Changes in Land Use Land Cover in Equatorial Coastal Forest of Kilifi County, Kenya

Changes in Land Use Land Cover in Equatorial Coastal Forest of Kilifi County, Kenya | 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 Changes in Land Use Land Cover in Equatorial Coastal Forest of Kilifi County, Kenya Lewis Mjomba Ndungu, László Zentai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7014006/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Land Use Land Cover (LULC) changes influence human activities and policies related to environmental protection and conservation. These changes are usually anthropogenic in nature with population increase driving urbanization and agricultural expansion. These changes may result in environmental degradation thereby contributing to global problems like global warming. This study focused on analysing LULC changes in Arabuko Sokoke Forest (ASF) and Arabuko Sokoke Forest Region (ASFR) between 2001 and 2023, identifying the LULC conversions, and determining the deforestation rate. Landsat 7 ETM + and Landsat 8 OLI/TIRS satellite images were analysed using Google Earth Engine (GEE) to create 2001, 2012, and 2023 LULC maps. Conversion maps were generated using QGIS MOLUSCE tool. Seven LULC classes (forest, bareland, wetland, woodland, built-up, cropland, and water) were classified with overall accuracies and Kappa coefficients of above 93% and 91% respectively. For the 22-year period ASF and ASFR experienced forest annual loss of 629 ha(hectares)/year and 289 ha/year respectively. Additionally, 6682 ha and 8399 ha of the forest was converted to woodland within ASF and ASFR respectively. Existing literature suggest that these changes are largely driven by the interplay of social, economic, technical, and policy factors at national and local level. Therefore, multi-stakeholder interventions are required for effective mitigation. Land Use Land Cover (LULC) Forest Ecosystem Remote Sensing Google Earth Engine (GEE) Kenya Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1.0 Introduction The concept of sustainable development began to be used in the 1980s, but it was the implementation of the Sustainable Development Goals (SDGs), adopted by consensus by the United Nations at the 2015 General Assembly, that made it more widely known. Putting the world on a sustainable development path is one of the UN's biggest undertakings to date and perhaps also has the potential to reduce the gap between developed and developing countries. Africa is the least developed region in the world, but there are also significant differences in natural and social conditions between African countries. The colonial past also plays a major role in African countries because the policies and economic role of the colonial countries still have a significant impact on the development of countries that have been independent for decades. The loss of biodiversity in Africa is threatening the livelihoods of tens of millions of people, reducing food security, leading to conflict as arable land shrinks, and increasing the likelihood of animal-to-human transmission of infections. Forests cover 26% of Africa's land area, most of which is in South Africa, Ethiopia and Nigeria, but the forest cover is steadily decreasing. Agriculture is the main direct cause of deforestation, accounting for about three-quarters of deforestation in Africa. To make matters worse, the continent's growing population clearly means an increasing demand for food. Twentieth-century colonialism has in many cases replaced natural vegetation with cocoa, coffee, oil palms and tea trees. Tropical Africa has lost about 22% of its forest cover since 1900, comparable to losses in the South America (Amazon Basin). Even less attention is paid to the shrinkage of dry forests in West, East, and Southern Africa, despite historically much greater deforestation in these areas. This is another reason why it can be instructive to look at land use and land cover change in Kenya. From a global perspective, it is estimated that over six decades (1960–2019), a third (32%, approximately 43 million km 2 ) of the global land area has been affected by land use changes [ 40 ]. Moreover, during the same period, global net forest area loss was 0.8 million km 2 , while global agriculture had an expansion of 1.0 and 0.9 million km 2 for cropland and pasture/rangeland respectively. These statistics underscores the significance of these land use changes. Land use describes people's activities, their inputs, and arrangements of how they utilize land, while land cover describes features that are observed on the earth's surface which can be anthropogenic or natural [ 37 ]. Land Use Land Cover (LULC) as a concept and a research area had been applied to various fields of studies. It can be applied in study areas such as erosion, landslides, global change and land planning [ 22 ]. Additionally, evaluation of LULC change is vital in resource management, sustainable development, environmental conservation [ 2 ], water management climate-resilient strategies [ 26 ], water resource planning and watershed management [ 38 ]. Overall, environmental, social, and economic systems are all impacted by LULC dynamics, making their monitoring and understanding critical for achieving sustainable development [ 35 ]. The effects of land use changes have also been captured on a local scale in various parts of the world. In the Kashmir Valley, India, between 1992 and 2001, there was a decrease in forest and pasture areas and an increase in shrubs, plantations, marshy areas, barren land, and built-up areas. However, between 2001 and 2015, forest and pasture areas increased [ 2 ]. In the Talihya North watershed of DR Congo, forest cover decreased from 253.11 km2 in 1987 to 201.12 km2 in 2001 and further to 123.04 km2 in 2020 [ 15 ]. In the Bafing area of Senegal, between 1986 and 2020, there was a significant increase in water bodies, vegetated areas, agricultural land, and settlements, while bare ground decreased [ 5 ]. In Ethiopia, construction and cropland areas increased by 46.95% and 15% respectively while vegetation, grassland, and waterbodies decreased by 70.02%, 38.1%, and 62.7% respectively [ 26 ]. In Western Uganda, there was an increase in forest (0.1%), agriculture (0.1%), and urban (0.1%), while wetland, grassland, and shrubland cover decreased by 0.05%, 0.22%, and 0.01% respectively [ 18 ]. Kenya as a country has also experienced LULC changes which have been researched at a localized level. In River Ruiru watershed, Kiambu County perennial crops (coffee and tea), annual crops, and built-up areas increased by 11.43%, 35.84% and 3.068% respectively, while forestland, shrubland, and grassland decreased by 29.79%, 13.25%, and 7.48% respectively, between 1976 and 2017 [ 38 ]. In Makueni County, results showed that built up area had the highest increase from 160.7 km 2 to 644.5 km 2 , while evergreen forest had the highest decrease from 3105.8 km 2 to 1372 km 2 between 2000 and 2016 respectively [ 8 ]. Land cover change analysis reveals substantial forest loss in both the Mau Forest Complex and Mt. Elgon complex in Kenya. The Mau Forest Complex experienced a 21.9% loss (88,493 ha) of forest cover between 1986 and 2017, while the Mt. Elgon complex experienced a 12.5% loss (27,201 ha) between 1984 and 2017 [ 33 ]. Further analysis specifically focusing on the Kenyan side of Mt. Elgon Forest ecosystem from 1973 to 2019 revealed specific changes in forest types. Natural forests declined by 18%, bamboo forests by 15.19%, and plantation forests by 15.6%, while mixed farming, fallow land, and tea plantations increased by 29%, 10%, and 0.13%, respectively [ 24 ]. These findings highlight pressure on forest ecosystems largely attributed by human activities. Drivers and causes of LULC change are a major global concern, prompting numerous investigations worldwide. One such study in Pakistan identified several key drivers [ 3 ]. Perceived and proximate drivers included natural conditions, increased infrastructure, unplanned urbanization, and agricultural decline, while underlying drivers were poor marketing, inadequate financial resources, and weak governance. Socioeconomic factors and climatic factors such as drought and rainfall can also drive the change [ 8 ]. Furthermore, natural conditions such as frost and drought can also damage forests, as observed in the Nagyerdő forest in Debrecen, Hungary [ 27 ]. Human activities have a long history of influencing forest cover. For example, forest conversion into farmlands and grazing land resulted in wood shortages in Europe, contributing to the development of silviculture and forestry practices [ 41 ]. These studies illustrate the diverse range of factors contributing to LULC change including both natural and anthropogenic activities factors. However, anthropogenic activities are widely recognized as key drivers of extensive land transformations [ 2 ] largely attributed to rapid population growth. For instance, a study in Ethiopia showed that population increases lead to high food demands causing conversion of natural forests into grasslands, urbanization, and agricultural land [ 26 ]. In Uganda, population growth is shown to have a positive relationship with the extent of urban areas, contributing to their increase [ 18 ]. The relationship between population growth and forest changes, however, is complex and context dependent. For instance, research has demonstrated a contrasting trend: while increased population in Bafing was associated with an increase in tree cover, likely due to the presence of a large dam and supportive policies, population growth in Faleme coincided with increased deforestation, likely due to different land use pressures and management strategies [ 5 ]. Generally, conversion of natural vegetation to grazing land, farmland, urban centers, and human settlements in East Africa is associated with land degradation, deforestation and loss of biodiversity [ 23 ]. Other activities such as conversion of wetlands into settlement and agricultural land were the main drivers of LULC changes in Nyando River Basin in Kisumu County, Kenya [ 32 ]. Agriculture was documented as the main cause of deforestation, accounting for 81.5% (70,612 ha) and 63.2% (24,077 ha) of the deforestation in Mau Forest and Mt. Elgon Forest respectively [ 33 ]. Different studies employ various LULC classification schemes, depending on their research objectives, location, and researcher preferences. While some adopt a more focused approach, like using four classes such as forest, savannah, bare lands and buildings, and croplands and fallows [ 15 ], or classifying land cover into natural forest, bamboo forest, grassland, mixed farming, and fallow land [ 24 ], others opt for broader, more detailed categories. For example, six classes including forest, built-up areas, agricultural land, maize, para rubber trees, and water were utilized [ 21 ], while other studies used seven classes like forest, moorland, agriculture large scale, agriculture small-scale, rangeland, settlement/urban, and water [ 33 ], or evergreen forests, grassland, bushlands, built-up areas, croplands, bare land, and water bodies [ 8 ]. Despite these specific variations, a common broad categorization of vegetation (natural and semi-natural) and human activities remains evident across studies. Arabuko Sokoke Forest (ASF) was first gazetted as a forest reserve in 1943 as Crown Forest, and later the reserve was subsequently expanded with additional forest land gazetted in 1968 and 1979 [ 39 ]. The management and governance of ASF has deteriorated since the colonial era. Several factors are responsible for this decline, including insufficient incentives for local community participation in forest management, widespread poverty, occurrences of unauthorized forest resource access, firewood overexploitation, and poaching of construction materials. Based on the background, this paper aims to (a) analyse LULC changes for ASF and Arabuko Sokoke Forest Region (ASFR) between 2001 and 2023; (b) identify conversions between the LULC classes for the period; (c) determine the annual deforest loss rate. 2.0 Materials and methods 2.1. Study Area ASF is the largest remaining fragment of East African coastal dry forest with high density of endemic species making it a global biodiversity hotspot [ 12 , 13 ]. It covers an area of approximately 41,846 ha, located along the Kenyan coast in Kilifi County. However, other scholars have documented it to have an area of 41,600 ha (103,740 acres) [ 4 , 13 , 39 ]. The elevation within ASF ranges from 40m to 160m above sea level, with three major vegetation type including mixed forest, Brachystegia Forest, and Cynometra Forest [ 4 ]. It experiences long rains are between April and June, and short rains in November and December. The climate is hot and humid with an average temperature of 29℃ [ 28 ]. The soil texture generally ranges from sandy-to-sandy loam. ASF management has four forest regions: Gede, Jilore, Kararacha, Sokoke, with three forest stations in Gede, Jilore and Sokoke [ 14 ]. Administratively, ASF is shared between Malindi, Ganze and Bahari Constituencies. Its boundaries extend to seven locations: Jilore, Mwahera, Vitengeni, Sokoke, Roka, Gede, and Ngerenya, which make up the ASFR (154160 ha), along longitude 40 o E and latitude 3 o S (Fig. 1 ). Indian Ocean borders the east of Gede, Roka, and Ngerenya Locations. Several villages surrounding the reserve are mostly small-scale farmers with the Giriama tribe who primarily dependent on forest resources. They also grow subsistence crops including cow peas, cassava, and maize, while cash crops include mango, coconut, and cashew-nut. 2.2. Data collection Top-of-atmosphere (TOA) reflectance calibrated Tire 1 collection 2 images of Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) were used in this study. Median composite images were generated using 46 images from 2000–2001, 49 images from 2011–2012, and 42 images from 2023. The study period 2001–2023 was selected based on the availability of a sufficient number of satellite images with minimal cloud cover in the study area. 2.3 Image processing, accuracy assessment, and classification Spectral indices were calculated in the Google Earth Engine (GEE) platform for the 2001, 2012, and 2023 median composite images and added as additional bands to improve classification accuracy. Training samples were derived from user-defined polygons, with 1442, 1736, and 2017 pixels used for training the 2001, 2012, and 2023 image datasets, respectively. High resolution Google earth Images was used as reference to digitize the training sample polygons with a total of seven LULC types as shown in Table 1 . LULC type and description is adapted from the Kenya Forest Service (KFS) [ 17 ]. Figure 2 shows the field photos depicting the LULC types. The sampling data was randomly split into 70% training data and 30% testing data. The Random Forest (RF) algorithm, a built-in classifier in GEE, was used to perform supervised classification on the 2001, 2012, and 2023 images using the training data. This process produced LULC maps for the selected time periods. Table 1 LULC types and description LULC types Description Forest (FO) Land area covering over 0.5 ha with trees above 2m height, and a crown cover greater than 15%. Not used for agriculture or other non-forest purposes Bareland (BL) Land area without vegetation covered by bare soil, rocks, rough roads, or degraded lands Wetland (WE) Wetland, vegetated wetland (Mash, Bogs, Papyrus) Woodland (WL) Woodland scrubs, thickets, open and wooded grass Built-up (BU) Lands dominated by huts, houses, industrial facilities and paved houses. Cropland (CL) Area under crop cultivation or tilled land. Water (WA) Water bodies including streams, rivers, ocean, and lakes. Evaluation of the supervised classification utilized GEE in-built accuracy assessment techniques. The overall accuracy (OA), kappa coefficient (K), consumer accuracy (CA), and producer accuracy (PA) were calculated for the classified images. Equations 1 , 2 , 3 , and 4 represent the formulas used. $$\:OA=\:\frac{Number\:of\:Correctly\:Classified\:Samples}{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{S}\text{a}\text{m}\text{p}\text{l}\text{e}\text{s}}\times\:100$$ 1 $$\:K=\:\frac{Overral\:Accuracy-Estimated\:Channce\:Agreement}{1-\text{E}\text{s}\text{t}\text{i}\text{m}\text{a}\text{t}\text{e}\text{d}\:\text{C}\text{h}\text{a}\text{n}\text{n}\text{c}\text{e}\:\text{A}\text{g}\text{r}\text{e}\text{e}\text{m}\text{e}\text{n}\text{t}}$$ 2 $$\:CA=\:\frac{Number\:of\:Correctly\:Classified\:Samples\:in\:each\:Class}{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{S}\text{a}\text{m}\text{p}\text{l}\text{e}\text{s}\:\text{C}\text{l}\text{a}\text{s}\text{s}\text{i}\text{f}\text{i}\text{e}\text{d}\:\text{t}\text{o}\:\text{t}\text{h}\text{a}\text{t}\:\text{C}\text{l}\text{a}\text{s}\text{s}}$$ 3 $$\:PA=\:\frac{Number\:of\:Correctly\:Classified\:Samples\:in\:each\:Class}{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{S}\text{a}\text{m}\text{p}\text{l}\text{e}\text{s}\:\text{f}\text{r}\text{o}\text{m}\:\text{R}\text{e}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e}\text{d}\:\text{d}\text{a}\text{t}\text{a}\:\text{i}\text{n}\:\text{e}\text{a}\text{c}\text{h}\:\text{C}\text{l}\text{a}\text{s}\text{s}}$$ 4 LULC values were reclassified, and LULC and conversion maps were generated and visualized using QGIS. Conversion maps were specifically generated using the MOLUSCE (Modules for Land Use Change Simulations) tool in QGIS [ 31 ]. A summary of the methodology flow and steps is shown in Fig. 3 . 2.4 Limitations of the study The ASFR is located near the equator along the Kenyan Indian coast, experiences persistent cloud cover throughout the year. This scarcity of suitable cloud-free images necessitated the use of a two-year period of satellite data to generate each median image composite. Combining two years of data improved the generation of a clearer, higher-quality composite. 3.0 Results 3.1 LULC Maps classification Accuracy The results of confusion matrixes, overall accuracy (OA), producer accuracy (PA), consumer accuracy (CA), and kappa coefficient (K) calculations are shown in Table 2 . 2001, 2012, and 2023 classified maps had an overall accuracy of 93%, 99%, and 94% respectively. The kappa coefficient was 91%, 98%, and 92% for 2001, 2012, and 2023 images. Urban and cropland classes for 2001 image had the lowest producer accuracy of 27% and 22% due to the scarcity of reference data for accurate training data identification. However, the high overall accuracy and kappa coefficient of the image allowed for the results to be used for change analysis. Table 2 Accuracy Assessment for 2001, 2012, and 2023 classified images Forest Bareland Wetland Woodland Built-up Cropland Water OA K 2001 CA 92% 85% 99% 30% 100% 50% 100% 93% 91% PA 96% 95% 90% 100% 27% 22% 100% 2012 CA 100% 89% 100% 100% 96% 86% 100% 99% 98% PA 100% 100% 100% 53% 96% 96% 99% 2023 CA 100% 93% 100% 85% 100% 96% 85% 94% 92% PA 94% 97% 82% 96% 74% 100% 100% 3.2 LULC maps Mapping and displaying the spatial distribution of the seven LULC types is shown in Fig. 4 . Spatially cropland has been spreading on the eastern side of ASFR, specifically Gede and Roka locations from 2001 to 2023. The water inlet from the Indian Ocean is seen shrinking over the years. Galana-sabaki river is visible in the northern side of the 2001 and 2012 map while in 2023 its size has significantly reduced. Bareland has increased rapidly on the eastern side of ASFR. Within the ASF, woodland area has increased, with a decrease in forest. 3.3 LULC Analysis and changes for ASF and ASFR 3.3.1 LULC analysis for ASF Throughout the period forest was the most dominant class, showing a declining trend from 2001 to 2023 (Fig. 5 ). Forest class was the dominant type covering 39,348 ha (94.0%), 35,240 ha (84.2%), and 33,000 ha (78.9%) in the year 2001, 2012, and 2023 respectively. Woodland, cropland, built-up, and bareland classes increased steadily throughout the period. Woodland had the highest increase from 2,345 ha to 8,245 ha. However, wetland showed a decreasing trend from 82 ha to 4 ha. The 2001–2012 period had a larger forest loss of 4,108 ha (10%), compared to 2,240 ha (6%) loss in the 2012–2023 period. On the other hand, woodland had a higher gain of 3790 ha (162%) in the 2001–2012 period compared to 2110 ha (34%) in the 2012–2013 period. There was also a large cropland area increase of 320 ha between 2001–2012 with a small decrease of 13 ha in 2012–2023 period. 3.3.2 LULC analysis for ASFR LULC analysis for ASFR is shown in Fig. 6 . Woodland class was the highest with 73,368 ha (47.6%), 57,952 ha (37.6%), and 47,726 ha (31%) of the area for 2001, 2012, and 2023 respectively. Generally, woodland, and grassland class have been decreasing throughout the 2001–2023 period while bareland, built-up, and cropland show an increasing trend. Wetland showed a slight decreasing trend, while water class increased and decreased. Generally, the 2001–2012 period had the largest decrease in forest and woodland as 11,495 ha (23%) and 15416 ha (21%) was lost respectively. Additionally, the 2001–2012 period saw the largest increase of bareland, cropland, and water with a gain of 13210 ha (62%), 12918 ha (214%), and 499 ha (32%) respectively. 3.4 LULC Changes 3.4.1 Total LULC Changes for 2001–2023 period The largest decline within ASFR was woodland losing 25,642 ha, followed by forest with a loss of 13,846 ha within the 22 years study period (Fig. 7 ). On the other hand, bareland and cropland increased by 20,320 ha and 17,815 ha respectively for ASFR. ASF had a woodland increase of 5,900 ha, while forest decreased by 6,348 ha. 3.4.2 Annual LULC changes The mean annual year gains and losses rates are shown in Fig. 8 . Within the ASFR, bareland had the highest yearly increase of 924 ha/year followed by cropland (810 ha/year) while woodland and forest had net annual losses of 1,166 ha/year and 629 ha/year respectively. Moreover, ASF has an annual woodland gain of 268 ha/year and an annual forest loss of 289 ha/year for the 2001–2023 period. During the study period both wetland and water classes had minor changes annually. ASFR experiences an annual built-up growth 87 ha/year during the 22-year period. 3.5 Land use land cover conversions in ASF Several LULC conversions were observed in the ASF over the 2001–2023 period (Table 3 ). Forest class had the largest conversion as 6682 ha, 183 ha and 43 ha was converted to woodland, cropland, and bareland respectively. On the other hand, 494 ha, 71 ha and 9 ha of woodland, wetland, and bareland respectively was converted to forest. Water and built-up areas experienced the least conversions. Table 3 Land Cover conversions in ASF (2001–2023) 2023 LULC type Forest Bareland Wetland Woodland Built-up Cropland Water Grand Total 2001 Forest 32421 43 0 6682 19 183 1 39348 Bareland 9 15 0 10 2 6 0 41 Wetland 71 1 4 7 0 0 0 82 Woodland 494 143 0 1544 38 126 0 2345 Built-up 2 0 0 0 0 1 0 4 Cropland 4 3 0 2 1 14 0 24 Water 1 0 0 0 0 0 0 2 Grand Total 33000 204 4 8245 60 331 1 41846 3.6 Land Cover conversions in ASFR Within the larger area of ASFR woodland had the largest conversion with 56% (41,167 ha) being converted into bareland (26950 ha), cropland (11514 ha), forest (1556 ha), built-up (1115 ha), water (26 ha) and wetland (6 ha). 31.9% (15,801 ha) of the forest area was converted into woodland (8399 ha), cropland (6150 ha), bareland (762 ha), wetland (209 ha), and water (8 ha) (Table 4 ). On the other hand, 19% (1,203 ha) of the cropland was converted into bareland (404 ha), built-up (274 ha), woodland (271 ha), and forest (254 ha). Table 4 Land Cover conversions in ASFR (2001–2023) 2023 LULC type Forest Bareland Wetland Woodland Built-up Cropland Water Grand Total 2001 Forest 33709 762 209 8399 274 6150 8 49510 Bareland 44 13171 4 6595 549 1039 20 21422 Wetland 74 34 1373 89 19 8 37 1634 Woodland 1556 26950 6 32201 1115 11514 26 73368 Built-up 25 46 3 19 239 300 0 631 Cropland 254 404 0 271 274 4832 0 6035 Water 3 376 9 152 74 7 939 1560 Grand Total 35664 2544 1030 47725 23850 41742 1604 154160 3.7 Spatial distribution of the LULC conversions Figure 9 shows the LULC conversions between different classes for ASF and ASFR for 2001–2012, 2012–2023, and 2001–2023 period. Generally, the ASF area experienced relatively larger conversions in the 2001–2012 period compared to 2012–2023 especially in the northwestern and eastern part. Large conversion of woodland to bareland is observed in Jilore, Mwahera, Vitengeni, Sokoke locations while Gede, Roka, and Ngerenya locations experienced conversion of woodland into cropland and other land uses. 4.0 Discussion This study analyzed LULC changes within the Arabuko Sokoke Forest (ASF) and the broader Arabuko Sokoke Forest Region (ASFR) between 2001 and 2022. Generally, the 22-year period saw significant LULC variation and conversions within ASF and ASFR. The ASF experienced a forest loss of 6348 ha with a 5900-ha woodland and 307-ha cropland increase. Consequently, the larger ASFR had a forest loss of 13,846 ha and a woodland decrease of 20,320 ha. This is largely contributed by increase in bareland and cropland of 20,320 ha and 17,815 ha. This shows forest loss was largely caused by expansion of woodland, bareland, and cropland area in ASFR while ASF loss was mainly cropland. The increase in woodlands and bareland areas may arise from the harvesting of trees by local communities. Built-up, wetland, and water areas had minimal effects on forest loss. These findings coincide with other LULC change studies confirming forests loss in Kenya including Cherengany hills forest ecosystem [ 36 ], Mau Forest and Mt. Elgon Forest [ 33 ]. Forest decrease is also a common occurrence as observed by studies in other African countries such as Tanzania [ 16 ], Uganda [ 18 ], Ethiopia [ 26 ], and Democratic Republic of Congo [ 15 ]. Additionally, these studies concede large cropland expansion. LULC changes observed within ASFR are closely linked to demographic and socio-economic factors. The study region is within Kilifi County which has seen increase in population over the years despite administrative boundary system changes which were instituted by the new constitution in 2010. Census reports show a growing population trend within the region of 544,303 (1999), 1,109,735 (2009), and 1,440,958 (2019) [ 7 , 19 , 20 ]. This study showed a 214% (12918 ha) cropland in the 2001–2012 period which may have been attributed to the 103.9% increase in population in the 1999–2009 period, who raise the increase the demand for agricultural products. Moreover, timber demand has increased as it is required for curving which contributes to further forest area reduction [ 4 ]. Notwithstanding the significant demographic and socio-economic influence, Kenya has a long history of implementing policies and legal frameworks to address forest-related challenges. The 1964 forest Master Plan, 1969 Forest Act, Forest Act of 2005, 2010 Constitution of Kenya, and the national forest policy of 2014, are some of the policy documents that have promoted conservation and management of forests in Kenya [ 9 ]. Specifically, Forest Act of 2005, legally recognised Participatory Forest Management (PFM), which was first introduced in ASF in 1997 as a pilot project [ 34 ]. Additionally, the act established the KFS which manages and administers all state forests in Kenya. The KFS works alongside other state institutions such as National Museum Kenya (NMK), Kenya Wildlife Service (KWS), and Kenya Forest Research Institute (KFRI) to ensure collective efforts in forest protection [ 1 ]. The state led institutions have tried their best to address various forest related challenges, however most forest communities felt marginalized in this process. The Forest Act of 2016 addressed this challenge by allowing the formation of Community Forest Associations (CFAs) thus enhancing community participation in forest management [ 30 ]. CFAs function similarly to Community Based Organisations (CBOs) with a one-time membership subscription [ 29 ]. CFAs and CBOs within ASF include Arabuko Sokoke Forest Adjacent Dwellers Association (ASFADA), Arabuko Sokoke Forest Management Team (ASFMT), Arabuko Sokoke Forest Guide Association (ASFGA), and Friends of Arabuko Sokoke Forest (FoASF) [ 1 ]. It is estimated that there are about 300 user groups involved in the management under ASFADA [ 34 ]. Despite having such a strong management institutions, structures, and support systems, ASF conservation and protection effort face various challenges. These challenges are centred around elite capture, limited resources, unequal representation, conflicting stakeholders’ interests and institutional mandates [ 9 ]. This can lead to marginalization of local communities and unequal distribution of socio-economic benefits derived from the forest. For instance, there are low survival rates of trees planted by the county government due to insufficient technical support and collaboration from organisations such as KEFRI and KFS [ 25 ]. Weak government structures and unclear stakeholders’ roles hinder effective implementation of Conservation strategies [ 14 ]. This ambiguity undermines conservation goals due to confusion and possible duplication of efforts. Inadequate financial, technical and human expertise to management of the forests has resulted in challenges such as encroachment, agricultural expansion, and indiscriminate tree cutting for charcoal [ 25 ]. The severe resource gap significantly undermines the capacity of relevant organisations to effectively monitor and enforce regulations. Additionally, the key drivers of destruction identified are illegal logging and natural resource exploitation, charcoal burning, and local elephant population exceeding the carrying capacity [ 14 ]. The demand for poles, carvings, and timber by the surrounding community has been identified as a key driver of illegal logging [ 12 ]. From an international perspective, [ 5 ] emphasize that agroforestry focusing on high-value trees produces high-quality logs, which can help mitigate regional timber shortages. The natural environmental challenge of frequent droughts, especially in Ganze constituency, leads to tree desiccation [ 10 , 11 ], which is a contributing factor to large conversion of woodland to bareland in Mwahera, Vitengeni, and Sokoke locations. Addressing these issues requires a multi-faceted approach, combining community engagement, habitat management and law enforcement. Locally, Kilifi County has advocated for community engagement in nature-based enterprises, strict forest laws enforcement, establishing tree nurseries and woodlots and the development of forest management plans [ 11 ]. They also plan to train and equip additional 30 forest guards and rehabilitation of degraded lands. Such efforts can improve institutional capacity, reduce overdependence of forest products and poverty levels and expansion of sources of livelihoods [ 1 ]. Electric fencing of ASF has helped to reduce human-wildlife conflict, which has been instrumental in reducing illegal settlement and deforestation [ 14 ]. CBOs have, to some extent, incorporated measures to reduce forest vulnerability and hazards such as anti-logging practices and policy, adhering to forest resource utilization zones, community educational and financial empowerment, and forest sustainability awareness programs [ 1 ]. Limited resource challenge can be addressed through innovative funding models, formulation of partnerships, and increasing budget allocations [ 9 ]. Continuous monitoring and evaluation of these strategies can ensure long term sustainability of conservation measures in ASF. Moreover, success of these strategies requires a strong political will and adaptive management. 5.0 Conclusion LULC studies generally provide a key insight on the interactions of human activities and the natural environment. Quantifying these interactions spatially and temporally is an effective way of monitoring hotspots areas requiring intervention and gaps in mitigation measures enforcement. Temporally there is an annual decrease of forest area of 629 ha/year (ASFR) and 289 ha/year (ASF) for the 2001–2023 period. Spatially, Jilore, Mwahera, Vitengeni, region experienced the largest conversion of woodland to bareland. These changes highlight the need for enacting and enforcing balanced mitigation measures for long-term sustainability. Socio-economic pressures, inadequate governance and poor management practices which have resulted into shifts and conversion in land cover uses within ASF and ASFR. Poverty and unemployment create local community overdependence on the forest for energy and food supply. This is evident from the increasing demand for agricultural land, firewood, and charcoal which leads to illegal logging and encroachment. These adverse effects are further encouraged by weak law enforcement, poor governance structure, and lack of coordinated efforts between forest related organisation. Fostering transparent and inclusive stakeholder engagement, enhancing equitable representation and robust governance to reduce elite capture, and amplifying marginalized groups' voices in decision-making can overcome such challenges [ 9 ]. The insights from this study can guide local and national policymakers and practitioners in developing tailored interventions to address forest degradation hotspots and contribute to the achievement of global biodiversity conservation goals. Declarations Funding: This study received no external funding. Clinical trial number: not applicable. Consent to Publish declaration: not applicable. Consent to Participate declaration: not applicable. Ethics declaration: not applicable. Competing interests: The authors have no conflicts of interest to declare. Data availability: All relevant data are contained within the manuscript. Author contributions : Lewis Mjomba Ndungu conceptualized the study, wrote, edited and reviewed the manuscript. László Zentai wrote, edited, and reviewed the manuscript. Country Affiliation: Lewis Mjomba Ndungu (Hungary, Kenya) and László Zentai (Hungary). References Adavize Julius A. Participatory Forest Management and Disaster Risk Reduction: The Case of Arabuko-Sakoke Forest in Kenya [dissertation]. Nairobi: University of Nairobi; 2015. Available from: https://erepository.uonbi.ac.ke/bitstream/handle/11295/94459/Adinoyi_Participatory%20forest%20management%20and%20disaster%20risk%20reduction:the%20case%20of%20arabuko-sakoke%20forest%20in%20kenya.pdf?sequence=1 Alam A, Bhat MS, Maheen M. Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. GeoJournal. 2020;85(6):1529–43. https://doi.org/10.1007/s10708-019-10037-x Anwar Z, Alam A, Elahi N, Shah I. Assessing the trends and drivers of land use land cover change in district Abbottabad lower Himalayan Region Pakistan. Geocarto Int. 2022;37(25):10855–70. https://doi.org/10.1080/10106049.2022.2040604 ASFMT (Arabuko-Sokoke Forest Management Team). Arabuko-Sokoke forest strategic forest management plan 2002–2007. Arabuko-Sokoke Forest Management Team; 2002. Available from: https://friendsarabukosokoke.org/wp-content/uploads/2021/08/Arabuko-Sokoke-Forest-Strategic-Management-Plan-2002-2027.pdf Astou Sambou MH, Albergel J, Vissin EW, Liersch S, Koch H, Szantoi Z, et al. Prediction of land use and land cover change in two watersheds in the Senegal River basin (West Africa) using the Multilayer Perceptron and Markov chain model. Eur J Remote Sens. 2023;56(1). https://doi.org/10.1080/22797254.2023.2231137 Báder M, Németh R, Vörös Á, Tóth Z, Novotni A. The effect of agroforestry farming on wood quality and timber industry and its supportation by Horizon 2020. Agrofor Syst. 2023;97(4):587–603. https://doi.org/10.1007/s10457-023-00812-8 CBS (Central Bureau of Statistics). The 1999 Population & Housing Census: Counting Our People for Development. 2001. Available from: https://www.knbs.or.ke/wp-content/uploads/2023/09/1999-Kenya-population-and-Housing-Census-Counting-Our-People-For-Development-Volume-1.pdf Cheruto MC, Kauti MK, Kisangau PD, Kariuki P. Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing Techniques: A Case Study of Makueni County, Kenya. 2016. https://doi.org/10.4175/2469-4134.1000175 Chisika SN, Yeom C. The Implication of the Changing Forest Management Paradigms in Formulating Forestry Policies in Kenya. 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The 2009 Kenya Population and Housing Census: Population distributions by political units. 2010. Available from: https://www.knbs.or.ke/wp-content/uploads/2023/09/2009-Kenya-population-and-Housing-Census-Volume-1B-Population-Distribution-by-Political-Units.pdf KNBS (Kenya National Bureau of Statistics). 2019 Kenya Population and Housing Census: Population by county and subcounty. 2019. Available from: https://www.knbs.or.ke/wp-content/uploads/2023/09/2019-Kenya-population-and-Housing-Census-Volume-1-Population-By-County-And-Sub-County.pdf Kruasilp J, Pattanakiat S, Phutthai T, Vardhanabindu P, Nakmuenwai P. Evaluation of Land Use Land Cover Changes in Nan Province, Thailand, Using Multi-Sensor Satellite Data and Google Earth Engine. Environ Nat Resour J. 2023;21(2):186–97. https://doi.org/10.32526/ennrj/21/202200200 Liping C, Yujun S, Saeed S. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE. 2018;13(7). https://doi.org/10.1371/journal.pone.0200493 Maitima JM, Mugatha SM, Reid RS, Gachimbi LN, Majule A, Lyaruu H, et al. The linkages between land use change, land degradation and biodiversity across East Africa. Afr J Environ Sci Technol. 2009;3(10). http://www.academicjournals.org/AJEST Masayi NN, Omondi P, Tsingalia M. Assessment of land use and land cover changes in Kenya’s Mt. Elgon forest ecosystem. Afr J Ecol. 2021;59(4):988–1003. https://doi.org/10.1111/aje.12886 Mbuvi MTE, Ndalilo L, Cheboiwo J. Challenges to Actualization of Decentralization Forest Management Functions: Experiences and Lessons on Devolving Forestry Management Functions in Kenya. 2018;8(10). Available from: www.iiste.org Mekonnen YA, Manderso TM. Land use/land cover change impact on streamflow using Arc-SWAT model, in case of Fetam watershed, Abbay Basin, Ethiopia. Appl Water Sci. 2023;13(5). https://doi.org/10.1007/s13201-023-01914-5 Molnár T, Király G. Forest Monitoring Based on Sentinel-2 Satellite Imagery, Google Earth Engine Cloud Computing, and Machine Learning. 2023. https://doi.org/10.20944/preprints202307.0800.v1 Muriithi S, Kenyon W. Conservation of biodiversity in the Arabuko Sokoke Forest, Kenya. Biodivers Conserv. 2002;11. https://doi.org/https://doi.org/10.1023/A:1016234224819 Mutune JM, Lund JF. Unpacking the impacts of “participatory” forestry policies: Evidence from Kenya. Forest Policy Econ. 2016;69:45–52. https://doi.org/10.1016/j.forpol.2016.03.004 Ndalila MN, Lala F, Makindi SM. Community perceptions on wildfires in Mount Kenya forest: implications for fire preparedness and community wildfire management. Fire Ecol. 2024;20(1). NextGIS. MOLUSCE-Land cover changes analysis in QGIS. 2024. Available from: https://nextgis.com/molusce/ Odaro DO, Ucakuwun EK, Daudi F. Analysis of land Use, land cover changes of Okana Wetland Ecosystems in Lower Nyando River Basin. J Res Innov Implic Educ. 2023;7(4):493. https://doi.org/10.59765/cwrs5385 Ojoatre S, Zhang C, Yesuf G, Rufino MC. Mapping deforestation and recovery of tropical montane forests of East Africa. Front Environ Sci. 2023;11. https://doi.org/10.3389/fenvs.2023.1084764 Ongugo PO, Mogoi JN, Obonyo E, Oeba VO. Examining the roles of Community Forest Associations (CFAs) in the decentralization process of Kenyan forests. 2008. Available from: https://vtechworks.lib.vt.edu/items/0c2667c1-5120-4c84-bd32-b6661d06ee5d Raza A, Syed NR, Fahmeed R, Acharki S, Aljohani TH, Hussain S, et al. Investigation of changes in land use/land cover using principal component analysis and supervised classification from Operational Land Imager Satellite Data: A case study of under developed regions, Pakistan. Discov Sustain. 2024;5(1). https://doi.org/10.1007/s43621-024-00263-w Rotich B, Ojwang D. Trends and drivers of forest cover change in the Cherangany hills forest ecosystem, western Kenya. Glob Ecol Conserv. 2021;30. https://doi.org/10.1016/j.gecco.2021.e01755 Tadese S, Soromessa T, Bekele T. Analysis of the Current and Future Prediction of Land Use/Land Cover Change Using Remote Sensing and the CA-Markov Model in Majang Forest Biosphere Reserves of Gambella, Southwestern Ethiopia. Sci World J. 2021. https://doi.org/10.1155/2021/6685045 Waithaka A. Analysis of Land Use/Land Cover Changes Using GIS and Remote Sensing Techniques in River Ruiru Watershed, Kiambu County, Kenya. J Geosci Geomat. 2023;11(4):97–101. https://doi.org/10.12691/jgg-11-4-1 Wekesa C. Biodiversity Status of Arabuko Sokoke Forest, Kenya. Ochieng’ D, Luvanda A, Mbuvi MTE Ndalilo L, editors. Kenya Forestry Research Institute; 2017. Available from: https://www.researchgate.net/publication/322752359 Winkler K, Fuchs R, Rounsevell M, Herold M. Global land use changes are four times greater than previously estimated. Nat Commun. 2021;12(1). https://doi.org/10.1038/s41467-021-22702-2 Zentai L. Discovery of forested areas on topographic maps: Development of Orienteering Maps. Lect Notes Geoinf Cartogr. 2013;295–308. https://doi.org/10.1007/978-3-642-33317-0_18 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7014006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487032774,"identity":"15599191-44d2-4576-9594-a418b8b8b569","order_by":0,"name":"Lewis Mjomba 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2","display":"","copyAsset":false,"role":"figure","size":1436629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePhotos showing the LULC types: a) forest, b) bareland, c) wetland, d) woodland, e) built-up, f) cropland, and g) water.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/3c761e836cba5e541d5d482a.png"},{"id":87242292,"identity":"918dd6bc-a1b8-48ac-a706-2fbceba5b3f7","added_by":"auto","created_at":"2025-07-22 01:44:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMethodology flow chart\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/8835db7a261ee36518ae9049.png"},{"id":87242301,"identity":"f4f23213-a7d6-412f-8e85-9d9a216bd562","added_by":"auto","created_at":"2025-07-22 01:44:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":543955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLULC maps for 2001, 2012, and 2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/649aecb65a372ef7523e873a.png"},{"id":87242298,"identity":"5834ef0f-398e-40fc-8e34-bcc474b83035","added_by":"auto","created_at":"2025-07-22 01:44:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLULC Analysis for ASF\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/cd5c0f789ece0de7198b6eda.png"},{"id":87242300,"identity":"ec3e5f07-ae8c-4fb5-901a-e95c44cd6c59","added_by":"auto","created_at":"2025-07-22 01:44:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLULC Analysis for ASFR\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/bd1ab3b76b3dbbb8d7d9bebf.png"},{"id":87243351,"identity":"29291ee6-45d2-4cf8-a1dc-c418a5473b3c","added_by":"auto","created_at":"2025-07-22 02:00:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":83073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTotal LULC change in ha (2001-2023)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/d78421b72226d7494b739c06.png"},{"id":87242798,"identity":"ab50e36b-a388-45cb-855f-7449b3780eb4","added_by":"auto","created_at":"2025-07-22 01:52:19","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":80160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual LULC Change rates in ha (2001-2023)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/0d477e7f1b54017ca6d60923.png"},{"id":87242790,"identity":"9b0583ce-dea2-4657-98eb-5a38c7aef751","added_by":"auto","created_at":"2025-07-22 01:52:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":775440,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLULC conversion Maps for 2001-2012, 2012-2023, and 2001-2023.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/ce7034ee9f556c95deac70cd.png"},{"id":90005192,"identity":"2f677a0a-e287-4031-b66b-f8d8b7541d96","added_by":"auto","created_at":"2025-08-27 09:24:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4383194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7014006/v1/b9f9a757-4eed-49cd-a7a9-6416e65895ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changes in Land Use Land Cover in Equatorial Coastal Forest of Kilifi County, Kenya","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThe concept of sustainable development began to be used in the 1980s, but it was the implementation of the Sustainable Development Goals (SDGs), adopted by consensus by the United Nations at the 2015 General Assembly, that made it more widely known. Putting the world on a sustainable development path is one of the UN's biggest undertakings to date and perhaps also has the potential to reduce the gap between developed and developing countries. Africa is the least developed region in the world, but there are also significant differences in natural and social conditions between African countries. The colonial past also plays a major role in African countries because the policies and economic role of the colonial countries still have a significant impact on the development of countries that have been independent for decades.\u003c/p\u003e\u003cp\u003eThe loss of biodiversity in Africa is threatening the livelihoods of tens of millions of people, reducing food security, leading to conflict as arable land shrinks, and increasing the likelihood of animal-to-human transmission of infections. Forests cover 26% of Africa's land area, most of which is in South Africa, Ethiopia and Nigeria, but the forest cover is steadily decreasing. Agriculture is the main direct cause of deforestation, accounting for about three-quarters of deforestation in Africa. To make matters worse, the continent's growing population clearly means an increasing demand for food. Twentieth-century colonialism has in many cases replaced natural vegetation with cocoa, coffee, oil palms and tea trees.\u003c/p\u003e\u003cp\u003eTropical Africa has lost about 22% of its forest cover since 1900, comparable to losses in the South America (Amazon Basin). Even less attention is paid to the shrinkage of dry forests in West, East, and Southern Africa, despite historically much greater deforestation in these areas. This is another reason why it can be instructive to look at land use and land cover change in Kenya. From a global perspective, it is estimated that over six decades (1960\u0026ndash;2019), a third (32%, approximately 43\u0026nbsp;million km\u003csup\u003e2\u003c/sup\u003e) of the global land area has been affected by land use changes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, during the same period, global net forest area loss was 0.8\u0026nbsp;million km\u003csup\u003e2\u003c/sup\u003e, while global agriculture had an expansion of 1.0 and 0.9\u0026nbsp;million km\u003csup\u003e2\u003c/sup\u003e for cropland and pasture/rangeland respectively. These statistics underscores the significance of these land use changes.\u003c/p\u003e\u003cp\u003eLand use describes people's activities, their inputs, and arrangements of how they utilize land, while land cover describes features that are observed on the earth's surface which can be anthropogenic or natural [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Land Use Land Cover (LULC) as a concept and a research area had been applied to various fields of studies. It can be applied in study areas such as erosion, landslides, global change and land planning [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, evaluation of LULC change is vital in resource management, sustainable development, environmental conservation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], water management climate-resilient strategies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], water resource planning and watershed management [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Overall, environmental, social, and economic systems are all impacted by LULC dynamics, making their monitoring and understanding critical for achieving sustainable development [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe effects of land use changes have also been captured on a local scale in various parts of the world. In the Kashmir Valley, India, between 1992 and 2001, there was a decrease in forest and pasture areas and an increase in shrubs, plantations, marshy areas, barren land, and built-up areas. However, between 2001 and 2015, forest and pasture areas increased [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the Talihya North watershed of DR Congo, forest cover decreased from 253.11 km2 in 1987 to 201.12 km2 in 2001 and further to 123.04 km2 in 2020 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In the Bafing area of Senegal, between 1986 and 2020, there was a significant increase in water bodies, vegetated areas, agricultural land, and settlements, while bare ground decreased [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Ethiopia, construction and cropland areas increased by 46.95% and 15% respectively while vegetation, grassland, and waterbodies decreased by 70.02%, 38.1%, and 62.7% respectively [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In Western Uganda, there was an increase in forest (0.1%), agriculture (0.1%), and urban (0.1%), while wetland, grassland, and shrubland cover decreased by 0.05%, 0.22%, and 0.01% respectively [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eKenya as a country has also experienced LULC changes which have been researched at a localized level. In River Ruiru watershed, Kiambu County perennial crops (coffee and tea), annual crops, and built-up areas increased by 11.43%, 35.84% and 3.068% respectively, while forestland, shrubland, and grassland decreased by 29.79%, 13.25%, and 7.48% respectively, between 1976 and 2017 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In Makueni County, results showed that built up area had the highest increase from 160.7 km\u003csup\u003e2\u003c/sup\u003e to 644.5 km\u003csup\u003e2\u003c/sup\u003e, while evergreen forest had the highest decrease from 3105.8 km\u003csup\u003e2\u003c/sup\u003e to 1372 km\u003csup\u003e2\u003c/sup\u003e between 2000 and 2016 respectively [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLand cover change analysis reveals substantial forest loss in both the Mau Forest Complex and Mt. Elgon complex in Kenya. The Mau Forest Complex experienced a 21.9% loss (88,493 ha) of forest cover between 1986 and 2017, while the Mt. Elgon complex experienced a 12.5% loss (27,201 ha) between 1984 and 2017 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Further analysis specifically focusing on the Kenyan side of Mt. Elgon Forest ecosystem from 1973 to 2019 revealed specific changes in forest types. Natural forests declined by 18%, bamboo forests by 15.19%, and plantation forests by 15.6%, while mixed farming, fallow land, and tea plantations increased by 29%, 10%, and 0.13%, respectively [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings highlight pressure on forest ecosystems largely attributed by human activities.\u003c/p\u003e\u003cp\u003eDrivers and causes of LULC change are a major global concern, prompting numerous investigations worldwide. One such study in Pakistan identified several key drivers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Perceived and proximate drivers included natural conditions, increased infrastructure, unplanned urbanization, and agricultural decline, while underlying drivers were poor marketing, inadequate financial resources, and weak governance. Socioeconomic factors and climatic factors such as drought and rainfall can also drive the change [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, natural conditions such as frost and drought can also damage forests, as observed in the Nagyerdő forest in Debrecen, Hungary [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Human activities have a long history of influencing forest cover. For example, forest conversion into farmlands and grazing land resulted in wood shortages in Europe, contributing to the development of silviculture and forestry practices [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These studies illustrate the diverse range of factors contributing to LULC change including both natural and anthropogenic activities factors.\u003c/p\u003e\u003cp\u003eHowever, anthropogenic activities are widely recognized as key drivers of extensive land transformations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] largely attributed to rapid population growth. For instance, a study in Ethiopia showed that population increases lead to high food demands causing conversion of natural forests into grasslands, urbanization, and agricultural land [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In Uganda, population growth is shown to have a positive relationship with the extent of urban areas, contributing to their increase [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relationship between population growth and forest changes, however, is complex and context dependent. For instance, research has demonstrated a contrasting trend: while increased population in Bafing was associated with an increase in tree cover, likely due to the presence of a large dam and supportive policies, population growth in Faleme coincided with increased deforestation, likely due to different land use pressures and management strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenerally, conversion of natural vegetation to grazing land, farmland, urban centers, and human settlements in East Africa is associated with land degradation, deforestation and loss of biodiversity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Other activities such as conversion of wetlands into settlement and agricultural land were the main drivers of LULC changes in Nyando River Basin in Kisumu County, Kenya [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Agriculture was documented as the main cause of deforestation, accounting for 81.5% (70,612 ha) and 63.2% (24,077 ha) of the deforestation in Mau Forest and Mt. Elgon Forest respectively [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDifferent studies employ various LULC classification schemes, depending on their research objectives, location, and researcher preferences. While some adopt a more focused approach, like using four classes such as forest, savannah, bare lands and buildings, and croplands and fallows [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], or classifying land cover into natural forest, bamboo forest, grassland, mixed farming, and fallow land [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], others opt for broader, more detailed categories. For example, six classes including forest, built-up areas, agricultural land, maize, para rubber trees, and water were utilized [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while other studies used seven classes like forest, moorland, agriculture large scale, agriculture small-scale, rangeland, settlement/urban, and water [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], or evergreen forests, grassland, bushlands, built-up areas, croplands, bare land, and water bodies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these specific variations, a common broad categorization of vegetation (natural and semi-natural) and human activities remains evident across studies.\u003c/p\u003e\u003cp\u003eArabuko Sokoke Forest (ASF) was first gazetted as a forest reserve in 1943 as Crown Forest, and later the reserve was subsequently expanded with additional forest land gazetted in 1968 and 1979 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The management and governance of ASF has deteriorated since the colonial era. Several factors are responsible for this decline, including insufficient incentives for local community participation in forest management, widespread poverty, occurrences of unauthorized forest resource access, firewood overexploitation, and poaching of construction materials. Based on the background, this paper aims to (a) analyse LULC changes for ASF and Arabuko Sokoke Forest Region (ASFR) between 2001 and 2023; (b) identify conversions between the LULC classes for the period; (c) determine the annual deforest loss rate.\u003c/p\u003e"},{"header":"2.0 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study Area\u003c/h2\u003e\u003cp\u003eASF is the largest remaining fragment of East African coastal dry forest with high density of endemic species making it a global biodiversity hotspot [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It covers an area of approximately 41,846 ha, located along the Kenyan coast in Kilifi County. However, other scholars have documented it to have an area of 41,600 ha (103,740 acres) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The elevation within ASF ranges from 40m to 160m above sea level, with three major vegetation type including mixed forest, Brachystegia Forest, and Cynometra Forest [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It experiences long rains are between April and June, and short rains in November and December. The climate is hot and humid with an average temperature of 29℃ [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The soil texture generally ranges from sandy-to-sandy loam.\u003c/p\u003e\u003cp\u003eASF management has four forest regions: Gede, Jilore, Kararacha, Sokoke, with three forest stations in Gede, Jilore and Sokoke [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Administratively, ASF is shared between Malindi, Ganze and Bahari Constituencies. Its boundaries extend to seven locations: Jilore, Mwahera, Vitengeni, Sokoke, Roka, Gede, and Ngerenya, which make up the ASFR (154160 ha), along longitude 40\u003csup\u003eo\u003c/sup\u003eE and latitude 3\u003csup\u003eo\u003c/sup\u003eS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Indian Ocean borders the east of Gede, Roka, and Ngerenya Locations. Several villages surrounding the reserve are mostly small-scale farmers with the Giriama tribe who primarily dependent on forest resources. They also grow subsistence crops including cow peas, cassava, and maize, while cash crops include mango, coconut, and cashew-nut.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data collection\u003c/h2\u003e\u003cp\u003eTop-of-atmosphere (TOA) reflectance calibrated Tire 1 collection 2 images of Landsat 7 Enhanced Thematic Mapper plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) were used in this study. Median composite images were generated using 46 images from 2000\u0026ndash;2001, 49 images from 2011\u0026ndash;2012, and 42 images from 2023. The study period 2001\u0026ndash;2023 was selected based on the availability of a sufficient number of satellite images with minimal cloud cover in the study area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Image processing, accuracy assessment, and classification\u003c/h2\u003e\u003cp\u003eSpectral indices were calculated in the Google Earth Engine (GEE) platform for the 2001, 2012, and 2023 median composite images and added as additional bands to improve classification accuracy. Training samples were derived from user-defined polygons, with 1442, 1736, and 2017 pixels used for training the 2001, 2012, and 2023 image datasets, respectively. High resolution Google earth Images was used as reference to digitize the training sample polygons with a total of seven LULC types as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. LULC type and description is adapted from the Kenya Forest Service (KFS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the field photos depicting the LULC types. The sampling data was randomly split into 70% training data and 30% testing data. The Random Forest (RF) algorithm, a built-in classifier in GEE, was used to perform supervised classification on the 2001, 2012, and 2023 images using the training data. This process produced LULC maps for the selected time periods.\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\u003eLULC types and description\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULC types\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest (FO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLand area covering over 0.5 ha with trees above 2m height, and a crown cover greater than 15%. Not used for agriculture or other non-forest purposes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBareland (BL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLand area without vegetation covered by bare soil, rocks, rough roads, or degraded lands\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWetland (WE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWetland, vegetated wetland (Mash, Bogs, Papyrus)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoodland (WL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWoodland scrubs, thickets, open and wooded grass\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-up (BU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLands dominated by huts, houses, industrial facilities and paved houses.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCropland (CL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea under crop cultivation or tilled land.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater (WA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater bodies including streams, rivers, ocean, and lakes.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eEvaluation of the supervised classification utilized GEE in-built accuracy assessment techniques. The overall accuracy (OA), kappa coefficient (K), consumer accuracy (CA), and producer accuracy (PA) were calculated for the classified images. Equations\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represent the formulas used.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:OA=\\:\\frac{Number\\:of\\:Correctly\\:Classified\\:Samples}{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{S}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}\\text{s}}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:K=\\:\\frac{Overral\\:Accuracy-Estimated\\:Channce\\:Agreement}{1-\\text{E}\\text{s}\\text{t}\\text{i}\\text{m}\\text{a}\\text{t}\\text{e}\\text{d}\\:\\text{C}\\text{h}\\text{a}\\text{n}\\text{n}\\text{c}\\text{e}\\:\\text{A}\\text{g}\\text{r}\\text{e}\\text{e}\\text{m}\\text{e}\\text{n}\\text{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:CA=\\:\\frac{Number\\:of\\:Correctly\\:Classified\\:Samples\\:in\\:each\\:Class}{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{S}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}\\text{s}\\:\\text{C}\\text{l}\\text{a}\\text{s}\\text{s}\\text{i}\\text{f}\\text{i}\\text{e}\\text{d}\\:\\text{t}\\text{o}\\:\\text{t}\\text{h}\\text{a}\\text{t}\\:\\text{C}\\text{l}\\text{a}\\text{s}\\text{s}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:PA=\\:\\frac{Number\\:of\\:Correctly\\:Classified\\:Samples\\:in\\:each\\:Class}{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{S}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}\\text{s}\\:\\text{f}\\text{r}\\text{o}\\text{m}\\:\\text{R}\\text{e}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e}\\text{d}\\:\\text{d}\\text{a}\\text{t}\\text{a}\\:\\text{i}\\text{n}\\:\\text{e}\\text{a}\\text{c}\\text{h}\\:\\text{C}\\text{l}\\text{a}\\text{s}\\text{s}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLULC values were reclassified, and LULC and conversion maps were generated and visualized using QGIS. Conversion maps were specifically generated using the MOLUSCE (Modules for Land Use Change Simulations) tool in QGIS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A summary of the methodology flow and steps is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Limitations of the study\u003c/h2\u003e\u003cp\u003eThe ASFR is located near the equator along the Kenyan Indian coast, experiences persistent cloud cover throughout the year. This scarcity of suitable cloud-free images necessitated the use of a two-year period of satellite data to generate each median image composite. Combining two years of data improved the generation of a clearer, higher-quality composite.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 LULC Maps classification Accuracy\u003c/h2\u003e\u003cp\u003eThe results of confusion matrixes, overall accuracy (OA), producer accuracy (PA), consumer accuracy (CA), and kappa coefficient (K) calculations are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. 2001, 2012, and 2023 classified maps had an overall accuracy of 93%, 99%, and 94% respectively. The kappa coefficient was 91%, 98%, and 92% for 2001, 2012, and 2023 images. Urban and cropland classes for 2001 image had the lowest producer accuracy of 27% and 22% due to the scarcity of reference data for accurate training data identification. However, the high overall accuracy and kappa coefficient of the image allowed for the results to be used for change analysis.\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\u003eAccuracy Assessment for 2001, 2012, and 2023 classified images\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\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBareland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e2001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e99%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30%\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\u003e50%\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e93%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePA\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\u003e95%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e2012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89%\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\u003e96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e86%\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e99%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e98%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\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\u003e53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e99%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93%\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\u003e85%\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\u003e96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100%\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=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 LULC maps\u003c/h2\u003e\u003cp\u003eMapping and displaying the spatial distribution of the seven LULC types is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Spatially cropland has been spreading on the eastern side of ASFR, specifically Gede and Roka locations from 2001 to 2023. The water inlet from the Indian Ocean is seen shrinking over the years. Galana-sabaki river is visible in the northern side of the 2001 and 2012 map while in 2023 its size has significantly reduced. Bareland has increased rapidly on the eastern side of ASFR. Within the ASF, woodland area has increased, with a decrease in forest.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 LULC Analysis and changes for ASF and ASFR\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 LULC analysis for ASF\u003c/h2\u003e\u003cp\u003eThroughout the period forest was the most dominant class, showing a declining trend from 2001 to 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Forest class was the dominant type covering 39,348 ha (94.0%), 35,240 ha (84.2%), and 33,000 ha (78.9%) in the year 2001, 2012, and 2023 respectively. Woodland, cropland, built-up, and bareland classes increased steadily throughout the period. Woodland had the highest increase from 2,345 ha to 8,245 ha. However, wetland showed a decreasing trend from 82 ha to 4 ha.\u003c/p\u003e\u003cp\u003eThe 2001\u0026ndash;2012 period had a larger forest loss of 4,108 ha (10%), compared to 2,240 ha (6%) loss in the 2012\u0026ndash;2023 period. On the other hand, woodland had a higher gain of 3790 ha (162%) in the 2001\u0026ndash;2012 period compared to 2110 ha (34%) in the 2012\u0026ndash;2013 period. There was also a large cropland area increase of 320 ha between 2001\u0026ndash;2012 with a small decrease of 13 ha in 2012\u0026ndash;2023 period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 LULC analysis for ASFR\u003c/h2\u003e\u003cp\u003eLULC analysis for ASFR is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Woodland class was the highest with 73,368 ha (47.6%), 57,952 ha (37.6%), and 47,726 ha (31%) of the area for 2001, 2012, and 2023 respectively. Generally, woodland, and grassland class have been decreasing throughout the 2001\u0026ndash;2023 period while bareland, built-up, and cropland show an increasing trend. Wetland showed a slight decreasing trend, while water class increased and decreased. Generally, the 2001\u0026ndash;2012 period had the largest decrease in forest and woodland as 11,495 ha (23%) and 15416 ha (21%) was lost respectively. Additionally, the 2001\u0026ndash;2012 period saw the largest increase of bareland, cropland, and water with a gain of 13210 ha (62%), 12918 ha (214%), and 499 ha (32%) respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 LULC Changes\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.4.1 Total LULC Changes for 2001\u0026ndash;2023 period\u003c/h2\u003e\u003cp\u003eThe largest decline within ASFR was woodland losing 25,642 ha, followed by forest with a loss of 13,846 ha within the 22 years study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). On the other hand, bareland and cropland increased by 20,320 ha and 17,815 ha respectively for ASFR. ASF had a woodland increase of 5,900 ha, while forest decreased by 6,348 ha.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.4.2 Annual LULC changes\u003c/h2\u003e\u003cp\u003eThe mean annual year gains and losses rates are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Within the ASFR, bareland had the highest yearly increase of 924 ha/year followed by cropland (810 ha/year) while woodland and forest had net annual losses of 1,166 ha/year and 629 ha/year respectively. Moreover, ASF has an annual woodland gain of 268 ha/year and an annual forest loss of 289 ha/year for the 2001\u0026ndash;2023 period. During the study period both wetland and water classes had minor changes annually. ASFR experiences an annual built-up growth 87 ha/year during the 22-year period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Land use land cover conversions in ASF\u003c/h2\u003e\u003cp\u003eSeveral LULC conversions were observed in the ASF over the 2001\u0026ndash;2023 period (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Forest class had the largest conversion as 6682 ha, 183 ha and 43 ha was converted to woodland, cropland, and bareland respectively. On the other hand, 494 ha, 71 ha and 9 ha of woodland, wetland, and bareland respectively was converted to forest. Water and built-up areas experienced the least conversions.\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\u003eLand Cover conversions in ASF (2001\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLULC type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBareland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eGrand Total\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003e2001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e32421\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e39348\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBareland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1544\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e2345\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGrand Total\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e33000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e204\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e8245\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e331\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e41846\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\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Land Cover conversions in ASFR\u003c/h2\u003e\u003cp\u003eWithin the larger area of ASFR woodland had the largest conversion with 56% (41,167 ha) being converted into bareland (26950 ha), cropland (11514 ha), forest (1556 ha), built-up (1115 ha), water (26 ha) and wetland (6 ha). 31.9% (15,801 ha) of the forest area was converted into woodland (8399 ha), cropland (6150 ha), bareland (762 ha), wetland (209 ha), and water (8 ha) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On the other hand, 19% (1,203 ha) of the cropland was converted into bareland (404 ha), built-up (274 ha), woodland (271 ha), and forest (254 ha).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand Cover conversions in ASFR (2001\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLULC type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBareland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eGrand Total\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003e2001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e33709\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e762\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e49510\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBareland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e13171\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e21422\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1373\u003c/b\u003e\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\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1634\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e32201\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e73368\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e239\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e631\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e4832\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e6035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e939\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e1560\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eGrand Total\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e35664\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2544\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e47725\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e23850\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e41742\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e1604\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e154160\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\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Spatial distribution of the LULC conversions\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows the LULC conversions between different classes for ASF and ASFR for 2001\u0026ndash;2012, 2012\u0026ndash;2023, and 2001\u0026ndash;2023 period. Generally, the ASF area experienced relatively larger conversions in the 2001\u0026ndash;2012 period compared to 2012\u0026ndash;2023 especially in the northwestern and eastern part. Large conversion of woodland to bareland is observed in Jilore, Mwahera, Vitengeni, Sokoke locations while Gede, Roka, and Ngerenya locations experienced conversion of woodland into cropland and other land uses.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis study analyzed LULC changes within the Arabuko Sokoke Forest (ASF) and the broader Arabuko Sokoke Forest Region (ASFR) between 2001 and 2022. Generally, the 22-year period saw significant LULC variation and conversions within ASF and ASFR. The ASF experienced a forest loss of 6348 ha with a 5900-ha woodland and 307-ha cropland increase. Consequently, the larger ASFR had a forest loss of 13,846 ha and a woodland decrease of 20,320 ha. This is largely contributed by increase in bareland and cropland of 20,320 ha and 17,815 ha. This shows forest loss was largely caused by expansion of woodland, bareland, and cropland area in ASFR while ASF loss was mainly cropland. The increase in woodlands and bareland areas may arise from the harvesting of trees by local communities. Built-up, wetland, and water areas had minimal effects on forest loss.\u003c/p\u003e\u003cp\u003eThese findings coincide with other LULC change studies confirming forests loss in Kenya including Cherengany hills forest ecosystem [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], Mau Forest and Mt. Elgon Forest [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Forest decrease is also a common occurrence as observed by studies in other African countries such as Tanzania [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], Uganda [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Ethiopia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and Democratic Republic of Congo [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, these studies concede large cropland expansion.\u003c/p\u003e\u003cp\u003eLULC changes observed within ASFR are closely linked to demographic and socio-economic factors. The study region is within Kilifi County which has seen increase in population over the years despite administrative boundary system changes which were instituted by the new constitution in 2010. Census reports show a growing population trend within the region of 544,303 (1999), 1,109,735 (2009), and 1,440,958 (2019) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study showed a 214% (12918 ha) cropland in the 2001\u0026ndash;2012 period which may have been attributed to the 103.9% increase in population in the 1999\u0026ndash;2009 period, who raise the increase the demand for agricultural products. Moreover, timber demand has increased as it is required for curving which contributes to further forest area reduction [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotwithstanding the significant demographic and socio-economic influence, Kenya has a long history of implementing policies and legal frameworks to address forest-related challenges. The 1964 forest Master Plan, 1969 Forest Act, Forest Act of 2005, 2010 Constitution of Kenya, and the national forest policy of 2014, are some of the policy documents that have promoted conservation and management of forests in Kenya [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Specifically, Forest Act of 2005, legally recognised Participatory Forest Management (PFM), which was first introduced in ASF in 1997 as a pilot project [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, the act established the KFS which manages and administers all state forests in Kenya. The KFS works alongside other state institutions such as National Museum Kenya (NMK), Kenya Wildlife Service (KWS), and Kenya Forest Research Institute (KFRI) to ensure collective efforts in forest protection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe state led institutions have tried their best to address various forest related challenges, however most forest communities felt marginalized in this process. The Forest Act of 2016 addressed this challenge by allowing the formation of Community Forest Associations (CFAs) thus enhancing community participation in forest management [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. CFAs function similarly to Community Based Organisations (CBOs) with a one-time membership subscription [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. CFAs and CBOs within ASF include Arabuko Sokoke Forest Adjacent Dwellers Association (ASFADA), Arabuko Sokoke Forest Management Team (ASFMT), Arabuko Sokoke Forest Guide Association (ASFGA), and Friends of Arabuko Sokoke Forest (FoASF) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is estimated that there are about 300 user groups involved in the management under ASFADA [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite having such a strong management institutions, structures, and support systems, ASF conservation and protection effort face various challenges. These challenges are centred around elite capture, limited resources, unequal representation, conflicting stakeholders\u0026rsquo; interests and institutional mandates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This can lead to marginalization of local communities and unequal distribution of socio-economic benefits derived from the forest. For instance, there are low survival rates of trees planted by the county government due to insufficient technical support and collaboration from organisations such as KEFRI and KFS [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Weak government structures and unclear stakeholders\u0026rsquo; roles hinder effective implementation of Conservation strategies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This ambiguity undermines conservation goals due to confusion and possible duplication of efforts. Inadequate financial, technical and human expertise to management of the forests has resulted in challenges such as encroachment, agricultural expansion, and indiscriminate tree cutting for charcoal [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The severe resource gap significantly undermines the capacity of relevant organisations to effectively monitor and enforce regulations.\u003c/p\u003e\u003cp\u003eAdditionally, the key drivers of destruction identified are illegal logging and natural resource exploitation, charcoal burning, and local elephant population exceeding the carrying capacity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The demand for poles, carvings, and timber by the surrounding community has been identified as a key driver of illegal logging [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. From an international perspective, [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] emphasize that agroforestry focusing on high-value trees produces high-quality logs, which can help mitigate regional timber shortages. The natural environmental challenge of frequent droughts, especially in Ganze constituency, leads to tree desiccation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which is a contributing factor to large conversion of woodland to bareland in Mwahera, Vitengeni, and Sokoke locations.\u003c/p\u003e\u003cp\u003eAddressing these issues requires a multi-faceted approach, combining community engagement, habitat management and law enforcement. Locally, Kilifi County has advocated for community engagement in nature-based enterprises, strict forest laws enforcement, establishing tree nurseries and woodlots and the development of forest management plans [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They also plan to train and equip additional 30 forest guards and rehabilitation of degraded lands. Such efforts can improve institutional capacity, reduce overdependence of forest products and poverty levels and expansion of sources of livelihoods [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Electric fencing of ASF has helped to reduce human-wildlife conflict, which has been instrumental in reducing illegal settlement and deforestation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCBOs have, to some extent, incorporated measures to reduce forest vulnerability and hazards such as anti-logging practices and policy, adhering to forest resource utilization zones, community educational and financial empowerment, and forest sustainability awareness programs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Limited resource challenge can be addressed through innovative funding models, formulation of partnerships, and increasing budget allocations [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Continuous monitoring and evaluation of these strategies can ensure long term sustainability of conservation measures in ASF. Moreover, success of these strategies requires a strong political will and adaptive management.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eLULC studies generally provide a key insight on the interactions of human activities and the natural environment. Quantifying these interactions spatially and temporally is an effective way of monitoring hotspots areas requiring intervention and gaps in mitigation measures enforcement. Temporally there is an annual decrease of forest area of 629 ha/year (ASFR) and 289 ha/year (ASF) for the 2001\u0026ndash;2023 period. Spatially, Jilore, Mwahera, Vitengeni, region experienced the largest conversion of woodland to bareland. These changes highlight the need for enacting and enforcing balanced mitigation measures for long-term sustainability.\u003c/p\u003e\u003cp\u003eSocio-economic pressures, inadequate governance and poor management practices which have resulted into shifts and conversion in land cover uses within ASF and ASFR. Poverty and unemployment create local community overdependence on the forest for energy and food supply. This is evident from the increasing demand for agricultural land, firewood, and charcoal which leads to illegal logging and encroachment. These adverse effects are further encouraged by weak law enforcement, poor governance structure, and lack of coordinated efforts between forest related organisation. Fostering transparent and inclusive stakeholder engagement, enhancing equitable representation and robust governance to reduce elite capture, and amplifying marginalized groups' voices in decision-making can overcome such challenges [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The insights from this study can guide local and national policymakers and practitioners in developing tailored interventions to address forest degradation hotspots and contribute to the achievement of global biodiversity conservation goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\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\u003eEthics declaration: \u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability: \u003c/strong\u003eAll relevant data are contained within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: Lewis Mjomba Ndungu conceptualized the study, wrote, edited and reviewed the manuscript. L\u0026aacute;szl\u0026oacute; Zentai wrote, edited, and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCountry Affiliation: \u003c/strong\u003eLewis Mjomba Ndungu (Hungary, Kenya) and L\u0026aacute;szl\u0026oacute; Zentai (Hungary).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdavize Julius A. Participatory Forest Management and Disaster Risk Reduction: The Case of Arabuko-Sakoke Forest in Kenya [dissertation]. Nairobi: University of Nairobi; 2015. Available from: https://erepository.uonbi.ac.ke/bitstream/handle/11295/94459/Adinoyi_Participatory%20forest%20management%20and%20disaster%20risk%20reduction:the%20case%20of%20arabuko-sakoke%20forest%20in%20kenya.pdf?sequence=1\u003c/li\u003e\n\u003cli\u003eAlam A, Bhat MS, Maheen M. Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley. 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Nat Commun. 2021;12(1). https://doi.org/10.1038/s41467-021-22702-2\u003c/li\u003e\n\u003cli\u003eZentai L. Discovery of forested areas on topographic maps: Development of Orienteering Maps. Lect Notes Geoinf Cartogr. 2013;295\u0026ndash;308. https://doi.org/10.1007/978-3-642-33317-0_18\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land Use Land Cover (LULC), Forest Ecosystem, Remote Sensing, Google Earth Engine (GEE), Kenya","lastPublishedDoi":"10.21203/rs.3.rs-7014006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7014006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand Use Land Cover (LULC) changes influence human activities and policies related to environmental protection and conservation. These changes are usually anthropogenic in nature with population increase driving urbanization and agricultural expansion. These changes may result in environmental degradation thereby contributing to global problems like global warming. This study focused on analysing LULC changes in Arabuko Sokoke Forest (ASF) and Arabuko Sokoke Forest Region (ASFR) between 2001 and 2023, identifying the LULC conversions, and determining the deforestation rate. Landsat 7 ETM\u0026thinsp;+\u0026thinsp;and Landsat 8 OLI/TIRS satellite images were analysed using Google Earth Engine (GEE) to create 2001, 2012, and 2023 LULC maps. Conversion maps were generated using QGIS MOLUSCE tool. Seven LULC classes (forest, bareland, wetland, woodland, built-up, cropland, and water) were classified with overall accuracies and Kappa coefficients of above 93% and 91% respectively. For the 22-year period ASF and ASFR experienced forest annual loss of 629 ha(hectares)/year and 289 ha/year respectively. Additionally, 6682 ha and 8399 ha of the forest was converted to woodland within ASF and ASFR respectively. Existing literature suggest that these changes are largely driven by the interplay of social, economic, technical, and policy factors at national and local level. Therefore, multi-stakeholder interventions are required for effective mitigation.\u003c/p\u003e","manuscriptTitle":"Changes in Land Use Land Cover in Equatorial Coastal Forest of Kilifi County, Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 01:44:13","doi":"10.21203/rs.3.rs-7014006/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ac70375-1e6d-4e74-947c-f221494d22dc","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T09:24:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 01:44:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7014006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7014006","identity":"rs-7014006","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}