Land-use monitoring of tree-crop diversification in eastern Côte d’Ivoire: Landscape structure changes and implications for sustainable landscape development

Land-use monitoring of tree-crop diversification in eastern Côte d’Ivoire: Landscape structure changes and implications for sustainable landscape development | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Land-use monitoring of tree-crop diversification in eastern Côte d’Ivoire: Landscape structure changes and implications for sustainable landscape development Evelyn Asante-Yeboah, Benoit Sarrazin, Bonna Antoinette Tokou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6613964/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Graphical Abstract Abstract Tree-crop diversification is increasingly adopted in tropical agricultural landscapes as a resilience strategy amidst fluctuating commodity markets, environmental change, and policy shifts. However, its spatial implications at the landscape level remain underexplored. This study examines the structure and dynamics of mosaic landscapes in eastern Côte d’Ivoire, a region characterized by heterogeneous landscapes, in response to tree-crop diversification trends and their implications for sustainable landscape management. Using multi-temporal Landsat imagery (1986, 2016, 2023), remote sensing classification with a Random Forest algorithm, and landscape metrics, we evaluated changes in land-use/land-cover (LULC), landscape composition (diversity, regularity), and landscape configurational heterogeneity (complexity and fragmentation). Results reveal a substantial increase in rubber plantations (net gain of 50.35%), with concurrent declines in cropland (−147%), cocoa (−45.28%), and sparse vegetation (−61.48%). Although landscape diversity increased slightly (Shannon index: 0.99 to 1.07), fragmentation intensified, with mean patch size decreasing by 12.3%. While tree-crop diversification introduced new compositional complexity, it often manifested as monoculture expansion rather than ecologically restorative land-use. The resulting structural transformations, characterized by high edge densities and smaller, isolated patches, suggest diminished functional connectivity of natural habitats and increasing ecological vulnerability. These trends raise critical questions about the long-term sustainability of current land-use trajectories. We argue that tree-crop diversification, while enhancing economic stability, can erode ecological resilience without integrated landscape-level planning and policy intervention. We recommend landscape-scale strategies that promote agroecological diversification, ecological corridor conservation, and inclusive land-use governance to mitigate fragmentation and maintain the multifunctionality of these rapidly transforming landscapes. Land-use diversification landscape metrics fragmentation geo-information science sustainable land management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Globally, landscapes are under increasing pressure from human activities, particularly the expansion of agricultural systems to meet the demands of a growing population and global markets (Bennett et al., 2021 ; Creutzig et al., 2019 ; McKenzie & Williams, 2015 ; Smith et al., 2016 ). These land-use changes often come at the expense of biodiversity and essential ecosystem services, which are crucial for food security and the well-being of local communities (Molotoks et al., 2017 ; Muluneh, 2021 ; Zhang et al., 2019 ). In the Global South, mosaic landscapes, characterized by their heterogeneous structure and multifunctionality, play a critical role in supporting diverse stakeholders across multiple scales. These landscapes contribute to food security, livelihoods, and overall socio-ecological resilience (Asante-Yeboah et al., 2024a ; Asubonteng et al., 2021 ; Dronova, 2017 ). However, they are increasingly contested spaces where socio-economic demands, conservation objectives, and climate mitigation efforts often compete. This competition arises from differing stakeholder interests and land-use choices, which can either align to create synergies or conflict, leading to trade-offs between land-use and related ecosystem services. Land-use decisions are influenced by a combination of government policies, international frameworks, and both global and domestic market demands. These overarching conditions shape the actions of micro-agents, such as households, landowners, firms, and farmers, who ultimately implement land-use strategies in response to these pressures. One prominent strategy is tree-crop diversification, involving crops such as coffee ( Coffea spp.) , cocoa ( Theobroma cacao ), coconut ( Cocos nucifera ), oil palm ( Elaeis guineensis ), and rubber ( Hevea brasiliensis ). Beyond supporting local livelihoods, these crops are primarily cultivated to supply high-end global markets (Sankaran et al., 2017 ; Schuler et al., 2022 ). Tree-crop diversification serves as a crucial strategy for smallholder farmers and their households, acting as a form of "self-insurance" that enhances resilience and sustainability in agricultural systems (Ahmad et al., 2024 ; Buckley, 2024 ). Smallholder farmers are particularly vulnerable to environmental and market-related shocks, such as sudden fluctuations in international commodity prices and changes in government policies (Aduhene-Chinbuah & Peprah, 2024 ; Mudzengi et al., 2025 ; Waldman et al., 2021 ). Unlike annual crop farmers, who can adjust their land-use decisions every year, tree-crop farmers face long-term commitments that limit their flexibility in responding to environmental and economic uncertainties. Through diversification, farmers can gradually adapt to environmental changes, including declining soil fertility, increasing weed and pest pressures, emerging crop diseases, reduced rainfall, and diminished microclimate regulation. Rather than relying on a single dominant crop, diversification allows for a more resilient and adaptive agricultural system, mitigating risks and improving long-term sustainability (Gil et al., 2017 ; Mustafa et al., 2019 ; Rosa-Schleich et al., 2019 ; van Zonneveld et al., 2020 ; Vernooy, 2022 ). There is substantial evidence that farmers of tropical tree-crops make diversification and land-use decisions primarily to enhance and stabilize their income (Hashmiu et al., 2024 ; Sivaraman et al., 2024 ; Waarts et al., 2021 ). Many farmers have turned to additional tree-crops due to the more favorable market prices compared to the previous crops, resulting in diversification (Aduhene-Chinbuah & Peprah, 2024 ; Asante-Yeboah et al., 2024a , b ). In the case of Côte d’Ivoire, before gaining independence, the nation concentrated its economic growth on agriculture, particularly coffee and subsequently cocoa (Ruf, 2015 ; Stryke, 1972 ). Following independence, various public policies have influenced farmers to favor cocoa, which has emerged as the primary source of agricultural revenue for both the Ivorian populace and the government. The government's pricing policy, which favored cocoa over coffee starting in the mid-1970s, combined with a decline in global coffee prices, significantly contributed to a nationwide shift in diversification (Heirman, 2016 ; Pereira, 2024 ; Vellema et al., 2016 ). This led many coffee farmers to initially intercrop coffee with cocoa before fully transitioning to cocoa (Ruf, 2015 ). Notwithstanding, the collapse of the government's price stabilization scheme in 1988, along with falling cocoa prices in the 1990s and 2000s, prompted a shift towards oil palm and, more notably, rubber (Coulibaly & Erbao, 2019 ; Ruf, 2015 ). The trend of diversifying cocoa farms into rubber, which currently benefits from favorable producer prices and is more resilient to slightly degraded environmental conditions, has become increasingly common across West and Central Africa (Odijie, 2019 ; Ruf & Schroth, 2015 ). Similarly, in Ghana during the 1970s and 1980s, low cocoa producer prices resulting from government pricing policies contributed to the rise of farms diversifying into oil palm and citrus while still maintaining some cocoa production (Michel-Dounias et al. 2013). As witnessed in West Africa, similar trends existed in Asia, where, in Indonesia, low coffee prices also encouraged coffee farmers in the last decade to switch to cocoa (Byrareddy et al., 2019 ). Similarly, Sulawesi's producers of clove ( Syzygium aromaticum ) responded to a declining clove-to-cocoa price ratio during the 1980s and 1990s with diversification into cocoa (Kumar et al., 2023 ). Cocoa also became the diversification choice for farmers who had previously depended mostly on the production of irrigated rice in Sulawesi. The increase of the cocoa-to-rice price ratio from 2 to 3 in the early 1980s contributed to launching a wave of diversification into cocoa. Many paddy farmers either sold their paddy fields or left them under sharecropping contracts and migrated to upland areas to plant cocoa. Some partially irrigated rice fields were even drained and planted with cocoa (Byrareddy et al., 2019 ; Ruf & Schroth, 2015 ). The same has happened in rice farms in southern Thailand with rubber instead of cocoa (Chambon et al., 2016 ; Ruf & Schroth, 2015 ). Despite these multiple examples of market-driven tree-crop diversification, the consequences of these developments for mosaic landscape structure, dynamics, and multifunctionality are poorly understood. Tree-crop diversification occurs at three distinct levels: i) at the plot level, where gaps in an existing tree-crop plantations are filled with alternative trees such as fruit trees, or timber trees; ii) at the farm level, where the oldest and least productive plots are replanted with economically viable tree-crops like rubber trees or pasture grass; and iii) at the landscape scale, where different farmers within a given landscape specialize in various crops, creating specialized patches within a diversified land-use mosaic (Schroth & Ruf, 2014 ). While the plot and farm level focus on optimizing economic and ecological benefits within individual farming units, at the landscape level, emphasis is placed on the large-scale interactions of the different patches, which are concerns for the sustainability and resilience of many landscape ecological functionalities (McGranahan, 2014 ). Research on tree-crop diversification has predominantly focused on the household-level farming system, often examining the economic, social, and ecological benefits for smallholder farmers. Such studies have explored how diversification enhances household income stability, mitigates risks associated with market fluctuations, and improves food security by reducing reliance on a single cash crop (Hashmiu et al., 2024 ; Khanal & Mishra, 2017 ; Premono et al., 2019 ; Tokou et al., 2025 ). However, fewer studies have assessed tree-crop diversification at the landscape level, despite its critical implications for land-use patterns, ecosystem services, and broader socio-ecological dynamics. Landscape-scale assessments can provide insights into how diversification influences habitat connectivity, biodiversity conservation, carbon sequestration, and overall ecosystem multifunctionality. For instance, Kremen and Merenlender ( 2018 ) emphasize that tree-crop diversification at a landscape scale contributes to ecological resilience by enhancing pollinator habitats and improving watershed health. Similarly, Duriaux Chavarria et al., (2018) suggest that integrating diverse tree-crop systems within landscapes can mitigate deforestation while sustaining agricultural productivity. However, tree-crop diversification can also have negative effects at the landscape level, particularly when poorly planned or driven by market incentives rather than ecological considerations (Asante-Yeboah et al., 2024a ; Asubonteng et al., 2020 ; Liu et al., 2020 ). One key concern is its potential to contribute to habitat fragmentation and biodiversity loss (Loh et al., 2022 ; Vogel et al., 2023 ). Studies have shown that the expansion of tree-crop plantations as diversification systems, particularly cocoa, oil palm, and rubber, often leads to the conversion of natural forests and other critical habitats, reducing overall landscape connectivity and threatening species that rely on intact ecosystems (Asante-Yeboah et al., 2024a , 2024b ; Asubonteng et al., 2020 ; Loh et al., 2022 ; Waarts et al., 2021 ; Wang & Pfister, 2024 ). Despite these broader ecological concerns of tree-crop diversification, research remains limited in integrating spatial metrics and geospatial analyses to assess how tree-crop diversification shapes landscape structure (composition and configuration) and functions (ecological connectivity, ecosystem services). Addressing this gap is essential for informing land-use planning and sustainable agricultural policies that balance production with conservation goals (Laurance et al., 2014 ; Meyfroidt et al., 2018 ; Sayer et al., 2015 ). For this purpose, this paper analyzes the structure and dynamics of the smallholder landscape in eastern Côte d'Ivoire, a region that has undergone substantial transformation due to tree-crop/land-use diversification, among other factors. We do so by using remote sensing techniques and landscape metrics, and reflect on these three objectives: To describe and analyze the spatial and temporal expansion of tree-crop diversification system and the resulting mosaic landscapes of eastern Côte d’Ivoire. To assess the structural changes on the study landscape pattern, including changes in patch composition and configuration, resulting from the tree-crop diversification. To synthesize the implications of tree-crop diversification for sustainable landscape management in eastern Côte d'Ivoire, drawing from the study’s analytical outcomes. Theoretical framework: relationships between landscape structure, functions, and changes From a spatial perspective, the structure, function, and change of a landscape are the three fundamental characteristics of landscape ecology (Ran et al., 2023 ; Sonter et al., 2017 ; Xu et al., 2020 ). A key principle of landscape ecology, the pattern of landscapes—defined by their composition and configuration—strongly influences ecological processes and characteristics (Karimi et al., 2021 ). This relationship serves as the foundation for the provision of ecosystem services. Changes in landscape structure lead to changes in landscape functions, and vice versa (Gashaw et al., 2018 ; Jing Luo, 2022) (Fig. 1 ). Anthropogenic factors are seen as the primary drivers of changes to landscape structure. The resultant effect can usually be negative, leading to biodiversity loss and ecosystem degradation, however, there is a global recognition of a positive effect of anthropogenic land-use changes to influence ecosystem services through human–nature co-produced land-use strategies that enhance landscape multifunctionality (Keesstra et al., 2018 ). For example, a heterogeneous landscape includes multiple land-cover types with a high degree of spatial interaction and multifunctionality. Such landscapes facilitate the dynamic flow of energy and materials, thereby supporting various ecological processes. In contrast, a homogeneous landscape comprises large, segregated land-cover types that primarily focus on providing food, habitat, and raw materials while overlooking other ecosystem services often leading to the loss of ecosystem services (Pickett et al., 2017 ; Sirami, 2016 ; Stephens et al., 2021 ). In the case of human-induced smallholder landscape changes, the first visible effect is an alteration in the composition of the landscape structure, usually marked by an increase in one ecosystem type at the expense of another, leading to ecosystem degradation and associated environmental risks (Asante-Yeboah, et al., 2024a , 2024b ; Howell et al., 2018 ; Tieskens et al., 2017 ). For instance, the conversion of smallholder heterogeneous farms into monoculture plantations aimed at export markets can negatively impact soil fertility, water regulation, biodiversity, and climate resilience leading to nutrient depletion, increased water consumption, reduced habitat diversity, and heightened vulnerability of agricultural systems to climate change, pests, and disease outbreaks (Mabhaudhi et al., 2022 ; Stratton et al., 2020 ). The second significant effect is a change in the configuration of the landscape structure, which impacts its overall function. Increased fragmentation and reduced connectivity can make ecosystems more vulnerable and impede the formation of essential ecosystem services. The third significant effect is in the structural complexity of the smallholder landscape composed of mosaics of rubber, cocoa, and other tree-crops. The structure often mimics high land-use diversity from a structural perspective, but this does not automatically mean high ecological resilience (Asubonteng et al., 2020 ; Ran et al., 2023 ). Structurally, such complexity reflects a greater variety in land-cover types, spatial patterns, and patch configurations, which suggests compositional diversity across the landscape (Ran et al., 2023 ). However, this diversity is largely superficial, as it typically involves monoculture plantations of different species rather than integrated ecologically diverse or multifunctional systems. True ecological resilience requires not just structural diversity but functional diversity, including native vegetation, heterogeneous microhabitats, and intact ecosystem processes (Ran et al., 2023 ). Therefore, maintaining landscape heterogeneity and connectivity between favorable ecological habitats is crucial for sustaining ecological balance and ensuring the continued provision of ecosystem services, which are key components of sustainable landscape management (Ran et al., 2023 ). We hypothesize that the diversification of tree-crop systems in the study area leads to structural changes in the composition and configuration of the landscape that negatively on the ecological resilience. We aimed to demonstrate the effects of this land-use transition on landscape configuration, probably including smaller, more fragmented or isolated patches and increasing landscape complexity. We also expected that, while tree-crop diversification may increase landscape complexity, this would not necessarily mean an enhancement in the ecological connectivity of the study area. This growing complexity could lead to the fragmentation of the remaining natural ecosystems, which may hinder species movement and increase edge density over time. Consequently, we argue that, without integrative spatial planning and ecologically informed management, such land-use diversification is likely to undermine sustainable landscape management by exacerbating ecological degradation and reducing ecosystem services. Materials and methods Study site The selected study area consists of a mosaic landscape in the eastern part of Côte d’lvoire in the Mé region within the towns of Adzopé, Yakassé Attobrou, and Akoupe and falls within latitudes 5°45'30"N and 6°38'10"N, and longitudes 3°80'30"W and 3°25'20"W (Fig. 2 ). The selected study area is bordered to the north by Indénié-Djuablin, to the northwest by Moronou, to the southwest by Agnéby-Tiassa and the District of Abidjan, and to the southeast by the Sud-Comoé region (CORENA and FADCI, 2016). The study area is situated at an elevation of zero meters (0 feet) above sea level and experiences a tropical wet and dry climate. The average annual temperature in this region is 28.42ºC (83.16ºF), which is 0.41% above the national average for Côte d'Ivoire (Walz et al., 2015 ). The area typically receives between 177.26 and 376 millimeters of rainfall, with approximately 285.77 rainy days each year, accounting for 78.29% of the time. Conversely, the dry season lasts from November to March, with January being the month that records the fewest wet days, averaging only 1.7 days with at least 0.04 inches of precipitation.(Deh et al., 2017 ; Walz et al., 2015 ). From an ecological perspective, the region is classified within the humid climate zone, specifically of the Attiéen type (Deh et al., 2017 ). This ecological zone has undergone substantial transformation due to activities such as mining, agriculture, and urban settlement. Forests are threatened with extinction due to large pioneer fronts linked to the development of cash crops (Ouattara et al., 2022 ). Historically, the selected area was part of the former cocoa belt in Côte d’Ivoire, which was recognized for its significant cocoa production during the 1980s (Läderach et al., 2013 )..Today, due to aging cocoa, environmental and economic shocks, diversification into other land-cover types, especially diversification into tree-crops, is taking dominance, necessitating the need for assessment of the landscape's structural and functional status to support sustainable landscape planning and management. Data Acquisition and image preprocessing. The study used three remote-sensing images from the Landsat sensor to generate the land-cover maps (Fig. 3 ). The Landsat satellite images were sourced from the USGS Earth Explorer platform ( https://earthexplorer.usgs.gov/ ) for the years 1986, 2016, and 2023. These dates were chosen to capture significant changes in land-cover over time, essential for long-term environmental monitoring and analysis and were based on availability, minimal cloud cover (with a maximum threshold of 10%), and low haze levels as cloudy pixels can impact the accuracy of the classification (Sabins Jr & Ellis, 2020). While these four images were not captured on the same anniversary dates, they were all taken during the dry season under comparable atmospheric and phenological conditions (Andrew et al., 2015 ). The Landsat images have been extensively used in environmental monitoring studies because of its broad temporal coverage (Li et al., 2024 ; Tesfaye et al., 2024 ). This study used scenes from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) for the land-cover classification (Fig. 3 ). The images were preprocessed using Environment for Visualizing Images (ENVI) 5.3 software, including radiometric calibration and atmospheric correction. Subsequently, the images were clipped to the area of interest (AOI) using the selected study area boundary shape file. Identification of classification scheme We identified the classification scheme for this study by reflecting on the land-cover map of Côte’d’lvoire prepared by the bnetd Carte d'occupation des sols de Côte d'Ivoire en 2020 ( https://africageoportal.maps.arcgis.com/apps/webappviewer/index.html?id=88c2493e722546c09c2a0a8b394c4454 ). This map served as a context specific land-cover types and reflected the needs of land-cover types under this study. We identified eight land-cover types for the land-cover mapping in this study (Table 1 ). Table 1 Description and classification scheme for the land-cover mapping Land-cover types Description Dense forest Area covered with dense tree vegetation, forest plantation, and reforestation areas Built-up Areas with bare ground, infrastructure and human habitat/settlement Cocoa plantation Areas covered with cocoa tree-crop plantation Rubber plantation Areas covered with rubber tree plantations Cropland Areas covered with coconut plantations, cashew plantation, arboriculture/fruit plantations, agricultural development, fallow lands, coffee plantations, and other crops Oil palm plantation Areas covered with oil palm trees Waterbody Areas covered with courses and bodies of water, marshy areas, swamp forest, forest on hydromorphic soil Sparse vegetation Areas covered with gallery forest, degraded forest, shrubs, herbaceous formations Field data collection and Land-cover mapping We conducted field data collection to collect training samples for the classification process. The field work took place between January to March 2024 to obtain ground truth datasets using a handheld Global Positioning System (GPS) unit. This process enabled us to generate representative datasets, including points and polygons, for each identified land-cover type. Given the highly heterogeneous nature of the landscape, the use of polygons enhanced the accuracy of matching the sampled areas with their corresponding land-cover types on the satellite imagery. A total of two hundred and fifty (250) ground truth data points were collected to classify the 2023 image. In addition, four hundred and eighty (480) polygons, representing various land-cover types, were extracted from high-spatial-resolution imagery available on Google Earth. These polygons were manually digitized on-screen to compensate for inaccessible areas (Diwediga et al., 2017 ). In total, seven hundred and thirty (730) points and polygons were gathered as reference data for the 2023 Landsat 8 Operational Land Imager (OLI), Subsequently, the data were randomly divided, with 80% allocated for training and 20% for testing or validation, to assess the accuracy of the classification (Bao Pham et al., 2024 ) A supervised classification was conducted using the Random Forest (RF) algorithm within the Scikit-learn framework, enhanced by the Scikit-eo extension tailored for Earth observation data in Python (Tarazona Coronel et al., 2024 ) (Fig. 3 ). A major advantage of scikit-eo is its powerful analytical features. It offers a comprehensive set of algorithms tailored for environmental research, covering areas such as statistical analysis, deep learning, data fusion, and spatial analysis (Tarazona Coronel et al., 2024 ). The RF algorithm is renowned for its robustness against overfitting and its proficiency in managing datasets with missing values and outliers (Patil & Panhalkar, 2023 ). Consequently, RF has become a prevalent choice in environmental monitoring endeavors, particularly in land-cover classification tasks (Marie Delalay et al., 2019 ; Tarazona Coronel et al., 2024 ). We classified and validated the images for 1986 and 2016 by generating historical LULC information from key local informants. This information was cross-referenced with unchanged areas in the 2023 image to ensure accuracy. Accuracy Assessment Following the land-cover classification, a confusion matrix and validation matrix are produced to calculate the producer’s and user’s accuracy, along with the Overall Accuracy (OA) and Kappa Coefficient (K), which are used to evaluate the performance of the Random Forest (RF) classification (Tariq et al., 2022 ). Overall accuracy and the Kappa statistics are essential indicators for assessing the precision of land-cover classification. A Kappa value of 0.4 or lower denotes poor consistency between the classified variables, a Kappa value ranging from 0.4 to 0.8 indicates moderate to strong consistency, and a value greater than 0.8 signifies excellent consistency (Tariq et al., 2022 ). Change detection analysis. This study utilized post-classification change detection to analyze land transitions across 1986, 2016, and 2023, identifying key land-cover types contributing to the landscape's change dynamics. The primary focus was on the Area/percent change , and Post classification or categorical change detection Area or percent change The area and percentage change (or rate of change) are commonly used quantitative methods for detecting changes in LCLU, as they effectively illustrate the extent of increase or decrease in each land-cover category within a given region. These changes are typically expressed in hectares and percentages (Chughtai et al., 2021 ; Tesfaye et al., 2024 ). The mathematical representation is given by the following equations: Categorical /post-classification change detection The method quantifies differences between independently classified land-cover maps for distinct periods, revealing spatial and temporal variations in land features. Conversions are analyzed using the “FROM-TO” change approach, which tracks transitions between different land-use categories (Chughtai et al., 2021 ). By comparing LULC maps from two different time periods, the analysis produced a detailed map of LULC changes along with a transition matrix. This matrix pinpointed specific areas where changes occurred, illustrating the shifts in land cover between the earlier and later periods. Persistent land-cover types appeared along the matrix’s diagonal, while transitions were represented by off-diagonal elements. The transition matrix offered key indicators such as gross gains, gross losses, and net change for each land-cover category. Gross gains referred to the total area acquired by a land-cover class from other categories, while gross losses indicated the area lost to other classes. Net change, calculated as the difference between gains and losses, revealed the scale and direction of transformation within the landscape (Asante-Yeboah et al., 2022 ). Landscape structural analysis Given that structural patterns shape landscape processes and ecosystem service delivery, it is essential to analyze the spatial configuration of land-cover types across temporal scales in landscape studies. Such analysis provides insights into how land-use changes affect habitat connectivity, biodiversity, and ecosystem resilience, informing both conservation strategies and land-use planning. We employed a 'Landscape Metrics Mapping' script using QGIS 3.x integrated with the Landscape Ecology Statistics (LecoS) plugin to calculate and visualize relevant landscape metrics for this study (Jung, 2016 ). This method allows for the computation of key landscape metrics across a defined study area, facilitating the analysis of structural changes over time and their potential ecological implications. Using the categorical land-cover maps from this study, we computed landscape metrics of diversity, fragmentation, and complexity at the landscape level (Ran et al., 2023 ). We calculated the Shannon diversity index to assess diversity, Shannon evenness for regularity edge density for complexity, and mean patch size for fragmentation. The classified land-use raster datasets were first filtered using an identical process of majority filters to eliminate isolated pixels, thereby reducing noise that could influence the indicator values. To enable spatially explicit metric calculation, a regular fishnet grid was generated over the study area using the "Create Grid" tool in QGIS. Grid cells of 5000 meters in a regular grid of points and a buffer distance of 2500 meters were used to segment the landscape into uniform spatial units, allowing for localized metric assessment. Each grid cell served as a unit of analysis in subsequent landscape metrics computation. We used the LecoS plugin within QGIS to compute a suite of landscape metrics at the grid-cell level (Jung, 2016 ). The classified land-cover raster was used as the input layer, and the generated grid was provided as the zonal mask. LecoS computed landscape-level metrics for each grid cell. Selected metrics included: Shannon diversity index (SDI): “Landscape diversity” SHDI= -ΣPi log Pi Landscape heterogeneity is most often characterized by the Shannon diversity index, where Pi is the proportion of element i in a landscape. This index corresponds to the diversity of landscape elements. The greater the value of the index, the more diverse the landscape. Shannon evenness index (SHDI): “Landscape regularity” SHEI = SHDI / Ln (m) with m as a number of different landscape elements. The SHEI values range from 0 to 1 Shannon diversity index locally depends on the number of LULC types. The Shannon evenness index (SHEI) relativizes the diversity index by the maximum possible diversity for the number of different elements and land-use types present. it allows local control of the balance between land-uses Edge density (ED): “Landscape complexity” Complexity is represented by edge density (ED): ED = L/A (meter) where L is the total length of edges within a landscape and A is the total area of the landscape. complexity will increase as the length of patch edges increases. Increasing the edge density can help a species to move from one patch to another, using potentially the edges between patches as intermediate refuges. The indices are also associated with complex landscape configuration and consequently better provision of several ecological services, except in the case of human disturbance land-use. Mean patch size (MPS): “Landscape fragmentation To characterize fragmentation, the average size of patches (polygons) is required: MPS (Mean Patch Size): MPS = A/N (hectare) where A is the total area of the landscape and N is the total number of patches in the landscape. The index value decreases as the number of patches increases and the landscape becomes increasingly fragmented. This is negatively correlated to the landscape diversity index value. When the patches are larger on average, this suggests greater spatial continuity of the habitat or land-use. For example, a forest with a high MPS may indicate a good state of ecological connectivity and habitat functionality. When the patches are more fragmented or smaller, this may reflect increased fragmentation (often anthropogenic), or a fine landscape mosaic, as in complex agricultural systems or small-scale agro-ecosystems. Results Land-use/land-cover maps of eastern Côte d’Ivoire for 1986, 2016, and 2023. The accuracy assessment derived from the error/confusion matrix is presented in Table 2 . The overall accuracies achieved for the LULC maps in 1986, 2016, and 2023 were 76%, 86%, and 88%, respectively. The classified LULC maps, along with their corresponding statistics for these years, are illustrated in Fig. 4 and detailed in Table 3 . At the beginning of the study period in 1986, dense forest (27%) and sparse vegetation (26%) were the predominant land-cover types. Cocoa accounted for 16%, rubber for 14%, and cropland for 7%, while built-up areas, oil palm, and water bodies each comprised less than 6% of the study area (Fig. 4 ). The distribution of dense forest was primarily concentrated in the southern and northern regions of the landscape. Sparse vegetation was spread throughout the entire area, while cocoa was predominantly found in the central part of the study landscape. Rubber exhibited a distribution pattern similar to that of cocoa, with a slight concentration towards the central-western part of the study area. In contrast, built-up areas and palm plantations were primarily located in the southwestern region. By 2016, no new land-cover types emerged; however, the spatial distributions and proportions of the existing eight land-cover types had shifted. Some land-cover types expanded in area, while others decreased. Sparse vegetation became the dominant feature of the study landscape, increasing from 26–31%. Similarly, rubber plantations grew from 14–22%, and palm areas rose from 5–10%. Conversely, dense forest cover declined from 27–22%. Cocoa cultivation decreased from 16–12%. Both cropland and built-up areas were reduced to 1%, while the size of water bodies remained unchanged. In 1986, cocoa was prevalent in the central part of the study area, while oil palm and built-up areas dominated the southwestern region. However, up to 2016, these areas diminished, making way for rubber plantations. By the end of the study period in 2023, no new land-cover types emerged; however, rubber plantations had significantly increased, covering 28% of the landscape, up from 22% in 2016 and 14% in 1986. The built-up area rose from 1% in 2016 to 8% in 2023, with a notable shift in location from the southwest in 1986 to the central region by 2023. Sparse vegetation has decreased in size by 16%, while dense forest has expanded by 24% compared to 2016. The area dedicated to oil palm has seen a slight decline of 9%, cocoa has further decreased, and cropland has experienced a modest increase to 3%. Throughout the study period, the water body remained unchanged at 1%. Table 2 Error/confusion matrix showing the accuracy assessment results of the land-cover classification Year 1986 2016 2023 LULC Class PA (%) UA (%) PA (%) UA (%) PA (%) UA (%) Dense Forest 65 61 62 83 56 67 Built-up 90 94 95 94 92 91 Cocoa Plantation 56 56 94 90 92 87 Rubber Plantation 41 46 83 80 85 82 Cropland 67 74 64 57 95 90 Oil Palm Plantation 90 92 83 84 91 87 Waterbody 98 100 98 98 95 97 Sparse Vegetation 71 59 73 73 51 66 Overall Accuracy 76 86 88 Kappa Coefficient 0.73 0.83 0.86 Changes in land-cover types and landscape transitions of eastern Côte d’Ivoire from 1986 to 2023. The results of the stock change analysis are presented in Table 3 . It indicates a significant decline in cropland, sparse vegetation, cocoa plantations, and dense forests over the entire study period (1986–2023). These changes were evident in both the area extent and spatial distribution. During the first study period (1986–2016), cropland experienced a reduction of -532.67% over 30 years. Although there was a slight increase in the second study period (2016–2023), the overall area extent and distribution of cropland showed a total loss of -147% for the entire study duration (1986–2023). Sparse vegetation saw a decrease of approximately − 17.88% during the first study period (1986–2016), while the second study period (2016–2023) recorded a dramatic reduction of -96.94%. This culminated in an overall decline of -60.96% in area extent by the end of the 37-year study period (1986–2023). The area dedicated to cocoa decreased significantly during the three study periods, with reductions of 39.73% in the first period, 3.97% in the second, and 45% in the third. Dense forest experienced a decline of approximately 12% in area from 1986 to 2023. In contrast, rubber plantations expanded in all three periods, showing increases of 35.35% in the first period (1986–2016), 23.21% in the second (2016–2023), and 50.35% in the third (1986–2023). Similarly, built-up areas saw a notable increase of 48.27% by the end of the study period (1986–2023). Additionally, both oil palm and water bodies experienced increased, with percentage changes of 41.39% and 35.03%, respectively (Table 3 ). Tables 4 and 5 present the transition matrix for the study landscape, detailing the changed areas and, persistent (Table 4 ), gross gains and losses as well as land-cover transfers among various categories over a span of 37 years (Table 5 ). The change matrix analysis indicates that approximately 216,872.76 ha (68%) of the land within the study area underwent LULC changes during the 37-year period from 1986–2023 (Table 5 ), while the remaining 102,536.07 ha (32%) remained stable. The extent of changes varied across different LULC types. For example, of the 23,127.6 ha of cropland recorded in 1986, only 156.5 ha (0.7%) remained unchanged throughout the study period, suggesting that about 99.3% of the cropland was converted to other land-cover types (Table 5 ). Similarly, among the 51,995.9 ha of cocoa plantations in 1986, only 23.2% remained unchanged during the same timeframe. In 2023, 76.7% of the cocoa plantations from 1986 had been converted to other land-use and land-cover types. This indicates that out of the 35,790.39 hectares still covered by cocoa, approximately 16,209.49 hectares that were once cocoa plantations in 1986 have transitioned into different land-cover types. Likewise, rubber plantation area of about 45,047.2 hectares in 1986 experienced a 61.5% reduction due to conversions to other land-cover types by the end of 2023. Dense forests saw about 45.9% of their area change, while the remaining 54.1% remained intact, making it the second land-cover type with the highest persistence, following water bodies, which maintained 73.5% of their area. Cropland had the highest percentage of land area changing, while sparse vegetation ranked second, with 81.1% of its initial area converted to other land-cover types, followed by built-up areas, which experienced a 69.6% change. Rubber plantations emerged as the most dominant contributor to land-cover changes, expanding by 64,888.83 hectares. However, it also experienced a loss of 22,106.40 hectares, resulting in a net gain of 42,728.42 hectares. Rubber became the dominant land-cover category, reflecting the highest net change. The transitions to rubber primarily occurred at the expense of sparse vegetation, dense forest, and cocoa plantations (Table 5 ). This indicates that rubber is the leading land-cover type with the most extensive changes from other land-uses on the study landscape. Although sparse vegetation became the second-largest gross gainer, increasing by 43,148.13 hectares. Yet, it also suffered the greatest loss, with a reduction of 66,663.18 hectares, leading to a significant negative net change of -23,515.05 hectares. Similarly, dense forest, cocoa plantations, and cropland all experienced negative net changes, suggesting that the area lost to other land-cover types surpassed the gains from these same categories. Table 3 Stock change analysis: Land-cover category area in hectares and percentage change from the Initial size Year 1986 2016 2023 Percentage change LULC types Area (ha) Area (ha) Area (ha) 1986–2016 2016–2023 1986–2023 Dense forest 84705.39 69854.76 74966.67 -21.26 6.82 -12.99 Built-up 12858.30 4775.76 24858.18 -169.24 80.79 48.27 Cocoa plantation 51995.88 37210.68 35790.39 -39.73 -3.97 -45.28 Rubber plantation 45047.16 69673.41 90737.01 35.35 23.21 50.35 Cropland 23137.56 3657.15 9366.66 -532.67 60.96 -147.02 Oil palm plantation 17360.01 31589.37 29618.64 45.04 -6.65 41.39 Waterbody 2024.28 2449.53 3115.89 17.36 21.39 35.03 Sparse vegetation 82280.25 100198.17 50955.39 17.88 -96.64 -61.48 Total 319,408.83 319,408.83 319,408.83 Table 4 Table showing persistent and changed areas from 1986–2023 Land-cover types Initial area(ha) 1986 Persistent Percentage Changed area (ha) (2023) Percentage Dense forest 84705.4 45824.2 54.1 38881.2 45.9 Built-up 12858.3 3903.8 30.4 8954.5 69.6 Cocoa plantation 51995.9 12104.2 23.3 39891.7 76.7 Rubber plantation 45047.2 17359.1 38.5 27688.1 61.5 Cropland 23137.6 156.5 0.7 22981.1 99.3 Oil palm plantation 17360.0 6129.8 35.3 11230.2 64.7 Waterbody 2024.3 1487.2 73.5 537.1 26.5 Sparse vegetation 82280.3 15571.2 18.9 66709.0 81.1 Total 319408.83 102536.1 216872.8 Table 5 transition matrix table for 1986–2023 showing the area (ha). The diagonal (in bold) values indicates the persistence of land-cover types and To 2023 From 1986 Dense forest Built-up Cocoa Plantation Rubber Plantation Cropland Oil palm Waterbody Sparse Vegetation Grand Total Gross loss Net change Dense Forest 45824.23 3145.82 7661.57 13355.48 1040.18 4745.75 368.16 17971.62 94112.81 48,288.58 -17,130.68 Built-up 72.50 3903.82 214.26 4791.27 606.80 1751.50 141.40 1351.50 12833.05 8,929.23 10,600.05 Cocoa Plantation 9375.83 1862.86 12104.19 11491.27 1072.56 5484.24 356.65 10032.11 51779.72 39,675.53 -16,009.27 Rubber Plantation 7317.06 3512.63 1570.88 17359.06 380.49 1575.38 181.07 7622.89 39519.46 22,160.40 42,728.42 Cropland 4150.92 2018.20 873.41 5726.66 156.51 1516.01 48.12 4887.96 19377.80 19,221.29 -10,016.36 Oil palm 561.83 1691.87 2277.53 4569.09 858.75 6129.81 113.61 1147.85 17350.34 11,220.52 12,251.89 Waterbody 120.71 28.42 45.60 116.57 23.66 66.74 1487.23 134.21 2023.15 535.92 1,091.00 Sparse Vegetation 9559.06 7269.48 11022.99 24838.48 5222.49 8332.77 417.91 15571.22 82234.39 66,663.18 -23,515.05 Grand Total 76,982.13 23,433.10 35,770.44 82,247.89 9,361.44 29,602.22 3,114.15 58,719.35 319,408.83 Gross gains 31,157.91 19,529.28 23,666.25 64,888.83 9,204.93 23,472.41 1,626.92 43,148.13 Landscape structural analysis (1986–2023) Overall, the average values of the landscape structural diversity indices remained relatively stable between 1986 and 2023, with some meaningful variations observed across the individual metrics. Notably, landscape diversity, as measured by the Shannon Diversity Index, showed a slight decrease between 1986 (0.99) and 2016 (0.94), followed by a notable increase to 1.07 in 2023 (Table 6 ). This suggests a diversification of land-cover types in recent years, potentially due to a more balanced distribution of the eight classified land-cover types. Landscape regularity, indicated by the Shannon Evenness Index, remained stable over the studied period, returning to its 1986 level of 0.60 after a slight dip in 2016 (0.55) (Table 6 ). This stability suggests a long-term trend of landscape elements being substituted over time, rather than any single land-cover type becoming dominant. In terms of configuration, landscape complexity increased steadily, as shown by rising edge density from 159.99 m/ha in 1986 to 180.54 m/ha in 2023 (Table 6 ). This upward trend points to more intricate and fragmented land-use boundaries, indicating increased configurational complexity in the study landscapes (Fig. 5 ). Simultaneously, the average patch size decreased from 406.56 hectares in 1986 to 356.79 hectares in 2023. This reduction indicates a rise in landscape fragmentation, where land-cover types are broken into smaller, more isolated patches, likely a result of ongoing land-use changes such as agriculture, settlement expansion, or infrastructure development. Although the regional averages suggest relative stability, significant spatial variation in these metrics emerged over time. Landscape diversity, for example, was concentrated in the southern part of the region in 1986, shifted to the east by 2016, and became more prominent in the central and northern areas by 2023 (Fig. 5 ). A similar trend was observed for landscape regularity, which was initially higher in the south but expanded toward the east between 1986 and 2016, then became widespread across the region by 2023. These shifts suggest a growing homogenization in the distribution of landscape elements. The spatial dynamics of landscape complexity closely mirrored those of diversity, with higher edge density values progressively extending eastward and northward (Fig. 5 ). This pattern supports the observation of increasing configurational complexity in these areas. Fragmentation, measured by mean patch size, was most pronounced around the two main cores of dense natural forest. These areas remained relatively intact in terms of low diversity and complexity, serving as important strongholds for contiguous forest cover (Fig. 5 ). However, the overall trend points to a decline in patch size, even near these dense forests, dropping from over 500 hectares locally in 1986 to under 300 hectares in 2023. This reinforces the concern about increasing fragmentation, even in previously undisturbed zones. Table 6 Annual average values of the landscape heterogeneity indices calculated from the land-use classification maps Landscape structure metrics (indicators average values) 1986 2016 2023 Landscape diversity: Shannon diversity index 0.99 0.94 1.07 Landscape regularity: Shannon evenness index 0.6 0.55 0.6 Landscape complexity: Edge density (m/ha) 159.99 173.03 180.54 Landscape fragmentation: Mean patch size (ha) 406.56 383.98 356.79 Discussion LULC changes The results of the LULC change analysis from 1986 to 2023 reveal profound transformations within the study landscape, which are captured in three folds. First , mosaic landscapes in sub-Saharan Africa have traditionally been the backbone for food production, but the assumption that cropland areas within these landscapes would remain stable is increasingly untenable in the face of shifting agricultural priorities and land-use pressures (Asante-Yeboah et al., 2024b ; Chirwa et al., 2024 ). Cropland underwent a sharp drop, decreasing by -532.67% between 1986 and 2016 (Table 3 ). Although a modest rebound of 60.96% occurred between 2016 and 2023, the overall trajectory remains negative, amounting to a total net reduction of -147% over the 37 years​. This pattern is consistent with broader regional and global trends, where cropland is gradually replaced by urban expansion and intensified agricultural land-use in tropical regions toward high commodity ends (Bren d’Amour et al., 2017 ; Goulart et al., 2023 ). For small cocoa producers, where productivity is stagnating and incomes are already low, this reduction in available land can exacerbate rural poverty and food insecurity (Tokou et al., 2025 ). The post-2016 resurgence may reflect targeted agricultural policies or a response to escalating food demand, yet it remains insufficient to reverse the long-term decline. Second is the compositional dynamics of sparse vegetation, the tree-crops (rubber, oil palm, and cocoa), and dense forest. Sparse vegetation followed a similar trend to cropland, with a modest increase until 2016, followed by a sharp 96.94% decline by 2023, resulting in a net loss of 60.96%, mainly due to the rapid expansion of rubber plantations (Table 3 ). From 1986 to 2023, rubber cultivation expanded by over 50%, largely replacing sparse vegetation, dense forest, and cocoa plantations, 76.7% of which were converted, highlighting a significant shift in land-use priorities toward more economically viable crops. According to Millard ( 2019 ), rubber has become a more attractive crop because of its higher market price and the growing global demand for rubber, driven by industries like automotive manufacturing. Oil palm cultivation also expanded substantially (41.39%) over the study period. However, a slight contraction of -6.65% from 2016 to 2023 may reflect land saturation, changing market dynamics, or regulatory interventions. This trajectory is consistent with observations from other tropical regions where the expansion of oil palm tends to decelerate after reaching peak land conversion thresholds (Marin-Burgos & Clancy, 2017 ). Dense forests experienced a moderate but persistent decline of approximately 12% over the study period. Although 54.1% of the 1986 forest cover persisted to 2023, making it one of the most stable LULC classes, about 45.9% was lost to other uses. While this rate of decline is comparatively lower than that observed for cropland or sparse vegetation in the study, it nonetheless reflects sustained deforestation, likely driven by agricultural expansion and logging (Houghton and Nassikas, 2018; Ordway et al., 2017). The resultant effect is the inability of the country to comply with new European regulations against deforestation (EU, 2023 ; Kouassi et al., 2021 ). The marginal deceleration in forest loss after 2016 may be indicative of emerging conservation initiatives or enhanced land governance. The third is observed in built-up areas exhibiting marked expansion, increasing by 48.27% between 1986 and 2023, with the most pronounced growth (80.79%) occurring between 2016–2023. This acceleration coincides with a resurgence in illegal mining activities, particularly in the central parts of the study landscape in 2023. High-resolution satellite imagery from 2023 corroborates this trend, revealing extensive land degradation characterized by exposed soil, abandoned pits, and sediment-laden water bodies, typical hallmarks of unregulated mining (Domingo et al., 2024 ). Comparable trends have been reported in Ghana, where artisanal and small-scale mining (ASM), in Ghana commonly referred to as galamsey , has contributed significantly to land degradation and rapid LULC changes (Cudjoe et al., 2023 ; Kwang et al., 2025 ; Obodai et al., 2019 ). These mining-induced changes not only result in vegetation loss but also impair soil quality, degrade water resources, and compromise the potential for ecological or agricultural recovery (Dumenu & Obeng, 2016 ; Kouassi et al., 2021 ). The spatial overlap between areas of cropland and sparse vegetation loss with increased built-up areas, which reveals illegal mined areas, suggests a direct and detrimental impact of informal economic activities on land productivity and environmental integrity. Unexpectedly, we also observed an increase in water bodies by 35.03%, with the largest proportion of this growth (21.39%) occurring in the 2016–2023 study period. This expansion is spatially correlated with mining zones, suggesting that excavation pits, subsequently filled by rainwater and groundwater seepage, drive the proliferation of artificial aquatic features. Similar phenomena have been documented in mining-intensive regions of Ghana and other parts of the world, such as Indonesia, the Philippines, Brazil, Peru, and Colombia, where such water-filled pits now dominate post-mining landscapes (Bansah et al., 2018 ; Benites, 2023 ; Meutia et al., 2023 ). While these water bodies may offer new ecological niches, they are also sources of heavy metal contamination, sedimentation, and aquatic ecosystem disruption (Bansah et al., 2018 ; Cudjoe et al., 2023 ; Meutia et al., 2023 ). Landscape structural changes , Tree-crop diversification, particularly the integration of rubber and oil palm into cocoa-dominated landscapes, has significantly altered the landscape structure of eastern Côte d'Ivoire, leading to both ecological and socio-economic implications. Between 1986 and 2023, landscape diversity increased slightly, with the Shannon diversity index rising from 0.99 to 1.07, indicating a more balanced distribution of land-use types across the region (Table 6 ). This trend reflects broader regional patterns reported in West Africa, where tree-crop expansion diversifies land-cover but often at the cost of native ecosystems (Asante-Yeboah et al., 2022 ; Asubonteng et al., 2020 ). While a structurally complex mosaic may appear diverse on maps, the ecological functions within these systems are often limited. Tree-crop monocultures like rubber, oil palm and cocoa generally lack species richness, provide minimal habitat for native fauna, and may disrupt ecological processes such as pollination, nutrient cycling, and water regulation (Ran et al., 2023 ). Furthermore, these land-uses often contribute to habitat fragmentation rather than supporting ecological connectivity. Despite this increase in compositional diversity, mean patch size declined from 406.56 ha to 356.79 ha, suggesting intensified fragmentation and reduced habitat contiguity, consistent with findings from similar agro-ecological contexts in Ghana and Southeast Asia (Arroyo-Rodríguez et al., 2017 ; Wong et al., 2022 ). Although edge density increased from 159.99 m/ha to 180.54 m/ha, implying enhanced structural connectivity, this may not equate to improved functional connectivity for biodiversity. Similar to patterns observed in monoculture-dominated landscapes in Southeast Asia, such structural changes can act as ecological traps, impeding the movement of forest-dependent species and fragmenting habitat networks (Ibrahim et al., 2018 ; Mohd-Azlan et al., 2019 ). Moreover, spatial trends reveal that areas of high landscape diversity and complexity have shifted from the south in 1986 to the central and northern zones by 2023, largely driven by the expansion of rubber plantations. While this may indicate a spatial redistribution of land-use types, the replacement of natural vegetation with monocultures raises concerns about long-term biodiversity resilience and ecosystem service provision (Kremen & Merenlender, 2018 ; Perfecto et al., 2019 ). The decline in patch size, even near dense forest cores, and the encroachment of fragmentation into previously contiguous areas underscore the ecological cost of unchecked land-use change. Despite the increase in heterogeneity, landscape functionality appears compromised, as high edge densities exacerbate edge effects such as microclimatic changes, increased predation, and vulnerability to invasive species (González et al., 2024 ; Haddad et al., 2015 ). In sum, while tree-crop diversification has increased structural complexity, the associated fragmentation and loss of natural habitats may undermine functional ecological connectivity and pose significant challenges to sustainable landscape management in eastern Côte d'Ivoire. Implications of tree-crop diversification for sustainable landscape management While mosaic landscapes in Sub-Saharan Africa have traditionally served as the backbone of household food production, the assumption that cropland areas within these landscapes would remain stable is increasingly untenable in the face of shifting agricultural priorities and land-use pressures. Empirical evidence shows that the expansion of export-oriented commodity crops, such as rubber, oil palm, and cocoa, is leading to the systematic reduction and fragmentation of cropland areas traditionally used for subsistence farming (Ordway et al., 2017; Giller et al., 2021). This trend contradicts the expectation of cropland stability. Rather than maintaining land for local food production, many rural households and governments are pivoting toward high-value cash crops due to their economic attractiveness and perceived developmental benefits (Ruf & Schroth, 2015 ). These market-driven transformations often lead to the conversion of diversified, food-producing mosaic landscapes into more homogenous, commodified land systems, which may undermine household food security in the long term (van Vliet et al., 2015). Moreover, this shift can have significant socio-ecological implications. As cropland dedicated to food staples is increasingly supplanted by monoculture export crops, the resilience and multifunctionality of these landscapes diminish. Food availability becomes more reliant on market purchases, which may be precarious due to price volatility, infrastructure limitations, and external shocks (Baudron & Giller, 2014). The observed rise in the Shannon diversity index (from 0.99 to 1.07) indicates a more balanced distribution of land-use types, suggesting a form of diversification. This aligns with broader regional patterns in West Africa, where smallholder and industrial agriculture have increased land-use heterogeneity (Asante-Yeboah et al., 2022 ). In theory, such diversification could be seen as a positive trend for landscape resilience and socio-economic sustainability. However, in the context of eastern Côte d'Ivoire, this heterogeneity is primarily driven by the expansion of monocultures rather than ecological restoration or agroecological practices. The conversion of forested or mixed-use areas into rubber and oil palm plantations can mask ecological degradation beneath a veneer of patch diversity. This undermines sustainability goals by reducing ecosystem services, limiting biodiversity, and simplifying ecological interactions, highlighting the need for more nuanced metrics of sustainability than land-cover diversity alone. The decline in mean patch size, from 406.56 ha to 356.79 ha, signals intensifying landscape fragmentation. In a region like eastern Côte d'Ivoire, where biodiversity hotspots and relic forest patches are crucial for endemic species, fragmentation poses serious threats. Smaller and more isolated patches suffer from reduced core habitat area and increased edge effects, which elevate the vulnerability of flora and fauna to predation, drought, and invasive species (Haddad et al., 2015 ; Kalarus and Nowicki, 2015). This trend mirrors fragmentation dynamics seen in neighboring Ghana and further suggests that economic drivers, especially commodity crop expansion, are overriding ecological considerations in land-use planning. Without strong conservation zoning or landscape-scale planning, continued fragmentation may irreversibly impair ecological connectivity and ecosystem integrity in the region. Conclusions This study employed remote sensing and geospatial techniques, combined with landscape metrics, to analyze the spatio-temporal dynamics and landscape heterogeneity associated with tree-crop diversification in eastern Côte d'Ivoire, with implications for sustainable landscape management. The land-use and land-cover changes observed reveal a landscape undergoing rapid and complex transformation, driven by economic incentives, policy shifts, and weak environmental governance. The expansion of rubber plantations, the emergence of land degradation from illegal mining, and the decline in both cropland and forest cover underscore the urgency of implementing integrated land management strategies that reconcile competing land-use demands while safeguarding ecological resilience. Weak enforcement of land-use regulations, the insufficiency of incentives for sustainable farming practices, and limited community engagement exacerbate these challenges. The ongoing landscape transformation reflects a pivotal moment for eastern Côte d'Ivoire, where tree-crop diversification, though superficially promising, is contributing to fragmentation, reduced functional connectivity, and biodiversity loss. Sustainable landscape management must therefore go beyond surface-level diversity and embrace a holistic, context-specific approach. These include the continued promotion of good agricultural practices such as agroforestry and mixed cropping systems to enhance biodiversity while maintaining productivity. The effective implementation of sustainability initiatives would limit the uncontrolled expansion of monocultures and encourage ecosystem-based land-use planning through the active involvement of local stakeholders. Without such a concerted and inclusive strategy, such as agroforestry systems, the region risks degrading the very ecosystems upon which its agricultural economy and future sustainability depend. Declarations Ethics approval and consent to participate : All authors have read, understood, and complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors Funding This work is conducted in the frame of the PRO-PLANTEURS Recherche project funded by the German Ministry of Economic Cooperation and Development (BMZ). The findings and conclusions contained within are those of the authors and do not necessarily reflect the positions or policies of the BMZ. Clinical trial number: not applicable Author Contribution EAY, BS, BAT, FO, SS, & KL worked on conceptualization. EAY, & BAT: Data collection. EAY, BS: Data analysis on maps and metrics. EAY wrote the main manuscript, and initial editing by BS. Supervision: KL, & SS. All authors reviewed the manuscript. Acknowledgement The authors gratefully acknowledge Dr. Abrou N'gouan Emmanuel Joel for his invaluable support during the data collection phase of this study. We also extend our sincere thanks to Eduzie and the Hen Mpoano team in Ghana and Dahan for their dedicated assistance and collaboration in the remote sensing dataset generation and land cover mapping exercise. Their contributions were instrumental to the success of this work. References Aduhene-Chinbuah, J., & Peprah, C. O. (2024). Multi-risk management in Ghana’s agricultural sector: Strategies, actors, and conceptual shifts—a review. Review of Agricultural, Food and Environmental Studies , 105 (4), 393–418. Ahmad, S., Bhat, S. S., Mir, N. H., Wani, A. A., & Pala, N. (2024). Temperate Agroforestry Systems for Diversification and Environmental Sustainability in Northwestern Himalayan Region. In S. Kumar, B. Alam, S. Taria, P. Singh, A. Yadav, & A. Arunachalam (Eds.), Agroforestry Solutions for Climate Change and Environmental Restoration (pp. 65–83). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5004-7_3 Andrew, M. E., Wulder, M. A., Nelson, T. A., & Coops, N. C. (2015). Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: A review. GIScience & Remote Sensing , 52 (3), 344–373. https://doi.org/10.1080/15481603.2015.1033809 Arroyo-Rodríguez, V., Melo, F. P. L., Martínez-Ramos, M., Bongers, F., Chazdon, R. L., Meave, J. A., Norden, N., Santos, B. A., Leal, I. R., & Tabarelli, M. (2017). Multiple successional pathways in human-modified tropical landscapes: New insights from forest succession, forest fragmentation and landscape ecology research: Multiple successional pathways. Biological Reviews , 92 (1), 326–340. https://doi.org/10.1111/brv.12231 Asante-Yeboah, E., Ashiagbor, G., Asubonteng, K., Sieber, S., Mensah, J. C., & Fürst, C. (2022). Analyzing Variations in Size and Intensities in Land Use Dynamics for Sustainable Land Use Management: A Case of the Coastal Landscapes of South-Western Ghana. Land , 11 (6), 815. Asante-Yeboah, E., Koo, H., Ros-Tonen, M. A. F., Sieber, S., & Fürst, C. (2024a). Participatory and Spatially Explicit Assessment to Envision the Future of Land-Use/Land-Cover Change Scenarios on Selected Ecosystem Services in Southwestern Ghana. Environmental Management . https://doi.org/10.1007/s00267-024-01943-z Asante-Yeboah, E., Koo, H., Sieber, S., & Fürst, C. (2024b). Designing mosaic landscapes for sustainable outcomes: Evaluating land-use options on ecosystem service provisioning in southwestern Ghana. Journal of Environmental Management , 353 , 120127. Asubonteng, K. O., Pfeffer, K., Ros-Tonen, M. A., Baud, I., & Benefoh, D. T. (2020). Integration versus segregation: Structural dynamics of a smallholder-dominated mosaic landscape under tree-crop expansion in Ghana. Applied Geography , 102201. Asubonteng, K. O., Ros-Tonen, M. A., Baud, I. S. A., & Pfeffer, K. (2021). Envisioning the future of mosaic landscapes: Actor perceptions in a mixed cocoa/oil-palm area in Ghana. Environmental Management , 68 (5), 701–719. Bansah, K. J., Dumakor-Dupey, N. K., Kansake, B. A., Assan, E., & Bekui, P. (2018). Socioeconomic and environmental assessment of informal artisanal and small-scale mining in Ghana. Journal of Cleaner Production , 202 , 465–475. https://doi.org/10.1016/j.jclepro.2018.08.150 Bao Pham, Q., Ajim Ali, S., Parvin, F., Van On, V., Mohd Sidek, L., Đurin, B., Cetl, V., Šamanović, S., & Nguyet Minh, N. (2024). Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network. Advances in Space Research , 74 (1), 17–47. https://doi.org/10.1016/j.asr.2024.03.027 Benites, G. V. (2023). Natures of concern: The criminalization of artisanal and small-scale mining in Colombia and Peru. The Extractive Industries and Society , 13 , 101105. https://doi.org/10.1016/j.exis.2022.101105 Bennett, E. M., Baird, J., Baulch, H., Chaplin-Kramer, R., Fraser, E., Loring, P., Morrison, P., Parrott, L., Sherren, K., & Winkler, K. J. (2021). Ecosystem services and the resilience of agricultural landscapes. In Advances in ecological research (Vol. 64, pp. 1–43). Elsevier. https://www.sciencedirect.com/science/article/pii/S0065250421000015 Bren d’Amour, C., Reitsma, F., Baiocchi, G., Barthel, S., Güneralp, B., Erb, K.-H., Haberl, H., Creutzig, F., & Seto, K. C. (2017). Future urban land expansion and implications for global croplands. Proceedings of the National Academy of Sciences , 114 (34), 8939–8944. https://doi.org/10.1073/pnas.1606036114 Buckley, R. (2024). Analyzing Income Diversification Strategies among Cashew Farming Communities in Ghana [Master’s Thesis, Norwegian University of Life Sciences]. https://nmbu.brage.unit.no/nmbu-xmlui/handle/11250/3163061 Byrareddy, V., Kouadio, L., Mushtaq, S., & Stone, R. (2019). Sustainable Production of Robusta Coffee under a Changing Climate: A 10-Year Monitoring of Fertilizer Management in Coffee Farms in Vietnam and Indonesia. Agronomy , 9 (9). https://doi.org/10.3390/agronomy9090499 Chambon, B., Ruf, F., Kongmanee, C., & Angthong, S. (2016). Can the cocoa cycle model explain the continuous growth of the rubber (Hevea brasiliensis) sector for more than a century in Thailand? Journal of Rural Studies , 44 , 187–197. https://doi.org/10.1016/j.jrurstud.2016.02.003 Chirwa, P. W., Syampungani, S., Mwamba, T. M., Kouami, K., Amusa, T. O., Kamwi, J. M., Babalola, F. D., Mng’omba, S. A., Yusuf, A. A., Kabwe, G., Assèdé, E. S. P., Sileshi, G. W., Thangata, P., & Chama, L. (2024). Securing Multifunctional Landscapes in Africa: What Is the Future Policy, Research and Development Direction? In P. W. Chirwa, S. Syampungani, & T. M. Mwamba (Eds.), Trees in a Sub-Saharan Multi-functional Landscape: Research, Management, and Policy (pp. 329–349). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-69812-5_14 Chughtai, A. H., Abbasi, H., & Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment , 22 , 100482. https://doi.org/10.1016/j.rsase.2021.100482 Coulibaly, S. K., & Erbao, C. (2019). An empirical analysis of the determinants of cocoa production in Cote d’Ivoire. Journal of Economic Structures , 8 (1), 5. https://doi.org/10.1186/s40008-019-0135-5 Creutzig, F., d’Amour, C. B., Weddige, U., Fuss, S., Beringer, T., Gläser, A., Kalkuhl, M., Steckel, J. C., Radebach, A., & Edenhofer, O. (2019). Assessing human and environmental pressures of global land-use change 2000–2010. Global Sustainability , 2 , e1. Cudjoe, K., Nyantakyi, E. K., Borkloe, J. K., Adjei, E. A., Siabi, E. K., Ackerson, N. O. B., Yeboah, S. I. I. K., Domfeh, M. K., Wezenamo, C. A., & Owusu, M. (2023). Assessing livelihood and environmental implications of artisanal and small-scale mining: A case of Akango mining, Nzema East Municipality, Western Region, Ghana. Environment, Development and Sustainability , 1–28. Deh, S. K., Kouame, K. J., Eba, A. E. L., Djemin, J. E., Kpan, A., & Jourda, J. P. (2017). Contribution of Geographic Information Systems in Protection Zones Delineation around a Surface Water Resource in Adzope Region (Southeast of Côte d’Ivoire). Journal of Environmental Protection , 8 (13), Article 13. https://doi.org/10.4236/jep.2017.813102 Diwediga, B., Agodzo, S., Wala, K., & Le, Q. B. (2017). Assessment of multifunctional landscapes dynamics in the mountainous basin of the Mo River (Togo, West Africa). Journal of Geographical Sciences , 27 (5), 579–605. Domingo, J. P. T., Jenkin, G. R., Quick, L., Williams, R. D., Hudson-Edwards, K. A., Tortajada, C., Byrne, P., Coulthard, T. J., Padrones, J. T., & Crane, R. (2024). Sustainable mining in tropical, biodiverse landscapes: Environmental challenges and opportunities in the archipelagic Philippines. Journal of Cleaner Production , 143114. Dronova, I. (2017). Environmental heterogeneity as a bridge between ecosystem service and visual quality objectives in management, planning and design. Landscape and Urban Planning , 163 , 90–106. Dumenu, W. K., & Obeng, E. A. (2016). Climate change and rural communities in Ghana: Social vulnerability, impacts, adaptations and policy implications. Environmental Science & Policy , 55 , 208–217. Duriaux Chavarría, J.-Y., Baudron, F., & Sunderland, T. (2018). Retaining forests within agricultural landscapes as a pathway to sustainable intensification: Evidence from Southern Ethiopia. Agriculture, Ecosystems & Environment , 263 , 41–52. https://doi.org/10.1016/j.agee.2018.04.020 EU. (2023). Regulation (Eu) 2023/1115 of the European Parliament and of the Council of 31 May 2023. Official Journal of the European Union . http://data.europa.eu/eli/reg/2 023/1115/oj Gashaw, T., Tulu, T., Argaw, M., Worqlul, A. W., Tolessa, T., & Kindu, M. (2018). Estimating the impacts of land use/land cover changes on Ecosystem Service Values: The case of the Andassa watershed in the Upper Blue Nile basin of Ethiopia. Ecosystem Services , 31 , 219–228. Gil, J. D. B., Cohn, A. S., Duncan, J., Newton, P., & Vermeulen, S. (2017). The resilience of integrated agricultural systems to climate change. WIREs Climate Change , 8 (4), e461. https://doi.org/10.1002/wcc.461 González, E., Rossetti, M. R., Moreno, M. L., Bernaschini, M. L., Cagnolo, L., Musicante, M. L., Salvo, A., & Valladares, G. (2024). Habitat Loss and Fragmentation in Chaco Forests: A Review of the Responses of Insect Communities and Consequences for Ecosystem Processes. In J. L. León-Cortés & A. Córdoba-Aguilar (Eds.), Insect Decline and Conservation in the Neotropics (pp. 129–162). Springer International Publishing. https://doi.org/10.1007/978-3-031-49255-6_7 Goulart, F. F., Chappell, M. J., Mertens, F., & Soares-Filho, B. (2023). Sparing or expanding? The effects of agricultural yields on farm expansion and deforestation in the tropics. Biodiversity and Conservation , 32 (3), 1089–1104. Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., Lovejoy, T. E., Sexton, J. O., Austin, M. P., Collins, C. D., Cook, W. M., Damschen, E. I., Ewers, R. M., Foster, B. L., Jenkins, C. N., King, A. J., Laurance, W. F., Levey, D. J., Margules, C. R., … Townshend, J. R. (2015). Habitat fragmentation and its lasting impact on Earth’s ecosystems. Science Advances , 1 (2), e1500052. https://doi.org/10.1126/sciadv.1500052 Hashmiu, I., Adams, F., Etuah, S., & Quaye, J. (2024). Food-cash crop diversification and farm household welfare in the Forest-Savannah Transition Zone of Ghana. Food Security , 16 (2), 487–509. Heirman, J. (2016). The impact of international actors on domestic agricultural Policy: A comparison of cocoa and rice in Ghana [PhD Thesis, University of Oxford]. https://ora.ox.ac.uk/objects/uuid:980ac41f-a591-4e23-ab16-deb6df121573 Howell, P. E., Muths, E., Hossack, B. R., Sigafus, B. H., & Chandler, R. B. (2018). Increasing connectivity between metapopulation ecology and landscape ecology. Ecology , 99 (5), 1119–1128. https://doi.org/10.1002/ecy.2189 Ibrahim, F.-H., Rawat, G. S., Yahara, T., Abi-Said, M., Corlett, R. T., Courchamp, F., Dai, R., Freitag, H., Haryoko, T., Hewitt, C. L., Hussain, T., Kadoya, T., Maheswaran, G., Miyashita, T., Mohan Kumar, B., Mohapatra, A., Nakashizuka, T., Piggott, J. J., Rawal, R., Yamakita, T. (2018). Status, trends and future dynamics of biodiversity and ecosystems underpinning nature’s contributions to people. In The IPBES regional assessment report on biodiversity and ecosystem services for Asia and the Pacific (pp. 220–336). Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem services. Jing Luo, X. H., Hong Cheng, Ning Jiang, Xuyu Mo, Xiuyu Wang. (2022). Impacts of landscape structure changes on ecosystem services in the Hunshandake Sandy Land. Journal of Desert Research , 42 (4), 99–109. Jung, M. (2016). LecoS — A python plugin for automated landscape ecology analysis. Ecological Informatics , 31 , 18–21. https://doi.org/10.1016/j.ecoinf.2015.11.006 Karimi, J. D., Corstanje, R., & Harris, J. A. (2021). Understanding the importance of landscape configuration on ecosystem service bundles at a high resolution in urban landscapes in the UK. Landscape Ecology , 36 (7), 2007–2024. https://doi.org/10.1007/s10980-021-01200-2 Keesstra, S., Nunes, J., Novara, A., Finger, D., Avelar, D., Kalantari, Z., & Cerdà, A. (2018). The superior effect of nature based solutions in land management for enhancing ecosystem services. Science of the Total Environment , 610 , 997–1009. Khanal, A. R., & Mishra, A. K. (2017). Enhancing food security: Food crop portfolio choice in response to climatic risk in India. Global Food Security , 12 , 22–30. https://doi.org/10.1016/j.gfs.2016.12.003 Kouassi, J.-L., Gyau, A., Diby, L., Bene, Y., & Kouamé, C. (2021). Assessing land use and land cover change and farmers’ perceptions of deforestation and land degradation in South-West Côte d’Ivoire, West Africa. Land , 10 (4), 429. Kremen, C., & Merenlender, A. M. (2018). Landscapes that work for biodiversity and people. Science , 362 (6412), eaau6020. Kumar, P., Uthappa, A. R., Chavan, S. B., Chichaghare, A. R., Debta, H., Bhat, S., & Dagar, J. C. (2023). Achieving Biodiversity Conservation, Livelihood Security and Sustainable Development Goals Through Agroforestry in Coastal and Island Regions of India and Southeast Asia. In J. C. Dagar, S. R. Gupta, & G. W. Sileshi (Eds.), Agroforestry for Sustainable Intensification of Agriculture in Asia and Africa (pp. 429–486). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-4602-8_14 Kwang, C., Afele, I. K., Yeboah, E., Sarfo, I., Batame, M., Okrah, A., Shwe, M. M., Siaw, W., Boyetey, D., Larbi, R. O., Mensah, A. O. K. N., El Rhadiouini, C., Jaffry, A. H., Siddique, F., Aftab, R., & Isinkaralar, O. (2025). Machine learning assessment of illegal mining (Galamsey) impacts on forest vegetation: A case study of Wassa Amenfi East District, Ghana. Earth Science Informatics , 18 (2), 348. https://doi.org/10.1007/s12145-025-01860-7 Läderach, P., Martinez-Valle, A., Schroth, G., & Castro, N. (2013). Predicting the future climatic suitability for cocoa farming of the world’s leading producer countries, Ghana and Côte d’Ivoire. Climatic Change , 119 (3), 841–854. https://doi.org/10.1007/s10584-013-0774-8 Laurance, W. F., Sayer, J., & Cassman, K. G. (2014). Agricultural expansion and its impacts on tropical nature. Trends in Ecology & Evolution , 29 (2), 107–116. Li, Y., Xie, W., Zhang, J., & Zhang, D. (2024). Spatiotemporal changes and driving factors of ecological environmental quality in the Yongding-Luan River Basin based on RSEI. Frontiers in Environmental Science , 12 . https://doi.org/10.3389/fenvs.2024.1494098 Liu, D., Chen, J., & Ouyang, Z. (2020). Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China. Science of The Total Environment , 710 , 136311. https://doi.org/10.1016/j.scitotenv.2019.136311 Loh, S. K., Asubonteng, K. O., & Adanu, S. K. (2022). Effects of monocropping on land cover transitions in the wet evergreen agro-ecological zone of Ghana. Land , 11 (7), 1063. Mabhaudhi, T., Hlahla, S., Chimonyo, V. G. P., Henriksson, R., Chibarabada, T. P., Murugani, V. G., Groner, V. P., Tadele, Z., Sobratee, N., & Slotow, R. (2022). Diversity and diversification: Ecosystem services derived from underutilized crops and their co-benefits for sustainable agricultural landscapes and resilient food systems in Africa. Frontiers in Agronomy , 4 , 859223. Marie Delalay, Varun Tiwari, Alan D. Ziegler, Vik Gopal, & Paul Passy. (2019). Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: Operational method and its implementation for a mountainous area of Nepal. Journal of Applied Remote Sensing , 13 (1), 014530. https://doi.org/10.1117/1.JRS.13.014530 Marin-Burgos, V., & Clancy, J. S. (2017). Understanding the expansion of energy crops beyond the global biofuel boom: Evidence from oil palm expansion in Colombia. Energy, Sustainability and Society , 7 (1), 21. https://doi.org/10.1186/s13705-017-0123-2 McGranahan, D. A. (2014). Ecologies of Scale: Multifunctionality Connects Conservation and Agriculture across Fields, Farms, and Landscapes. Land , 3 (3), 739–769. https://doi.org/10.3390/land3030739 McKenzie, F. C., & Williams, J. (2015). Sustainable food production: Constraints, challenges and choices by 2050. Food Security , 7 (2), 221–233. Meutia, A. A., Bachriadi, D., & Gafur, N. A. (2023). Environment Degradation, Health Threats, and Legality at the Artisanal Small-Scale Gold Mining Sites in Indonesia. International Journal of Environmental Research and Public Health , 20 (18). https://doi.org/10.3390/ijerph20186774 Meyfroidt, P., Chowdhury, R. R., de Bremond, A., Ellis, E. C., Erb, K.-H., Filatova, T., Garrett, R. D., Grove, J. M., Heinimann, A., & Kuemmerle, T. (2018). Middle-range theories of land system change. Global Environmental Change , 53 , 52–67. Millard, E. (2019). Recent Experiences from the Natural Rubber Industry and Its Movement Towards Sustainability. In M. Schmidt, D. Giovannucci, D. Palekhov, & B. Hansmann (Eds.), Sustainable Global Value Chains (pp. 499–520). Springer International Publishing. https://doi.org/10.1007/978-3-319-14877-9_27 Mohd-Azlan, J., Kaicheen, S. S., Lok, L., & Lawes, M. J. (2019). The role of forest fragments in small mammal conservation in an oil palm plantation in northern Sarawak, Borneo. Journal of Oil Palm Research , 31 (3), 422–436. Molotoks, A., Kuhnert, M., Dawson, T. P., & Smith, P. (2017). Global hotspots of conflict risk between food security and biodiversity conservation. Land , 6 (4), 67. Mudzengi, C. P., Dahwa, E., Mubvuma, M. T., Chapungu, L., & Poshiwa, X. (2025). Environmental and Socio-economic Shocks on Smallholder Farmers: Can Resilience Be Achieved Without Livestock in Zimbabwe? In L. Mhlanga, K. Nyikahadzoi, & J. Stack (Eds.), Building Resilience to Climate Change in Selected Districts in Zimbabwe (pp. 109–129). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80529-5_6 Muluneh, M. G. (2021). Impact of climate change on biodiversity and food security: A global perspective—a review article. Agriculture & Food Security , 10 (1), 1–25. Mustafa, M. A., Mayes, S., & Massawe, F. (2019). Crop Diversification Through a Wider Use of Underutilised Crops: A Strategy to Ensure Food and Nutrition Security in the Face of Climate Change. In A. Sarkar, S. R. Sensarma, & G. W. vanLoon (Eds.), Sustainable Solutions for Food Security (pp. 125–149). Springer International Publishing. https://doi.org/10.1007/978-3-319-77878-5_7 Obodai, J., Adjei, K. A., Odai, S. N., & Lumor, M. (2019). Land use/land cover dynamics using landsat data in a gold mining basin-the Ankobra, Ghana. Remote Sensing Applications: Society and Environment , 13 , 247–256. Odijie, M. (2019). Environmental change and normalization of cash crop systems in Africa: Preventing agrarian change in West Africa cocoa. International Journal of Sustainable Development & World Ecology , 26 (7), 597–611. https://doi.org/10.1080/13504509.2019.1641763 Ouattara, T. A., Sokeng, V.-C. J., Zo-Bi, I. C., Kouamé, K. F., Grinand, C., & Vaudry, R. (2022). Detection of Forest Tree Losses in Côte d’Ivoire Using Drone Aerial Images. Drones , 6 (4). https://doi.org/10.3390/drones6040083 Patil, A., & Panhalkar, S. (2023). A comparative analysis of machine learning algorithms for land use and land cover classification using google earth engine platform. Journal of Geomatics , 17 , 111–118. https://doi.org/10.58825/jog.2023.17.2.96 Pereira, L. S. (2024). Analyzing the Impact of Technological Innovations on Smallholder Farmers Within Ivory Coast’s Cocoa Supply Chain. Law and Economy , 3 (3), 1–11. Perfecto, I., Jiménez-Soto, M. E., & Vandermeer, J. (2019). Coffee landscapes shaping the Anthropocene: Forced simplification on a complex agroecological landscape. Current Anthropology , 60 (S20), S236–S250. Pickett, S. T. A., Cadenasso, M. L., Rosi-Marshall, E. J., Belt, K. T., Groffman, P. M., Grove, J. M., Irwin, E. G., Kaushal, S. S., LaDeau, S. L., Nilon, C. H., Swan, C. M., & Warren, P. S. (2017). Dynamic heterogeneity: A framework to promote ecological integration and hypothesis generation in urban systems. Urban Ecosystems , 20 (1), 1–14. https://doi.org/10.1007/s11252-016-0574-9 Premono, B. T., Lestari, S., & Winarno, B. (2019). Tree-crop diversification by smallholder farmers in Southern Sumatra to diversify sources of income. IOP Conference Series: Earth and Environmental Science , 298 (1), 012032. https://iopscience.iop.org/article/10.1088/1755-1315/298/1/012032/meta Ran, P., Hu, S., Frazier, A. E., Yang, S., Song, X., & Qu, S. (2023). The dynamic relationships between landscape structure and ecosystem services: An empirical analysis from the Wuhan metropolitan area, China. Journal of Environmental Management , 325 , 116575. https://doi.org/10.1016/j.jenvman.2022.116575 Rosa-Schleich, J., Loos, J., Mußhoff, O., & Tscharntke, T. (2019). Ecological-economic trade-offs of diversified farming systems–a review. Ecological Economics , 160 , 251–263. Ruf, F. (2015). Diversification of Cocoa Farms in Côte d’Ivoire: Complementarity of and Competition from Rubber Rent. In F. Ruf & G. Schroth (Eds.), Economics and Ecology of Diversification (pp. 41–86). Springer Netherlands. https://doi.org/10.1007/978-94-017-7294-5_2 Ruf, F., & Schroth, G. (2015). Introduction—Economic and Ecological Aspects of Diversification of Tropical Tree Crops. In F. Ruf & G. Schroth (Eds.), Economics and Ecology of Diversification (pp. 1–40). Springer Netherlands. https://doi.org/10.1007/978-94-017-7294-5_1 Sabins Jr, F. F., & Ellis, J. M. (2020). Remote sensing: Principles, interpretation, and applications . Waveland Press. https://books.google.com/books?hl=en&lr=&id=rAnaDwAAQBAJ&oi=fnd&pg=PR1&dq=principles+of+remote+sensing,+accuracy+assessment&ots=_GYt5Cxy2d&sig=HfnU0cDQpownc5pefCVJXQBU5G8 Sankaran, K. V., Tjitrosemito, S., & Sastroutomo, S. S. (2017). Impacts and management options for Mikania micrantha in plantations. In C. A. Ellison, K. V. Sankaran, & S. T. Murphy (Eds.), Invasive alien plants: Impacts on development and options for management (pp. 39–58). CABI. https://doi.org/10.1079/9781780646275.0039 Sayer, J., Margules, C., Boedhihartono, A. K., Dale, A., Sunderland, T., Supriatna, J., & Saryanthi, R. (2015). Landscape approaches; what are the pre-conditions for success? Sustainability Science , 10 , 345–355. Schroth, G., & Ruf, F. (2014). Farmer strategies for tree crop diversification in the humid tropics. A review. Agronomy for Sustainable Development , 34 (1), 139–154. Schuler, H. R., Alarcon, G. G., Joner, F., dos Santos, K. L., Siminski, A., & Siddique, I. (2022). Ecosystem services from ecological agroforestry in Brazil: A systematic map of scientific evidence. Land , 11 (01), 83. Sirami, C. (2016). Biodiversity in heterogeneous and dynamic landscapes. In Oxford research encyclopedia of environmental science . https://oxfordre.com/environmentalscience/display/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-33 Sivaraman, K., Thankamani, C. K., & Srinivasan, V. (2024). Crop Diversification: Cropping/System Approach for Enhancing Farmers’ Income. In P. N. Ravindran, K. Sivaraman, S. Devasahayam, & K. N. Babu (Eds.), Handbook of Spices in India: 75 Years of Research and Development (pp. 3847–3926). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-3728-6_61 Smith, P., House, J. I., Bustamante, M., Sobocká, J., Harper, R., Pan, G., West, P. C., Clark, J. M., Adhya, T., Rumpel, C., Paustian, K., Kuikman, P., Cotrufo, M. F., Elliott, J. A., McDowell, R., Griffiths, R. I., Asakawa, S., Bondeau, A., Jain, A. K., … Pugh, T. A. M. (2016). Global change pressures on soils from land use and management. Global Change Biology , 22 (3), 1008–1028. https://doi.org/10.1111/gcb.13068 Sonter, L. J., Tomsett, N., Wu, D., & Maron, M. (2017). Biodiversity offsetting in dynamic landscapes: Influence of regulatory context and counterfactual assumptions on achievement of no net loss. Biological Conservation , 206 , 314–319. https://doi.org/10.1016/j.biocon.2016.11.025 Stephens, C. M., Lall, U., Johnson, F. M., & Marshall, L. A. (2021). Landscape changes and their hydrologic effects: Interactions and feedbacks across scales. Earth-Science Reviews , 212 , 103466. Stratton, A. E., Kuhl, L., & Blesh, J. (2020). Ecological and nutritional functions of agroecosystems as indicators of smallholder resilience. Frontiers in Sustainable Food Systems , 4 , 543914. Stryke, J. D. (1972). Exports and Growth in the Ivory Coast: Timber, Cocoa and Coffee . Center discussion paper. https://www.econstor.eu/bitstream/10419/160076/1/cdp147.pdf Tarazona Coronel, Y., Benitez-Paez, F., Nowosad, J., Drenkhan, F., & Timana, M. (2024). scikit-eo: A Python package for Remote Sensing Data Analysis. The Journal of Open Source Software , 9 , 6692. https://doi.org/10.21105/joss.06692 Tariq, A., Mumtaz, F., Majeed, M., & Zeng, X. (2022). Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan. Environmental Monitoring and Assessment , 195 (1), 114. https://doi.org/10.1007/s10661-022-10738-w Tesfaye, W., Elias, E., Warkineh, B., Tekalign, M., & Abebe, G. (2024). Modeling of land use and land cover changes using google earth engine and machine learning approach: Implications for landscape management. Environmental Systems Research , 13 (1), 31. https://doi.org/10.1186/s40068-024-00366-3 Tieskens, K. F., Schulp, C. J., Levers, C., Lieskovskỳ, J., Kuemmerle, T., Plieninger, T., & Verburg, P. H. (2017). Characterizing European cultural landscapes: Accounting for structure, management intensity and value of agricultural and forest landscapes. Land Use Policy , 62 , 29–39. Tokou, B. A., Coral, C., Montiel, F. I., Adou Yao, C. Y., Sieber, S., & Löhr, K. (2025). Diversification strategies to improve cocoa farmers’ household income: The case of Côte d’Ivoire. Frontiers in Sustainable Food Systems , 9 , 1524997. van Zonneveld, M., Turmel, M.-S., & Hellin, J. (2020). Decision-making to diversify farm systems for climate change adaptation. Frontiers in Sustainable Food Systems , 4 , 32. Vellema, S., Laven, A., Ton, G., & Muilerman, S. (2016). Policy reform and supply chain governance. The Economics of Chocolate, Oxford University Press, Oxford , 228–246. Vernooy, R. (2022). Does crop diversification lead to climate-related resilience? Improving the theory through insights on practice. Agroecology and Sustainable Food Systems , 46 (6), 877–901. https://doi.org/10.1080/21683565.2022.2076184 Vogel, C., Poveda, K., Iverson, A., Boetzl, F. A., Mkandawire, T., Chunga, T. L., Küstner, G., Keller, A., Bezner Kerr, R., & Steffan-Dewenter, I. (2023). The effects of crop type, landscape composition and agroecological practices on biodiversity and ecosystem services in tropical smallholder farms. Journal of Applied Ecology , 60 (5), 859–874. https://doi.org/10.1111/1365-2664.14380 Waarts, Y. R., Janssen, V., Aryeetey, R., Onduru, D., Heriyanto, D., Aprillya, S. T., N’Guessan, A., Courbois, L., Bakker, D., & Ingram, V. J. (2021). Multiple pathways towards achieving a living income for different types of smallholder tree-crop commodity farmers. Food Security , 13 (6), 1467–1496. https://doi.org/10.1007/s12571-021-01220-5 Waldman, K. B., Giroux, S. A., Farmer, J. R., Heaberlin, B. M., Blekking, J. P., & Todd, P. M. (2021). Socioeconomic threats are more salient to farmers than environmental threats. Journal of Rural Studies , 86 , 508–517. Walz, Y., Wegmann, M., Dech, S., Vounatsou, P., Poda, J.-N., N’Goran, E. K., Utzinger, J., & Raso, G. (2015). Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing. PLOS Neglected Tropical Diseases , 9 (11), e0004217. https://doi.org/10.1371/journal.pntd.0004217 Wang, S., & Pfister, S. (2024). Landscape-Scale Biodiversity Impacts Analysis of Côte d’Ivoire’s Cocoa Cultivation along Export Supply Chains. Environmental Science & Technology , 58 (22), 9601–9611. https://doi.org/10.1021/acs.est.3c07795 Wong, G. Y., Holm, M., Pietarinen, N., Ville, A., & Brockhaus, M. (2022). The making of resource frontier spaces in the Congo Basin and Southeast Asia: A critical analysis of narratives, actors and drivers in the scientific literature. World Development Perspectives , 27 , 100451 Xu, M., Luo ,T., & and Wang, Z. (2020). Urbanization diverges residents’ landscape preferences but towards a more natural landscape: Case to complement landscape ecology from the lens of landscape perception. International Journal of Sustainable Development & World Ecology , 27 (3), 250–260. https://doi.org/10.1080/13504509.2020.1727989 Zhang, W., Dulloo, E., Kennedy, G., Bailey, A., Sandhu, H., & Nkonya, E. (2019). Biodiversity and ecosystem services. In Sustainable Food and Agriculture (pp. 137–152). Elsevier. https://www.sciencedirect.com/science/article/pii/B978012812134400008X Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6613964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456467081,"identity":"75cafca6-4063-409c-921f-9bc9449b927c","order_by":0,"name":"Evelyn Asante-Yeboah","email":"data:image/png;base64,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","orcid":"","institution":"Leibniz Centre for Agricultural Landscape Research (ZALF)","correspondingAuthor":true,"prefix":"","firstName":"Evelyn","middleName":"","lastName":"Asante-Yeboah","suffix":""},{"id":456467082,"identity":"cbd2a09e-e662-46c6-954f-1ec501c01a91","order_by":1,"name":"Benoit Sarrazin","email":"","orcid":"","institution":"ISARA","correspondingAuthor":false,"prefix":"","firstName":"Benoit","middleName":"","lastName":"Sarrazin","suffix":""},{"id":456467083,"identity":"27c7995e-abb1-4f6c-a432-fd7708834971","order_by":2,"name":"Bonna Antoinette Tokou","email":"","orcid":"","institution":"Leibniz Centre for Agricultural Landscape Research (ZALF)","correspondingAuthor":false,"prefix":"","firstName":"Bonna","middleName":"Antoinette","lastName":"Tokou","suffix":""},{"id":456467085,"identity":"0b6c93ed-1c7e-4691-a1f6-f8b7a5fc48ae","order_by":3,"name":"Franziska Ollendorf","email":"","orcid":"","institution":"Leibniz Centre for Agricultural Landscape Research (ZALF)","correspondingAuthor":false,"prefix":"","firstName":"Franziska","middleName":"","lastName":"Ollendorf","suffix":""},{"id":456467089,"identity":"15fe7f8b-ec8f-453e-aa88-bd28c6a008ae","order_by":4,"name":"Stefan Sieber","email":"","orcid":"","institution":"Leibniz Centre for Agricultural Landscape Research (ZALF)","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Sieber","suffix":""},{"id":456467092,"identity":"7f2fceab-8428-4346-ad05-df44578e89c1","order_by":5,"name":"Katharina Löhr","email":"","orcid":"","institution":"Leibniz Centre for Agricultural Landscape Research (ZALF)","correspondingAuthor":false,"prefix":"","firstName":"Katharina","middleName":"","lastName":"Löhr","suffix":""}],"badges":[],"createdAt":"2025-05-07 16:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6613964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6613964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82829731,"identity":"cfcee786-588f-4dbf-acca-9715f5be7e60","added_by":"auto","created_at":"2025-05-15 16:59:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62925,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagram illustrates the vicious cycle of negative landscape change and ecological degradation in landscape ecology. Landscape structure (composition and configuration) influences landscape functions (ecological processes). In turn, landscape changes such as fragmentation and land shift, alter both the structure and function of the landscape, potentially leading to ecological degradation.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6613964/v1/060067d43a479131034a3e3c.jpg"},{"id":82829732,"identity":"ea7b6fee-91c6-40da-b486-ab35ff6fd91f","added_by":"auto","created_at":"2025-05-15 16:59:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109836,"visible":true,"origin":"","legend":"\u003cp\u003eThe selected study area located within the Adzope, Akoupe and Yakasse-Attobrou regions of eastern Côte d’Ivoire\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6613964/v1/5b95157661b0f73f7dd62d1b.jpg"},{"id":82829733,"identity":"95d82e0a-99cf-4f37-91d0-001910766ccc","added_by":"auto","created_at":"2025-05-15 16:59:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145618,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the flow for the land-cover mapping and the description of Landsat images used in this study.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6613964/v1/81c5af12c0a8139ad466dde9.jpg"},{"id":82829996,"identity":"c70db8fc-a6f6-4524-9ea6-6f33587b7e42","added_by":"auto","created_at":"2025-05-15 17:07:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170090,"visible":true,"origin":"","legend":"\u003cp\u003eLand-cover maps and proportion of area for 1986, 2016, and 2023.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6613964/v1/5b1f1853886bc7eee01deca3.jpg"},{"id":82829995,"identity":"066040b4-109f-4c99-8400-39e45aca5516","added_by":"auto","created_at":"2025-05-15 17:07:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":131303,"visible":true,"origin":"","legend":"\u003cp\u003eLandscape heterogeneity for 1986, 2016, and 2023, measured from landscape diversity (Shannon index), landscape regularity (Shannon evenness), landscape complexity (edge density of patches) and landscape fragmentation (mean patches size).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6613964/v1/71d53a7f3f776f5c9c66e4b9.jpg"},{"id":82830655,"identity":"a0b814b3-fc45-42f1-9f3b-623bff78b1a7","added_by":"auto","created_at":"2025-05-15 17:15:27","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"graphical-abstract","size":271304,"visible":true,"origin":"","legend":"Tree-crop diversification is increasingly adopted in tropical agricultural landscapes as a resilience strategy amidst fluctuating commodity markets, environmental change, and policy shifts. However, its spatial implications at the landscape level remain underexplored. This study examines the structure and dynamics of mosaic landscapes in eastern C\u0026ocirc;te d\u0026rsquo;Ivoire, a region characterized by heterogeneous landscapes, in response to tree-crop diversification trends and their implications for sustainable landscape management. Using multi-temporal Landsat imagery (1986, 2016, 2023), remote sensing classification with a Random Forest algorithm, and landscape metrics, we evaluated changes in land-use/land-cover (LULC), landscape composition (diversity, regularity), and landscape configurational heterogeneity (complexity and fragmentation). Results reveal a substantial increase in rubber plantations (net gain of 50.35%), with concurrent declines in cropland (\u0026minus;\u0026thinsp;147%), cocoa (\u0026minus;\u0026thinsp;45.28%), and sparse vegetation (\u0026minus;\u0026thinsp;61.48%). Although landscape diversity increased slightly (Shannon index: 0.99 to 1.07), fragmentation intensified, with mean patch size decreasing by 12.3%. While tree-crop diversification introduced new compositional complexity, it often manifested as monoculture expansion rather than ecologically restorative land-use. The resulting structural transformations, characterized by high edge densities and smaller, isolated patches, suggest diminished functional connectivity of natural habitats and increasing ecological vulnerability. These trends raise critical questions about the long-term sustainability of current land-use trajectories. We argue that tree-crop diversification, while enhancing economic stability, can erode ecological resilience without integrated landscape-level planning and policy intervention. We recommend landscape-scale strategies that promote agroecological diversification, ecological corridor conservation, and inclusive land-use governance to mitigate fragmentation and maintain the multifunctionality of these rapidly transforming landscapes.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6613964/v1/ff138910b1f8d106395da4b9.png"},{"id":85887662,"identity":"15af7b04-be32-42ae-bee9-30eb5d6c6f66","added_by":"auto","created_at":"2025-07-02 18:16:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2051113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6613964/v1/7d37292c-27cf-458e-b5bf-bd332470b120.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land-use monitoring of tree-crop diversification in eastern Côte d’Ivoire: Landscape structure changes and implications for sustainable landscape development","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, landscapes are under increasing pressure from human activities, particularly the expansion of agricultural systems to meet the demands of a growing population and global markets (Bennett et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Creutzig et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; McKenzie \u0026amp; Williams, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These land-use changes often come at the expense of biodiversity and essential ecosystem services, which are crucial for food security and the well-being of local communities (Molotoks et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Muluneh, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the Global South, mosaic landscapes, characterized by their heterogeneous structure and multifunctionality, play a critical role in supporting diverse stakeholders across multiple scales. These landscapes contribute to food security, livelihoods, and overall socio-ecological resilience (Asante-Yeboah et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Asubonteng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Dronova, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, they are increasingly contested spaces where socio-economic demands, conservation objectives, and climate mitigation efforts often compete. This competition arises from differing stakeholder interests and land-use choices, which can either align to create synergies or conflict, leading to trade-offs between land-use and related ecosystem services. Land-use decisions are influenced by a combination of government policies, international frameworks, and both global and domestic market demands. These overarching conditions shape the actions of micro-agents, such as households, landowners, firms, and farmers, who ultimately implement land-use strategies in response to these pressures. One prominent strategy is tree-crop diversification, involving crops such as coffee (\u003cem\u003eCoffea spp.)\u003c/em\u003e, cocoa (\u003cem\u003eTheobroma cacao\u003c/em\u003e), coconut (\u003cem\u003eCocos nucifera\u003c/em\u003e), oil palm (\u003cem\u003eElaeis guineensis\u003c/em\u003e), and rubber (\u003cem\u003eHevea brasiliensis\u003c/em\u003e). Beyond supporting local livelihoods, these crops are primarily cultivated to supply high-end global markets (Sankaran et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schuler et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTree-crop diversification serves as a crucial strategy for smallholder farmers and their households, acting as a form of \"self-insurance\" that enhances resilience and sustainability in agricultural systems (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Buckley, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Smallholder farmers are particularly vulnerable to environmental and market-related shocks, such as sudden fluctuations in international commodity prices and changes in government policies (Aduhene-Chinbuah \u0026amp; Peprah, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mudzengi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Waldman et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Unlike annual crop farmers, who can adjust their land-use decisions every year, tree-crop farmers face long-term commitments that limit their flexibility in responding to environmental and economic uncertainties. Through diversification, farmers can gradually adapt to environmental changes, including declining soil fertility, increasing weed and pest pressures, emerging crop diseases, reduced rainfall, and diminished microclimate regulation. Rather than relying on a single dominant crop, diversification allows for a more resilient and adaptive agricultural system, mitigating risks and improving long-term sustainability (Gil et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mustafa et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rosa-Schleich et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; van Zonneveld et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vernooy, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is substantial evidence that farmers of tropical tree-crops make diversification and land-use decisions primarily to enhance and stabilize their income (Hashmiu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sivaraman et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Waarts et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many farmers have turned to additional tree-crops due to the more favorable market prices compared to the previous crops, resulting in diversification (Aduhene-Chinbuah \u0026amp; Peprah, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Asante-Yeboah et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003eb\u003c/span\u003e). In the case of C\u0026ocirc;te d\u0026rsquo;Ivoire, before gaining independence, the nation concentrated its economic growth on agriculture, particularly coffee and subsequently cocoa (Ruf, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stryke, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Following independence, various public policies have influenced farmers to favor cocoa, which has emerged as the primary source of agricultural revenue for both the Ivorian populace and the government. The government's pricing policy, which favored cocoa over coffee starting in the mid-1970s, combined with a decline in global coffee prices, significantly contributed to a nationwide shift in diversification (Heirman, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pereira, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vellema et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This led many coffee farmers to initially intercrop coffee with cocoa before fully transitioning to cocoa (Ruf, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notwithstanding, the collapse of the government's price stabilization scheme in 1988, along with falling cocoa prices in the 1990s and 2000s, prompted a shift towards oil palm and, more notably, rubber (Coulibaly \u0026amp; Erbao, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ruf, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The trend of diversifying cocoa farms into rubber, which currently benefits from favorable producer prices and is more resilient to slightly degraded environmental conditions, has become increasingly common across West and Central Africa (Odijie, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ruf \u0026amp; Schroth, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, in Ghana during the 1970s and 1980s, low cocoa producer prices resulting from government pricing policies contributed to the rise of farms diversifying into oil palm and citrus while still maintaining some cocoa production (Michel-Dounias et al. 2013). As witnessed in West Africa, similar trends existed in Asia, where, in Indonesia, low coffee prices also encouraged coffee farmers in the last decade to switch to cocoa (Byrareddy et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, Sulawesi's producers of clove (\u003cem\u003eSyzygium aromaticum\u003c/em\u003e) responded to a declining clove-to-cocoa price ratio during the 1980s and 1990s with diversification into cocoa (Kumar et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cocoa also became the diversification choice for farmers who had previously depended mostly on the production of irrigated rice in Sulawesi. The increase of the cocoa-to-rice price ratio from 2 to 3 in the early 1980s contributed to launching a wave of diversification into cocoa. Many paddy farmers either sold their paddy fields or left them under sharecropping contracts and migrated to upland areas to plant cocoa. Some partially irrigated rice fields were even drained and planted with cocoa (Byrareddy et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ruf \u0026amp; Schroth, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The same has happened in rice farms in southern Thailand with rubber instead of cocoa (Chambon et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ruf \u0026amp; Schroth, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these multiple examples of market-driven tree-crop diversification, the consequences of these developments for mosaic landscape structure, dynamics, and multifunctionality are poorly understood. Tree-crop diversification occurs at three distinct levels: i) at the plot level, where gaps in an existing tree-crop plantations are filled with alternative trees such as fruit trees, or timber trees; ii) at the farm level, where the oldest and least productive plots are replanted with economically viable tree-crops like rubber trees or pasture grass; and iii) at the landscape scale, where different farmers within a given landscape specialize in various crops, creating specialized patches within a diversified land-use mosaic (Schroth \u0026amp; Ruf, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While the plot and farm level focus on optimizing economic and ecological benefits within individual farming units, at the landscape level, emphasis is placed on the large-scale interactions of the different patches, which are concerns for the sustainability and resilience of many landscape ecological functionalities (McGranahan, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch on tree-crop diversification has predominantly focused on the household-level farming system, often examining the economic, social, and ecological benefits for smallholder farmers. Such studies have explored how diversification enhances household income stability, mitigates risks associated with market fluctuations, and improves food security by reducing reliance on a single cash crop (Hashmiu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khanal \u0026amp; Mishra, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Premono et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tokou et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, fewer studies have assessed tree-crop diversification at the landscape level, despite its critical implications for land-use patterns, ecosystem services, and broader socio-ecological dynamics. Landscape-scale assessments can provide insights into how diversification influences habitat connectivity, biodiversity conservation, carbon sequestration, and overall ecosystem multifunctionality. For instance, Kremen and Merenlender (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasize that tree-crop diversification at a landscape scale contributes to ecological resilience by enhancing pollinator habitats and improving watershed health. Similarly, Duriaux Chavarria et al., (2018) suggest that integrating diverse tree-crop systems within landscapes can mitigate deforestation while sustaining agricultural productivity. However, tree-crop diversification can also have negative effects at the landscape level, particularly when poorly planned or driven by market incentives rather than ecological considerations (Asante-Yeboah et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Asubonteng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One key concern is its potential to contribute to habitat fragmentation and biodiversity loss (Loh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vogel et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies have shown that the expansion of tree-crop plantations as diversification systems, particularly cocoa, oil palm, and rubber, often leads to the conversion of natural forests and other critical habitats, reducing overall landscape connectivity and threatening species that rely on intact ecosystems (Asante-Yeboah et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Asubonteng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Loh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Waarts et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang \u0026amp; Pfister, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these broader ecological concerns of tree-crop diversification, research remains limited in integrating spatial metrics and geospatial analyses to assess how tree-crop diversification shapes landscape structure (composition and configuration) and functions (ecological connectivity, ecosystem services). Addressing this gap is essential for informing land-use planning and sustainable agricultural policies that balance production with conservation goals (Laurance et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Meyfroidt et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sayer et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For this purpose, this paper analyzes the structure and dynamics of the smallholder landscape in eastern C\u0026ocirc;te d'Ivoire, a region that has undergone substantial transformation due to tree-crop/land-use diversification, among other factors. We do so by using remote sensing techniques and landscape metrics, and reflect on these three objectives:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo describe and analyze the spatial and temporal expansion of tree-crop diversification system and the resulting mosaic landscapes of eastern C\u0026ocirc;te d\u0026rsquo;Ivoire.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo assess the structural changes on the study landscape pattern, including changes in patch composition and configuration, resulting from the tree-crop diversification.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo synthesize the implications of tree-crop diversification for sustainable landscape management in eastern C\u0026ocirc;te d'Ivoire, drawing from the study\u0026rsquo;s analytical outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eTheoretical framework: relationships between landscape structure, functions, and changes\u003c/h3\u003e\n\u003cp\u003eFrom a spatial perspective, the structure, function, and change of a landscape are the three fundamental characteristics of landscape ecology (Ran et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sonter et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A key principle of landscape ecology, the pattern of landscapes\u0026mdash;defined by their composition and configuration\u0026mdash;strongly influences ecological processes and characteristics (Karimi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This relationship serves as the foundation for the provision of ecosystem services.\u003c/p\u003e \u003cp\u003eChanges in landscape structure lead to changes in landscape functions, and vice versa (Gashaw et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jing Luo, 2022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Anthropogenic factors are seen as the primary drivers of changes to landscape structure. The resultant effect can usually be negative, leading to biodiversity loss and ecosystem degradation, however, there is a global recognition of a positive effect of anthropogenic land-use changes to influence ecosystem services through human\u0026ndash;nature co-produced land-use strategies that enhance landscape multifunctionality (Keesstra et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, a heterogeneous landscape includes multiple land-cover types with a high degree of spatial interaction and multifunctionality. Such landscapes facilitate the dynamic flow of energy and materials, thereby supporting various ecological processes. In contrast, a homogeneous landscape comprises large, segregated land-cover types that primarily focus on providing food, habitat, and raw materials while overlooking other ecosystem services often leading to the loss of ecosystem services (Pickett et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sirami, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stephens et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the case of human-induced smallholder landscape changes, the \u003cem\u003efirst\u003c/em\u003e visible effect is an alteration in the composition of the landscape structure, usually marked by an increase in one ecosystem type at the expense of another, leading to ecosystem degradation and associated environmental risks (Asante-Yeboah, et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Howell et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tieskens et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, the conversion of smallholder heterogeneous farms into monoculture plantations aimed at export markets can negatively impact soil fertility, water regulation, biodiversity, and climate resilience leading to nutrient depletion, increased water consumption, reduced habitat diversity, and heightened vulnerability of agricultural systems to climate change, pests, and disease outbreaks (Mabhaudhi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stratton et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The \u003cem\u003esecond\u003c/em\u003e significant effect is a change in the configuration of the landscape structure, which impacts its overall function. Increased fragmentation and reduced connectivity can make ecosystems more vulnerable and impede the formation of essential ecosystem services. The \u003cem\u003ethird\u003c/em\u003e significant effect is in the structural complexity of the smallholder landscape composed of mosaics of rubber, cocoa, and other tree-crops. The structure often mimics high land-use diversity from a structural perspective, but this does not automatically mean high ecological resilience (Asubonteng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ran et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Structurally, such complexity reflects a greater variety in land-cover types, spatial patterns, and patch configurations, which suggests compositional diversity across the landscape (Ran et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this diversity is largely superficial, as it typically involves monoculture plantations of different species rather than integrated ecologically diverse or multifunctional systems. True ecological resilience requires not just structural diversity but functional diversity, including native vegetation, heterogeneous microhabitats, and intact ecosystem processes (Ran et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, maintaining landscape heterogeneity and connectivity between favorable ecological habitats is crucial for sustaining ecological balance and ensuring the continued provision of ecosystem services, which are key components of sustainable landscape management (Ran et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe hypothesize that the diversification of tree-crop systems in the study area leads to structural changes in the composition and configuration of the landscape that negatively on the ecological resilience. We aimed to demonstrate the effects of this land-use transition on landscape configuration, probably including smaller, more fragmented or isolated patches and increasing landscape complexity. We also expected that, while tree-crop diversification may increase landscape complexity, this would not necessarily mean an enhancement in the ecological connectivity of the study area. This growing complexity could lead to the fragmentation of the remaining natural ecosystems, which may hinder species movement and increase edge density over time. Consequently, we argue that, without integrative spatial planning and ecologically informed management, such land-use diversification is likely to undermine sustainable landscape management by exacerbating ecological degradation and reducing ecosystem services.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThe selected study area consists of a mosaic landscape in the eastern part of C\u0026ocirc;te d\u0026rsquo;lvoire in the M\u0026eacute; region within the towns of Adzop\u0026eacute;, Yakass\u0026eacute; Attobrou, and Akoupe and falls within latitudes 5\u0026deg;45'30\"N and 6\u0026deg;38'10\"N, and longitudes 3\u0026deg;80'30\"W and 3\u0026deg;25'20\"W (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The selected study area is bordered to the north by Ind\u0026eacute;ni\u0026eacute;-Djuablin, to the northwest by Moronou, to the southwest by Agn\u0026eacute;by-Tiassa and the District of Abidjan, and to the southeast by the Sud-Como\u0026eacute; region (CORENA and FADCI, 2016).\u003c/p\u003e \u003cp\u003eThe study area is situated at an elevation of zero meters (0 feet) above sea level and experiences a tropical wet and dry climate. The average annual temperature in this region is 28.42\u0026ordm;C (83.16\u0026ordm;F), which is 0.41% above the national average for C\u0026ocirc;te d'Ivoire (Walz et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The area typically receives between 177.26 and 376 millimeters of rainfall, with approximately 285.77 rainy days each year, accounting for 78.29% of the time. Conversely, the dry season lasts from November to March, with January being the month that records the fewest wet days, averaging only 1.7 days with at least 0.04 inches of precipitation.(Deh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Walz et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom an ecological perspective, the region is classified within the humid climate zone, specifically of the Atti\u0026eacute;en type (Deh et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This ecological zone has undergone substantial transformation due to activities such as mining, agriculture, and urban settlement. Forests are threatened with extinction due to large pioneer fronts linked to the development of cash crops (Ouattara et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Historically, the selected area was part of the former cocoa belt in C\u0026ocirc;te d\u0026rsquo;Ivoire, which was recognized for its significant cocoa production during the 1980s (L\u0026auml;derach et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)..Today, due to aging cocoa, environmental and economic shocks, diversification into other land-cover types, especially diversification into tree-crops, is taking dominance, necessitating the need for assessment of the landscape's structural and functional status to support sustainable landscape planning and management.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData Acquisition and image preprocessing.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study used three remote-sensing images from the Landsat sensor to generate the land-cover maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Landsat satellite images were sourced from the USGS Earth Explorer platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e for the years 1986, 2016, and 2023. These dates were chosen to capture significant changes in land-cover over time, essential for long-term environmental monitoring and analysis and were based on availability, minimal cloud cover (with a maximum threshold of 10%), and low haze levels as cloudy pixels can impact the accuracy of the classification (Sabins Jr \u0026amp; Ellis, 2020). While these four images were not captured on the same anniversary dates, they were all taken during the dry season under comparable atmospheric and phenological conditions (Andrew et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The Landsat images have been extensively used in environmental monitoring studies because of its broad temporal coverage (Li et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tesfaye et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study used scenes from Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) for the land-cover classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The images were preprocessed using Environment for Visualizing Images (ENVI) 5.3 software, including radiometric calibration and atmospheric correction. Subsequently, the images were clipped to the area of interest (AOI) using the selected study area boundary shape file.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of classification scheme\u003c/h3\u003e\n\u003cp\u003eWe identified the classification scheme for this study by reflecting on the land-cover map of C\u0026ocirc;te\u0026rsquo;d\u0026rsquo;lvoire prepared by the \u003cem\u003ebnetd Carte d'occupation des sols de C\u0026ocirc;te d'Ivoire en 2020\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://africageoportal.maps.arcgis.com/apps/webappviewer/index.html?id=88c2493e722546c09c2a0a8b394c4454\u003c/span\u003e\u003cspan address=\"https://africageoportal.maps.arcgis.com/apps/webappviewer/index.html?id=88c2493e722546c09c2a0a8b394c4454\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This map served as a context specific land-cover types and reflected the needs of land-cover types under this study. We identified eight land-cover types for the land-cover mapping in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription and classification scheme for the land-cover mapping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand-cover types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea covered with dense tree vegetation, forest plantation, and reforestation areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas with bare ground, infrastructure and human habitat/settlement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCocoa plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with cocoa tree-crop plantation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRubber plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with rubber tree plantations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with coconut plantations, cashew plantation, arboriculture/fruit plantations, agricultural development, fallow lands, coffee plantations, and other crops\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil palm plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with oil palm trees\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with courses and bodies of water, marshy areas, swamp forest, forest on hydromorphic soil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSparse vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAreas covered with gallery forest, degraded forest, shrubs, herbaceous formations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eField data collection and Land-cover mapping\u003c/h3\u003e\n\u003cp\u003eWe conducted field data collection to collect training samples for the classification process. The field work took place between January to March 2024 to obtain ground truth datasets using a handheld Global Positioning System (GPS) unit. This process enabled us to generate representative datasets, including points and polygons, for each identified land-cover type. Given the highly heterogeneous nature of the landscape, the use of polygons enhanced the accuracy of matching the sampled areas with their corresponding land-cover types on the satellite imagery. A total of two hundred and fifty (250) ground truth data points were collected to classify the 2023 image. In addition, four hundred and eighty (480) polygons, representing various land-cover types, were extracted from high-spatial-resolution imagery available on Google Earth. These polygons were manually digitized on-screen to compensate for inaccessible areas (Diwediga et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In total, seven hundred and thirty (730) points and polygons were gathered as reference data for the 2023 Landsat 8 Operational Land Imager (OLI), Subsequently, the data were randomly divided, with 80% allocated for training and 20% for testing or validation, to assess the accuracy of the classification (Bao Pham et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eA supervised classification was conducted using the Random Forest (RF) algorithm within the Scikit-learn framework, enhanced by the Scikit-eo extension tailored for Earth observation data in Python (Tarazona Coronel et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A major advantage of scikit-eo is its powerful analytical features. It offers a comprehensive set of algorithms tailored for environmental research, covering areas such as statistical analysis, deep learning, data fusion, and spatial analysis (Tarazona Coronel et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The RF algorithm is renowned for its robustness against overfitting and its proficiency in managing datasets with missing values and outliers (Patil \u0026amp; Panhalkar, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, RF has become a prevalent choice in environmental monitoring endeavors, particularly in land-cover classification tasks (Marie Delalay et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tarazona Coronel et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We classified and validated the images for 1986 and 2016 by generating historical LULC information from key local informants. This information was cross-referenced with unchanged areas in the 2023 image to ensure accuracy.\u003c/p\u003e\n\u003ch3\u003eAccuracy Assessment\u003c/h3\u003e\n\u003cp\u003eFollowing the land-cover classification, a confusion matrix and validation matrix are produced to calculate the producer\u0026rsquo;s and user\u0026rsquo;s accuracy, along with the Overall Accuracy (OA) and Kappa Coefficient (K), which are used to evaluate the performance of the Random Forest (RF) classification (Tariq et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overall accuracy and the Kappa statistics are essential indicators for assessing the precision of land-cover classification. A Kappa value of 0.4 or lower denotes poor consistency between the classified variables, a Kappa value ranging from 0.4 to 0.8 indicates moderate to strong consistency, and a value greater than 0.8 signifies excellent consistency (Tariq et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eChange detection analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study utilized post-classification change detection to analyze land transitions across 1986, 2016, and 2023, identifying key land-cover types contributing to the landscape's change dynamics. The primary focus was on the \u003cem\u003eArea/percent change\u003c/em\u003e, and \u003cem\u003ePost classification or categorical change detection\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eArea or percent change\u003c/h2\u003e \u003cp\u003eThe area and percentage change (or rate of change) are commonly used quantitative methods for detecting changes in LCLU, as they effectively illustrate the extent of increase or decrease in each land-cover category within a given region. These changes are typically expressed in hectares and percentages (Chughtai et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tesfaye et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The mathematical representation is given by the following equations:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"715\" height=\"241\"\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCategorical /post-classification change detection\u003c/h3\u003e\n\u003cp\u003eThe method quantifies differences between independently classified land-cover maps for distinct periods, revealing spatial and temporal variations in land features. Conversions are analyzed using the \u0026ldquo;FROM-TO\u0026rdquo; change approach, which tracks transitions between different land-use categories (Chughtai et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By comparing LULC maps from two different time periods, the analysis produced a detailed map of LULC changes along with a transition matrix. This matrix pinpointed specific areas where changes occurred, illustrating the shifts in land cover between the earlier and later periods. Persistent land-cover types appeared along the matrix\u0026rsquo;s diagonal, while transitions were represented by off-diagonal elements. The transition matrix offered key indicators such as gross gains, gross losses, and net change for each land-cover category. Gross gains referred to the total area acquired by a land-cover class from other categories, while gross losses indicated the area lost to other classes. Net change, calculated as the difference between gains and losses, revealed the scale and direction of transformation within the landscape (Asante-Yeboah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLandscape structural analysis\u003c/h3\u003e\n\u003cp\u003eGiven that structural patterns shape landscape processes and ecosystem service delivery, it is essential to analyze the spatial configuration of land-cover types across temporal scales in landscape studies. Such analysis provides insights into how land-use changes affect habitat connectivity, biodiversity, and ecosystem resilience, informing both conservation strategies and land-use planning.\u003c/p\u003e \u003cp\u003eWe employed a 'Landscape Metrics Mapping' script using QGIS 3.x integrated with the Landscape Ecology Statistics (LecoS) plugin to calculate and visualize relevant landscape metrics for this study (Jung, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This method allows for the computation of key landscape metrics across a defined study area, facilitating the analysis of structural changes over time and their potential ecological implications. Using the categorical land-cover maps from this study, we computed landscape metrics of diversity, fragmentation, and complexity at the landscape level (Ran et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We calculated the Shannon diversity index to assess diversity, Shannon evenness for regularity edge density for complexity, and mean patch size for fragmentation. The classified land-use raster datasets were first filtered using an identical process of majority filters to eliminate isolated pixels, thereby reducing noise that could influence the indicator values. To enable spatially explicit metric calculation, a regular fishnet grid was generated over the study area using the \"Create Grid\" tool in QGIS. Grid cells of 5000 meters in a regular grid of points and a buffer distance of 2500 meters were used to segment the landscape into uniform spatial units, allowing for localized metric assessment. Each grid cell served as a unit of analysis in subsequent landscape metrics computation. We used the LecoS plugin within QGIS to compute a suite of landscape metrics at the grid-cell level (Jung, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The classified land-cover raster was used as the input layer, and the generated grid was provided as the zonal mask. LecoS computed landscape-level metrics for each grid cell. Selected metrics included:\u003c/p\u003e \u003cp\u003eShannon diversity index (SDI): \u0026ldquo;Landscape diversity\u0026rdquo;\u003c/p\u003e \u003cp\u003eSHDI= -ΣPi log Pi\u003c/p\u003e \u003cp\u003eLandscape heterogeneity is most often characterized by the Shannon diversity index, where Pi is the proportion of element i in a landscape. This index corresponds to the diversity of landscape elements. The greater the value of the index, the more diverse the landscape.\u003c/p\u003e \u003cp\u003eShannon evenness index (SHDI): \u0026ldquo;Landscape regularity\u0026rdquo;\u003c/p\u003e \u003cp\u003eSHEI\u0026thinsp;=\u0026thinsp;SHDI / Ln (m) with m as a number of different landscape elements. The SHEI values range from 0 to 1\u003c/p\u003e \u003cp\u003eShannon diversity index locally depends on the number of LULC types. The Shannon evenness index (SHEI) relativizes the diversity index by the maximum possible diversity for the number of different elements and land-use types present. it allows local control of the balance between land-uses\u003c/p\u003e \u003cp\u003eEdge density (ED): \u0026ldquo;Landscape complexity\u0026rdquo;\u003c/p\u003e \u003cp\u003eComplexity is represented by edge density (ED):\u003c/p\u003e \u003cp\u003eED\u0026thinsp;=\u0026thinsp;L/A (meter)\u003c/p\u003e \u003cp\u003ewhere L is the total length of edges within a landscape and A is the total area of the landscape. complexity will increase as the length of patch edges increases. Increasing the edge density can help a species to move from one patch to another, using potentially the edges between patches as intermediate refuges. The indices are also associated with complex landscape configuration and consequently better provision of several ecological services, except in the case of human disturbance land-use.\u003c/p\u003e \u003cp\u003eMean patch size (MPS): \u0026ldquo;Landscape fragmentation\u003c/p\u003e \u003cp\u003eTo characterize fragmentation, the average size of patches (polygons) is required: MPS (Mean Patch Size):\u003c/p\u003e \u003cp\u003eMPS\u0026thinsp;=\u0026thinsp;A/N (hectare)\u003c/p\u003e \u003cp\u003ewhere A is the total area of the landscape and N is the total number of patches in the landscape. The index value decreases as the number of patches increases and the landscape becomes increasingly fragmented. This is negatively correlated to the landscape diversity index value.\u003c/p\u003e \u003cp\u003eWhen the patches are larger on average, this suggests greater spatial continuity of the habitat or land-use. For example, a forest with a high MPS may indicate a good state of ecological connectivity and habitat functionality. When the patches are more fragmented or smaller, this may reflect increased fragmentation (often anthropogenic), or a fine landscape mosaic, as in complex agricultural systems or small-scale agro-ecosystems.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eLand-use/land-cover maps of eastern C\u0026ocirc;te d\u0026rsquo;Ivoire for 1986, 2016, and 2023.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe accuracy assessment derived from the error/confusion matrix is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The overall accuracies achieved for the LULC maps in 1986, 2016, and 2023 were 76%, 86%, and 88%, respectively. The classified LULC maps, along with their corresponding statistics for these years, are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. At the beginning of the study period in 1986, dense forest (27%) and sparse vegetation (26%) were the predominant land-cover types. Cocoa accounted for 16%, rubber for 14%, and cropland for 7%, while built-up areas, oil palm, and water bodies each comprised less than 6% of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The distribution of dense forest was primarily concentrated in the southern and northern regions of the landscape. Sparse vegetation was spread throughout the entire area, while cocoa was predominantly found in the central part of the study landscape. Rubber exhibited a distribution pattern similar to that of cocoa, with a slight concentration towards the central-western part of the study area. In contrast, built-up areas and palm plantations were primarily located in the southwestern region.\u003c/p\u003e \u003cp\u003eBy 2016, no new land-cover types emerged; however, the spatial distributions and proportions of the existing eight land-cover types had shifted. Some land-cover types expanded in area, while others decreased. Sparse vegetation became the dominant feature of the study landscape, increasing from 26\u0026ndash;31%. Similarly, rubber plantations grew from 14\u0026ndash;22%, and palm areas rose from 5\u0026ndash;10%. Conversely, dense forest cover declined from 27\u0026ndash;22%. Cocoa cultivation decreased from 16\u0026ndash;12%. Both cropland and built-up areas were reduced to 1%, while the size of water bodies remained unchanged. In 1986, cocoa was prevalent in the central part of the study area, while oil palm and built-up areas dominated the southwestern region. However, up to 2016, these areas diminished, making way for rubber plantations.\u003c/p\u003e \u003cp\u003eBy the end of the study period in 2023, no new land-cover types emerged; however, rubber plantations had significantly increased, covering 28% of the landscape, up from 22% in 2016 and 14% in 1986. The built-up area rose from 1% in 2016 to 8% in 2023, with a notable shift in location from the southwest in 1986 to the central region by 2023. Sparse vegetation has decreased in size by 16%, while dense forest has expanded by 24% compared to 2016. The area dedicated to oil palm has seen a slight decline of 9%, cocoa has further decreased, and cropland has experienced a modest increase to 3%. Throughout the study period, the water body remained unchanged at 1%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eError/confusion matrix showing the accuracy assessment results of the land-cover classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUA (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCocoa Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRubber Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil Palm Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSparse Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa Coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eChanges in land-cover types and landscape transitions of eastern C\u0026ocirc;te d\u0026rsquo;Ivoire from 1986 to 2023.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results of the stock change analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. It indicates a significant decline in cropland, sparse vegetation, cocoa plantations, and dense forests over the entire study period (1986\u0026ndash;2023). These changes were evident in both the area extent and spatial distribution. During the first study period (1986\u0026ndash;2016), cropland experienced a reduction of -532.67% over 30 years. Although there was a slight increase in the second study period (2016\u0026ndash;2023), the overall area extent and distribution of cropland showed a total loss of -147% for the entire study duration (1986\u0026ndash;2023). Sparse vegetation saw a decrease of approximately \u0026minus;\u0026thinsp;17.88% during the first study period (1986\u0026ndash;2016), while the second study period (2016\u0026ndash;2023) recorded a dramatic reduction of -96.94%. This culminated in an overall decline of -60.96% in area extent by the end of the 37-year study period (1986\u0026ndash;2023). The area dedicated to cocoa decreased significantly during the three study periods, with reductions of 39.73% in the first period, 3.97% in the second, and 45% in the third. Dense forest experienced a decline of approximately 12% in area from 1986 to 2023. In contrast, rubber plantations expanded in all three periods, showing increases of 35.35% in the first period (1986\u0026ndash;2016), 23.21% in the second (2016\u0026ndash;2023), and 50.35% in the third (1986\u0026ndash;2023). Similarly, built-up areas saw a notable increase of 48.27% by the end of the study period (1986\u0026ndash;2023). Additionally, both oil palm and water bodies experienced increased, with percentage changes of 41.39% and 35.03%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the transition matrix for the study landscape, detailing the changed areas and, persistent (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), gross gains and losses as well as land-cover transfers among various categories over a span of 37 years (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The change matrix analysis indicates that approximately 216,872.76 ha (68%) of the land within the study area underwent LULC changes during the 37-year period from 1986\u0026ndash;2023 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), while the remaining 102,536.07 ha (32%) remained stable. The extent of changes varied across different LULC types. For example, of the 23,127.6 ha of cropland recorded in 1986, only 156.5 ha (0.7%) remained unchanged throughout the study period, suggesting that about 99.3% of the cropland was converted to other land-cover types (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Similarly, among the 51,995.9 ha of cocoa plantations in 1986, only 23.2% remained unchanged during the same timeframe. In 2023, 76.7% of the cocoa plantations from 1986 had been converted to other land-use and land-cover types. This indicates that out of the 35,790.39 hectares still covered by cocoa, approximately 16,209.49 hectares that were once cocoa plantations in 1986 have transitioned into different land-cover types. Likewise, rubber plantation area of about 45,047.2 hectares in 1986 experienced a 61.5% reduction due to conversions to other land-cover types by the end of 2023. Dense forests saw about 45.9% of their area change, while the remaining 54.1% remained intact, making it the second land-cover type with the highest persistence, following water bodies, which maintained 73.5% of their area. Cropland had the highest percentage of land area changing, while sparse vegetation ranked second, with 81.1% of its initial area converted to other land-cover types, followed by built-up areas, which experienced a 69.6% change.\u003c/p\u003e \u003cp\u003eRubber plantations emerged as the most dominant contributor to land-cover changes, expanding by 64,888.83 hectares. However, it also experienced a loss of 22,106.40 hectares, resulting in a net gain of 42,728.42 hectares. Rubber became the dominant land-cover category, reflecting the highest net change. The transitions to rubber primarily occurred at the expense of sparse vegetation, dense forest, and cocoa plantations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This indicates that rubber is the leading land-cover type with the most extensive changes from other land-uses on the study landscape.\u003c/p\u003e \u003cp\u003eAlthough sparse vegetation became the second-largest gross gainer, increasing by 43,148.13 hectares. Yet, it also suffered the greatest loss, with a reduction of 66,663.18 hectares, leading to a significant negative net change of -23,515.05 hectares. Similarly, dense forest, cocoa plantations, and cropland all experienced negative net changes, suggesting that the area lost to other land-cover types surpassed the gains from these same categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStock change analysis: Land-cover category area in hectares and percentage change from the Initial size\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003ePercentage change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1986\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1986\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84705.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69854.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74966.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-21.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12858.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4775.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24858.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-169.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCocoa plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51995.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37210.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35790.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-39.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-45.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRubber plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45047.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69673.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90737.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23137.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3657.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9366.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-532.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-147.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil palm plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17360.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31589.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29618.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2449.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3115.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSparse vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82280.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100198.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50955.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-96.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-61.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e319,408.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319,408.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319,408.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable showing persistent and changed areas from 1986\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand-cover types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial area(ha) 1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePersistent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChanged area (ha) (2023)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDense forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84705.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45824.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38881.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12858.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3903.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8954.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCocoa plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51995.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12104.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39891.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRubber plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45047.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17359.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27688.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23137.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22981.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil palm plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17360.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6129.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11230.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1487.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e537.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSparse vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82280.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15571.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66709.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e319408.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e102536.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e216872.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003etransition matrix table for 1986\u0026ndash;2023 showing the area (ha). The diagonal (in bold) values indicates the persistence of land-cover types and\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eTo 2023\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eFrom\u003c/p\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDense forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCocoa Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRubber Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOil palm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSparse Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGrand Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eGross loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNet change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDense Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e45824.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3145.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7661.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13355.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1040.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4745.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e368.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17971.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e94112.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48,288.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-17,130.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3903.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4791.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e606.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1751.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e141.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1351.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12833.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8,929.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10,600.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCocoa Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9375.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1862.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12104.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11491.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1072.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5484.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e356.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10032.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e51779.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e39,675.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-16,009.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRubber Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7317.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3512.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1570.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e17359.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e380.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1575.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e181.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7622.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39519.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22,160.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e42,728.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4150.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2018.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e873.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5726.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e156.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1516.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4887.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19377.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e19,221.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-10,016.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOil palm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e561.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1691.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2277.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4569.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e858.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e6129.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e113.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1147.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17350.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11,220.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12,251.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaterbody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1487.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e134.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2023.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e535.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1,091.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSparse Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9559.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7269.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11022.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24838.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5222.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8332.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e417.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e15571.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82234.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e66,663.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-23,515.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrand Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76,982.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,433.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35,770.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82,247.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,361.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29,602.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3,114.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e58,719.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e319,408.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross gains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31,157.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,529.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23,666.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64,888.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9,204.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23,472.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,626.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43,148.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLandscape structural analysis (1986\u0026ndash;2023)\u003c/h2\u003e \u003cp\u003eOverall, the average values of the landscape structural diversity indices remained relatively stable between 1986 and 2023, with some meaningful variations observed across the individual metrics. Notably, landscape diversity, as measured by the Shannon Diversity Index, showed a slight decrease between 1986 (0.99) and 2016 (0.94), followed by a notable increase to 1.07 in 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This suggests a diversification of land-cover types in recent years, potentially due to a more balanced distribution of the eight classified land-cover types. Landscape regularity, indicated by the Shannon Evenness Index, remained stable over the studied period, returning to its 1986 level of 0.60 after a slight dip in 2016 (0.55) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This stability suggests a long-term trend of landscape elements being substituted over time, rather than any single land-cover type becoming dominant.\u003c/p\u003e \u003cp\u003eIn terms of configuration, landscape complexity increased steadily, as shown by rising edge density from 159.99 m/ha in 1986 to 180.54 m/ha in 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This upward trend points to more intricate and fragmented land-use boundaries, indicating increased configurational complexity in the study landscapes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Simultaneously, the average patch size decreased from 406.56 hectares in 1986 to 356.79 hectares in 2023. This reduction indicates a rise in landscape fragmentation, where land-cover types are broken into smaller, more isolated patches, likely a result of ongoing land-use changes such as agriculture, settlement expansion, or infrastructure development.\u003c/p\u003e \u003cp\u003eAlthough the regional averages suggest relative stability, significant spatial variation in these metrics emerged over time. Landscape diversity, for example, was concentrated in the southern part of the region in 1986, shifted to the east by 2016, and became more prominent in the central and northern areas by 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A similar trend was observed for landscape regularity, which was initially higher in the south but expanded toward the east between 1986 and 2016, then became widespread across the region by 2023. These shifts suggest a growing homogenization in the distribution of landscape elements. The spatial dynamics of landscape complexity closely mirrored those of diversity, with higher edge density values progressively extending eastward and northward (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This pattern supports the observation of increasing configurational complexity in these areas.\u003c/p\u003e \u003cp\u003eFragmentation, measured by mean patch size, was most pronounced around the two main cores of dense natural forest. These areas remained relatively intact in terms of low diversity and complexity, serving as important strongholds for contiguous forest cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the overall trend points to a decline in patch size, even near these dense forests, dropping from over 500 hectares locally in 1986 to under 300 hectares in 2023. This reinforces the concern about increasing fragmentation, even in previously undisturbed zones.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnnual average values of the landscape heterogeneity indices calculated from the land-use classification maps\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape structure metrics\u003c/p\u003e \u003cp\u003e(indicators average values)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape diversity:\u003c/p\u003e \u003cp\u003eShannon diversity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape regularity:\u003c/p\u003e \u003cp\u003eShannon evenness index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape complexity:\u003c/p\u003e \u003cp\u003eEdge density (m/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape fragmentation:\u003c/p\u003e \u003cp\u003eMean patch size (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e406.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e383.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLULC changes\u003c/h2\u003e \u003cp\u003eThe results of the LULC change analysis from 1986 to 2023 reveal profound transformations within the study landscape, which are captured in three folds. \u003cem\u003eFirst\u003c/em\u003e, mosaic landscapes in sub-Saharan Africa have traditionally been the backbone for food production, but the assumption that cropland areas within these landscapes would remain stable is increasingly untenable in the face of shifting agricultural priorities and land-use pressures (Asante-Yeboah et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Chirwa et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cropland underwent a sharp drop, decreasing by -532.67% between 1986 and 2016 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although a modest rebound of 60.96% occurred between 2016 and 2023, the overall trajectory remains negative, amounting to a total net reduction of -147% over the 37 years​. This pattern is consistent with broader regional and global trends, where cropland is gradually replaced by urban expansion and intensified agricultural land-use in tropical regions toward high commodity ends (Bren d\u0026rsquo;Amour et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Goulart et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For small cocoa producers, where productivity is stagnating and incomes are already low, this reduction in available land can exacerbate rural poverty and food insecurity (Tokou et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The post-2016 resurgence may reflect targeted agricultural policies or a response to escalating food demand, yet it remains insufficient to reverse the long-term decline.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSecond\u003c/em\u003e is the compositional dynamics of sparse vegetation, the tree-crops (rubber, oil palm, and cocoa), and dense forest. Sparse vegetation followed a similar trend to cropland, with a modest increase until 2016, followed by a sharp 96.94% decline by 2023, resulting in a net loss of 60.96%, mainly due to the rapid expansion of rubber plantations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). From 1986 to 2023, rubber cultivation expanded by over 50%, largely replacing sparse vegetation, dense forest, and cocoa plantations, 76.7% of which were converted, highlighting a significant shift in land-use priorities toward more economically viable crops. According to Millard (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), rubber has become a more attractive crop because of its higher market price and the growing global demand for rubber, driven by industries like automotive manufacturing. Oil palm cultivation also expanded substantially (41.39%) over the study period. However, a slight contraction of -6.65% from 2016 to 2023 may reflect land saturation, changing market dynamics, or regulatory interventions. This trajectory is consistent with observations from other tropical regions where the expansion of oil palm tends to decelerate after reaching peak land conversion thresholds (Marin-Burgos \u0026amp; Clancy, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Dense forests experienced a moderate but persistent decline of approximately 12% over the study period. Although 54.1% of the 1986 forest cover persisted to 2023, making it one of the most stable LULC classes, about 45.9% was lost to other uses. While this rate of decline is comparatively lower than that observed for cropland or sparse vegetation in the study, it nonetheless reflects sustained deforestation, likely driven by agricultural expansion and logging (Houghton and Nassikas, 2018; Ordway et al., 2017). The resultant effect is the inability of the country to comply with new European regulations against deforestation (EU, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kouassi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The marginal deceleration in forest loss after 2016 may be indicative of emerging conservation initiatives or enhanced land governance.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ethird\u003c/em\u003e is observed in built-up areas exhibiting marked expansion, increasing by 48.27% between 1986 and 2023, with the most pronounced growth (80.79%) occurring between 2016\u0026ndash;2023. This acceleration coincides with a resurgence in illegal mining activities, particularly in the central parts of the study landscape in 2023. High-resolution satellite imagery from 2023 corroborates this trend, revealing extensive land degradation characterized by exposed soil, abandoned pits, and sediment-laden water bodies, typical hallmarks of unregulated mining (Domingo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Comparable trends have been reported in Ghana, where artisanal and small-scale mining (ASM), in Ghana commonly referred to as \u003cem\u003egalamsey\u003c/em\u003e, has contributed significantly to land degradation and rapid LULC changes (Cudjoe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kwang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Obodai et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These mining-induced changes not only result in vegetation loss but also impair soil quality, degrade water resources, and compromise the potential for ecological or agricultural recovery (Dumenu \u0026amp; Obeng, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kouassi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The spatial overlap between areas of cropland and sparse vegetation loss with increased built-up areas, which reveals illegal mined areas, suggests a direct and detrimental impact of informal economic activities on land productivity and environmental integrity. Unexpectedly, we also observed an increase in water bodies by 35.03%, with the largest proportion of this growth (21.39%) occurring in the 2016\u0026ndash;2023 study period. This expansion is spatially correlated with mining zones, suggesting that excavation pits, subsequently filled by rainwater and groundwater seepage, drive the proliferation of artificial aquatic features. Similar phenomena have been documented in mining-intensive regions of Ghana and other parts of the world, such as Indonesia, the Philippines, Brazil, Peru, and Colombia, where such water-filled pits now dominate post-mining landscapes (Bansah et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Benites, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Meutia et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While these water bodies may offer new ecological niches, they are also sources of heavy metal contamination, sedimentation, and aquatic ecosystem disruption (Bansah et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cudjoe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Meutia et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLandscape structural changes\u003c/b\u003e,\u003c/p\u003e \u003cp\u003eTree-crop diversification, particularly the integration of rubber and oil palm into cocoa-dominated landscapes, has significantly altered the landscape structure of eastern C\u0026ocirc;te d'Ivoire, leading to both ecological and socio-economic implications. Between 1986 and 2023, landscape diversity increased slightly, with the Shannon diversity index rising from 0.99 to 1.07, indicating a more balanced distribution of land-use types across the region (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This trend reflects broader regional patterns reported in West Africa, where tree-crop expansion diversifies land-cover but often at the cost of native ecosystems (Asante-Yeboah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Asubonteng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While a structurally complex mosaic may appear diverse on maps, the ecological functions within these systems are often limited. Tree-crop monocultures like rubber, oil palm and cocoa generally lack species richness, provide minimal habitat for native fauna, and may disrupt ecological processes such as pollination, nutrient cycling, and water regulation (Ran et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, these land-uses often contribute to habitat fragmentation rather than supporting ecological connectivity. Despite this increase in compositional diversity, mean patch size declined from 406.56 ha to 356.79 ha, suggesting intensified fragmentation and reduced habitat contiguity, consistent with findings from similar agro-ecological contexts in Ghana and Southeast Asia (Arroyo-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough edge density increased from 159.99 m/ha to 180.54 m/ha, implying enhanced structural connectivity, this may not equate to improved functional connectivity for biodiversity. Similar to patterns observed in monoculture-dominated landscapes in Southeast Asia, such structural changes can act as ecological traps, impeding the movement of forest-dependent species and fragmenting habitat networks (Ibrahim et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohd-Azlan et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, spatial trends reveal that areas of high landscape diversity and complexity have shifted from the south in 1986 to the central and northern zones by 2023, largely driven by the expansion of rubber plantations. While this may indicate a spatial redistribution of land-use types, the replacement of natural vegetation with monocultures raises concerns about long-term biodiversity resilience and ecosystem service provision (Kremen \u0026amp; Merenlender, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Perfecto et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe decline in patch size, even near dense forest cores, and the encroachment of fragmentation into previously contiguous areas underscore the ecological cost of unchecked land-use change. Despite the increase in heterogeneity, landscape functionality appears compromised, as high edge densities exacerbate edge effects such as microclimatic changes, increased predation, and vulnerability to invasive species (Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Haddad et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In sum, while tree-crop diversification has increased structural complexity, the associated fragmentation and loss of natural habitats may undermine functional ecological connectivity and pose significant challenges to sustainable landscape management in eastern C\u0026ocirc;te d'Ivoire.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications of tree-crop diversification for sustainable landscape management\u003c/h2\u003e \u003cp\u003eWhile mosaic landscapes in Sub-Saharan Africa have traditionally served as the backbone of household food production, the assumption that cropland areas within these landscapes would remain stable is increasingly untenable in the face of shifting agricultural priorities and land-use pressures. Empirical evidence shows that the expansion of export-oriented commodity crops, such as rubber, oil palm, and cocoa, is leading to the systematic reduction and fragmentation of cropland areas traditionally used for subsistence farming (Ordway et al., 2017; Giller et al., 2021). This trend contradicts the expectation of cropland stability. Rather than maintaining land for local food production, many rural households and governments are pivoting toward high-value cash crops due to their economic attractiveness and perceived developmental benefits (Ruf \u0026amp; Schroth, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These market-driven transformations often lead to the conversion of diversified, food-producing mosaic landscapes into more homogenous, commodified land systems, which may undermine household food security in the long term (van Vliet et al., 2015). Moreover, this shift can have significant socio-ecological implications. As cropland dedicated to food staples is increasingly supplanted by monoculture export crops, the resilience and multifunctionality of these landscapes diminish. Food availability becomes more reliant on market purchases, which may be precarious due to price volatility, infrastructure limitations, and external shocks (Baudron \u0026amp; Giller, 2014).\u003c/p\u003e \u003cp\u003eThe observed rise in the Shannon diversity index (from 0.99 to 1.07) indicates a more balanced distribution of land-use types, suggesting a form of diversification. This aligns with broader regional patterns in West Africa, where smallholder and industrial agriculture have increased land-use heterogeneity (Asante-Yeboah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In theory, such diversification could be seen as a positive trend for landscape resilience and socio-economic sustainability. However, in the context of eastern C\u0026ocirc;te d'Ivoire, this heterogeneity is primarily driven by the expansion of monocultures rather than ecological restoration or agroecological practices. The conversion of forested or mixed-use areas into rubber and oil palm plantations can mask ecological degradation beneath a veneer of patch diversity. This undermines sustainability goals by reducing ecosystem services, limiting biodiversity, and simplifying ecological interactions, highlighting the need for more nuanced metrics of sustainability than land-cover diversity alone.\u003c/p\u003e \u003cp\u003eThe decline in mean patch size, from 406.56 ha to 356.79 ha, signals intensifying landscape fragmentation. In a region like eastern C\u0026ocirc;te d'Ivoire, where biodiversity hotspots and relic forest patches are crucial for endemic species, fragmentation poses serious threats. Smaller and more isolated patches suffer from reduced core habitat area and increased edge effects, which elevate the vulnerability of flora and fauna to predation, drought, and invasive species (Haddad et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kalarus and Nowicki, 2015). This trend mirrors fragmentation dynamics seen in neighboring Ghana and further suggests that economic drivers, especially commodity crop expansion, are overriding ecological considerations in land-use planning. Without strong conservation zoning or landscape-scale planning, continued fragmentation may irreversibly impair ecological connectivity and ecosystem integrity in the region.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study employed remote sensing and geospatial techniques, combined with landscape metrics, to analyze the spatio-temporal dynamics and landscape heterogeneity associated with tree-crop diversification in eastern C\u0026ocirc;te d'Ivoire, with implications for sustainable landscape management. The land-use and land-cover changes observed reveal a landscape undergoing rapid and complex transformation, driven by economic incentives, policy shifts, and weak environmental governance. The expansion of rubber plantations, the emergence of land degradation from illegal mining, and the decline in both cropland and forest cover underscore the urgency of implementing integrated land management strategies that reconcile competing land-use demands while safeguarding ecological resilience. Weak enforcement of land-use regulations, the insufficiency of incentives for sustainable farming practices, and limited community engagement exacerbate these challenges. The ongoing landscape transformation reflects a pivotal moment for eastern C\u0026ocirc;te d'Ivoire, where tree-crop diversification, though superficially promising, is contributing to fragmentation, reduced functional connectivity, and biodiversity loss. Sustainable landscape management must therefore go beyond surface-level diversity and embrace a holistic, context-specific approach. These include the continued promotion of good agricultural practices such as agroforestry and mixed cropping systems to enhance biodiversity while maintaining productivity. The effective implementation of sustainability initiatives would limit the uncontrolled expansion of monocultures and encourage ecosystem-based land-use planning through the active involvement of local stakeholders. Without such a concerted and inclusive strategy, such as agroforestry systems, the region risks degrading the very ecosystems upon which its agricultural economy and future sustainability depend.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003e \u003cb\u003eEthics approval and consent to participate\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eAll authors have read, understood, and complied as applicable with the statement on \"Ethical responsibilities of Authors\" as found in the Instructions for Authors\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work is conducted in the frame of the PRO-PLANTEURS Recherche project funded by the\u003c/p\u003e \u003cp\u003eGerman Ministry of Economic Cooperation and Development (BMZ). The findings and\u003c/p\u003e \u003cp\u003econclusions contained within are those of the authors and do not necessarily reflect the positions or policies of the BMZ.\u003c/p\u003e \u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEAY, BS, BAT, FO, SS, \u0026amp; KL worked on conceptualization. EAY, \u0026amp; BAT: Data collection. EAY, BS: Data analysis on maps and metrics. EAY wrote the main manuscript, and initial editing by BS. Supervision: KL, \u0026amp; SS. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge Dr. Abrou N'gouan Emmanuel Joel for his invaluable support during the data collection phase of this study. We also extend our sincere thanks to Eduzie and the Hen Mpoano team in Ghana and Dahan for their dedicated assistance and collaboration in the remote sensing dataset generation and land cover mapping exercise. Their contributions were instrumental to the success of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAduhene-Chinbuah, J., \u0026amp; Peprah, C. O. (2024). Multi-risk management in Ghana\u0026rsquo;s agricultural sector: Strategies, actors, and conceptual shifts\u0026mdash;a review. \u003cem\u003eReview of Agricultural, Food and Environmental Studies\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e(4), 393\u0026ndash;418.\u003c/li\u003e\n\u003cli\u003eAhmad, S., Bhat, S. S., Mir, N. H., Wani, A. A., \u0026amp; Pala, N. (2024). Temperate Agroforestry Systems for Diversification and Environmental Sustainability in Northwestern Himalayan Region. In S. Kumar, B. Alam, S. Taria, P. Singh, A. Yadav, \u0026amp; A. Arunachalam (Eds.), \u003cem\u003eAgroforestry Solutions for Climate Change and Environmental Restoration\u003c/em\u003e (pp. 65\u0026ndash;83). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5004-7_3\u003c/li\u003e\n\u003cli\u003eAndrew, M. E., Wulder, M. A., Nelson, T. A., \u0026amp; Coops, N. C. (2015). Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: A review. \u003cem\u003eGIScience \u0026amp; Remote Sensing\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(3), 344\u0026ndash;373. https://doi.org/10.1080/15481603.2015.1033809\u003c/li\u003e\n\u003cli\u003eArroyo-Rodr\u0026iacute;guez, V., Melo, F. P. L., Mart\u0026iacute;nez-Ramos, M., Bongers, F., Chazdon, R. L., Meave, J. A., Norden, N., Santos, B. A., Leal, I. R., \u0026amp; Tabarelli, M. (2017). Multiple successional pathways in human-modified tropical landscapes: New insights from forest succession, forest fragmentation and landscape ecology research: Multiple successional pathways. \u003cem\u003eBiological Reviews\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e(1), 326\u0026ndash;340. https://doi.org/10.1111/brv.12231\u003c/li\u003e\n\u003cli\u003eAsante-Yeboah, E., Ashiagbor, G., Asubonteng, K., Sieber, S., Mensah, J. C., \u0026amp; F\u0026uuml;rst, C. (2022). Analyzing Variations in Size and Intensities in Land Use Dynamics for Sustainable Land Use Management: A Case of the Coastal Landscapes of South-Western Ghana. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(6), 815.\u003c/li\u003e\n\u003cli\u003eAsante-Yeboah, E., Koo, H., Ros-Tonen, M. A. F., Sieber, S., \u0026amp; F\u0026uuml;rst, C. (2024a). Participatory and Spatially Explicit Assessment to Envision the Future of Land-Use/Land-Cover Change Scenarios on Selected Ecosystem Services in Southwestern Ghana. \u003cem\u003eEnvironmental Management\u003c/em\u003e. https://doi.org/10.1007/s00267-024-01943-z\u003c/li\u003e\n\u003cli\u003eAsante-Yeboah, E., Koo, H., Sieber, S., \u0026amp; F\u0026uuml;rst, C. (2024b). Designing mosaic landscapes for sustainable outcomes: Evaluating land-use options on ecosystem service provisioning in southwestern Ghana. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e353\u003c/em\u003e, 120127.\u003c/li\u003e\n\u003cli\u003eAsubonteng, K. O., Pfeffer, K., Ros-Tonen, M. A., Baud, I., \u0026amp; Benefoh, D. T. (2020). Integration versus segregation: Structural dynamics of a smallholder-dominated mosaic landscape under tree-crop expansion in Ghana. \u003cem\u003eApplied Geography\u003c/em\u003e, 102201.\u003c/li\u003e\n\u003cli\u003eAsubonteng, K. O., Ros-Tonen, M. A., Baud, I. S. A., \u0026amp; Pfeffer, K. (2021). Envisioning the future of mosaic landscapes: Actor perceptions in a mixed cocoa/oil-palm area in Ghana. \u003cem\u003eEnvironmental Management\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(5), 701\u0026ndash;719.\u003c/li\u003e\n\u003cli\u003eBansah, K. J., Dumakor-Dupey, N. K., Kansake, B. A., Assan, E., \u0026amp; Bekui, P. (2018). Socioeconomic and environmental assessment of informal artisanal and small-scale mining in Ghana. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, \u003cem\u003e202\u003c/em\u003e, 465\u0026ndash;475. https://doi.org/10.1016/j.jclepro.2018.08.150\u003c/li\u003e\n\u003cli\u003eBao Pham, Q., Ajim Ali, S., Parvin, F., Van On, V., Mohd Sidek, L., Đurin, B., Cetl, V., \u0026Scaron;amanović, S., \u0026amp; Nguyet Minh, N. (2024). Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network. \u003cem\u003eAdvances in Space Research\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(1), 17\u0026ndash;47. https://doi.org/10.1016/j.asr.2024.03.027\u003c/li\u003e\n\u003cli\u003eBenites, G. V. (2023). Natures of concern: The criminalization of artisanal and small-scale mining in Colombia and Peru. \u003cem\u003eThe Extractive Industries and Society\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 101105. https://doi.org/10.1016/j.exis.2022.101105\u003c/li\u003e\n\u003cli\u003eBennett, E. M., Baird, J., Baulch, H., Chaplin-Kramer, R., Fraser, E., Loring, P., Morrison, P., Parrott, L., Sherren, K., \u0026amp; Winkler, K. J. (2021). Ecosystem services and the resilience of agricultural landscapes. In \u003cem\u003eAdvances in ecological research\u003c/em\u003e (Vol. 64, pp. 1\u0026ndash;43). Elsevier. https://www.sciencedirect.com/science/article/pii/S0065250421000015\u003c/li\u003e\n\u003cli\u003eBren d\u0026rsquo;Amour, C., Reitsma, F., Baiocchi, G., Barthel, S., G\u0026uuml;neralp, B., Erb, K.-H., Haberl, H., Creutzig, F., \u0026amp; Seto, K. C. (2017). Future urban land expansion and implications for global croplands. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e114\u003c/em\u003e(34), 8939\u0026ndash;8944. https://doi.org/10.1073/pnas.1606036114\u003c/li\u003e\n\u003cli\u003eBuckley, R. (2024). \u003cem\u003eAnalyzing Income Diversification Strategies among Cashew Farming Communities in Ghana\u003c/em\u003e [Master\u0026rsquo;s Thesis, Norwegian University of Life Sciences]. https://nmbu.brage.unit.no/nmbu-xmlui/handle/11250/3163061\u003c/li\u003e\n\u003cli\u003eByrareddy, V., Kouadio, L., Mushtaq, S., \u0026amp; Stone, R. (2019). Sustainable Production of Robusta Coffee under a Changing Climate: A 10-Year Monitoring of Fertilizer Management in Coffee Farms in Vietnam and Indonesia. \u003cem\u003eAgronomy\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(9). https://doi.org/10.3390/agronomy9090499\u003c/li\u003e\n\u003cli\u003eChambon, B., Ruf, F., Kongmanee, C., \u0026amp; Angthong, S. (2016). Can the cocoa cycle model explain the continuous growth of the rubber (Hevea brasiliensis) sector for more than a century in Thailand? \u003cem\u003eJournal of Rural Studies\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e, 187\u0026ndash;197. https://doi.org/10.1016/j.jrurstud.2016.02.003\u003c/li\u003e\n\u003cli\u003eChirwa, P. W., Syampungani, S., Mwamba, T. M., Kouami, K., Amusa, T. O., Kamwi, J. M., Babalola, F. D., Mng\u0026rsquo;omba, S. A., Yusuf, A. A., Kabwe, G., Ass\u0026egrave;d\u0026eacute;, E. S. P., Sileshi, G. W., Thangata, P., \u0026amp; Chama, L. (2024). Securing Multifunctional Landscapes in Africa: What Is the Future Policy, Research and Development Direction? In P. W. Chirwa, S. Syampungani, \u0026amp; T. M. Mwamba (Eds.), \u003cem\u003eTrees in a Sub-Saharan Multi-functional Landscape: Research, Management, and Policy\u003c/em\u003e (pp. 329\u0026ndash;349). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-69812-5_14\u003c/li\u003e\n\u003cli\u003eChughtai, A. H., Abbasi, H., \u0026amp; Karas, I. R. (2021). A review on change detection method and accuracy assessment for land use land cover. \u003cem\u003eRemote Sensing Applications: Society and Environment\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 100482. https://doi.org/10.1016/j.rsase.2021.100482\u003c/li\u003e\n\u003cli\u003eCoulibaly, S. K., \u0026amp; Erbao, C. (2019). An empirical analysis of the determinants of cocoa production in Cote d\u0026rsquo;Ivoire. \u003cem\u003eJournal of Economic Structures\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 5. https://doi.org/10.1186/s40008-019-0135-5\u003c/li\u003e\n\u003cli\u003eCreutzig, F., d\u0026rsquo;Amour, C. B., Weddige, U., Fuss, S., Beringer, T., Gl\u0026auml;ser, A., Kalkuhl, M., Steckel, J. C., Radebach, A., \u0026amp; Edenhofer, O. (2019). Assessing human and environmental pressures of global land-use change 2000\u0026ndash;2010. \u003cem\u003eGlobal Sustainability\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, e1.\u003c/li\u003e\n\u003cli\u003eCudjoe, K., Nyantakyi, E. K., Borkloe, J. K., Adjei, E. A., Siabi, E. K., Ackerson, N. O. B., Yeboah, S. I. I. K., Domfeh, M. K., Wezenamo, C. A., \u0026amp; Owusu, M. (2023). Assessing livelihood and environmental implications of artisanal and small-scale mining: A case of Akango mining, Nzema East Municipality, Western Region, Ghana. \u003cem\u003eEnvironment, Development and Sustainability\u003c/em\u003e, 1\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eDeh, S. K., Kouame, K. J., Eba, A. E. L., Djemin, J. E., Kpan, A., \u0026amp; Jourda, J. P. (2017). Contribution of Geographic Information Systems in Protection Zones Delineation around a Surface Water Resource in Adzope Region (Southeast of C\u0026ocirc;te d\u0026rsquo;Ivoire). \u003cem\u003eJournal of Environmental Protection\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(13), Article 13. https://doi.org/10.4236/jep.2017.813102\u003c/li\u003e\n\u003cli\u003eDiwediga, B., Agodzo, S., Wala, K., \u0026amp; Le, Q. B. (2017). Assessment of multifunctional landscapes dynamics in the mountainous basin of the Mo River (Togo, West Africa). \u003cem\u003eJournal of Geographical Sciences\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(5), 579\u0026ndash;605.\u003c/li\u003e\n\u003cli\u003eDomingo, J. P. T., Jenkin, G. R., Quick, L., Williams, R. D., Hudson-Edwards, K. A., Tortajada, C., Byrne, P., Coulthard, T. J., Padrones, J. T., \u0026amp; Crane, R. (2024). Sustainable mining in tropical, biodiverse landscapes: Environmental challenges and opportunities in the archipelagic Philippines. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 143114.\u003c/li\u003e\n\u003cli\u003eDronova, I. (2017). Environmental heterogeneity as a bridge between ecosystem service and visual quality objectives in management, planning and design. \u003cem\u003eLandscape and Urban Planning\u003c/em\u003e, \u003cem\u003e163\u003c/em\u003e, 90\u0026ndash;106.\u003c/li\u003e\n\u003cli\u003eDumenu, W. K., \u0026amp; Obeng, E. A. (2016). Climate change and rural communities in Ghana: Social vulnerability, impacts, adaptations and policy implications. \u003cem\u003eEnvironmental Science \u0026amp; Policy\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 208\u0026ndash;217.\u003c/li\u003e\n\u003cli\u003eDuriaux Chavarr\u0026iacute;a, J.-Y., Baudron, F., \u0026amp; Sunderland, T. (2018). Retaining forests within agricultural landscapes as a pathway to sustainable intensification: Evidence from Southern Ethiopia. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e263\u003c/em\u003e, 41\u0026ndash;52. https://doi.org/10.1016/j.agee.2018.04.020\u003c/li\u003e\n\u003cli\u003eEU. (2023). \u003cem\u003eRegulation (Eu) 2023/1115 of the European Parliament and of the Council of 31 May 2023. Official Journal of the European Union\u003c/em\u003e. http://data.europa.eu/eli/reg/2 023/1115/oj\u003c/li\u003e\n\u003cli\u003eGashaw, T., Tulu, T., Argaw, M., Worqlul, A. W., Tolessa, T., \u0026amp; Kindu, M. (2018). Estimating the impacts of land use/land cover changes on Ecosystem Service Values: The case of the Andassa watershed in the Upper Blue Nile basin of Ethiopia. \u003cem\u003eEcosystem Services\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 219\u0026ndash;228.\u003c/li\u003e\n\u003cli\u003eGil, J. D. B., Cohn, A. S., Duncan, J., Newton, P., \u0026amp; Vermeulen, S. (2017). The resilience of integrated agricultural systems to climate change. \u003cem\u003eWIREs Climate Change\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4), e461. https://doi.org/10.1002/wcc.461\u003c/li\u003e\n\u003cli\u003eGonz\u0026aacute;lez, E., Rossetti, M. R., Moreno, M. L., Bernaschini, M. L., Cagnolo, L., Musicante, M. L., Salvo, A., \u0026amp; Valladares, G. (2024). Habitat Loss and Fragmentation in Chaco Forests: A Review of the Responses of Insect Communities and Consequences for Ecosystem Processes. In J. L. Le\u0026oacute;n-Cort\u0026eacute;s \u0026amp; A. C\u0026oacute;rdoba-Aguilar (Eds.), \u003cem\u003eInsect Decline and Conservation in the Neotropics\u003c/em\u003e (pp. 129\u0026ndash;162). Springer International Publishing. https://doi.org/10.1007/978-3-031-49255-6_7\u003c/li\u003e\n\u003cli\u003eGoulart, F. F., Chappell, M. J., Mertens, F., \u0026amp; Soares-Filho, B. (2023). Sparing or expanding? The effects of agricultural yields on farm expansion and deforestation in the tropics. \u003cem\u003eBiodiversity and Conservation\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(3), 1089\u0026ndash;1104.\u003c/li\u003e\n\u003cli\u003eHaddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., Lovejoy, T. E., Sexton, J. O., Austin, M. P., Collins, C. D., Cook, W. M., Damschen, E. I., Ewers, R. M., Foster, B. L., Jenkins, C. N., King, A. J., Laurance, W. F., Levey, D. J., Margules, C. R., \u0026hellip; Townshend, J. R. (2015). Habitat fragmentation and its lasting impact on Earth\u0026rsquo;s ecosystems. \u003cem\u003eScience Advances\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(2), e1500052. https://doi.org/10.1126/sciadv.1500052\u003c/li\u003e\n\u003cli\u003eHashmiu, I., Adams, F., Etuah, S., \u0026amp; Quaye, J. (2024). Food-cash crop diversification and farm household welfare in the Forest-Savannah Transition Zone of Ghana. \u003cem\u003eFood Security\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 487\u0026ndash;509.\u003c/li\u003e\n\u003cli\u003eHeirman, J. (2016). \u003cem\u003eThe impact of international actors on domestic agricultural Policy: A comparison of cocoa and rice in Ghana\u003c/em\u003e [PhD Thesis, University of Oxford]. https://ora.ox.ac.uk/objects/uuid:980ac41f-a591-4e23-ab16-deb6df121573\u003c/li\u003e\n\u003cli\u003eHowell, P. E., Muths, E., Hossack, B. R., Sigafus, B. H., \u0026amp; Chandler, R. B. (2018). Increasing connectivity between metapopulation ecology and landscape ecology. \u003cem\u003eEcology\u003c/em\u003e, \u003cem\u003e99\u003c/em\u003e(5), 1119\u0026ndash;1128. https://doi.org/10.1002/ecy.2189\u003c/li\u003e\n\u003cli\u003eIbrahim, F.-H., Rawat, G. S., Yahara, T., Abi-Said, M., Corlett, R. T., Courchamp, F., Dai, R., Freitag, H., Haryoko, T., Hewitt, C. L., Hussain, T., Kadoya, T., Maheswaran, G., Miyashita, T., Mohan Kumar, B., Mohapatra, A., Nakashizuka, T., Piggott, J. J., Rawal, R., Yamakita, T. (2018). Status, trends and future dynamics of biodiversity and ecosystems underpinning nature\u0026rsquo;s contributions to people. In \u003cem\u003eThe IPBES regional assessment report on biodiversity and ecosystem services for Asia and the Pacific\u003c/em\u003e (pp. 220\u0026ndash;336). Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem services.\u003c/li\u003e\n\u003cli\u003eJing Luo, X. H., Hong Cheng, Ning Jiang, Xuyu Mo, Xiuyu Wang. (2022). Impacts of landscape structure changes on ecosystem services in the Hunshandake Sandy Land. \u003cem\u003eJournal of Desert Research\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(4), 99\u0026ndash;109.\u003c/li\u003e\n\u003cli\u003eJung, M. (2016). LecoS \u0026mdash; A python plugin for automated landscape ecology analysis. \u003cem\u003eEcological Informatics\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 18\u0026ndash;21. https://doi.org/10.1016/j.ecoinf.2015.11.006\u003c/li\u003e\n\u003cli\u003eKarimi, J. D., Corstanje, R., \u0026amp; Harris, J. A. (2021). Understanding the importance of landscape configuration on ecosystem service bundles at a high resolution in urban landscapes in the UK. \u003cem\u003eLandscape Ecology\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(7), 2007\u0026ndash;2024. https://doi.org/10.1007/s10980-021-01200-2\u003c/li\u003e\n\u003cli\u003eKeesstra, S., Nunes, J., Novara, A., Finger, D., Avelar, D., Kalantari, Z., \u0026amp; Cerd\u0026agrave;, A. (2018). The superior effect of nature based solutions in land management for enhancing ecosystem services. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e610\u003c/em\u003e, 997\u0026ndash;1009.\u003c/li\u003e\n\u003cli\u003eKhanal, A. R., \u0026amp; Mishra, A. K. (2017). Enhancing food security: Food crop portfolio choice in response to climatic risk in India. \u003cem\u003eGlobal Food Security\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 22\u0026ndash;30. https://doi.org/10.1016/j.gfs.2016.12.003\u003c/li\u003e\n\u003cli\u003eKouassi, J.-L., Gyau, A., Diby, L., Bene, Y., \u0026amp; Kouam\u0026eacute;, C. (2021). Assessing land use and land cover change and farmers\u0026rsquo; perceptions of deforestation and land degradation in South-West C\u0026ocirc;te d\u0026rsquo;Ivoire, West Africa. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(4), 429.\u003c/li\u003e\n\u003cli\u003eKremen, C., \u0026amp; Merenlender, A. M. (2018). Landscapes that work for biodiversity and people. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e362\u003c/em\u003e(6412), eaau6020.\u003c/li\u003e\n\u003cli\u003eKumar, P., Uthappa, A. R., Chavan, S. B., Chichaghare, A. R., Debta, H., Bhat, S., \u0026amp; Dagar, J. C. (2023). Achieving Biodiversity Conservation, Livelihood Security and Sustainable Development Goals Through Agroforestry in Coastal and Island Regions of India and Southeast Asia. In J. C. Dagar, S. R. Gupta, \u0026amp; G. W. Sileshi (Eds.), \u003cem\u003eAgroforestry for Sustainable Intensification of Agriculture in Asia and Africa\u003c/em\u003e (pp. 429\u0026ndash;486). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-4602-8_14\u003c/li\u003e\n\u003cli\u003eKwang, C., Afele, I. K., Yeboah, E., Sarfo, I., Batame, M., Okrah, A., Shwe, M. M., Siaw, W., Boyetey, D., Larbi, R. O., Mensah, A. O. K. N., El Rhadiouini, C., Jaffry, A. H., Siddique, F., Aftab, R., \u0026amp; Isinkaralar, O. (2025). Machine learning assessment of illegal mining (Galamsey) impacts on forest vegetation: A case study of Wassa Amenfi East District, Ghana. \u003cem\u003eEarth Science Informatics\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2), 348. https://doi.org/10.1007/s12145-025-01860-7\u003c/li\u003e\n\u003cli\u003eL\u0026auml;derach, P., Martinez-Valle, A., Schroth, G., \u0026amp; Castro, N. (2013). Predicting the future climatic suitability for cocoa farming of the world\u0026rsquo;s leading producer countries, Ghana and C\u0026ocirc;te d\u0026rsquo;Ivoire. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e(3), 841\u0026ndash;854. https://doi.org/10.1007/s10584-013-0774-8\u003c/li\u003e\n\u003cli\u003eLaurance, W. F., Sayer, J., \u0026amp; Cassman, K. G. (2014). Agricultural expansion and its impacts on tropical nature. \u003cem\u003eTrends in Ecology \u0026amp; Evolution\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 107\u0026ndash;116.\u003c/li\u003e\n\u003cli\u003eLi, Y., Xie, W., Zhang, J., \u0026amp; Zhang, D. (2024). Spatiotemporal changes and driving factors of ecological environmental quality in the Yongding-Luan River Basin based on RSEI. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e. https://doi.org/10.3389/fenvs.2024.1494098\u003c/li\u003e\n\u003cli\u003eLiu, D., Chen, J., \u0026amp; Ouyang, Z. (2020). Responses of landscape structure to the ecological restoration programs in the farming-pastoral ecotone of Northern China. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e710\u003c/em\u003e, 136311. https://doi.org/10.1016/j.scitotenv.2019.136311\u003c/li\u003e\n\u003cli\u003eLoh, S. K., Asubonteng, K. O., \u0026amp; Adanu, S. K. (2022). Effects of monocropping on land cover transitions in the wet evergreen agro-ecological zone of Ghana. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(7), 1063.\u003c/li\u003e\n\u003cli\u003eMabhaudhi, T., Hlahla, S., Chimonyo, V. G. P., Henriksson, R., Chibarabada, T. P., Murugani, V. G., Groner, V. P., Tadele, Z., Sobratee, N., \u0026amp; Slotow, R. (2022). Diversity and diversification: Ecosystem services derived from underutilized crops and their co-benefits for sustainable agricultural landscapes and resilient food systems in Africa. \u003cem\u003eFrontiers in Agronomy\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 859223.\u003c/li\u003e\n\u003cli\u003eMarie Delalay, Varun Tiwari, Alan D. Ziegler, Vik Gopal, \u0026amp; Paul Passy. (2019). Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: Operational method and its implementation for a mountainous area of Nepal. \u003cem\u003eJournal of Applied Remote Sensing\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 014530. https://doi.org/10.1117/1.JRS.13.014530 \u003c/li\u003e\n\u003cli\u003eMarin-Burgos, V., \u0026amp; Clancy, J. S. (2017). Understanding the expansion of energy crops beyond the global biofuel boom: Evidence from oil palm expansion in Colombia. \u003cem\u003eEnergy, Sustainability and Society\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 21. https://doi.org/10.1186/s13705-017-0123-2\u003c/li\u003e\n\u003cli\u003eMcGranahan, D. A. (2014). Ecologies of Scale: Multifunctionality Connects Conservation and Agriculture across Fields, Farms, and Landscapes. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 739\u0026ndash;769. https://doi.org/10.3390/land3030739\u003c/li\u003e\n\u003cli\u003eMcKenzie, F. C., \u0026amp; Williams, J. (2015). Sustainable food production: Constraints, challenges and choices by 2050. \u003cem\u003eFood Security\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 221\u0026ndash;233.\u003c/li\u003e\n\u003cli\u003eMeutia, A. A., Bachriadi, D., \u0026amp; Gafur, N. A. (2023). Environment Degradation, Health Threats, and Legality at the Artisanal Small-Scale Gold Mining Sites in Indonesia. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(18). https://doi.org/10.3390/ijerph20186774\u003c/li\u003e\n\u003cli\u003eMeyfroidt, P., Chowdhury, R. R., de Bremond, A., Ellis, E. C., Erb, K.-H., Filatova, T., Garrett, R. D., Grove, J. M., Heinimann, A., \u0026amp; Kuemmerle, T. (2018). Middle-range theories of land system change. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 52\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eMillard, E. (2019). Recent Experiences from the Natural Rubber Industry and Its Movement Towards Sustainability. In M. Schmidt, D. Giovannucci, D. Palekhov, \u0026amp; B. Hansmann (Eds.), \u003cem\u003eSustainable Global Value Chains\u003c/em\u003e (pp. 499\u0026ndash;520). Springer International Publishing. https://doi.org/10.1007/978-3-319-14877-9_27\u003c/li\u003e\n\u003cli\u003eMohd-Azlan, J., Kaicheen, S. S., Lok, L., \u0026amp; Lawes, M. J. (2019). The role of forest fragments in small mammal conservation in an oil palm plantation in northern Sarawak, Borneo. \u003cem\u003eJournal of Oil Palm Research\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(3), 422\u0026ndash;436.\u003c/li\u003e\n\u003cli\u003eMolotoks, A., Kuhnert, M., Dawson, T. P., \u0026amp; Smith, P. (2017). Global hotspots of conflict risk between food security and biodiversity conservation. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), 67.\u003c/li\u003e\n\u003cli\u003eMudzengi, C. P., Dahwa, E., Mubvuma, M. T., Chapungu, L., \u0026amp; Poshiwa, X. (2025). Environmental and Socio-economic Shocks on Smallholder Farmers: Can Resilience Be Achieved Without Livestock in Zimbabwe? In L. Mhlanga, K. Nyikahadzoi, \u0026amp; J. Stack (Eds.), \u003cem\u003eBuilding Resilience to Climate Change in Selected Districts in Zimbabwe\u003c/em\u003e (pp. 109\u0026ndash;129). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80529-5_6\u003c/li\u003e\n\u003cli\u003eMuluneh, M. G. (2021). Impact of climate change on biodiversity and food security: A global perspective\u0026mdash;a review article. \u003cem\u003eAgriculture \u0026amp; Food Security\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eMustafa, M. A., Mayes, S., \u0026amp; Massawe, F. (2019). Crop Diversification Through a Wider Use of Underutilised Crops: A Strategy to Ensure Food and Nutrition Security in the Face of Climate Change. In A. Sarkar, S. R. Sensarma, \u0026amp; G. W. vanLoon (Eds.), \u003cem\u003eSustainable Solutions for Food Security\u003c/em\u003e (pp. 125\u0026ndash;149). Springer International Publishing. https://doi.org/10.1007/978-3-319-77878-5_7\u003c/li\u003e\n\u003cli\u003eObodai, J., Adjei, K. A., Odai, S. N., \u0026amp; Lumor, M. (2019). Land use/land cover dynamics using landsat data in a gold mining basin-the Ankobra, Ghana. \u003cem\u003eRemote Sensing Applications: Society and Environment\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 247\u0026ndash;256.\u003c/li\u003e\n\u003cli\u003eOdijie, M. (2019). Environmental change and normalization of cash crop systems in Africa: Preventing agrarian change in West Africa cocoa. \u003cem\u003eInternational Journal of Sustainable Development \u0026amp; World Ecology\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(7), 597\u0026ndash;611. https://doi.org/10.1080/13504509.2019.1641763\u003c/li\u003e\n\u003cli\u003eOuattara, T. A., Sokeng, V.-C. J., Zo-Bi, I. C., Kouam\u0026eacute;, K. F., Grinand, C., \u0026amp; Vaudry, R. (2022). Detection of Forest Tree Losses in C\u0026ocirc;te d\u0026rsquo;Ivoire Using Drone Aerial Images. \u003cem\u003eDrones\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4). https://doi.org/10.3390/drones6040083\u003c/li\u003e\n\u003cli\u003ePatil, A., \u0026amp; Panhalkar, S. (2023). A comparative analysis of machine learning algorithms for land use and land cover classification using google earth engine platform. \u003cem\u003eJournal of Geomatics\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 111\u0026ndash;118. https://doi.org/10.58825/jog.2023.17.2.96\u003c/li\u003e\n\u003cli\u003ePereira, L. S. (2024). Analyzing the Impact of Technological Innovations on Smallholder Farmers Within Ivory Coast\u0026rsquo;s Cocoa Supply Chain. \u003cem\u003eLaw and Economy\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003ePerfecto, I., Jim\u0026eacute;nez-Soto, M. E., \u0026amp; Vandermeer, J. (2019). Coffee landscapes shaping the Anthropocene: Forced simplification on a complex agroecological landscape. \u003cem\u003eCurrent Anthropology\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(S20), S236\u0026ndash;S250.\u003c/li\u003e\n\u003cli\u003ePickett, S. T. A., Cadenasso, M. L., Rosi-Marshall, E. J., Belt, K. T., Groffman, P. M., Grove, J. M., Irwin, E. G., Kaushal, S. S., LaDeau, S. L., Nilon, C. H., Swan, C. M., \u0026amp; Warren, P. S. (2017). Dynamic heterogeneity: A framework to promote ecological integration and hypothesis generation in urban systems. \u003cem\u003eUrban Ecosystems\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 1\u0026ndash;14. https://doi.org/10.1007/s11252-016-0574-9 \u003c/li\u003e\n\u003cli\u003ePremono, B. T., Lestari, S., \u0026amp; Winarno, B. (2019). Tree-crop diversification by smallholder farmers in Southern Sumatra to diversify sources of income. \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e, \u003cem\u003e298\u003c/em\u003e(1), 012032. https://iopscience.iop.org/article/10.1088/1755-1315/298/1/012032/meta\u003c/li\u003e\n\u003cli\u003eRan, P., Hu, S., Frazier, A. E., Yang, S., Song, X., \u0026amp; Qu, S. (2023). The dynamic relationships between landscape structure and ecosystem services: An empirical analysis from the Wuhan metropolitan area, China. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cem\u003e325\u003c/em\u003e, 116575. https://doi.org/10.1016/j.jenvman.2022.116575\u003c/li\u003e\n\u003cli\u003eRosa-Schleich, J., Loos, J., Mu\u0026szlig;hoff, O., \u0026amp; Tscharntke, T. (2019). Ecological-economic trade-offs of diversified farming systems\u0026ndash;a review. \u003cem\u003eEcological Economics\u003c/em\u003e, \u003cem\u003e160\u003c/em\u003e, 251\u0026ndash;263.\u003c/li\u003e\n\u003cli\u003eRuf, F. (2015). Diversification of Cocoa Farms in C\u0026ocirc;te d\u0026rsquo;Ivoire: Complementarity of and Competition from Rubber Rent. In F. Ruf \u0026amp; G. Schroth (Eds.), \u003cem\u003eEconomics and Ecology of Diversification\u003c/em\u003e (pp. 41\u0026ndash;86). Springer Netherlands. https://doi.org/10.1007/978-94-017-7294-5_2 \u003c/li\u003e\n\u003cli\u003eRuf, F., \u0026amp; Schroth, G. (2015). Introduction\u0026mdash;Economic and Ecological Aspects of Diversification of Tropical Tree Crops. In F. Ruf \u0026amp; G. Schroth (Eds.), \u003cem\u003eEconomics and Ecology of Diversification\u003c/em\u003e (pp. 1\u0026ndash;40). Springer Netherlands. https://doi.org/10.1007/978-94-017-7294-5_1 \u003c/li\u003e\n\u003cli\u003eSabins Jr, F. F., \u0026amp; Ellis, J. M. (2020). \u003cem\u003eRemote sensing: Principles, interpretation, and applications\u003c/em\u003e. Waveland Press. https://books.google.com/books?hl=en\u0026amp;lr=\u0026amp;id=rAnaDwAAQBAJ\u0026amp;oi=fnd\u0026amp;pg=PR1\u0026amp;dq=principles+of+remote+sensing,+accuracy+assessment\u0026amp;ots=_GYt5Cxy2d\u0026amp;sig=HfnU0cDQpownc5pefCVJXQBU5G8 \u003c/li\u003e\n\u003cli\u003eSankaran, K. V., Tjitrosemito, S., \u0026amp; Sastroutomo, S. S. (2017). Impacts and management options for Mikania micrantha in plantations. In C. A. Ellison, K. V. Sankaran, \u0026amp; S. T. Murphy (Eds.), \u003cem\u003eInvasive alien plants: Impacts on development and options for management\u003c/em\u003e (pp. 39\u0026ndash;58). CABI. https://doi.org/10.1079/9781780646275.0039 \u003c/li\u003e\n\u003cli\u003eSayer, J., Margules, C., Boedhihartono, A. K., Dale, A., Sunderland, T., Supriatna, J., \u0026amp; Saryanthi, R. (2015). Landscape approaches; what are the pre-conditions for success? \u003cem\u003eSustainability Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 345\u0026ndash;355.\u003c/li\u003e\n\u003cli\u003eSchroth, G., \u0026amp; Ruf, F. (2014). Farmer strategies for tree crop diversification in the humid tropics. A review. \u003cem\u003eAgronomy for Sustainable Development\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 139\u0026ndash;154.\u003c/li\u003e\n\u003cli\u003eSchuler, H. R., Alarcon, G. G., Joner, F., dos Santos, K. L., Siminski, A., \u0026amp; Siddique, I. (2022). Ecosystem services from ecological agroforestry in Brazil: A systematic map of scientific evidence. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(01), 83.\u003c/li\u003e\n\u003cli\u003eSirami, C. (2016). Biodiversity in heterogeneous and dynamic landscapes. In \u003cem\u003eOxford research encyclopedia of environmental science\u003c/em\u003e. https://oxfordre.com/environmentalscience/display/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-33 \u003c/li\u003e\n\u003cli\u003eSivaraman, K., Thankamani, C. K., \u0026amp; Srinivasan, V. (2024). Crop Diversification: Cropping/System Approach for Enhancing Farmers\u0026rsquo; Income. In P. N. Ravindran, K. Sivaraman, S. Devasahayam, \u0026amp; K. N. Babu (Eds.), \u003cem\u003eHandbook of Spices in India: 75 Years of Research and Development\u003c/em\u003e (pp. 3847\u0026ndash;3926). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-3728-6_61 \u003c/li\u003e\n\u003cli\u003eSmith, P., House, J. I., Bustamante, M., Sobock\u0026aacute;, J., Harper, R., Pan, G., West, P. C., Clark, J. M., Adhya, T., Rumpel, C., Paustian, K., Kuikman, P., Cotrufo, M. F., Elliott, J. A., McDowell, R., Griffiths, R. I., Asakawa, S., Bondeau, A., Jain, A. K., \u0026hellip; Pugh, T. A. M. (2016). Global change pressures on soils from land use and management. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), 1008\u0026ndash;1028. https://doi.org/10.1111/gcb.13068 \u003c/li\u003e\n\u003cli\u003eSonter, L. J., Tomsett, N., Wu, D., \u0026amp; Maron, M. (2017). Biodiversity offsetting in dynamic landscapes: Influence of regulatory context and counterfactual assumptions on achievement of no net loss. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e206\u003c/em\u003e, 314\u0026ndash;319. https://doi.org/10.1016/j.biocon.2016.11.025 \u003c/li\u003e\n\u003cli\u003eStephens, C. M., Lall, U., Johnson, F. M., \u0026amp; Marshall, L. A. (2021). Landscape changes and their hydrologic effects: Interactions and feedbacks across scales. \u003cem\u003eEarth-Science Reviews\u003c/em\u003e, \u003cem\u003e212\u003c/em\u003e, 103466.\u003c/li\u003e\n\u003cli\u003eStratton, A. E., Kuhl, L., \u0026amp; Blesh, J. (2020). Ecological and nutritional functions of agroecosystems as indicators of smallholder resilience. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 543914.\u003c/li\u003e\n\u003cli\u003eStryke, J. D. (1972). \u003cem\u003eExports and Growth in the Ivory Coast: Timber, Cocoa and Coffee\u003c/em\u003e. Center discussion paper. https://www.econstor.eu/bitstream/10419/160076/1/cdp147.pdf\u003c/li\u003e\n\u003cli\u003eTarazona Coronel, Y., Benitez-Paez, F., Nowosad, J., Drenkhan, F., \u0026amp; Timana, M. (2024). scikit-eo: A Python package for Remote Sensing Data Analysis. \u003cem\u003eThe Journal of Open Source Software\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 6692. https://doi.org/10.21105/joss.06692 \u003c/li\u003e\n\u003cli\u003eTariq, A., Mumtaz, F., Majeed, M., \u0026amp; Zeng, X. (2022). Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, \u003cem\u003e195\u003c/em\u003e(1), 114. https://doi.org/10.1007/s10661-022-10738-w \u003c/li\u003e\n\u003cli\u003eTesfaye, W., Elias, E., Warkineh, B., Tekalign, M., \u0026amp; Abebe, G. (2024). Modeling of land use and land cover changes using google earth engine and machine learning approach: Implications for landscape management. \u003cem\u003eEnvironmental Systems Research\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 31. https://doi.org/10.1186/s40068-024-00366-3 \u003c/li\u003e\n\u003cli\u003eTieskens, K. F., Schulp, C. J., Levers, C., Lieskovskỳ, J., Kuemmerle, T., Plieninger, T., \u0026amp; Verburg, P. H. (2017). Characterizing European cultural landscapes: Accounting for structure, management intensity and value of agricultural and forest landscapes. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e, 29\u0026ndash;39.\u003c/li\u003e\n\u003cli\u003eTokou, B. A., Coral, C., Montiel, F. I., Adou Yao, C. Y., Sieber, S., \u0026amp; L\u0026ouml;hr, K. (2025). Diversification strategies to improve cocoa farmers\u0026rsquo; household income: The case of C\u0026ocirc;te d\u0026rsquo;Ivoire. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 1524997.\u003c/li\u003e\n\u003cli\u003evan Zonneveld, M., Turmel, M.-S., \u0026amp; Hellin, J. (2020). Decision-making to diversify farm systems for climate change adaptation. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 32.\u003c/li\u003e\n\u003cli\u003eVellema, S., Laven, A., Ton, G., \u0026amp; Muilerman, S. (2016). Policy reform and supply chain governance. \u003cem\u003eThe Economics of Chocolate, Oxford University Press, Oxford\u003c/em\u003e, 228\u0026ndash;246.\u003c/li\u003e\n\u003cli\u003eVernooy, R. (2022). Does crop diversification lead to climate-related resilience? Improving the theory through insights on practice. \u003cem\u003eAgroecology and Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(6), 877\u0026ndash;901. https://doi.org/10.1080/21683565.2022.2076184 \u003c/li\u003e\n\u003cli\u003eVogel, C., Poveda, K., Iverson, A., Boetzl, F. A., Mkandawire, T., Chunga, T. L., K\u0026uuml;stner, G., Keller, A., Bezner Kerr, R., \u0026amp; Steffan-Dewenter, I. (2023). The effects of crop type, landscape composition and agroecological practices on biodiversity and ecosystem services in tropical smallholder farms. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(5), 859\u0026ndash;874. https://doi.org/10.1111/1365-2664.14380 \u003c/li\u003e\n\u003cli\u003eWaarts, Y. R., Janssen, V., Aryeetey, R., Onduru, D., Heriyanto, D., Aprillya, S. T., N\u0026rsquo;Guessan, A., Courbois, L., Bakker, D., \u0026amp; Ingram, V. J. (2021). Multiple pathways towards achieving a living income for different types of smallholder tree-crop commodity farmers. \u003cem\u003eFood Security\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), 1467\u0026ndash;1496. https://doi.org/10.1007/s12571-021-01220-5 \u003c/li\u003e\n\u003cli\u003eWaldman, K. B., Giroux, S. A., Farmer, J. R., Heaberlin, B. M., Blekking, J. P., \u0026amp; Todd, P. M. (2021). Socioeconomic threats are more salient to farmers than environmental threats. \u003cem\u003eJournal of Rural Studies\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e, 508\u0026ndash;517.\u003c/li\u003e\n\u003cli\u003eWalz, Y., Wegmann, M., Dech, S., Vounatsou, P., Poda, J.-N., N\u0026rsquo;Goran, E. K., Utzinger, J., \u0026amp; Raso, G. (2015). Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing. \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(11), e0004217. https://doi.org/10.1371/journal.pntd.0004217 \u003c/li\u003e\n\u003cli\u003eWang, S., \u0026amp; Pfister, S. (2024). Landscape-Scale Biodiversity Impacts Analysis of C\u0026ocirc;te d\u0026rsquo;Ivoire\u0026rsquo;s Cocoa Cultivation along Export Supply Chains. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(22), 9601\u0026ndash;9611. https://doi.org/10.1021/acs.est.3c07795\u003c/li\u003e\n\u003cli\u003eWong, G. Y., Holm, M., Pietarinen, N., Ville, A., \u0026amp; Brockhaus, M. (2022). The making of resource frontier spaces in the Congo Basin and Southeast Asia: A critical analysis of narratives, actors and drivers in the scientific literature. \u003cem\u003eWorld Development Perspectives\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e, 100451 \u003c/li\u003e\n\u003cli\u003eXu, M., Luo ,T., \u0026amp; and Wang, Z. (2020). Urbanization diverges residents\u0026rsquo; landscape preferences but towards a more natural landscape: Case to complement landscape ecology from the lens of landscape perception. \u003cem\u003eInternational Journal of Sustainable Development \u0026amp; World Ecology\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(3), 250\u0026ndash;260. https://doi.org/10.1080/13504509.2020.1727989 \u003c/li\u003e\n\u003cli\u003eZhang, W., Dulloo, E., Kennedy, G., Bailey, A., Sandhu, H., \u0026amp; Nkonya, E. (2019). Biodiversity and ecosystem services. In \u003cem\u003eSustainable Food and Agriculture\u003c/em\u003e (pp. 137\u0026ndash;152). Elsevier. https://www.sciencedirect.com/science/article/pii/B978012812134400008X\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land-use diversification, landscape metrics, fragmentation, geo-information science, sustainable land management","lastPublishedDoi":"10.21203/rs.3.rs-6613964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6613964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tree-crop diversification is increasingly adopted in tropical agricultural landscapes as a resilience strategy amidst fluctuating commodity markets, environmental change, and policy shifts. However, its spatial implications at the landscape level remain underexplored. This study examines the structure and dynamics of mosaic landscapes in eastern Côte d’Ivoire, a region characterized by heterogeneous landscapes, in response to tree-crop diversification trends and their implications for sustainable landscape management. Using multi-temporal Landsat imagery (1986, 2016, 2023), remote sensing classification with a Random Forest algorithm, and landscape metrics, we evaluated changes in land-use/land-cover (LULC), landscape composition (diversity, regularity), and landscape configurational heterogeneity (complexity and fragmentation). Results reveal a substantial increase in rubber plantations (net gain of 50.35%), with concurrent declines in cropland (−147%), cocoa (−45.28%), and sparse vegetation (−61.48%). Although landscape diversity increased slightly (Shannon index: 0.99 to 1.07), fragmentation intensified, with mean patch size decreasing by 12.3%. While tree-crop diversification introduced new compositional complexity, it often manifested as monoculture expansion rather than ecologically restorative land-use. The resulting structural transformations, characterized by high edge densities and smaller, isolated patches, suggest diminished functional connectivity of natural habitats and increasing ecological vulnerability. These trends raise critical questions about the long-term sustainability of current land-use trajectories. We argue that tree-crop diversification, while enhancing economic stability, can erode ecological resilience without integrated landscape-level planning and policy intervention. We recommend landscape-scale strategies that promote agroecological diversification, ecological corridor conservation, and inclusive land-use governance to mitigate fragmentation and maintain the multifunctionality of these rapidly transforming landscapes.","manuscriptTitle":"Land-use monitoring of tree-crop diversification in eastern Côte d’Ivoire: Landscape structure changes and implications for sustainable landscape development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 16:59:22","doi":"10.21203/rs.3.rs-6613964/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2da8fcd-4383-477b-b9d9-318ddc7dc597","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-02T18:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 16:59:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6613964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6613964","identity":"rs-6613964","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}