Four Decades of Habitat Change and Spatio-Temporal Dynamics in the Liuwa Ecosystem of Zambia

Four Decades of Habitat Change and Spatio-Temporal Dynamics in the Liuwa Ecosystem of Zambia | 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 Four Decades of Habitat Change and Spatio-Temporal Dynamics in the Liuwa Ecosystem of Zambia Danny Chisanga Musenge, Darius Phiri, Ngawo Namukonde, Donald Zulu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8523664/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Human encroachment into wildlife habitats has led to extensive habitat loss and fragmentation, often resulting in the displacement and decline of wildlife population across African Ecosystems. This study assessed four decades (1984–2024) of spatio-temporal habitat dynamics in Liuwa Plain National Park, a human - wildlife co - existence landscape of Zambia. Using Landsat satellite imagery classified at 10-year intervals (1984, 1994, 2004, 2014, and 2024) and a random forest algorithm with a two-step approach including pixel - based classification followed by an object-based refinement we produced thematic maps with accuracies of 82–89% with the 2024 map being the most accurate. Results show 20% decline in forest cover, 5% loss of grasslands and 3% reduction was observed in 2024 in wetlands and water bodies, likely due to the 2023/2024 drought. Particularly the changes were observed on the southern Kalabo Urban Zone and more evident outside the park boundaries. These shifts indicate rising shifts of anthropogenic pressure on habitat critical for wildlife persistence. The study underscores the need for integrated land-use planning, habitat management, adaptive park zonation and support the identification of ecological hotspots to sustain co - existence and ecological integrity in Liuwa NP and similar landscapes. Biodiversity conservation Blue-wildebeest Human-wildlife interaction Land cover changes Landsat Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Landscapes in protected areas such as National Parks change because of natural disruptions and anthropogenic activities, however, anthropogenic activities are by far the most significant threat to biodiversity in protected areas [ 1 ]. Many scholars have highlighted how human encroachment into wildlife habitats has led to widespread habitat loss and fragmentation, often resulting in the displacement and decline of wildlife populations [ 2 , 3 ]. Traditionally, the establishment of protected areas such as game reserves and national parks has involved the relocation of rural communities, frequently implemented with limited consultation or inadequate compensation [ 4 , 5 ]. In contrast, Liuwa Plain NP represents a unique conservation model where human and wildlife populations co-exist within the landscape [ 6 , 7 ] .This approach challenges conventional conservation paradigms and offers a valuable opportunity to examine alternative strategies to biodiversity conservation, while potentially reducing human-wildlife conflicts [ 8 ]. Wildlife-based land use planning within protected areas, such as national parks, holds significant potential to support community livelihoods through sustainable tourism and the provision of ecosystem services [ 9 ]. However, in regions like Liuwa ecosystem, consisting both the national park and its surrounding areas where agricultural potential is low the effectiveness of protected areas in delivering economic benefits from wildlife-based enterprises is often limited by natural resource abundance [ 6 , 10 ]. As a result, many protected area networks are falling short of their full potential to contribute to ecological conservation, economic development, and social well-being [ 8 ]. This highlights the urgent need for effective management strategies and sustainable financing mechanisms [ 6 ]. Therefore, understanding and monitoring of long-term spatio-temporal habitat dynamics in the Liuwa Plain NP and the surrounding areas will provide crucial information for management regimes and restoration of stressed land cover types within the ecosystem and provide conducive habitats for wildlife and other secondary dependents such as humans as well as establishing informed conservation priorities. In Zambia, where issues, such as encroachment and illegal wildlife trade remain prevalent, there is a pressing need for comprehensive data on habitat dynamics in terms of spatio-temporal extent to guide conservation practice and policy decisions. Numerous studies have explored various aspects of resource management in Zambia’s protected areas, addressing themes such as illegal wildlife harvesting [ 11 , 12 ], land encroachment and human-wildlife conflicts [ 13 , 14 ], policy frameworks[ 8 ], ecological dynamics [ 15 , 16 ], and community participation [ 17 , 18 ], however, few have combined robust spatio – temporal habitat modelling with the lens of a shared landscape [ 10 ]. This leaves a critical gap in understanding how habitat extent and quality change over multiple decades. Additionally, advances in remote sensing, spatio modelling and change detection offer new opportunities to fill the gap, for instance recent work by [ 19 ] utilised geospatial and machine learning approaches to parse long term land cover changes and urbanisation patterns in other contexts for example the spatio temporal expansion in Lucknow, India and to model complex environmental changes under combined anthropogenic and natural pressures as employed in the study by [ 20 ]. As such these studies highlighted above indicate a feasibility and power to integrate spatio - temporal and analytical remote sensing techniques to untangle landscape dynamics. The objective of this study is to assess four decades of spatio-temporal habitat dynamics in Liuwa Plain NP in a human-wildlife co - existence landscape of Zambia and its surrounding areas focusing on forests, settlements, wetlands, grasslands, water, cropland and bareland. Further, the study endeavored to achieve key specific objectives aimed at understanding the long-term land cover changes within the Liuwa ecosystem. Specifically, it sought to 1. Examine how land cover types have changed over the four- decade period from 1984–2024 through the development of change maps for each decade 2. Explore the spatio variations in habitat dynamics by comparing inside the Liuwa Plain NP and the surrounding buffer of approximately 40 km. 3. Investigating the patterns of habitat loss and gains across the ecosystem in order to determine the extent and spatial distribution of these changes, lastly, 4. To analyse the habitat transition matrices to illustrate the magnitude and direction of the land cover conversions highlighting how land cover classes have been transformed over time within the ecosystem. Furthermore, the study hypothesised that increasing human demands for natural resources within and around the park are exerting growing pressure on both the extent and quality of habitat in the Liuwa ecosystem. The findings are expected to offer new insights into the sustainable management of wildlife resources in settings where human communities and natural ecosystems share spaces. 2. Methods 2.1 Study area Liuwa Plain NP, situated at latitude − 14.64 and longitude 22.74 (Fig. 1 ), is a unique conservation area in Zambia where wildlife co-exists with humans [ 10 ]. Established in 1972, the park's conservation legacy dates to the 19th century when it was managed as a royal hunting ground under King Lewanika of the Lozi People. The park is characterized by a floodplain with a mean elevation of 1, 050m above sea level [ 21 ]. The area is inhabited by local communities engaging in small-scale mixed-farming, fishing, and other natural resource-based enterprises. Since 2003, the park has been jointly managed by the Department of National Parks and Wildlife (DNPW), African Parks Network (APN), and the Barotse Royal Establishment (BRE), promoting effective natural resource management and community participation[ 50 ]. The Liuwa ecosystem is situated within Agro-Ecological Regional IIB, which is dominated by sandy soils and receives mean annual rainfall of 800-1,000 mm and has mean annual temperatures ranging from 8.1–34.9 ˚C. The park's ecosystem is dominated by grasslands, and seasonally flooded, supporting diverse biodiversity [ 10 ]. Liuwa Plain NP is home to over 300 bird species, including endangered wattled cranes, grey crowned cranes, and saddlebill storks. Large mammals inhabiting the park include blue wildebeest, Burchell's zebra, and tsessebe, with a large annual migratory blue wildebeest population, second to Serengeti in East Africa [ 50 ]. The park also hosts predators such as lions, cheetahs, and spotted hyenas, making it a significant conservation area in Zambia. 2.2. Materials and Methods 2.2.1 Overall workflow Figure 2 shows the workflow used to achieve the objectives of this study. The methodological approach followed a systematic workflow, ensuring clear and logical progression from data acquisition to analysis. The study integrated remote sensing, GIS, and analytical techniques to assess habitat dynamics over 40 years (1984–2024) in Liuwa Plain NP a human-wildlife co-existence landscape of Zambia. The methodology is divided into four key phases: (1) data acquisition and pre-processing, (2) LULC classification using a combination of pixel-based classification (PBC) and object-based post-classification refinement (OBPR), (3) Change detection and analysis to examine spatial trends in landscape transformation both within and outside park boundaries; and (4) Results interpretation. 2.2.2 Data acquisition and pre-processing 2.2.1.1. Imagery dataset This study employed multi-temporal Landsat imagery (1984–2024) to derive land use and land cover (LULC) maps. Landsat 5 TM images (1984, 1994, 2004, and 2014) and Landsat 8 OLI/TIRS imagery (2024) were obtained from the USGS EarthExplorer platform. Analysis-ready Collection 2 Level 2 products with a 30 m spatial resolution and less than 10% cloud cover were selected. To ensure classification consistency, all images were acquired during the late dry season (August–September), when vegetation phenology is most distinct. The imagery was projected to UTM Zone 35S (EPSG:32735), and analysis was conducted within a 40 km buffer surrounding the study area. Table 1 Details of the images used in this study include the type of Landsat images, date of acquisition, spatial resolution, and satellite path/row. Sensor Date of Acquisition Spatial resolution (m) Path/Row Landsat 5 TM 1984/09/13 30 172/068, 172/069 Landsat 5 TM 1994/08/29 30 172/068, 172/069 Landsat 5 TM 2004/08/14 30 172/068, 172/069 Landsat 8 OLI 2014/09/08 30 172/068, 172/069 Landsat 8 OLI 2024/09/12 30 172/068, 172/069 TM: Thematic Mapper; OLI: Operation Land Imager; USGS: United States Geological Survey 2.2.1.2. Auxiliary dataset Auxiliary spatial datasets, including boundary shapefiles, digital elevation models (DEMs), topographic maps, and Google Earth imagery, were incorporated to support spatial analysis. Settlement extent data were obtained from the GRID3 Zambia Settlement Extents v3.0 dataset( https://data.grid3.org/datasets/GRID3::grid3-zmb-settlement-extents-v3-0/about ). These complementary datasets were integrated to enhance the accuracy and interpretation of habitat dynamics within the study area. 2.2.3 LULC Classification 2.2.3.1 Classification Scheme Development The LULC classification was conducted using a scheme comprising seven distinct classes: Settlements, Forest, Cropland, Wetlands, Grassland, Bare land, and Water (Table 2 ). The development of these classes was informed by reconnaissance surveys and expert knowledge of the study area, ensuring relevance and accuracy. This classification system allowed for a nuanced understanding of land use patterns and changes within the study area. Table 2 Land use/cover scheme used to characterize the habitat in Liuwa ecosystem, western Zambia. Land use/cover classes Description Forest Deciduous forests, evergreen forests, mixed forest lands, coniferous forests, orchards, commercial plantations, protected areas, and nurseries Settlements Urban/rural settlements with transportation and communication facilities Wetland Areas characterized by water and close to water sources, such as rivers Grasslands Stunted height degraded forest, shrubs, or grasses. Water Rivers, lakes, ponds, reservoirs/dams, streams Cropland Pastures, orchards, home gardens, and areas covered with perennial and annual crops, may be fallow. Bare lands Barren areas devoid of vegetation cover and consisting of exposed soils and rock outcrops or sandy surfaces 2.2.3.2 Image classification This study implemented a two-step approach combining pixel-based classification (PBC) and object-based post-classification refinement (OBPR). The accuracy and reliability of this integrated classification methodology for LULC mapping have been established in earlier studies [ 22 ]. 2.2.3.3 Pixel-Based Classification Training datasets were developed for each of the seven LULC classes at each time point (1984, 1994, 2004, 2014, and 2024) to support supervised classification. The pixel-based classification was performed in ArcGIS Pro 3.5.1 using the Image Classification Wizard, which provides an integrated workflow for supervised classification of remote sensing imagery. A Random Forest Machine-learning classifier was applied, with parameters set to a maximum of 100 trees, a maximum tree depth of 30, and a maximum of 2,000 samples per class. [ 23 , 24 ] reported the superior performance of Random Forest compared to other classifiers. A minimum of 100 training sets were selected per class to ensure robust classification. Spectral signatures were generated from specified areas of interest, topographical maps, ESRI imagery hybrid base map and Google Earth images and Google Earth images were used as supplementary data to aid in the classification process and improve accuracy. Classification using PBC alone resulted in notable misclassifications due to spectral overlap, particularly between agricultural land, built-up areas, Grassland/wetlands and bareland. 2.2.3.4 Object-Based Post-Classification Refinement Following pixel-based classification, OBPR was implemented to address misclassifications arising from spectral confusion between land cover classes [ 22 ]. Using eCognition Developer software (version 10.3), multi-resolution segmentation was applied to segment Landsat imagery into homogeneous image objects for each time point [ 25 ]. Three critical parameters, scale, shape, and compactness were optimised to generate meaningful image objects, where shape and compactness-controlled object homogeneity while scale determined maximum object size [ 23 ]. In this study we used fixed optimized scale factor of 13, shape of 0.2 and compaction of 0.8. Segmentation of parameters were differentiated based on feature characteristics: large-scale values for extensive features such as grasslands, wetland and agricultural areas, and smaller-scale values for fine-resolution features including scattered settlements and small farming plots characteristic of the study area [ 26 ]. Smaller image objects representing the same LULC classes were merged before the final image objects were used for post-classification refinement. Misclassified pixels were identified through overlay analysis combining object-based segments, auxiliary spatial data, and expert visual interpretation [ 27 ]. Misclassified pixels were reclassified and mosaicked back into the original maps, producing five refined LULC classifications for 1984, 1994, 2004, 2014, and 2024 [ 26 ]. 2.2.4 Accuracy Assessment To assess the reliability of our LULC maps, we conducted a stratified random sampling accuracy assessment (Eq. 1–3) using 300 validation points (≥ 40 per class) for each time point. Reference labels were determined by visually inspecting each point against high-resolution Google Earth imagery, topographic maps, and expert knowledge from reconnaissance surveys [ 28 , 29 ].The classified and reference labels were compared and entered into an error matrix (confusion matrix), from which the following metrics were computed: Overall Accuracy (OA): \(\:OA=\frac{\begin{array}{c}\sum\:{x}_{ii}\\\:i=1\end{array}}{n}\times\:100\) (1) Producer's Accuracy (PA) : \(\:PAi=\frac{{x}_{ii}}{{x}_{i}+}\times\:100\) (2) User's Accuracy (UA) : \(\:UAi=\frac{{x}_{ii}}{x+i}\times\:100\) (3) Where : \(\:{x}_{ii}\) = number of correctly classified pixels for class i (the diagonal element) \(\:{x}_{i+}\) = total reference pixels for class i (row sum) \(\:{x}_{+i}\) = total classified pixels for class i (column sum) \(\:n\) = total number of reference points \(\:r\) = total number of classes 2.2.5 Change Detection Analysis Post-classification change detection analysis was performed to quantify LULC changes across four-time intervals: 1984–1994, 1994–2004, 2004–2014 and 2014–2024. By overlaying the initial and final land cover maps for each period, the change detection process identified the direction and magnitude of changes between land cover classes. The ArcGIS Pro change detection workflow was utilized to analyze the change trajectories and determine the rate of change for each time step (Eq. 4–6). The rate of change was calculated using the formulas: \(\:Annual\:Rate\:ochange=\frac{{A}_{2}-{A}_{1}}{{A}_{1}\times\:t}\times\:100\) (4) \(\:\varDelta\:={A}_{2}-{A}_{1}\) (5) \(\:\%\varDelta\:A=\frac{{A}_{2}-{A}_{1}}{{A}_{1}}\times\:100\) (6) Where : \(\:{A}_{1}\) = Area of a given LULC class at the beginning of the period \(\:{A}_{2}\) = Area of the same class at the end of the period \(\:t\) = Length of the time interval in years 2.2.6 Spatial Analysis and Comparison A separate analysis was conducted inside and outside the Liuwa Plain NP to evaluate conservation effectiveness (Eq. 7). The park shapefile was used to clip classified maps into two zones; The inside park area, representing the protected zone where habitat change was measured for ecological stability and the outside 40 km buffer zone, representing surrounding unprotected areas within the Liuwa Ecosystem of Zambia. \(\:CEI=\frac{{\text{C}\text{h}\text{a}\text{n}\text{g}\text{e}}_{outside}=\:-{\text{Change}}_{inside}}{{\text{Change}}_{outside}}\times\:100\) Where : \(\:{\text{Change}}_{inside}\) = total habitat change (area or %Δ) within park boundaries \(\:{\text{Change}}_{outside}\) = total habitat change (area or %Δ) in the 40 km buffer zone 2.2.7Habitat Transition Matrix Analysis (7) Transition matrices were generated to identify donor (loss) and beneficiary (gain) classes, between the different temporal intervals. This approach enabled a detailed assessment of how specific habitat types were transformed over time and the extent to which each class benefited or contributed to the overall landscape change (Formulas 8,9 and 10). $$\:{T}_{ij}=area\:transitioning\:from\:class\:i\:at\:time\:1\:to\:class\:j\:at\:time\:2$$ $$\:Then:$$ \(\:Gai{n}_{j}=\begin{array}{c}\sum\:{T}_{ij}\\\:i\ne\:1\end{array}\:\) (8) \(\:Los{s}_{i}=\:\begin{array}{c}\sum\:{T}_{ij}\\\:j\ne\:1\end{array}\) (9) \(\:Persistence\:j=\frac{{T}_{ii}}{{A}_{i\left(t1\right)}}\times\:100\) (10) Where : \(\:{T}_{ij}\) = area transitioning from LULC class i to class j \(\:Gai{n}_{j}\) = total area gained by class j from all other classes \(\:Los{s}_{i}\) = total area lost by class i to all other classes \(\:{T}_{ii}\) = area persisting in class i (diagonal element) \(\:{A}_{i\left(t1\right)}\) = area of class i at the initial time step 3. Results 3.1 Land use/cover classification The land cover maps generated for the study area demonstrated overall classification accuracies ranging from 82% to 89% (Table 3 ), indicating a high level of reliability in the classification methodology employed. The lowest accuracy was recorded for the 1994 map, while the 2024 classification achieved the highest accuracy. User's and producer's accuracies ranged from 78% to 100%, further supporting the consistency and reliability of the maps in representing real-world land cover conditions throughout the study period. Table 3 Accuracy assessment of the thematic maps which reports the user, producer and overall accuracies. 1984 1994 2004 2014 2024 Land cover PA (%) UA (%) PA (%) UA (%) PA (%) UA (%) PA (%) UA (%) PA (%) UA (%) Bareland 97 99 97 97 97 97 94 95 93 70 Cropland 92 86 98 98 98 98 93 93 78 82 Water 100 81 100 100 100 100 97 100 100 95 Grassland 100 93 79 94 79 94 91 100 82 90 Settlement 81 96 89 89 89 89 88 82 80 39 Forest 85 82 85 87 85 87 87 82 84 84 Wetland 100 98 98 98 98 98 84 88 100 87 OA (%) 82 84 85 87 89 Figure 3 shows that the Liuwa ecosystem is predominantly covered by grassland, which occupies over 75% of the total area. However, both grassland and forest cover have declined over the study period. Between 1994 and 2024, notable land cover changes occurred across the Liuwa ecosystem, with decreases in natural land cover types particularly forest and grassland and concurrent expansions in built-up areas and cropland. These changes are spatially concentrated in the southern part of the park, where development activity appears to be most intense, while the northern region remains relatively less affected. A visual inspection of Fig. 2 further highlights increases in land cover types, such as Bareland, cropland, and settlements, particularly evident in 2024. 3.2 Habitat dynamics Between 1984 and 2004, grassland increased slightly by 0.83% but subsequently declined by 0.65% between 2004 and 2024 (Fig. 4). In contrast to the fluctuating trend observed in grassland, forest cover exhibited a consistent decline throughout the study period, decreasing by up to 15%, apart from a brief stabilization around 2014. Human-influenced land cover types, such as cropland and settlements, showed a continuous upward trend, with their combined spatial extent doubling from 1984 to 2024. Notably, wetlands and water bodies experienced a marked reduction in 2024, potentially because of the drought conditions reported during that year. Figure 4: Extent of land cover types including key habitats, such as grasslands, forests and wetlands in Liuwa Plain NP, western Zambia, between 1984 and 2024. A comparison of land cover changes inside and outside Liuwa National Park reveals a clear divergence in trends between the two areas. While both regions were dominated by grassland followed by forests and wetlands throughout the study period, the extent and nature of change differed markedly. Inside the park (Fig. 5 A), grassland increased by up to 5%, whereas outside the park (Fig. 5 B), the increase was minimal, reaching only about 1%. Forest cover declined significantly outside the park, with a loss of up to 20%, compared to a more moderate 5% decline within the park. Furthermore, cropland and settlements expanded to more than three times their original extent outside the national parks, substantially greater than the changes observed within the protected area. These differences suggest that Liuwa National Park has played a critical role in mitigating human-induced land cover change, thereby contributing positively to habitat preservation within the park. 3.3 Habitat loss and gain Figure 6 shows the spatial distribution of major habitat dynamics within and surrounding Liuwa Plain NP. The most significant habitat losses were concentrated in the southern portion of the park, adjacent to Kalabo town. This area has experienced considerable expansion of built-up areas, agricultural land, and infrastructure developments, such as roads, all of which are contributing to habitat degradation and fragmentation. In contrast, the northern part of the park shows signs of persistent regrowth in both grassland and forest cover, suggesting a greater potential for ecological sustainability in that region. A contextual analysis of land cover gains and losses within and outside Liuwa Plain NP reveals dynamic changes across all land cover types over the study period. Figure 7 indicates that both grassland and wetland experienced net gains, with gains exceeding losses in both areas. Although some forest regeneration was observed, particularly within the park, forest losses especially outside the park were substantially greater than the gains. Notably, land cover types associated with habitat degradation, such as cropland and settlements, expanded significantly, with increases exceeding 300%, highlighting intense human pressure on the ecosystem. 3.4: Habitat transition Table 4 presents habitat transitions by identifying the sources of gains and the destinations of losses for each land cover type. Both inside and outside Liuwa Plain NP, over 60% of the losses from forests and grasslands were converted into cropland and settlements. Overall, grassland and forest areas acted as the primary contributors (donor classes), while cropland, settlements, and bareland emerged as the main recipients (beneficiary classes). Although changes in wetlands and water bodies were relatively minor, most of the observed transitions for these classes occurred as exchanges between each other. Table 4: Habitat transition between 1984 and 2024 within (A) and outside (B) Liuwa Plain NP, western Zambia. 4. Discussion The results of this study provide empirical evidence on the long-term habitat dynamics within the unique shared landscape in Liuwa ecosystem. The analysis is based on a series of high-accuracy thematic maps, each achieving classification accuracy exceeding 80% (Table 3 ). According to [ 29 ], such levels of accuracy are essential for reliable post-classification change detection and any further analysis. The accuracies achieved in this study are generally consistent with other studies involving Landsat image classifications [ 30 , 31 ] with Landsat 8 OLI/TIRS producing the highest accuracy. Besides, [ 32 ] indicates that the high accuracy of the OLI/TIRS image is likely due to the high quality of the Landsat sensor. The findings reveal a notable decline in land cover types associated with habitat integrity particularly outside the Liuwa Plain NP where forest cover declined by up to 20% and grassland by approximately 5% between 1984 and 2024, these results are consistent with a study in the Barotse Floodplain, the Zambezi river Sub Basin of Zambia which assessed and reported a general decline of the ecosystem services by [ 33 ] and also in line with a recent study in Malawi’s Kasungu National Park [ 1 ] were substantial loss of forest cover, vegetation fragmentation and increased aggregation of remnant habitat between 1997 and 2018 were observed and these patterns were attributed to anthropogenic pressures and climate variability. In addition, similar habitat decline trends have been documented in other co-managed protected areas across Southern Africa, such as Zimbabwe’s Gonarezhou National Park, where human-wildlife coexistence has led to complex land-cover transformations driven by settlement expansion and high elephant populations as reported by [ 34 ], in the same vein, a recent study in Okavango Delta of Botswana, revealed strong links between population growth and land cover changes, where rapid urban expansion from 0.68 km² to 9.61 km² between 1990 and 2020 corresponded with a marked decline in shrub and tree cover from 13.53 km² to 7.06 km², alongside notable water and vegetation fluctuations [ 35 ]. Over the same period, human-induced land cover types, such as cropland and settlements, expanded more than threefold across the Liuwa ecosystem, highlighting growing anthropogenic pressure. Wetlands and water bodies experienced a relatively modest decline of less than 3%, largely attributed to the severe drought conditions that affected Zambia in 2023/2024 [ 36 , 37 ]. However, the interpretation of habitat dynamics in Liuwa Plain NP, must also account for fire regimes, floodplain hydrology and migration corridors which acts as confounding ecological drivers, for instance seasonal burning, annual flooding of the Liuwa ecosystem and the long-term herbivore movements continuously to reshape vegetation structure and spectral reflectance patterns. This study demonstrates that the habitat within and around Liuwa Plain NP has undergone noticeable changes. Although the overall margins of change remain minimal, suggesting that ecological integration is still largely intact, the ecosystem is under increasing pressure. Over the past decades, the human population that depends on and interacts with these natural resources has more than doubled, intensifying pressure on land and other ecosystem services. According to demographic data from the Zambia Statistical Agency, the population of Kalabo District increased from 89,689 in 1990 to approximately 120,000 in 2022 [ 38 ] and these trend is consisted with the trend reported by [ 35 ] reported in Okavango, Delta of Botswana in a similar recent study. This population growth has fueled demand for land for settlement, agriculture, and fishing consistent with urban expansion trends observed elsewhere. For instance a geospatial analysis of Lucknow city, India, by [ 19 ] revealed fivefold built up are from 53.86 km² in 1991 to 261.45 km² in 2021, driven by rapid population growth and socio economic development. In the same vein the study aligned to Kolkata which has threefold between 1991 and 2018 [ 39 ] and Bengaluru with a fourfold growth in between 1992–2016 [ 40 ] in India as well as consistent with the results revealed in a study by [ 20 ] in Afghanistan. Similarly, in Liuwa Plain NP population pressure has emerged has a key driver of land use transformations, highlighting the shared dynamics between rural and urban landscapes undergoing human induced spatial expansion in this rare case of human-wildlife co-existence landscape. In such shared ecosystems, the risk and frequency of human-wildlife conflict tend to rise significantly, requiring pragmatic countermeasures [ 41 , 42 , 43 ]. Our results reveal a notable impact of the 2023/2024 drought on the Liuwa landscape, with a significant decline in the extent of wetlands and water resources observed across the study area. These results resonates with a study within Liuwa Plain NP were the study by [ 44 ] reported through community perspectives that droughts have been observed as a cause of decline in water levels in wetlands, contributing to some of the observed negative impacts on fish abundance and crop yield declines within the ecosystem. Further, to our interpretation that the 2023/2023 precipitated observable decline of wetlands and water in the Liuwa Plain NP and the surrounding areas is consistent with national scale diagnostics for the same season. Using CHIRPS v2 rainfall (1984 – March 2024) and SPI, computed at ,1- 3- and 6- month windows [ 36 ] report statistically significant trends indicative of short term water deficit across Zambia and characterise seasons as increasingly dry, particularly at shorter accumulation periods relevant to hydrologic stress (ONDJFM) [ 36 ]. The same study documents CHIRPS source and availability (0.05° resolution, Jan 1981–Mar 2024). Complementing these meteorological indicators is a study by [ 45 ] showing concurrent hydrological impacts between December 2023 and April 2024, Zambia experienced significant surface water losses in major reservoirs e.g Itezhi -Tezhi and Lake Kariba, reflecting basin - wide water deficits during the same period [ 45 ]. In this regard, taken together, nation -wide SPI anomalies from CHIRPS and independently observed reservoir shrinkage during the 2023/2024 provide convergent, quantitative support for attributing the observed wetland and water declines in Liuwa Plain NP and the surrounding areas to the exceptional drought conditions of that season. Despite the drought's severity, its effects did not immediately translate to wildlife mortality, suggesting a complex behavioral plasticity relationship between climate-driven resource scarcity and wildlife population dynamics. However, wildlife populations tend to respond to the environmental stressors, such as effects of climate change, variedly [ 43 , 46 ]. The drought's consequences, including reduced water sources, diminished food quality and quantity, potential limitations in breeding habitats, and substantial declines in fish stocks, a crucial resource for local communities underscore the increasing vulnerability of both wildlife and human populations within this ecosystem. The remote location of the park may further exacerbate these vulnerabilities. These findings are consistent with existing literature, such as [ 43 ] and [ 45 ], which highlights the role of escalating drought frequency and severity in exacerbating human-wildlife conflicts in Southern Africa, as observed in Zimbabwe. This emphasizes the urgent need for concerted efforts to enhance adaptation measures, thereby increasing the resilience of both wildlife and human populations to the impacts of climate change. By reducing vulnerability through targeted interventions, it is possible to mitigate the adverse effects of climate-driven stressors on ecosystems and communities [ 47 , 48 ]. Although the management of Liuwa Plain NP has evolved over time, the habitat quality for wildlife remains sufficiently intact to support a rich and diverse ecosystem. While this study did not assess changes in wildlife populations directly, once severely depleted of wildlife, the park has since experienced a significant recovery [ 6 , 50 ]. Since 2003, Liuwa has been managed through a formal co-management arrangement involving three key stakeholders: the Department of National Parks and Wildlife (DNPW), the Barotse Royal Establishment (BRE), and African Parks Network (APN). This collaborative governance model emphasizes conservation, law enforcement, land use planning, and the sustainable utilization of natural resources. The current healthy state of the park’s ecosystems stands as a testament to the effectiveness of this partnership and the long-term benefits of integrated management strategies and this is supported by the findings in a similar study conducted in Zimbabwe by [ 34 ] who strongly recommended and highlighted the importance of innovative partnerships between state and non-state profit organisations, local communities and local international partners in managing protected areas in Zimbabwe and elsewhere as they provide, financial, and technical assistance crucial in protected area administration and resource governance. The findings from this study make three key contributions to the management of Liuwa Plain NP: (1) they provide essential data to inform land use planning and park management; (2) they highlight the impact of the 2023/2024 drought, underscoring the need for integrated climate adaptation strategies for both wildlife and human communities; and (3) they emphasize the importance of strengthening conservation efforts to enhance habitat quality and support a wide range of biodiversity. [ 49 ] have previously demonstrated the value of geospatial information in wildlife habitat evaluation and planning. Building on that foundation, this study offers spatial insights that can be used to identify areas of persistent habitat loss, enabling more targeted zonation and the development of adaptive management strategies. These insights are critical to supporting long-term human-wildlife co-existence in Liuwa ecosystem. The findings of this study should be interpreted with consideration of several limitations. First, although the land cover classifications achieved high accuracy, they were not error-free, and misclassifications are still possible. Consequently, some land cover transitions may have been missed, or inaccurately estimated, either over or under-represented during the post-classification analysis. Second, the classification was conducted at 10-year intervals, which limits the ability to detect short-term or seasonal changes, such as those caused by fire, droughts, or other episodic events. In addition, potential influence of fire regimes, flood dynamics, or migration corridors which are highly relevant to the Liuwa ecosystem might have not been investigated in this study; there conclusions of these aspects are not addressed in details here. Future research should consider shorter temporal intervals to capture such dynamics more effectively as well as influence of factors such as fire, floods and migration. The aspect of drought which is central to this study need to be studied in more detail using climate data such as CHIRPS datasets in order to draw more conclusive results. Finally, this study did not investigate the underlying drivers of habitat dynamics. In-depth understanding of these drivers is essential for informed conservation planning, and future studies should aim to identify and analyze the socio-economic, environmental, and institutional factors influencing habitat dynamics in Liuwa ecosystem. 5. Conclusions This study aimed to assess four decades of habitat dynamics in Liuwa Plain NP a human-wildlife co-existence ecosystem of Western, Zambia. The Liuwa ecosystem uniquely supports the co-occurrence of human and wildlife populations, creating significant potential for competition over space and resources. The results revealed a general decline in key land cover types closely associated with wildlife habitats particularly forests and grasslands. While similar trends were observed both inside and outside the park, habitat loss was significantly more pronounced in areas outside the park boundaries. Human-induced land uses, including cropland and settlements, showed a marked increase in spatial extent over the study period. Although wetlands and water bodies did not change remarkably over the period, a noticeable decline was observed in 2024, likely due to the 2023/2024 drought and this is in line with nation -wide SPI anomalies from CHIRPS and independently observed reservoir shrinkage during the 2023/2024 which provides a convergent, quantitative support for attributing the observed wetland and water declines in Liuwa Plain NP and the surrounding areas to the exceptional drought conditions of that season. These findings provide valuable insights that can support more effective conservation planning and inform habitat management strategies aimed at minimizing habitat loss. Therefore, the study recommends that protected area managers should utilise remote sensing-based monitoring systems that provide near real time data on habitat condition and change. Such monitoring tools can guide targeted restoration interventions, for instance identifying priority zones for afforestation, corridor establishment and natural regeneration to mitigate fragmentation and strengthen ecosystem resilience to climate change. Finally the study recommends the integration of satellite derived information into adaptive management frameworks and that will enable evidence based decision making ensuring that conservation actions in Liuwa Plain NP and similar protected areas across the globe remain responsive to spatio and temporal habitat trends Future research should prioritize higher temporal resolution mapping using high-resolution imagery to better capture short-term changes and focus on identifying the underlying drivers of land cover change in the Liuwa ecosystem. Declarations Ethical Approval: This study was conducted under the ethical clearance No.2023/2/13/1 issued by The Copperbelt University, and research permit No. NPW/8/27/1 issued by Department of National Parks and Wildlife (DNPW). Consent to Publish declaration: Not applicable Consent to Participate declaration: Not applicable Funding: Funding was provided by Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) under the Enhanced Livelihoods and Natural Resource Management under accelerated Climate Change (ELNAC) project supported by the Federal ministry of Education and Research of Germany. Author’s contribution: DCM and GM analysed, DP and DCM drafted the manuscript, while NN, DZ and VRN conceptualized and reviewed the manuscript. Competing Interests : The authors declare that there are no competing interests. Clinical trial number: Not applicable. Acknowledgements: We wish to thank Copperbelt University for providing support during the preparation of the manuscript. Availability of Data and Material: Data will be provided upon reasonable request to the corresponding author. The data is held in Google Drive and a link will be provided. 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Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers invited by journal 26 Jan, 2026 Editor invited by journal 19 Jan, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 14 Jan, 2026 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. 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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-8523664","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581144304,"identity":"e6f90045-9200-4965-a4b4-04351028ee48","order_by":0,"name":"Danny Chisanga Musenge","email":"","orcid":"","institution":"Copperbelt University","correspondingAuthor":false,"prefix":"","firstName":"Danny","middleName":"Chisanga","lastName":"Musenge","suffix":""},{"id":581144305,"identity":"05fae4fd-4fa4-4810-a231-f85fb5f3fd1b","order_by":1,"name":"Darius Phiri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYDCCA2DEkMDGwNj4AMgwAIsmFBCnpdkArIUNxDXArwVsLBCzScC1MODRwnf87MPDPH/u5PFJH26rrqiwM+aX70788MCAQZ5f7ABWLZJn0g0O87Y9K2bjS2y7eeZMsplkG+9mCaDDDGfOTsCqxeBAGsNh3obDiW08jG03G9sO2Bgc490A0pJgcBuHlvPPGIAOg2gpBGmxP8a7+QdeLTeAtvCwQbQwArWYGbDxbsNri+SNZwwH57Y9A2lplmw4k2wscSx3m0WCgQROv/CdT2P+8ObPncT5PewPPzZU2Bn2N5/dfPNHhY08vzR2LVBwAENEAp9y7FpGwSgYBaNgFMABAD9OY2x1+2WgAAAAAElFTkSuQmCC","orcid":"","institution":"Copperbelt University","correspondingAuthor":true,"prefix":"","firstName":"Darius","middleName":"","lastName":"Phiri","suffix":""},{"id":581144306,"identity":"4c0a0d08-8623-4594-b090-83a423b96578","order_by":2,"name":"Ngawo Namukonde","email":"","orcid":"","institution":"Copperbelt University","correspondingAuthor":false,"prefix":"","firstName":"Ngawo","middleName":"","lastName":"Namukonde","suffix":""},{"id":581144307,"identity":"127c65c0-b16a-45df-a13b-8c07951293f4","order_by":3,"name":"Donald Zulu","email":"","orcid":"","institution":"Copperbelt University","correspondingAuthor":false,"prefix":"","firstName":"Donald","middleName":"","lastName":"Zulu","suffix":""},{"id":581144308,"identity":"ee60804f-5f42-44ea-b3a2-c95c40a22a65","order_by":4,"name":"Gift Mulenga","email":"","orcid":"","institution":"Copperbelt University","correspondingAuthor":false,"prefix":"","firstName":"Gift","middleName":"","lastName":"Mulenga","suffix":""},{"id":581144309,"identity":"62330004-353a-413a-8646-95c00d8151cd","order_by":5,"name":"Vincent R. Nyirenda","email":"","orcid":"","institution":"Copperbelt University","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"R.","lastName":"Nyirenda","suffix":""}],"badges":[],"createdAt":"2026-01-05 17:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8523664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8523664/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101306483,"identity":"2dd1d015-ef93-417c-a6ff-0b828467afec","added_by":"auto","created_at":"2026-01-28 10:16:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":317981,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Liuwa Plain NP in Western Province of Zambia. The park is surrounded by the Upper Zambezi GMA which provides a buffer to the available resources.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/3606d0dc7f0d5d06fe56cf74.jpg"},{"id":101397882,"identity":"b013e0f0-d450-4a7f-b31d-a2a15fd3fbb9","added_by":"auto","created_at":"2026-01-29 09:37:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95769,"visible":true,"origin":"","legend":"\u003cp\u003eData major steps involved in data processing which include data acquisition, image classification and change detection (see a detailed flow chart under the Supplementary Material).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/054112809875d98123a38d1f.jpg"},{"id":101306477,"identity":"125e92f8-72da-4fc1-9c48-b6d1133a47bb","added_by":"auto","created_at":"2026-01-28 10:16:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":396413,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover maps for the Liuwa ecosystem between 1984 and 2024. This map shows the trends of the wildlife habitat in Liuwa ecosystem, both inside and outside the Liuwa Plain NP, western Zambia.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/9384718a71d822933f9dfc06.jpg"},{"id":101306478,"identity":"c8202cc9-990c-4939-9dec-b14de4ee90fb","added_by":"auto","created_at":"2026-01-28 10:16:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115781,"visible":true,"origin":"","legend":"\u003cp\u003eExtent of land cover types including key habitats, such as grasslands, forests and wetlands in Liuwa Plain NP, western Zambia, between 1984 and 2024.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/ce6eb86e06f05f28ffeb3423.jpg"},{"id":101306480,"identity":"51ad3494-b555-4a5a-9d85-06f5dab84953","added_by":"auto","created_at":"2026-01-28 10:16:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68963,"visible":true,"origin":"","legend":"\u003cp\u003eExtent of land cover types including key habitats, such as grasslands, forests and wetlands with (a) inside and (b) outside the Liuwa Plain NP, western Zambia, between 1984 and 2024.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/bab2836001171107bd72b458.jpg"},{"id":101306479,"identity":"d6fad5e5-cff1-49b0-abc6-86700bb03f7c","added_by":"auto","created_at":"2026-01-28 10:16:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":211774,"visible":true,"origin":"","legend":"\u003cp\u003eHabitat losses within and outside Liuwa Plain NP, western Zambia, between 1984 and 2024.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/9a403ed8772734812b660c01.jpg"},{"id":101751343,"identity":"8b3f5451-cb89-4db9-b60b-fa453d200518","added_by":"auto","created_at":"2026-02-03 10:19:28","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":61483,"visible":true,"origin":"","legend":"\u003cp\u003eLosses and gains within and outside the Liuwa Plain NP between 1984 (A) and 2024 (B).\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/5734b5b57b45c15e10d84f1c.jpg"},{"id":103049123,"identity":"828fd7e9-a985-4e30-9225-42f2cd1653a6","added_by":"auto","created_at":"2026-02-20 07:33:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2482622,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8523664/v1/a2079710-46ef-4bbd-abf6-cee40273ebac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Four Decades of Habitat Change and Spatio-Temporal Dynamics in the Liuwa Ecosystem of Zambia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLandscapes in protected areas such as National Parks change because of natural disruptions and anthropogenic activities, however, anthropogenic activities are by far the most significant threat to biodiversity in protected areas [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Many scholars have highlighted how human encroachment into wildlife habitats has led to widespread habitat loss and fragmentation, often resulting in the displacement and decline of wildlife populations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Traditionally, the establishment of protected areas such as game reserves and national parks has involved the relocation of rural communities, frequently implemented with limited consultation or inadequate compensation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In contrast, Liuwa Plain NP represents a unique conservation model where human and wildlife populations co-exist within the landscape [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] .This approach challenges conventional conservation paradigms and offers a valuable opportunity to examine alternative strategies to biodiversity conservation, while potentially reducing human-wildlife conflicts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWildlife-based land use planning within protected areas, such as national parks, holds significant potential to support community livelihoods through sustainable tourism and the provision of ecosystem services [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, in regions like Liuwa ecosystem, consisting both the national park and its surrounding areas where agricultural potential is low the effectiveness of protected areas in delivering economic benefits from wildlife-based enterprises is often limited by natural resource abundance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As a result, many protected area networks are falling short of their full potential to contribute to ecological conservation, economic development, and social well-being [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This highlights the urgent need for effective management strategies and sustainable financing mechanisms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, understanding and monitoring of long-term spatio-temporal habitat dynamics in the Liuwa Plain NP and the surrounding areas will provide crucial information for management regimes and restoration of stressed land cover types within the ecosystem and provide conducive habitats for wildlife and other secondary dependents such as humans as well as establishing informed conservation priorities. In Zambia, where issues, such as encroachment and illegal wildlife trade remain prevalent, there is a pressing need for comprehensive data on habitat dynamics in terms of spatio-temporal extent to guide conservation practice and policy decisions.\u003c/p\u003e \u003cp\u003eNumerous studies have explored various aspects of resource management in Zambia\u0026rsquo;s protected areas, addressing themes such as illegal wildlife harvesting [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], land encroachment and human-wildlife conflicts [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], policy frameworks[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], ecological dynamics [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and community participation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], however, few have combined robust spatio \u0026ndash; temporal habitat modelling with the lens of a shared landscape [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This leaves a critical gap in understanding how habitat extent and quality change over multiple decades. Additionally, advances in remote sensing, spatio modelling and change detection offer new opportunities to fill the gap, for instance recent work by [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] utilised geospatial and machine learning approaches to parse long term land cover changes and urbanisation patterns in other contexts for example the spatio temporal expansion in Lucknow, India and to model complex environmental changes under combined anthropogenic and natural pressures as employed in the study by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As such these studies highlighted above indicate a feasibility and power to integrate spatio - temporal and analytical remote sensing techniques to untangle landscape dynamics.\u003c/p\u003e \u003cp\u003eThe objective of this study is to assess four decades of spatio-temporal habitat dynamics in Liuwa Plain NP in a human-wildlife co - existence landscape of Zambia and its surrounding areas focusing on forests, settlements, wetlands, grasslands, water, cropland and bareland. Further, the study endeavored to achieve key specific objectives aimed at understanding the long-term land cover changes within the Liuwa ecosystem. Specifically, it sought to 1. Examine how land cover types have changed over the four- decade period from 1984\u0026ndash;2024 through the development of change maps for each decade 2. Explore the spatio variations in habitat dynamics by comparing inside the Liuwa Plain NP and the surrounding buffer of approximately 40 km. 3. Investigating the patterns of habitat loss and gains across the ecosystem in order to determine the extent and spatial distribution of these changes, lastly, 4. To analyse the habitat transition matrices to illustrate the magnitude and direction of the land cover conversions highlighting how land cover classes have been transformed over time within the ecosystem. Furthermore, the study hypothesised that increasing human demands for natural resources within and around the park are exerting growing pressure on both the extent and quality of habitat in the Liuwa ecosystem. The findings are expected to offer new insights into the sustainable management of wildlife resources in settings where human communities and natural ecosystems share spaces.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eLiuwa Plain NP, situated at latitude \u0026minus;\u0026thinsp;14.64 and longitude 22.74 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), is a unique conservation area in Zambia where wildlife co-exists with humans [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Established in 1972, the park's conservation legacy dates to the 19th century when it was managed as a royal hunting ground under King Lewanika of the Lozi People. The park is characterized by a floodplain with a mean elevation of 1, 050m above sea level [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The area is inhabited by local communities engaging in small-scale mixed-farming, fishing, and other natural resource-based enterprises. Since 2003, the park has been jointly managed by the Department of National Parks and Wildlife (DNPW), African Parks Network (APN), and the Barotse Royal Establishment (BRE), promoting effective natural resource management and community participation[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Liuwa ecosystem is situated within Agro-Ecological Regional IIB, which is dominated by sandy soils and receives mean annual rainfall of 800-1,000 mm and has mean annual temperatures ranging from 8.1\u0026ndash;34.9 ˚C. The park's ecosystem is dominated by grasslands, and seasonally flooded, supporting diverse biodiversity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Liuwa Plain NP is home to over 300 bird species, including endangered wattled cranes, grey crowned cranes, and saddlebill storks. Large mammals inhabiting the park include blue wildebeest, Burchell's zebra, and tsessebe, with a large annual migratory blue wildebeest population, second to Serengeti in East Africa [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The park also hosts predators such as lions, cheetahs, and spotted hyenas, making it a significant conservation area in Zambia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Materials and Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Overall workflow\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the workflow used to achieve the objectives of this study. The methodological approach followed a systematic workflow, ensuring clear and logical progression from data acquisition to analysis. The study integrated remote sensing, GIS, and analytical techniques to assess habitat dynamics over 40 years (1984\u0026ndash;2024) in Liuwa Plain NP a human-wildlife co-existence landscape of Zambia. The methodology is divided into four key phases: (1) data acquisition and pre-processing, (2) LULC classification using a combination of pixel-based classification (PBC) and object-based post-classification refinement (OBPR), (3) Change detection and analysis to examine spatial trends in landscape transformation both within and outside park boundaries; and (4) Results interpretation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Data acquisition and pre-processing\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section4\"\u003e \u003ch2\u003e2.2.1.1. Imagery dataset\u003c/h2\u003e \u003cp\u003eThis study employed multi-temporal Landsat imagery (1984\u0026ndash;2024) to derive land use and land cover (LULC) maps. Landsat 5 TM images (1984, 1994, 2004, and 2014) and Landsat 8 OLI/TIRS imagery (2024) were obtained from the USGS EarthExplorer platform. Analysis-ready Collection 2 Level 2 products with a 30 m spatial resolution and less than 10% cloud cover were selected. To ensure classification consistency, all images were acquired during the late dry season (August\u0026ndash;September), when vegetation phenology is most distinct. The imagery was projected to UTM Zone 35S (EPSG:32735), and analysis was conducted within a 40 km buffer surrounding the study area.\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\u003eDetails of the images used in this study include the type of Landsat images, date of acquisition, spatial resolution, and satellite path/row.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDate of Acquisition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial resolution (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePath/Row\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLandsat 5 TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1984/09/13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172/068, 172/069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLandsat 5 TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1994/08/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172/068, 172/069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLandsat 5 TM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2004/08/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172/068, 172/069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLandsat 8 OLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2014/09/08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172/068, 172/069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLandsat 8 OLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2024/09/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172/068, 172/069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTM: Thematic Mapper; OLI: Operation Land Imager; USGS: United States Geological Survey\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section4\"\u003e \u003ch2\u003e2.2.1.2. Auxiliary dataset\u003c/h2\u003e \u003cp\u003eAuxiliary spatial datasets, including boundary shapefiles, digital elevation models (DEMs), topographic maps, and Google Earth imagery, were incorporated to support spatial analysis. Settlement extent data were obtained from the GRID3 Zambia Settlement Extents v3.0 dataset(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.grid3.org/datasets/GRID3::grid3-zmb-settlement-extents-v3-0/about\u003c/span\u003e\u003cspan address=\"https://data.grid3.org/datasets/GRID3::grid3-zmb-settlement-extents-v3-0/about\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These complementary datasets were integrated to enhance the accuracy and interpretation of habitat dynamics within the study area.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 LULC Classification\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.1 Classification Scheme Development\u003c/h2\u003e \u003cp\u003eThe LULC classification was conducted using a scheme comprising seven distinct classes: Settlements, Forest, Cropland, Wetlands, Grassland, Bare land, and Water (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The development of these classes was informed by reconnaissance surveys and expert knowledge of the study area, ensuring relevance and accuracy. This classification system allowed for a nuanced understanding of land use patterns and changes within the study area.\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\u003eLand use/cover scheme used to characterize the habitat in Liuwa ecosystem, western Zambia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLand use/cover\u003c/p\u003e \u003cp\u003eclasses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\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\u003e\u003cb\u003eForest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDeciduous forests, evergreen forests, mixed forest lands, coniferous forests, orchards, commercial plantations, protected areas, and nurseries\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSettlements\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUrban/rural settlements with transportation and communication facilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWetland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAreas characterized by water and close to water sources, such as rivers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrasslands\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStunted height degraded forest, shrubs, or grasses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWater\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRivers, lakes, ponds, reservoirs/dams, streams\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCropland\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePastures, orchards, home gardens, and areas covered with perennial and annual crops, may be fallow.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBare lands\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBarren areas devoid of vegetation cover and consisting of exposed soils and rock outcrops or sandy surfaces\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.2 Image classification\u003c/h2\u003e \u003cp\u003eThis study implemented a two-step approach combining pixel-based classification (PBC) and object-based post-classification refinement (OBPR). The accuracy and reliability of this integrated classification methodology for LULC mapping have been established in earlier studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.3 Pixel-Based Classification\u003c/h2\u003e \u003cp\u003eTraining datasets were developed for each of the seven LULC classes at each time point (1984, 1994, 2004, 2014, and 2024) to support supervised classification. The pixel-based classification was performed in ArcGIS Pro 3.5.1 using the Image Classification Wizard, which provides an integrated workflow for supervised classification of remote sensing imagery. A Random Forest Machine-learning classifier was applied, with parameters set to a maximum of 100 trees, a maximum tree depth of 30, and a maximum of 2,000 samples per class. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported the superior performance of Random Forest compared to other classifiers. A minimum of 100 training sets were selected per class to ensure robust classification. Spectral signatures were generated from specified areas of interest, topographical maps, ESRI imagery hybrid base map and Google Earth images and Google Earth images were used as supplementary data to aid in the classification process and improve accuracy. Classification using PBC alone resulted in notable misclassifications due to spectral overlap, particularly between agricultural land, built-up areas, Grassland/wetlands and bareland.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003e2.2.3.4 Object-Based Post-Classification Refinement\u003c/h2\u003e \u003cp\u003eFollowing pixel-based classification, OBPR was implemented to address misclassifications arising from spectral confusion between land cover classes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Using eCognition Developer software (version 10.3), multi-resolution segmentation was applied to segment Landsat imagery into homogeneous image objects for each time point [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Three critical parameters, scale, shape, and compactness were optimised to generate meaningful image objects, where shape and compactness-controlled object homogeneity while scale determined maximum object size [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study we used fixed optimized scale factor of 13, shape of 0.2 and compaction of 0.8.\u003c/p\u003e \u003cp\u003eSegmentation of parameters were differentiated based on feature characteristics: large-scale values for extensive features such as grasslands, wetland and agricultural areas, and smaller-scale values for fine-resolution features including scattered settlements and small farming plots characteristic of the study area [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Smaller image objects representing the same LULC classes were merged before the final image objects were used for post-classification refinement.\u003c/p\u003e \u003cp\u003eMisclassified pixels were identified through overlay analysis combining object-based segments, auxiliary spatial data, and expert visual interpretation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Misclassified pixels were reclassified and mosaicked back into the original maps, producing five refined LULC classifications for 1984, 1994, 2004, 2014, and 2024 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Accuracy Assessment\u003c/h2\u003e \u003cp\u003eTo assess the reliability of our LULC maps, we conducted a stratified random sampling accuracy assessment (Eq.\u0026nbsp;1\u0026ndash;3) using 300 validation points (\u0026ge;\u0026thinsp;40 per class) for each time point. Reference labels were determined by visually inspecting each point against high-resolution Google Earth imagery, topographic maps, and expert knowledge from reconnaissance surveys [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].The classified and reference labels were compared and entered into an error matrix (confusion matrix), from which the following metrics were computed:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Accuracy (OA):\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:OA=\\frac{\\begin{array}{c}\\sum\\:{x}_{ii}\\\\\\:i=1\\end{array}}{n}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProducer's Accuracy (PA)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PAi=\\frac{{x}_{ii}}{{x}_{i}+}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUser's Accuracy (UA)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:UAi=\\frac{{x}_{ii}}{x+i}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eWhere\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{ii}\\)\u003c/span\u003e \u003c/span\u003e= number of correctly classified pixels for class \u003cem\u003ei\u003c/em\u003e (the diagonal element)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i+}\\)\u003c/span\u003e \u003c/span\u003e= total reference pixels for class \u003cem\u003ei\u003c/em\u003e (row sum)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{+i}\\)\u003c/span\u003e \u003c/span\u003e= total classified pixels for class \u003cem\u003ei\u003c/em\u003e (column sum)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e \u003c/span\u003e= total number of reference points\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e \u003c/span\u003e= total number of classes\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Change Detection Analysis\u003c/h2\u003e \u003cp\u003ePost-classification change detection analysis was performed to quantify LULC changes across four-time intervals: 1984\u0026ndash;1994, 1994\u0026ndash;2004, 2004\u0026ndash;2014 and 2014\u0026ndash;2024. By overlaying the initial and final land cover maps for each period, the change detection process identified the direction and magnitude of changes between land cover classes. The ArcGIS Pro change detection workflow was utilized to analyze the change trajectories and determine the rate of change for each time step (Eq.\u0026nbsp;4\u0026ndash;6).\u003c/p\u003e \u003cp\u003eThe rate of change was calculated using the formulas:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\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\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Annual\\:Rate\\:ochange=\\frac{{A}_{2}-{A}_{1}}{{A}_{1}\\times\\:t}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:={A}_{2}-{A}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\%\\varDelta\\:A=\\frac{{A}_{2}-{A}_{1}}{{A}_{1}}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eWhere\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{1}\\)\u003c/span\u003e \u003c/span\u003e= Area of a given LULC class at the beginning of the period\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{2}\\)\u003c/span\u003e \u003c/span\u003e= Area of the same class at the end of the period\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e \u003c/span\u003e= Length of the time interval in years\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Spatial Analysis and Comparison\u003c/h2\u003e \u003cp\u003eA separate analysis was conducted inside and outside the Liuwa Plain NP to evaluate conservation effectiveness (Eq.\u0026nbsp;7). The park shapefile was used to clip classified maps into two zones; The inside park area, representing the protected zone where habitat change was measured for ecological stability and the outside 40 km buffer zone, representing surrounding unprotected areas within the Liuwa Ecosystem of Zambia.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:CEI=\\frac{{\\text{C}\\text{h}\\text{a}\\text{n}\\text{g}\\text{e}}_{outside}=\\:-{\\text{Change}}_{inside}}{{\\text{Change}}_{outside}}\\times\\:100\\)\u003c/span\u003e \u003c/span\u003e \u003cb\u003eWhere\u003c/b\u003e:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Change}}_{inside}\\)\u003c/span\u003e\u003c/span\u003e= total habitat change (area or %Δ) within park boundaries\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Change}}_{outside}\\)\u003c/span\u003e\u003c/span\u003e= total habitat change (area or %Δ) in the 40 km buffer zone\u003cb\u003e2.2.7Habitat Transition Matrix Analysis\u003c/b\u003e(7)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTransition matrices were generated to identify donor (loss) and beneficiary (gain) classes, between the different temporal intervals. This approach enabled a detailed assessment of how specific habitat types were transformed over time and the extent to which each class benefited or contributed to the overall landscape change (Formulas 8,9 and 10).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{T}_{ij}=area\\:transitioning\\:from\\:class\\:i\\:at\\:time\\:1\\:to\\:class\\:j\\:at\\:time\\:2$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Then:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\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\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Gai{n}_{j}=\\begin{array}{c}\\sum\\:{T}_{ij}\\\\\\:i\\ne\\:1\\end{array}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Los{s}_{i}=\\:\\begin{array}{c}\\sum\\:{T}_{ij}\\\\\\:j\\ne\\:1\\end{array}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Persistence\\:j=\\frac{{T}_{ii}}{{A}_{i\\left(t1\\right)}}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eWhere\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{ij}\\)\u003c/span\u003e \u003c/span\u003e= area transitioning from LULC class \u003cem\u003ei\u003c/em\u003e to class \u003cem\u003ej\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Gai{n}_{j}\\)\u003c/span\u003e \u003c/span\u003e= total area gained by class \u003cem\u003ej\u003c/em\u003e from all other classes\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Los{s}_{i}\\)\u003c/span\u003e \u003c/span\u003e= total area lost by class \u003cem\u003ei\u003c/em\u003e to all other classes\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{ii}\\)\u003c/span\u003e \u003c/span\u003e= area persisting in class \u003cem\u003ei\u003c/em\u003e (diagonal element)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i\\left(t1\\right)}\\)\u003c/span\u003e \u003c/span\u003e= area of class \u003cem\u003ei\u003c/em\u003e at the initial time step\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Land use/cover classification\u003c/h2\u003e \u003cp\u003eThe land cover maps generated for the study area demonstrated overall classification accuracies ranging from 82% to 89% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating a high level of reliability in the classification methodology employed. The lowest accuracy was recorded for the 1994 map, while the 2024 classification achieved the highest accuracy. User's and producer's accuracies ranged from 78% to 100%, further supporting the consistency and reliability of the maps in representing real-world land cover conditions throughout the study period.\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\u003eAccuracy assessment of the thematic maps which reports the user, producer and overall accuracies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1984\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBareland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e70\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\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\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\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79\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\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87\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\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\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\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e89\u003c/b\u003e\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\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the Liuwa ecosystem is predominantly covered by grassland, which occupies over 75% of the total area. However, both grassland and forest cover have declined over the study period. Between 1994 and 2024, notable land cover changes occurred across the Liuwa ecosystem, with decreases in natural land cover types particularly forest and grassland and concurrent expansions in built-up areas and cropland. These changes are spatially concentrated in the southern part of the park, where development activity appears to be most intense, while the northern region remains relatively less affected. A visual inspection of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e further highlights increases in land cover types, such as Bareland, cropland, and settlements, particularly evident in 2024.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e3.2 Habitat dynamics\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBetween 1984 and 2004, grassland increased slightly by 0.83% but subsequently declined by 0.65% between 2004 and 2024 (Fig.\u0026nbsp;4). In contrast to the fluctuating trend observed in grassland, forest cover exhibited a consistent decline throughout the study period, decreasing by up to 15%, apart from a brief stabilization around 2014. Human-influenced land cover types, such as cropland and settlements, showed a continuous upward trend, with their combined spatial extent doubling from 1984 to 2024. Notably, wetlands and water bodies experienced a marked reduction in 2024, potentially because of the drought conditions reported during that year.\u003c/p\u003e \u003cp\u003eFigure 4: Extent of land cover types including key habitats, such as grasslands, forests and wetlands in Liuwa Plain NP, western Zambia, between 1984 and 2024.\u003c/p\u003e \u003cp\u003eA comparison of land cover changes inside and outside Liuwa National Park reveals a clear divergence in trends between the two areas. While both regions were dominated by grassland followed by forests and wetlands throughout the study period, the extent and nature of change differed markedly. Inside the park (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), grassland increased by up to 5%, whereas outside the park (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), the increase was minimal, reaching only about 1%. Forest cover declined significantly outside the park, with a loss of up to 20%, compared to a more moderate 5% decline within the park. Furthermore, cropland and settlements expanded to more than three times their original extent outside the national parks, substantially greater than the changes observed within the protected area. These differences suggest that Liuwa National Park has played a critical role in mitigating human-induced land cover change, thereby contributing positively to habitat preservation within the park.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Habitat loss and gain\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the spatial distribution of major habitat dynamics within and surrounding Liuwa Plain NP. The most significant habitat losses were concentrated in the southern portion of the park, adjacent to Kalabo town. This area has experienced considerable expansion of built-up areas, agricultural land, and infrastructure developments, such as roads, all of which are contributing to habitat degradation and fragmentation. In contrast, the northern part of the park shows signs of persistent regrowth in both grassland and forest cover, suggesting a greater potential for ecological sustainability in that region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA contextual analysis of land cover gains and losses within and outside Liuwa Plain NP reveals dynamic changes across all land cover types over the study period. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicates that both grassland and wetland experienced net gains, with gains exceeding losses in both areas. Although some forest regeneration was observed, particularly within the park, forest losses especially outside the park were substantially greater than the gains. Notably, land cover types associated with habitat degradation, such as cropland and settlements, expanded significantly, with increases exceeding 300%, highlighting intense human pressure on the ecosystem.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4: Habitat transition\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents habitat transitions by identifying the sources of gains and the destinations of losses for each land cover type. Both inside and outside Liuwa Plain NP, over 60% of the losses from forests and grasslands were converted into cropland and settlements. Overall, grassland and forest areas acted as the primary contributors (donor classes), while cropland, settlements, and bareland emerged as the main recipients (beneficiary classes). Although changes in wetlands and water bodies were relatively minor, most of the observed transitions for these classes occurred as exchanges between each other.\u003c/p\u003e \u003cp\u003eTable 4: Habitat transition between 1984 and 2024 within (A) and outside (B) Liuwa Plain NP, western Zambia.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"724\" height=\"586\"\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe results of this study provide empirical evidence on the long-term habitat dynamics within the unique shared landscape in Liuwa ecosystem. The analysis is based on a series of high-accuracy thematic maps, each achieving classification accuracy exceeding 80% (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], such levels of accuracy are essential for reliable post-classification change detection and any further analysis. The accuracies achieved in this study are generally consistent with other studies involving Landsat image classifications [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] with Landsat 8 OLI/TIRS producing the highest accuracy. Besides, [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] indicates that the high accuracy of the OLI/TIRS image is likely due to the high quality of the Landsat sensor. The findings reveal a notable decline in land cover types associated with habitat integrity particularly outside the Liuwa Plain NP where forest cover declined by up to 20% and grassland by approximately 5% between 1984 and 2024, these results are consistent with a study in the Barotse Floodplain, the Zambezi river Sub Basin of Zambia which assessed and reported a general decline of the ecosystem services by [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and also in line with a recent study in Malawi\u0026rsquo;s Kasungu National Park [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] were substantial loss of forest cover, vegetation fragmentation and increased aggregation of remnant habitat between 1997 and 2018 were observed and these patterns were attributed to anthropogenic pressures and climate variability. In addition, similar habitat decline trends have been documented in other co-managed protected areas across Southern Africa, such as Zimbabwe\u0026rsquo;s Gonarezhou National Park, where human-wildlife coexistence has led to complex land-cover transformations driven by settlement expansion and high elephant populations as reported by [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], in the same vein, a recent study in Okavango Delta of Botswana, revealed strong links between population growth and land cover changes, where rapid urban expansion from 0.68 km\u0026sup2; to 9.61 km\u0026sup2; between 1990 and 2020 corresponded with a marked decline in shrub and tree cover from 13.53 km\u0026sup2; to 7.06 km\u0026sup2;, alongside notable water and vegetation fluctuations [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Over the same period, human-induced land cover types, such as cropland and settlements, expanded more than threefold across the Liuwa ecosystem, highlighting growing anthropogenic pressure. Wetlands and water bodies experienced a relatively modest decline of less than 3%, largely attributed to the severe drought conditions that affected Zambia in 2023/2024 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, the interpretation of habitat dynamics in Liuwa Plain NP, must also account for fire regimes, floodplain hydrology and migration corridors which acts as confounding ecological drivers, for instance seasonal burning, annual flooding of the Liuwa ecosystem and the long-term herbivore movements continuously to reshape vegetation structure and spectral reflectance patterns.\u003c/p\u003e \u003cp\u003eThis study demonstrates that the habitat within and around Liuwa Plain NP has undergone noticeable changes. Although the overall margins of change remain minimal, suggesting that ecological integration is still largely intact, the ecosystem is under increasing pressure. Over the past decades, the human population that depends on and interacts with these natural resources has more than doubled, intensifying pressure on land and other ecosystem services. According to demographic data from the Zambia Statistical Agency, the population of Kalabo District increased from 89,689 in 1990 to approximately 120,000 in 2022 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and these trend is consisted with the trend reported by [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] reported in Okavango, Delta of Botswana in a similar recent study. This population growth has fueled demand for land for settlement, agriculture, and fishing consistent with urban expansion trends observed elsewhere. For instance a geospatial analysis of Lucknow city, India, by [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] revealed fivefold built up are from 53.86 km\u0026sup2; in 1991 to 261.45 km\u0026sup2; in 2021, driven by rapid population growth and socio economic development. In the same vein the study aligned to Kolkata which has threefold between 1991 and 2018 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and Bengaluru with a fourfold growth in between 1992\u0026ndash;2016 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in India as well as consistent with the results revealed in a study by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in Afghanistan. Similarly, in Liuwa Plain NP population pressure has emerged has a key driver of land use transformations, highlighting the shared dynamics between rural and urban landscapes undergoing human induced spatial expansion in this rare case of human-wildlife co-existence landscape. In such shared ecosystems, the risk and frequency of human-wildlife conflict tend to rise significantly, requiring pragmatic countermeasures [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results reveal a notable impact of the 2023/2024 drought on the Liuwa landscape, with a significant decline in the extent of wetlands and water resources observed across the study area. These results resonates with a study within Liuwa Plain NP were the study by [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] reported through community perspectives that droughts have been observed as a cause of decline in water levels in wetlands, contributing to some of the observed negative impacts on fish abundance and crop yield declines within the ecosystem. Further, to our interpretation that the 2023/2023 precipitated observable decline of wetlands and water in the Liuwa Plain NP and the surrounding areas is consistent with national scale diagnostics for the same season. Using CHIRPS v2 rainfall (1984 \u0026ndash; March 2024) and SPI, computed at ,1- 3- and 6- month windows [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] report statistically significant trends indicative of short term water deficit across Zambia and characterise seasons as increasingly dry, particularly at shorter accumulation periods relevant to hydrologic stress (ONDJFM) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The same study documents CHIRPS source and availability (0.05\u0026deg; resolution, Jan 1981\u0026ndash;Mar 2024). Complementing these meteorological indicators is a study by [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] showing concurrent hydrological impacts between December 2023 and April 2024, Zambia experienced significant surface water losses in major reservoirs e.g Itezhi -Tezhi and Lake Kariba, reflecting basin - wide water deficits during the same period [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In this regard, taken together, nation -wide SPI anomalies from CHIRPS and independently observed reservoir shrinkage during the 2023/2024 provide convergent, quantitative support for attributing the observed wetland and water declines in Liuwa Plain NP and the surrounding areas to the exceptional drought conditions of that season.\u003c/p\u003e \u003cp\u003eDespite the drought's severity, its effects did not immediately translate to wildlife mortality, suggesting a complex behavioral plasticity relationship between climate-driven resource scarcity and wildlife population dynamics. However, wildlife populations tend to respond to the environmental stressors, such as effects of climate change, variedly [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The drought's consequences, including reduced water sources, diminished food quality and quantity, potential limitations in breeding habitats, and substantial declines in fish stocks, a crucial resource for local communities underscore the increasing vulnerability of both wildlife and human populations within this ecosystem. The remote location of the park may further exacerbate these vulnerabilities. These findings are consistent with existing literature, such as [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] and [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which highlights the role of escalating drought frequency and severity in exacerbating human-wildlife conflicts in Southern Africa, as observed in Zimbabwe. This emphasizes the urgent need for concerted efforts to enhance adaptation measures, thereby increasing the resilience of both wildlife and human populations to the impacts of climate change. By reducing vulnerability through targeted interventions, it is possible to mitigate the adverse effects of climate-driven stressors on ecosystems and communities [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the management of Liuwa Plain NP has evolved over time, the habitat quality for wildlife remains sufficiently intact to support a rich and diverse ecosystem. While this study did not assess changes in wildlife populations directly, once severely depleted of wildlife, the park has since experienced a significant recovery [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Since 2003, Liuwa has been managed through a formal co-management arrangement involving three key stakeholders: the Department of National Parks and Wildlife (DNPW), the Barotse Royal Establishment (BRE), and African Parks Network (APN). This collaborative governance model emphasizes conservation, law enforcement, land use planning, and the sustainable utilization of natural resources. The current healthy state of the park\u0026rsquo;s ecosystems stands as a testament to the effectiveness of this partnership and the long-term benefits of integrated management strategies and this is supported by the findings in a similar study conducted in Zimbabwe by [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] who strongly recommended and highlighted the importance of innovative partnerships between state and non-state profit organisations, local communities and local international partners in managing protected areas in Zimbabwe and elsewhere as they provide, financial, and technical assistance crucial in protected area administration and resource governance.\u003c/p\u003e \u003cp\u003eThe findings from this study make three key contributions to the management of Liuwa Plain NP: (1) they provide essential data to inform land use planning and park management; (2) they highlight the impact of the 2023/2024 drought, underscoring the need for integrated climate adaptation strategies for both wildlife and human communities; and (3) they emphasize the importance of strengthening conservation efforts to enhance habitat quality and support a wide range of biodiversity. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] have previously demonstrated the value of geospatial information in wildlife habitat evaluation and planning. Building on that foundation, this study offers spatial insights that can be used to identify areas of persistent habitat loss, enabling more targeted zonation and the development of adaptive management strategies. These insights are critical to supporting long-term human-wildlife co-existence in Liuwa ecosystem.\u003c/p\u003e \u003cp\u003eThe findings of this study should be interpreted with consideration of several limitations. First, although the land cover classifications achieved high accuracy, they were not error-free, and misclassifications are still possible. Consequently, some land cover transitions may have been missed, or inaccurately estimated, either over or under-represented during the post-classification analysis. Second, the classification was conducted at 10-year intervals, which limits the ability to detect short-term or seasonal changes, such as those caused by fire, droughts, or other episodic events. In addition, potential influence of fire regimes, flood dynamics, or migration corridors which are highly relevant to the Liuwa ecosystem might have not been investigated in this study; there conclusions of these aspects are not addressed in details here. Future research should consider shorter temporal intervals to capture such dynamics more effectively as well as influence of factors such as fire, floods and migration. The aspect of drought which is central to this study need to be studied in more detail using climate data such as CHIRPS datasets in order to draw more conclusive results. Finally, this study did not investigate the underlying drivers of habitat dynamics. In-depth understanding of these drivers is essential for informed conservation planning, and future studies should aim to identify and analyze the socio-economic, environmental, and institutional factors influencing habitat dynamics in Liuwa ecosystem.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study aimed to assess four decades of habitat dynamics in Liuwa Plain NP a human-wildlife co-existence ecosystem of Western, Zambia. The Liuwa ecosystem uniquely supports the co-occurrence of human and wildlife populations, creating significant potential for competition over space and resources. The results revealed a general decline in key land cover types closely associated with wildlife habitats particularly forests and grasslands. While similar trends were observed both inside and outside the park, habitat loss was significantly more pronounced in areas outside the park boundaries. Human-induced land uses, including cropland and settlements, showed a marked increase in spatial extent over the study period. Although wetlands and water bodies did not change remarkably over the period, a noticeable decline was observed in 2024, likely due to the 2023/2024 drought and this is in line with nation -wide SPI anomalies from CHIRPS and independently observed reservoir shrinkage during the 2023/2024 which provides a convergent, quantitative support for attributing the observed wetland and water declines in Liuwa Plain NP and the surrounding areas to the exceptional drought conditions of that season. These findings provide valuable insights that can support more effective conservation planning and inform habitat management strategies aimed at minimizing habitat loss. Therefore, the study recommends that protected area managers should utilise remote sensing-based monitoring systems that provide near real time data on habitat condition and change. Such monitoring tools can guide targeted restoration interventions, for instance identifying priority zones for afforestation, corridor establishment and natural regeneration to mitigate fragmentation and strengthen ecosystem resilience to climate change. Finally the study recommends the integration of satellite derived information into adaptive management frameworks and that will enable evidence based decision making ensuring that conservation actions in Liuwa Plain NP and similar protected areas across the globe remain responsive to spatio and temporal habitat trends Future research should prioritize higher temporal resolution mapping using high-resolution imagery to better capture short-term changes and focus on identifying the underlying drivers of land cover change in the Liuwa ecosystem.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eThis study was conducted under the ethical clearance No.2023/2/13/1 issued by The Copperbelt University, and research permit No. NPW/8/27/1 issued by Department of National Parks and Wildlife (DNPW).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Funding was provided by Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) under the Enhanced Livelihoods and Natural Resource Management under accelerated Climate Change (ELNAC) project supported by the Federal ministry of Education and Research of Germany.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution:\u003c/strong\u003e DCM and GM analysed, DP and DCM drafted the manuscript, while NN, DZ and VRN conceptualized and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e: The authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe wish to thank Copperbelt University for providing support during the preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material:\u003c/strong\u003e Data will be provided upon reasonable request to the corresponding author. 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Lusaka, Zambia: Ministry of Tourism and Arts; 2020.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biodiversity conservation, Blue-wildebeest, Human-wildlife interaction, Land cover changes, Landsat","lastPublishedDoi":"10.21203/rs.3.rs-8523664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8523664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman encroachment into wildlife habitats has led to extensive habitat loss and fragmentation, often resulting in the displacement and decline of wildlife population across African Ecosystems. This study assessed four decades (1984\u0026ndash;2024) of spatio-temporal habitat dynamics in Liuwa Plain National Park, a human - wildlife co - existence landscape of Zambia. Using Landsat satellite imagery classified at 10-year intervals (1984, 1994, 2004, 2014, and 2024) and a random forest algorithm with a two-step approach including pixel - based classification followed by an object-based refinement we produced thematic maps with accuracies of 82\u0026ndash;89% with the 2024 map being the most accurate. Results show 20% decline in forest cover, 5% loss of grasslands and 3% reduction was observed in 2024 in wetlands and water bodies, likely due to the 2023/2024 drought. Particularly the changes were observed on the southern Kalabo Urban Zone and more evident outside the park boundaries. These shifts indicate rising shifts of anthropogenic pressure on habitat critical for wildlife persistence. The study underscores the need for integrated land-use planning, habitat management, adaptive park zonation and support the identification of ecological hotspots to sustain co - existence and ecological integrity in Liuwa NP and similar landscapes.\u003c/p\u003e","manuscriptTitle":"Four Decades of Habitat Change and Spatio-Temporal Dynamics in the Liuwa Ecosystem of Zambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 10:16:39","doi":"10.21203/rs.3.rs-8523664/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T12:59:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T00:10:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T02:37:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"268751544698074167648910383877765669055","date":"2026-02-10T12:33:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67613212886886991053193548505701528362","date":"2026-02-05T15:17:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T05:45:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145928169645260336709465289744439772337","date":"2026-01-29T13:41:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15677712233763563255636017279697541232","date":"2026-01-28T11:41:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T04:47:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T07:30:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T11:09:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T05:46:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2026-01-14T05:39:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f024c194-191c-4afd-8f19-4154d7e9ef10","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:10:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 10:16:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8523664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8523664","identity":"rs-8523664","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}