A Spatial Analysis of Habitat Connectivity in Simalaha Community Conservancy of Zambia

A Spatial Analysis of Habitat Connectivity in Simalaha Community Conservancy of Zambia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Spatial Analysis of Habitat Connectivity in Simalaha Community Conservancy of Zambia Danny Chisanga Musenge, Darius Phiri, Ngawo Namukonde, Gift Mulenga, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7506925/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Landscape ecology and conservation are essential for supporting biodiversity, particularly for migratory species. However, increasing habitat fragmentation continues to reduce landscape connectivity, threatening ecological integrity. The Kavango-Zambezi Transfrontier Conservation Area (KAZA TFCA), which hosts key biodiversity, faces such a challenge. This study assessed the impact of a community-based conservation approach specifically the Simalaha Community Conservancy in Western Zambia on improving forest connectivity and habitat integrity. Using freely available Landsat imagery, we analyzed land cover changes before the conservancy’s establishment in 2010 and 15 years later in 2025. Image classification was conducted using pixel-based and object-based methods, followed by landscape metrics analysis using FRAGSTATS and the Landscape Fragmentation Tool in ArcMap by focusing on indices, such as Number of Patches (NP), Patch area (PA) and Connectivity Index (CI). The results, based on classification maps with over 80% accuracy, showed increasing habitat loss and fragmentation outside the conservancy, especially in 2025. Within the conservancy, there was a notable shift from forest-dominated to grassland habitat, though forest loss remained below 1%, compared to 1.2% outside. The eastern part of the conservancy was dominated by croplands and settlements, identifying agricultural expansion as a primary driver of forest loss. Overall, the study demonstrates that the community conservancy model has contributed to maintaining and enhancing habitat connectivity and integrity within its boundaries, despite external anthropogenic pressures. These findings provide important insights for conservation planning and wildlife management in transboundary landscapes, especially for migratory species, such as elephants ( Loxodonta africana ) and blue wildebeest (Connochaetes taurinus) . Community-Based Conservation Conservation planning Habitat fragmentation Landscape KAZA - TFCA Wildlife movement ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Landscape connectivity has gained increasing attention towards biodiversity conservation in both policy and ecological research [ 1 , 2 ]. The Post-2020 Global Biodiversity Framework emphasizes connectivity across several goals and targets, notably the commitment to conserve 30% of land and water within a well-connected network of protected and conserved areas by the year 2030 [ 3 ]. In response, connectivity studies have become central to landscape ecology and conservation science, incorporating diverse data types, such as species movement data, genetic information, and point-based ecological observations. To assess landscape connectivity at multiple scales, for instance, at local and landscape levels, studies have increasingly employed high-resolution GPS telemetry data from collared wildlife to map and analyze the movement patterns of wide-ranging species, such as African savanna elephants ( Loxodonta africana ), lions ( Panthera leo ), spotted hyenas ( Crocuta crocuta ), and African wild dogs ( Lycaon pictus ). In addition, biologgers can be used to monitor wildlife physiological activities, such as heart rate, body temperature and movements, while using the landscapes[ 4 ]. In Sub-Saharan Africa, Transfrontier Conservation Areas (TFCAs), such as the Kavango-Zambezi Transfrontier Conservation Area (KAZA TFCA) play a crucial role in enhancing connectivity by coordinated multi-stakeholder conservation efforts across national borders [ 5 ]. These initiatives aim to link protected areas (PAs) and key habitats outside them, fostering collaboration among countries with diverse legislative frameworks, land-use practices, conservation priorities, and stages of socio-economic development [ 6 ]. The KAZA-TFCA, established in 2011 by Angola, Botswana, Namibia, Zambia, and Zimbabwe, is a key initiative aimed at fostering regional economic development and conserving the region’s unique biodiversity, particularly by facilitating large-scale wildlife migrations [ 7 ]. Home to the world’s largest elephant population, as well as globally threatened species like the black rhinoceros ( Diceros bicornis ) and African wild dog ( Lycaon pictus ), KAZA also supports several endemic species across multiple taxa [ 6 , 8 ]. A core objective of the TFCA is to enable the free movement of wildlife across borders, expanding dispersal areas, relieving population pressure, and enhancing ecological stability through metapopulation support. However, this increased connectivity has led to some regions becoming wildlife transit corridors, escalating human–wildlife conflicts due to more frequent interactions between wildlife and local communities. While landscape connectivity is vital for conservation, it can inadvertently intensify conflict as recovering wildlife populations encounter expanding human activities [ 9 ]. Addressing these challenges requires the implementation of effective, collaborative conflict mitigation strategies that are not only critical for the KAZA region but also offer valuable lessons for transboundary conservation globally. Protected areas (PAs) and complementary conservation initiatives beyond their borders play a crucial role in safeguarding natural resources by promoting sustainable land use and biodiversity management [ 6 , 10 ]. However, declining connectivity among core habitat patches both within and outside PAs poses a significant threat to ecological integrity [ 9 , 11 ]. Fragmentation disrupts migratory and dispersal routes, increases genetic isolation, diminishes resilience to disease, and reduces the adaptive capacity of wildlife to climate change [ 11 , 12 ]. Furthermore, the loss of intact habitats surrounding PAs intensifies edge effects, which can further degrade wildlife populations along PA boundaries (Fletcher Jr 2005 ;Ullah et al. 2024). These challenges highlight the urgent need for landscape-scale conservation approaches that improve habitat quality and connectivity across the broader matrix of protected and non-protected lands. Previous research has emphasized the importance of understanding how wildlife navigates and utilize these mixed-use landscapes to inform effective conservation planning [ 13 , 14 ]. Identifying the ecological and anthropogenic factors influencing wildlife space use is essential for prioritizing the protection of PAs, wildlife corridors, and buffer zones especially in light of growing climate impacts, escalating human pressures, and the chronic underfunding of many protected areas [ 15 , 16 ]. One increasingly recognized strategy to address these challenges is the establishment of community conservancies, such as the Simalaha Community Conservancy (SCC) in western Zambia, which aims to restore habitat connectivity, while supporting local livelihoods [ 17 , 18 ]. The SCC plays a vital role in advancing the KAZA-TFCA goal of restoring ecological connectivity across fragmented landscapes by establishing a functional mosaic of protected areas and transboundary wildlife corridors [ 17 , 18 ]. Strategically located between Chobe National Park in Botswana and Kafue National Park in Zambia, SCC serves as a key linkage within the larger Zambezi-Chobe dispersal area [ 19 ], enabling cross-border wildlife movement and promoting genetic exchange among subpopulations. Despite its ecological importance, there has been a limited empirical assessment of SCC’s effectiveness in enhancing landscape connectivity since its establishment in 2012. This study addresses this gap by employing remote sensing data (e.g., Landsat and Sentinel imagery) [ 20 , 21 ] and spatial analysis tools, such as FRAGSTATS [ 22 ] to quantify changes in habitat connectivity before and after the conservancy’s formation. The results are intended to inform conservation planning and corridor management efforts within the broader KAZA landscape. This study advances the field of landscape connectivity research in two keyways: (1) by explicitly evaluating connectivity at multiple spatial scales surrounding the SCC, and (2) by providing empirical evidence on the effectiveness of emerging community conservancy models in enhancing habitat connectivity. We hypothesize that the establishment of SCC has resulted in measurable improvements in landscape connectivity by reducing habitat fragmentation and facilitating ecological linkages across the broader transboundary landscape. By adopting this approach, the study aims to underscore the ecological significance of community-managed conservation areas in regional connectivity planning and biodiversity conservation, particularly within complex and dynamic socio-ecological systems, such as the KAZA Transfrontier Conservation Area. 2. Methodology 2.1 Description of study area Simalaha ecosystem (180,000 hectares) is a community driven conservation area established in 2012 to enhance conservation of wildlife and improve rural livelihoods. Management innervations in the area include fencing and restricting human activities, which serve as a temporal measure to contain huma pressure; once there is remarkable change in community behaviour, the fencing will be removed in the long run. The ecosystem is located on 17 o 31’27.3S and 24 o 57’35.9E in Sesheke and Sekute chiefdoms in Sesheke and Kazungula districts of Southern and Western provinces of Zambia (Fig. 1 ), respectively. The ecosystem also forms nexus to KAZA-TFCA which encompasses significant wetlands and includes large areas of the Miombo-Mopane and the Kalahari-Namib Wilderness Areas [ 18 ] (Fig. 2 ). Like other similar environments in the region, it is characterized by infertile sand and alluvial soils with grasslands and mixed woodland vegetation. Simalaha Community Conservancy is characterized by annual precipitation of 400 to 700 mm and temperature ranges from 16.4 o C to 32 o C, of which the higher temperature influences frequency and intensity of unprescribed wildfire [ 18 ]. The landscape experiences three seasons: hot-wet season from November to April, cold-dry season from April to August and hot-dry season from September to November. The elevation is about 170 m to 800 m above sea level on the lower altitude and about 600 m to 1400 m above sea level [ 23 ]. Due to poor soil, the landscape is unfavorable for arable cropping, but both livestock and wildlife can thrive. The SCC is restocked with more than 1,600 animals comprised of about thirteen large mammal species which include: red lechwe (Kobus leche leche) , buffalo (Syncerus caffer) , giraffe (Giraffa camelopardalis) , puku (Kobus vardonii) , hartebeest (Alcelaphus buselaphus) , sable antelope (Hippotragus niger) , eland (Taurotragus oryx) , impala (Aepyceros melampus) , zebra (Equus burchelli) and blue wildebeest (Connochaetes taurinus) [ 18 ]. 2.2. Materials and Methods 2.2.1 Method overview The methodological approach followed a systematic workflow, ensuring clear and logical progression from data acquisition to analysis (Fig. 2 ). The methodology is divided into five key phases: (1) data acquisition and pre-processing, (2) LULC classification (3) Change detection between 2012 and 2025; and (4) connectivity and fragmentation analysis. 2.2.2 Data acquisition and pre-processing 2.2.1.1. Imagery dataset This study utilized a multi-temporal Landsat imagery dataset, comprising Landsat 5 TM images for 2012, and Landsat 8 OLI/TIRS images for 2025 (Table 1 ), downloaded from the United States Geological Survey (USGS) website ( https://earthexplorer.usgs.gov/ ) to extract LULC maps. For the optimal LULC classification, atmospherically corrected and pre-processed (Level 2) Landsat images with a spatial resolution of 30 m were acquired within the month of September when the cloud cover is minimum. All images were projected to UTM Zone 35S (EPSG:32735) coordinate system, and a 40 km buffer zone was established around the park. Images underwent atmospheric correction and were selected based on cloud cover below 10% [ 24 , 25 ]. 2.2.1.2. Auxiliary dataset Auxiliary datasets were integrated to support the analysis and interpretation of habitat dynamics within the study area. These spatial data include boundary shapefiles, digital elevation models (DEM), topographical maps, and Google Earth imagery, including boundary shapefiles, digital elevation models (DEM), topographical maps, and Google Earth imagery. Additionally, settlement extent data were obtained from GRID3 ZMB - Settlement Extents v3.0, accessed through ( https://data.grid3.org/datasets/GRID3::grid3-zmb-settlement-extents-v3-0/about ). Table 1 Details of the images used in this study include the type of Landsat images, date of acquisition, spatial resolution, and satellite path/row. Sensor Date of Acquisition Spatial resolution (m) Path/Row Landsat 5 TM 2010/09/08 30 172/068, 172/069 Landsat 8 OLI 2025/05/12 30 172/068, 172/069 TM: Thematic Mapper; OLI: Operation Land Imager; USGS: United States Geological Survey 2.2.3 LULC Classification 2.2.3.1 Image classification The LULC classes include settlements, forest, cropland, wetlands, grassland, bare land, and water were used in the classification (Table 2 ). The development of these classes was informed by reconnaissance surveys and expert knowledge of the study area, ensuring relevance and accuracy. This classification system allowed for an understanding of land use patterns and changes within the study area. Table 2 Land use/cover classes used to characterize the habitat in Simahala ecosystem in Southern and Western Zambia. Land use/cover classes Description Forest Deciduous forests, evergreen forests, mixed forest lands, coniferous forests, orchards, commercial plantations, protected areas, and nurseries Settlements Urban/rural settlements with transportation and communication facilities Wetland Areas characterized by water and close to water sources, such as rivers Grasslands Stunted height degraded forest, shrubs, or grasses. Water Rivers, lakes, ponds, reservoirs/dams, streams Cropland Pastures, orchards, home gardens, and areas covered with perennial and annual crops, may be fallow. Bare lands Barren areas devoid of vegetation cover and consisting of exposed soils and rock outcrops or sandy surfaces 2.2.3.2 Image classification This study implemented a two-step approach combining pixel-based classification (PBC) and object-based refinement. The accuracy and reliability of this integrated classification methodology for LULC mapping have been established in earlier studies by Mulenga [ 26 ]. Pixel-Based Classification Training datasets were developed for each of the seven LULC classes for the two-time points (2012 and 2025) to support supervised classification. The pixel-based classification was performed in ArcGIS Pro 3.5.1 using the Image Classification Wizard, which provides an integrated workflow for supervised classification of remote sensing imagery. A Random Forest Machine-learning classifier was applied, with parameters set to a maximum of 100 trees, a maximum tree depth of 30, and a maximum of 2, 000 samples per class. Kotsiantis [ 27 ] reported the superior performance of Random Forest compared to other classifiers. To ensure more accurately a more accurate classification results, Spectral signatures were generated from well-defined areas of interest (AOIs), representing each LULC class. These signatures were cross referenced with supplementary topographical maps, ESRI imagery hybrid base maps, and high-resolution Google Earth images which provided additional visual and contextual information. The integration of these datasets helped to correctly identify and distinguish land cover types that exhibited similar spectral characteristics, thereby reducing classification errors and improving overall accuracy. Object-Based Post-Classification Refinement Object-based image analysis was conducted using the multi-resolution segmentation algorithm in eCognition Developer software. Three key parameters including scale, shape, and compactness—were applied to create homogeneous image objects. An overlay technique was then utilized, combining object-based image segments with supplementary spatial data and expert visual interpretation. This approach enabled the extraction and reclassification of misclassified pixels, which were subsequently mosaicked back into the classification to replace incorrectly classified areas, thereby enhancing the overall accuracy of the LULC classification. 2.2.4 Accuracy Assessment and change detection The accuracy of the LULC maps was assessed using a stratified random sampling approach, with 300 random reference points generated for validation which were derived based on a 7:3 ratio from the training dataset. Ground truthing dataset which was collected in July, 2025 and complemented by high-resolution Google Earth images serve as reference data. A confusion matrix was employed to evaluate the accuracy of the maps, comparing mapped data with reference data to quantify errors by generating matrices, such as user's and producer's accuracy. Post-classification change detection analysis was performed to quantify LULC changes across two-time intervals: 2012–2025. By overlaying the initial and final land cover maps for each period, the change detection process identified the direction (positive or negative) and magnitude (size) of changes between land cover classes. The ArcGIS Pro change detection workflow was utilized to analyze the change trajectories and determine the rate of change using Eq. 1 [ 28 ]. r = ( \(\:\frac{1}{t2-t1}\) ) x ln ( \(\:\frac{A1}{A2}\) ) Eq. (1) 2.2.4 Connectivity analysis Habitat connectivity was assessed using class-level landscape metrics with and outside the conservancy, with patches defined as clusters of interconnected cells based on the 8-neighbour rule [ 29 ]. The key metrics used to evaluate connectivity and fragmentation were Patch Area (PA), Number of Patches (NP), and the Connectivity Index (CI). PA measured variation in patch size, NP indicated the degree of fragmentation based on the number of patches, and CI quantified probabilistic connectivity between patches (ranging from 0 to 1 or as a percentage) through network analysis of patch relationships using Eq. 2. CI = \(\:\left[\:\:\:\frac{{\sum\:}_{j\ne\:k}^{n}{\:c}_{ijk}}{\frac{{n}_{\text{i}}\:\:\:({n}_{\text{i}}-1)}{2}}\:\:\:\:\:\right]\) (100) Eq. (2) where CI is connectivity index, C ijk is the connection between patch j and k of type i, and n i is the number of patches for a specific class. 3. Results 3.1 Classification Figure 3 shows the land cover of the SCC and its surrounding areas for the years 2010 and 2025. In both time periods, the landscape is predominantly characterized by forest and grassland, with wetlands primarily concentrated in the southern portion of the study area. Cropland and bare land are more prevalent in the eastern region, particularly outside the conservancy boundaries. A notable change observed between the two points is the expansion of grasslands in 2025. The land cover classification maps were produced with an overall accuracy exceeding 80% threshold. Specifically, the 2010 maps achieved an overall accuracy of 88%, while the 2025 maps reached 91%. Producer’s and user’s accuracies for individual land cover classes ranged from 78% to 100%. Water bodies consistently exhibited the highest classification accuracy, whereas cropland had the lowest, particularly in the 2025 dataset. Table 2 presents the overall confusion matrices for the 2010 and 2025 classifications, detailing the accuracy assessment results for each land cover category. Table 2: Confusion matrices for 2010 (A) and 2025 (B) maps. The accuracy ranges from 78.3 to 100%. Classified Data Reference data Built-up Forest Grassland cropland Bare Land Water Total UA (%) Built-up 86 2 1 0 0 10 99 86.87 Forest 0 82 17 0 1 0 100 82.00 Grassland 0 9 82 0 9 0 100 82.00 Cropland 0 6 0 94 0 0 100 94.00 Bare land 1 1 4 0 94 0 100 94.00 Water 0 0 0 0 2 98 100 98.00 Total 87 100 104 94 106 108 599 PA (%) 80.38 84.49 81.36 100 93.95 98.15 Overall Classification Accuracy = 88.12% Overall Kappa Coefficient =81.88% Classified Data Reference data Built-up Forest Grassland Copland Bare Land Water Total UA (%) Built-up 98 0 0 2 0 0 100 87.00 Forest 3 90 7 0 0 0 100 91.40 Grassland 0 0 88 7 5 0 100 82.00 Cropland 18 5 5 72 0 0 100 94.00 Bare land 2 0 1 7 90 0 100 94.00 Water 0 0 0 4 0 96 100 98.00 Total 121 95 101 92 95 96 600 PA (%) 80.99 94.74 87.13 78.26 94.74 100 Overall Classification Accuracy = 91.44% Overall Kappa Coefficient = 89.73% The percentage of different land cover types varied between the 2010 and 2025 maps, with comparisons made both within (Figure 4A) and outside (Figure 4B) the boundaries of the SCC, as shown in Figure 4. In both years, forest cover was the dominant land cover type, accounting for over 60% of the total surface area. However, forest cover declined between 2010 and 2025 in both zones, indicating ongoing habitat loss. Concurrently, grassland cover increased, with a particularly significant rise observed within the conservancy. Additionally, cropland and settlement areas expanded during the study period, with a more substantial increase occurring outside the conservancy. 3.2 Habitat changes Across all three scenarios analyzed—within the conservancy, outside the conservancy, and the combined area—forest cover, wetlands, and water bodies exhibited a general decline, while grassland, settlement, and cropland areas showed an overall increase (Figure 5). The most significant decline was observed in forest cover, which decreased by 1.1%, whereas grassland experienced the most notable gain, increasing by 1.05%. Habitat losses, particularly in forest and wetland areas, were more pronounced outside the conservancy boundaries than within. Similarly, increases in cropland and grassland were also higher outside the conservancy. Land cover types changed at varying rates, particularly when comparing areas within and outside the conservancy. Forest cover experienced the greatest decline outside the conservancy, decreasing by 1.05%, compared to a smaller decline of 0.92% within the conservancy. Across the entire landscape, forest cover declined by 1.05% ( Table 3 ) . Grassland cover increased by 1.09% outside the conservancy and by 0.98% within. Cropland also expanded more rapidly outside the conservancy, with a 0.41% increase compared to 0.24% within. Settlement areas showed a similar pattern, with the rate of increase nearly doubling outside the conservancy relative to inside. Table 3: Summary of the major transition that occurred within and outside the conservancy between 2010 and 2025. Section Landcover 2010 2025 Changes Area (ha) % Area (ha) % Change (ha) Change % Rate (ha) Rate % IN Settlement 432.54 0.22 828.33 0.42 395.79 0.20 26.39 0.01 Forest 120,646.05 60.71 102,457.51 51.56 -18,188.54 -9.15 -1,212.57 -0.92 Cropland 2,094.79 1.05 6,945.47 3.50 4,850.68 2.44 323.38 0.24 Wetland 31,532.44 15.87 26,011.88 13.09 -5,520.56 -2.78 -368.04 -0.28 Grassland 39,548.31 19.90 59,021.54 29.70 19,473.23 9.80 1,298.22 0.98 Bareland 3,171.82 1.60 1,990.27 1.00 -1,181.54 -0.59 -78.77 -0.06 Water 1,291.05 0.65 1,462.00 0.74 170.95 0.09 11.40 0.01 OUT Landcover Area (ha) % Area (ha) % Change (ha) Change % Rate (ha) Rate % Settlement 2,380.65 0.20 5088.01 0.43 2707.36 0.23 180.49 0.02 Forest 849,181.65 71.07 723,481.37 60.55 -125,700.27 -10.52 -8,380.02 -1.05 Cropland 18,235.20 1.53 67,671.90 5.66 49,436.70 4.14 3,295.78 0.41 Wetland 180,092.02 15.07 147,374.93 12.33 -32,717.09 -2.74 -2,181.14 -0.27 Grassland 77,773.64 6.51 208,138.04 17.42 130,364.40 10.91 8,690.96 1.09 Bareland 35,092.16 2.94 31,761.84 2.66 -3,330.31 -0.28 -222.02 -0.03 Water 32,168.04 2.69 11,098.07 0.93 -21,069.97 -1.76 -1,404.66 -0.18 ALL Landcover Area (ha) % Area (ha) % Change (ha) Change % Rate (ha) Rate % Settlement 2813.19 0.20 5,916.34 0.42 3103.15 0.22 206.88 0.02 Forest 969,827.70 69.60 825,938.88 59.28 -143,888.82 -10.33 -9,592.59 -1.03 Cropland 20,329.99 1.46 74,617.37 5.36 54,287.38 3.90 3,619.16 0.39 Wetland 211,624.46 15.19 173,386.81 12.44 -38,237.65 -2.74 -2,549.18 -0.27 Grassland 117,321.95 8.42 267,159.58 19.17 149,837.63 10.75 9,989.18 1.08 Bareland 38,263.97 2.75 33,752.12 2.42 -4,511.86 -0.32 -300.79 -0.03 Water 33,459.09 2.40 12,560.07 0.90 -20,899.02 -1.50 -1,393.27 -0.15 Figure 6 illustrates land cover transitions within and outside the conservancy between 2010 and 2025. The most significant transition observed was the conversion of forest to grassland, a more prominent change within the conservancy. Interestingly, there were also signs of emerging forest habitat within the conservancy. 2.3 Habitat Connectivity Figure 7 illustrates the patch sizes of the three habitat types including grassland, wetlands, and forests between 2010 (2 years before the establishment of the conservancy) and 2025 (15 years after the establishment measures began). Among the three, grasslands had the largest patch sizes, followed by wetlands and then forests. Overall, patch sizes were larger in 2010 than in 2025, indicating a general decline over time. In 2020, patch sizes outside the conservancy were generally larger than those inside; however, the differences were statistically insignificant (Wilcoxon Rank Test: W = 28; p-value = 0.06), except for grasslands, which exhibited greater variation. The number of patches between 2010 and 2025 showed some variation (Figure 8). Forests had the highest number of patches, followed by wetlands. Overall, there was an increase in patch numbers over the period, particularly outside the conservancy. Among the three habitat types, grasslands remained relatively stable throughout the analysis period. Figure 9 presents the connectivity index from 2010 to 2025. Connectivity remained high inside the conservancy throughout the period. Among the three habitats, wetlands and grasslands were generally more connected than forests. Notably, connectivity for forests and grasslands showed significant improvement over time, while wetlands experienced a slight decline in connectivity. 4. Discussions Our findings demonstrate that the SCC has contributed meaningfully to improving habitat integrity and connectivity across the landscape. By comparing land cover changes from 2010 to 2025, we observed distinct differences between areas within and outside the conservancy. Although forest cover declined by 1.1% across the entire study area over the 15-year period, the rate of loss was significantly lower within the conservancy, indicating the positive influence of community-based conservation interventions contingent upon inclusive governance, fair benefit sharing/distribution, and adaptive management [7, 30]. Concurrently, grassland cover expanded particularly within the conservancy, suggesting an ongoing shift toward a grassland-dominated system which suggests a notable shift from forest to grassland over time, suggesting a shift in vegetation cover and possibly land use dynamics over the 15-year period. The landscape metrics further support this trend, showing consistently higher habitat quality and connectivity within the conservancy compared to adjacent, unprotected areas, with disparities becoming more evident by 2025. These results underscore how community conservancies can attain the effectiveness in maintaining ecological function. Furthermore, the results align with previous studies [18] that highlight improved habitat connectivity and integrity in regions where targeted conservation actions are actively implemented and well-managed. The results of this study underscore the positive impact of the SCC on landscape connectivity within its boundaries. While the establishment of the conservancy has not yet produced measurable effects beyond its borders, connectivity inside the conservancy remains clearly distinct and strong. This suggests that fencing and restricting human activities within conservancy have effectively contributed to maintaining habitat connectivity. However, the comparatively low connectivity levels surrounding the conservancy highlight the urgent need for increased efforts to enhance habitat connectivity in adjacent areas. These trends highlight the stronger anthropogenic pressure and habitat transformation occurring in areas lacking formal protection, highlighting greater land use pressure in unprotected areas. Such efforts may include improved land-use planning, promotion of alternative livelihoods, and fostering changes in local community attitudes toward conservation. The observed expansion of grasslands may indicate habitat recovery in areas formerly used for cropland or settlements. Alternatively, it is possible that rising wildlife populations are suppressing the growth of young vegetation into mature forests, a dynamic that warrants further investigation. These findings identify important ecological and socio-economic factors for future research and planning for conservation priorities. While the establishment of the conservancy has brought numerous positive effects on habitat connectivity[31], it has also introduced social complexities that may threaten habitat integrity. Notably, without significant livelihood alternatives outside the conservancy, restricted access to resources within the conservancy has increased pressure on adjacent areas outside its boundaries. This has resulted in intensified grazing, cropland expansion, and greater exploitation of water sources in the surrounding landscape. Such increased resource use adversely affects vegetation and soil quality, leading to further habitat degradation. In contrast, most of the habitat losses, particularly forest and wetland areas, were driven by expansion of the cropland settlements which were predominantly occurring outside the SCC boundaries. These patterns underscore the effectiveness of the conservancy in buffering against land use pressures, while highlighting the need for greater management attention in surrounding areas, indicating greater land use change and anthropogenic pressure in unprotected areas [32]. Consequently, these pressures are driving a decline in habitat connectivity outside the conservancy, creating ‘biological islands’, which could ultimately undermine the broader conservation benefits achieved within the community-managed area [33, 34]. This study demonstrated the significant potential of geospatial tools in evaluating the impacts of conservation interventions, particularly the establishment of community conservancies like SCC. The use of open-source data and freely available analytical tools greatly enhances the feasibility of monitoring conservation outcomes, especially in resource-limited settings. By leveraging publicly accessible satellite imagery, such as Landsat and Sentinel data, combined with landscape analysis software like FRAGSTATS and the Landscape Fragmentation Tool [29], we were able to generate detailed land cover maps and quantify changes in habitat connectivity over time. Therefore, technological advancements allow integration of spatio-temporal analysis in the nexus of biodiversity conservation and livelihoods strategies. These approaches offer cost-effective and replicable methods for tracking landscape dynamics and assessing the effectiveness of conservation measures in multi-use landscapes. Importantly, this demonstrates that advanced conservation monitoring does not necessarily require expensive proprietary software or extensive field data collection, making it more accessible to conservation practitioners and community managers. Moving forward, the integration of these geospatial techniques with community knowledge and ground-based observations could further strengthen adaptive management strategies and support evidence-based decision-making in community-led conservation initiatives. Traditional ecological knowledge can play critical role in biodiversity conservation [35]. Landscape connectivity around the SCC is shaped by a complex interplay of socio-economic activities, biophysical characteristics, and climatic conditions [36, 37]. Most of the local population practice small-scale farming, which often requires periodic expansion to meet the needs of a growing population. Biophysically, the area is dominated by sandy soil that has limited agricultural productivity, thereby intensifying the demand for more land and exerting pressure on natural habitats. Additionally, climate change impacts are increasingly evident; for instance, the 2023/24 drought severely curtailed vegetation growth, further stressing the ecosystem and indicating more substantial expansion in unprotected areas and suggesting some localized forest regeneration or restoration. These challenges underscore the urgent need for enhanced mitigation and adaptation strategies that simultaneously promote habitat restoration and improve local livelihoods. This study makes several important contributions to both scientific literature and practical resource management. It demonstrates a straightforward and cost-effective approach to assessing the role of community conservation initiatives, such as SCC, in enhancing habitat connectivity. The findings highlight both the potential benefits and limitations of conservation efforts within community settings, which are often strongly influenced by traditional norms and practices. The study reveals that increased conservation efforts outside the conservancy boundaries are necessary to strengthen habitat connectivity at the broader KAZA landscape scale and beyond. Expansive connectivity has potential to positively contribute to resolution of socio-economic challenges, including human-wildlife conflicts in the region [8, 33]. Additionally, given that the conservancy model is relatively new in Zambia, this research underscores the need for further studies addressing both management challenges and policy development to optimize community-based conservation outcomes. Despite the promising findings, this study has inherent limitations. First, only two time points (2010 and 2025) were used to assess changes in connectivity and habitat integration, which may overlook gradual or interim landscape dynamics. Conducting analyses at shorter intervals would provide a more nuanced understanding of temporal changes. Second, although the classification maps achieved high overall accuracy, they may not entirely be free from errors. Finally, this study relied on landscape metrics derived from FRAGSTATS and the Landscape Fragmentation Tool; incorporating more advanced spatial modeling techniques could offer deeper insights and enhance the practical relevance of the findings for community conservancy management. 5. Conclusions This study evaluated the effectiveness of the SCC in enhancing habitat connectivity and integrity in Southern and Western Zambia, using freely available Landsat imagery to compare land cover before its establishment in 2010 and 15 years later in 2025. The analysis, based on land cover classification maps with an accuracy exceeding 80%, revealed contrasting trends inside and outside the conservancy. While habitat loss and fragmentation increased significantly outside the conservancy particularly by 2025 habitats within the conservancy showed greater stability. Notably, there was a shift from forest-dominated landscapes to grasslands within the conservancy, although forest loss remained below 1%, compared to 1.2% outside. Cropland expansion and settlement growth, especially in the eastern region of the conservancy, emerged as key drivers of forest degradation. Despite these anthropogenic pressures, the conservancy demonstrated a positive impact by maintaining higher landscape connectivity and reducing fragmentation within its boundaries. These findings highlight the potential of community-based conservation initiatives to restore and maintain ecological integrity, particularly in supporting species with large home ranges, such as elephants and blue wildebeests. The study provides valuable insights for future conservation planning and wildlife corridor development, emphasizing the importance of community engagement in achieving sustainable land use and biodiversity conservation goals. Declarations Acknowledgements: We wish to thank Copperbelt University (CBU) and the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) for providing support during the preparation of the manuscript. Ethics Approval: This study was conducted under the ethical clearance No.2023/2/13/1 issued by The Copperbelt University, and research permit No. NPW/8/27/1 issued by Department of National Parks and Wildlife (DNPW). The Copperbelt University has a functional ethics committee under the Directorate of Research, which provides oversight on research. Ethics and Accordance: This study involved limited human participation. Nonetheless, all procedures related to human data collection were guided by the ethical clearance No. 2023/2/13/1 issued by The Copperbelt University and research permit No. NPW/8/27/1 issued by the Department of National Parks and Wildlife (DNPW). Availability of Data and Material: Data will be provided upon reasonable request to the corresponding author. The data is held in Google Drive and a link will be provided. Competing Interests : The authors declare that there are no competing interests. Clinical trial number: Not applicable. 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1","display":"","copyAsset":false,"role":"figure","size":200705,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Simalaha Community Conservancy in Southern/Western provinces of Zambia.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/9d7bd5fe8f7268b3956cfd7d.jpg"},{"id":92583178,"identity":"b8822d8f-8001-4630-bc3c-a2639267094e","added_by":"auto","created_at":"2025-10-01 10:05:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108949,"visible":true,"origin":"","legend":"\u003cp\u003eData major steps involved in data processing which include data acquisition, image classification and change detection.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/28c8b3d5c8e4bef1efcc80eb.jpg"},{"id":92584607,"identity":"f5f99ab2-3ffb-43dc-9d76-bf7ecbe8fc6a","added_by":"auto","created_at":"2025-10-01 10:13:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256932,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover maps for Simalaha Community Conservancy for 2010 (2 years before its establishment) and 2025 (15 years after its establishment measures began).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/3afbae0e304e088c3a654dec.jpg"},{"id":92583174,"identity":"a8e6e97c-b783-4fce-a2b3-b6ccb8934700","added_by":"auto","created_at":"2025-10-01 10:05:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111955,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of different land covers within (A) and outside (B) the conservancy between 2010 and 2025.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/c16f979809215afb8ebebec6.jpg"},{"id":92583179,"identity":"d525cb3d-dc3a-4fc9-b88a-098f6c7086b4","added_by":"auto","created_at":"2025-10-01 10:05:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74302,"visible":true,"origin":"","legend":"\u003cp\u003eHabitat change with, outside and combined between 2010 and 2025 for Simalaha Community Conservancy.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/e799778cfda401ad4a83964b.jpg"},{"id":92583180,"identity":"69775e84-2ac1-4980-8a1b-89039e95e12a","added_by":"auto","created_at":"2025-10-01 10:05:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":481354,"visible":true,"origin":"","legend":"\u003cp\u003eHabitat transitions between 2010 and 2025 in Simalaha Community Conservancy in Southern and Western Zambia.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/ae079d2f9485a673b882a114.jpg"},{"id":92583181,"identity":"57a3c678-f5e0-4934-830b-9dad6efb88ec","added_by":"auto","created_at":"2025-10-01 10:05:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43820,"visible":true,"origin":"","legend":"\u003cp\u003eArea Index between 2010 and 2025 in three habitats within and outside the Simalaha Community Conservancy in Southern and Western Zambia.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/069d521782cb46bb461281ea.jpg"},{"id":92583182,"identity":"d810f17a-8604-4596-96b2-6806336728f6","added_by":"auto","created_at":"2025-10-01 10:05:30","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":44846,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of patches between 2010 and 2025 in three habitats within and outside the Simalaha Community Conservancy in Southern and Western Zambia.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/8c057d966ef376687d4f92eb.jpg"},{"id":92584611,"identity":"32b5a889-af6e-49f2-8b71-ca111b14c3e7","added_by":"auto","created_at":"2025-10-01 10:13:30","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":52630,"visible":true,"origin":"","legend":"\u003cp\u003eConnectivity index between 2010 and 2025 for the three habitat types within and outside the Simalaha Community Conservancy in Southern and Western Zambia.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/fff62a2b3dbf046052d1e00e.jpg"},{"id":92585629,"identity":"e4b04bcb-30b3-4a39-bc98-f2415ae5f998","added_by":"auto","created_at":"2025-10-01 10:29:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2751391,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7506925/v1/cacd895c-cd2f-4ec6-8ea6-a3e995fa5473.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Spatial Analysis of Habitat Connectivity in Simalaha Community Conservancy of Zambia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLandscape connectivity has gained increasing attention towards biodiversity conservation in both policy and ecological research [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Post-2020 Global Biodiversity Framework emphasizes connectivity across several goals and targets, notably the commitment to conserve 30% of land and water within a well-connected network of protected and conserved areas by the year 2030 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In response, connectivity studies have become central to landscape ecology and conservation science, incorporating diverse data types, such as species movement data, genetic information, and point-based ecological observations. To assess landscape connectivity at multiple scales, for instance, at local and landscape levels, studies have increasingly employed high-resolution GPS telemetry data from collared wildlife to map and analyze the movement patterns of wide-ranging species, such as African savanna elephants (\u003cem\u003eLoxodonta africana\u003c/em\u003e), lions (\u003cem\u003ePanthera leo\u003c/em\u003e), spotted hyenas (\u003cem\u003eCrocuta crocuta\u003c/em\u003e), and African wild dogs (\u003cem\u003eLycaon pictus\u003c/em\u003e). In addition, biologgers can be used to monitor wildlife physiological activities, such as heart rate, body temperature and movements, while using the landscapes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In Sub-Saharan Africa, Transfrontier Conservation Areas (TFCAs), such as the Kavango-Zambezi Transfrontier Conservation Area (KAZA TFCA) play a crucial role in enhancing connectivity by coordinated multi-stakeholder conservation efforts across national borders [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These initiatives aim to link protected areas (PAs) and key habitats outside them, fostering collaboration among countries with diverse legislative frameworks, land-use practices, conservation priorities, and stages of socio-economic development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe KAZA-TFCA, established in 2011 by Angola, Botswana, Namibia, Zambia, and Zimbabwe, is a key initiative aimed at fostering regional economic development and conserving the region\u0026rsquo;s unique biodiversity, particularly by facilitating large-scale wildlife migrations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Home to the world\u0026rsquo;s largest elephant population, as well as globally threatened species like the black rhinoceros (\u003cem\u003eDiceros bicornis\u003c/em\u003e) and African wild dog (\u003cem\u003eLycaon pictus\u003c/em\u003e), KAZA also supports several endemic species across multiple taxa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A core objective of the TFCA is to enable the free movement of wildlife across borders, expanding dispersal areas, relieving population pressure, and enhancing ecological stability through metapopulation support. However, this increased connectivity has led to some regions becoming wildlife transit corridors, escalating human\u0026ndash;wildlife conflicts due to more frequent interactions between wildlife and local communities. While landscape connectivity is vital for conservation, it can inadvertently intensify conflict as recovering wildlife populations encounter expanding human activities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Addressing these challenges requires the implementation of effective, collaborative conflict mitigation strategies that are not only critical for the KAZA region but also offer valuable lessons for transboundary conservation globally.\u003c/p\u003e\u003cp\u003eProtected areas (PAs) and complementary conservation initiatives beyond their borders play a crucial role in safeguarding natural resources by promoting sustainable land use and biodiversity management [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, declining connectivity among core habitat patches both within and outside PAs poses a significant threat to ecological integrity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Fragmentation disrupts migratory and dispersal routes, increases genetic isolation, diminishes resilience to disease, and reduces the adaptive capacity of wildlife to climate change [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, the loss of intact habitats surrounding PAs intensifies edge effects, which can further degrade wildlife populations along PA boundaries (Fletcher Jr 2005 ;Ullah et al. 2024). These challenges highlight the urgent need for landscape-scale conservation approaches that improve habitat quality and connectivity across the broader matrix of protected and non-protected lands. Previous research has emphasized the importance of understanding how wildlife navigates and utilize these mixed-use landscapes to inform effective conservation planning [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Identifying the ecological and anthropogenic factors influencing wildlife space use is essential for prioritizing the protection of PAs, wildlife corridors, and buffer zones especially in light of growing climate impacts, escalating human pressures, and the chronic underfunding of many protected areas [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. One increasingly recognized strategy to address these challenges is the establishment of community conservancies, such as the Simalaha Community Conservancy (SCC) in western Zambia, which aims to restore habitat connectivity, while supporting local livelihoods [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe SCC plays a vital role in advancing the KAZA-TFCA goal of restoring ecological connectivity across fragmented landscapes by establishing a functional mosaic of protected areas and transboundary wildlife corridors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Strategically located between Chobe National Park in Botswana and Kafue National Park in Zambia, SCC serves as a key linkage within the larger Zambezi-Chobe dispersal area [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], enabling cross-border wildlife movement and promoting genetic exchange among subpopulations. Despite its ecological importance, there has been a limited empirical assessment of SCC\u0026rsquo;s effectiveness in enhancing landscape connectivity since its establishment in 2012. This study addresses this gap by employing remote sensing data (e.g., Landsat and Sentinel imagery) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and spatial analysis tools, such as FRAGSTATS [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to quantify changes in habitat connectivity before and after the conservancy\u0026rsquo;s formation. The results are intended to inform conservation planning and corridor management efforts within the broader KAZA landscape.\u003c/p\u003e\u003cp\u003eThis study advances the field of landscape connectivity research in two keyways: (1) by explicitly evaluating connectivity at multiple spatial scales surrounding the SCC, and (2) by providing empirical evidence on the effectiveness of emerging community conservancy models in enhancing habitat connectivity. We hypothesize that the establishment of SCC has resulted in measurable improvements in landscape connectivity by reducing habitat fragmentation and facilitating ecological linkages across the broader transboundary landscape. By adopting this approach, the study aims to underscore the ecological significance of community-managed conservation areas in regional connectivity planning and biodiversity conservation, particularly within complex and dynamic socio-ecological systems, such as the KAZA Transfrontier Conservation Area.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Description of study area\u003c/h2\u003e\u003cp\u003eSimalaha ecosystem (180,000 hectares) is a community driven conservation area established in 2012 to enhance conservation of wildlife and improve rural livelihoods. Management innervations in the area include fencing and restricting human activities, which serve as a temporal measure to contain huma pressure; once there is remarkable change in community behaviour, the fencing will be removed in the long run. The ecosystem is located on 17\u003csup\u003eo\u003c/sup\u003e31\u0026rsquo;27.3S and 24\u003csup\u003eo\u003c/sup\u003e57\u0026rsquo;35.9E in Sesheke and Sekute chiefdoms in Sesheke and Kazungula districts of Southern and Western provinces of Zambia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), respectively. The ecosystem also forms nexus to KAZA-TFCA which encompasses significant wetlands and includes large areas of the Miombo-Mopane and the Kalahari-Namib Wilderness Areas [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Like other similar environments in the region, it is characterized by infertile sand and alluvial soils with grasslands and mixed woodland vegetation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimalaha Community Conservancy is characterized by annual precipitation of 400 to 700 mm and temperature ranges from 16.4 \u003csup\u003eo\u003c/sup\u003eC to 32 \u003csup\u003eo\u003c/sup\u003eC, of which the higher temperature influences frequency and intensity of unprescribed wildfire [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The landscape experiences three seasons: hot-wet season from November to April, cold-dry season from April to August and hot-dry season from September to November. The elevation is about 170 m to 800 m above sea level on the lower altitude and about 600 m to 1400 m above sea level [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Due to poor soil, the landscape is unfavorable for arable cropping, but both livestock and wildlife can thrive.\u003c/p\u003e\u003cp\u003eThe SCC is restocked with more than 1,600 animals comprised of about thirteen large mammal species which include: red lechwe \u003cem\u003e(Kobus leche leche)\u003c/em\u003e, buffalo \u003cem\u003e(Syncerus caffer)\u003c/em\u003e, giraffe \u003cem\u003e(Giraffa camelopardalis)\u003c/em\u003e, puku \u003cem\u003e(Kobus vardonii)\u003c/em\u003e, hartebeest \u003cem\u003e(Alcelaphus buselaphus)\u003c/em\u003e, sable antelope \u003cem\u003e(Hippotragus niger)\u003c/em\u003e, eland \u003cem\u003e(Taurotragus oryx)\u003c/em\u003e, impala \u003cem\u003e(Aepyceros melampus)\u003c/em\u003e, zebra \u003cem\u003e(Equus burchelli)\u003c/em\u003e and blue wildebeest \u003cem\u003e(Connochaetes taurinus)\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Materials and Methods\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Method overview\u003c/h2\u003e\u003cp\u003eThe methodological approach followed a systematic workflow, ensuring clear and logical progression from data acquisition to analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The methodology is divided into five key phases: (1) data acquisition and pre-processing, (2) LULC classification (3) Change detection between 2012 and 2025; and (4) connectivity and fragmentation analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Data acquisition and pre-processing\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section4\"\u003e\u003ch2\u003e2.2.1.1. Imagery dataset\u003c/h2\u003e\u003cp\u003eThis study utilized a multi-temporal Landsat imagery dataset, comprising Landsat 5 TM images for 2012, and Landsat 8 OLI/TIRS images for 2025 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), downloaded from the United States Geological Survey (USGS) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to extract LULC maps. For the optimal LULC classification, atmospherically corrected and pre-processed (Level 2) Landsat images with a spatial resolution of 30 m were acquired within the month of September when the cloud cover is minimum. All images were projected to UTM Zone 35S (EPSG:32735) coordinate system, and a 40 km buffer zone was established around the park. Images underwent atmospheric correction and were selected based on cloud cover below 10% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section4\"\u003e\u003ch2\u003e2.2.1.2. Auxiliary dataset\u003c/h2\u003e\u003cp\u003eAuxiliary datasets were integrated to support the analysis and interpretation of habitat dynamics within the study area. These spatial data include boundary shapefiles, digital elevation models (DEM), topographical maps, and Google Earth imagery, including boundary shapefiles, digital elevation models (DEM), topographical maps, and Google Earth imagery. Additionally, settlement extent data were obtained from GRID3 ZMB - Settlement Extents v3.0, accessed through (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.grid3.org/datasets/GRID3::grid3-zmb-settlement-extents-v3-0/about\u003c/span\u003e\u003cspan address=\"https://data.grid3.org/datasets/GRID3::grid3-zmb-settlement-extents-v3-0/about\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetails of the images used in this study include the type of Landsat images, date of acquisition, spatial resolution, and satellite path/row.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDate of Acquisition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpatial resolution (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePath/Row\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLandsat 5 TM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2010/09/08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e172/068, 172/069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLandsat 8 OLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2025/05/12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e172/068, 172/069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eTM: Thematic Mapper; OLI: Operation Land Imager; USGS: United States Geological Survey\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 LULC Classification\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section4\"\u003e\u003ch2\u003e2.2.3.1 Image classification\u003c/h2\u003e\u003cp\u003eThe LULC classes include settlements, forest, cropland, wetlands, grassland, bare land, and water were used in the classification (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The development of these classes was informed by reconnaissance surveys and expert knowledge of the study area, ensuring relevance and accuracy. This classification system allowed for an understanding of land use patterns and changes within the study area.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand use/cover classes used to characterize the habitat in Simahala ecosystem in Southern and Western Zambia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLand use/cover\u003c/p\u003e\u003cp\u003eclasses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eForest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDeciduous forests, evergreen forests, mixed forest lands, coniferous forests, orchards, commercial plantations, protected areas, and nurseries\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSettlements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUrban/rural settlements with transportation and communication facilities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWetland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAreas characterized by water and close to water sources, such as rivers\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrasslands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eStunted height degraded forest, shrubs, or grasses.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWater\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRivers, lakes, ponds, reservoirs/dams, streams\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCropland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePastures, orchards, home gardens, and areas covered with perennial and annual crops, may be fallow.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBare lands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBarren areas devoid of vegetation cover and consisting of exposed soils and rock outcrops or sandy surfaces\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section4\"\u003e\u003ch2\u003e2.2.3.2 Image classification\u003c/h2\u003e\u003cp\u003eThis study implemented a two-step approach combining pixel-based classification (PBC) and object-based refinement. The accuracy and reliability of this integrated classification methodology for LULC mapping have been established in earlier studies by Mulenga [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePixel-Based Classification\u003c/strong\u003e\u003cp\u003eTraining datasets were developed for each of the seven LULC classes for the two-time points (2012 and 2025) to support supervised classification. The pixel-based classification was performed in ArcGIS Pro 3.5.1 using the Image Classification Wizard, which provides an integrated workflow for supervised classification of remote sensing imagery. A Random Forest Machine-learning classifier was applied, with parameters set to a maximum of 100 trees, a maximum tree depth of 30, and a maximum of 2, 000 samples per class. Kotsiantis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] reported the superior performance of Random Forest compared to other classifiers. To ensure more accurately a more accurate classification results, Spectral signatures were generated from well-defined areas of interest (AOIs), representing each LULC class. These signatures were cross referenced with supplementary topographical maps, ESRI imagery hybrid base maps, and high-resolution Google Earth images which provided additional visual and contextual information. The integration of these datasets helped to correctly identify and distinguish land cover types that exhibited similar spectral characteristics, thereby reducing classification errors and improving overall accuracy.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eObject-Based Post-Classification Refinement\u003c/strong\u003e\u003cp\u003eObject-based image analysis was conducted using the multi-resolution segmentation algorithm in eCognition Developer software. Three key parameters including scale, shape, and compactness\u0026mdash;were applied to create homogeneous image objects. An overlay technique was then utilized, combining object-based image segments with supplementary spatial data and expert visual interpretation. This approach enabled the extraction and reclassification of misclassified pixels, which were subsequently mosaicked back into the classification to replace incorrectly classified areas, thereby enhancing the overall accuracy of the LULC classification.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Accuracy Assessment and change detection\u003c/h2\u003e\u003cp\u003eThe accuracy of the LULC maps was assessed using a stratified random sampling approach, with 300 random reference points generated for validation which were derived based on a 7:3 ratio from the training dataset. Ground truthing dataset which was collected in July, 2025 and complemented by high-resolution Google Earth images serve as reference data. A confusion matrix was employed to evaluate the accuracy of the maps, comparing mapped data with reference data to quantify errors by generating matrices, such as user's and producer's accuracy.\u003c/p\u003e\u003cp\u003ePost-classification change detection analysis was performed to quantify LULC changes across two-time intervals: 2012\u0026ndash;2025. By overlaying the initial and final land cover maps for each period, the change detection process identified the direction (positive or negative) and magnitude (size) of changes between land cover classes. The ArcGIS Pro change detection workflow was utilized to analyze the change trajectories and determine the rate of change using Eq.\u0026nbsp;1 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003er = ( \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{t2-t1}\\)\u003c/span\u003e\u003c/span\u003e) x ln (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{A1}{A2}\\)\u003c/span\u003e\u003c/span\u003e) Eq.\u0026nbsp;(1)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Connectivity analysis\u003c/h2\u003e\u003cp\u003eHabitat connectivity was assessed using class-level landscape metrics with and outside the conservancy, with patches defined as clusters of interconnected cells based on the 8-neighbour rule [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The key metrics used to evaluate connectivity and fragmentation were Patch Area (PA), Number of Patches (NP), and the Connectivity Index (CI). PA measured variation in patch size, NP indicated the degree of fragmentation based on the number of patches, and CI quantified probabilistic connectivity between patches (ranging from 0 to 1 or as a percentage) through network analysis of patch relationships using Eq.\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eCI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[\\:\\:\\:\\frac{{\\sum\\:}_{j\\ne\\:k}^{n}{\\:c}_{ijk}}{\\frac{{n}_{\\text{i}}\\:\\:\\:({n}_{\\text{i}}-1)}{2}}\\:\\:\\:\\:\\:\\right]\\)\u003c/span\u003e\u003c/span\u003e (100) Eq.\u0026nbsp;(2)\u003c/p\u003e\u003cp\u003ewhere CI is connectivity index, C\u003csub\u003eijk\u003c/sub\u003e is the connection between patch j and k of type i, and n\u003csub\u003ei\u003c/sub\u003e is the number of patches for a specific class.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e shows the land cover of the SCC and its surrounding areas for the years 2010 and 2025. In both time periods, the landscape is predominantly characterized by forest and grassland, with wetlands primarily concentrated in the southern portion of the study area. Cropland and bare land are more prevalent in the eastern region, particularly outside the conservancy boundaries. A notable change observed between the two points is the expansion of grasslands in 2025.\u003c/p\u003e\n\u003cp\u003eThe land cover classification maps were produced with an overall accuracy exceeding 80% threshold. Specifically, the 2010 maps achieved an overall accuracy of 88%, while the 2025 maps reached 91%. Producer\u0026rsquo;s and user\u0026rsquo;s accuracies for individual land cover classes ranged from 78% to 100%. Water bodies consistently exhibited the highest classification accuracy, whereas cropland had the lowest, particularly in the 2025 dataset. Table 2 presents the overall confusion matrices for the 2010 and 2025 classifications, detailing the accuracy assessment results for each land cover category.\u003c/p\u003e\n\u003cp\u003eTable 2: Confusion matrices for 2010 (A) and 2025 (B) maps. The accuracy ranges from 78.3 to 100%.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003cstrong\u003eClassified\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 435px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eBuilt-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ecropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eUA (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBuilt-up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e86.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e82.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrassland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e82.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCropland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e94.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBare land\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e94.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e98.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePA (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e80.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e84.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e81.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e93.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e98.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 693px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Classification Accuracy = 88.12% \u0026nbsp; \u0026nbsp; \u0026nbsp; Overall Kappa Coefficient =81.88%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClassified\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eBuilt-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003eCopland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eUA (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBuilt-up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e87.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e91.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrassland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e82.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCropland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e94.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBare land\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e94.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e98.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePA (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e80.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e94.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e87.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e78.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e94.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall Classification Accuracy = 91.44% \u0026nbsp; \u0026nbsp; \u0026nbsp;Overall Kappa Coefficient = 89.73%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe percentage of different land cover types varied between the 2010 and 2025 maps, with comparisons made both within (Figure 4A) and outside (Figure 4B) the boundaries of the SCC, as shown in Figure 4. In both years, forest cover was the dominant land cover type, accounting for over 60% of the total surface area. However, forest cover declined between 2010 and 2025 in both zones, indicating ongoing habitat loss. Concurrently, grassland cover increased, with a particularly significant rise observed within the conservancy. Additionally, cropland and settlement areas expanded during the study period, with a more substantial increase occurring outside the conservancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Habitat changes\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all three scenarios analyzed\u0026mdash;within the conservancy, outside the conservancy, and the combined area\u0026mdash;forest cover, wetlands, and water bodies exhibited a general decline, while grassland, settlement, and cropland areas showed an overall increase (Figure 5). The most significant decline was observed in forest cover, which decreased by 1.1%, whereas grassland experienced the most notable gain, increasing by 1.05%. Habitat losses, particularly in forest and wetland areas, were more pronounced outside the conservancy boundaries than within. Similarly, increases in cropland and grassland were also higher outside the conservancy.\u003c/p\u003e\n\u003cp\u003eLand cover types changed at varying rates, particularly when comparing areas within and outside the conservancy. Forest cover experienced the greatest decline outside the conservancy, decreasing by 1.05%, compared to a smaller decline of 0.92% within the conservancy. Across the entire landscape, forest cover declined by 1.05% (\u003cstrong\u003eTable 3\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003e Grassland cover increased by 1.09% outside the conservancy and by 0.98% within. Cropland also expanded more rapidly outside the conservancy, with a 0.41% increase compared to 0.24% within. Settlement areas showed a similar pattern, with the rate of increase nearly doubling outside the conservancy relative to inside.\u003c/p\u003e\n\u003cp\u003eTable 3: Summary of the major transition that occurred within and outside the conservancy between 2010 and 2025.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSection\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLandcover\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChanges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e432.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e828.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e395.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e26.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e120,646.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e60.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e102,457.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e51.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-18,188.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-9.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-1,212.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2,094.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e6,945.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4,850.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e323.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e31,532.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e15.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e26,011.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e13.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-5,520.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-368.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e39,548.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e19.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e59,021.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e29.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e19,473.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1,298.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBareland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3,171.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1,990.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-1,181.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-78.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1,291.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1,462.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e170.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e11.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOUT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLandcover\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2,380.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5088.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2707.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e180.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e849,181.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e71.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e723,481.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e60.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-125,700.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-10.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-8,380.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e18,235.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e67,671.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e49,436.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3,295.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e180,092.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e15.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e147,374.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-32,717.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-2,181.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e77,773.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e208,138.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e17.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e130,364.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e8,690.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBareland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e35,092.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e31,761.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-3,330.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-222.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e32,168.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e11,098.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-21,069.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-1,404.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLandcover\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange \u0026nbsp; (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; (ha)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSettlement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2813.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5,916.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3103.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e206.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e969,827.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e69.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e825,938.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e59.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-143,888.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-10.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-9,592.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e20,329.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e74,617.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e54,287.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3,619.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e211,624.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e173,386.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-38,237.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-2,549.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e117,321.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e267,159.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e19.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e149,837.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e9,989.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eBareland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e38,263.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e33,752.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-4,511.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-300.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e33,459.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e12,560.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-20,899.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-1,393.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 6\u003c/strong\u003e illustrates land cover transitions within and outside the conservancy between 2010 and 2025. The most significant transition observed was the conversion of forest to grassland, a more prominent change within the conservancy. Interestingly, there were also signs of emerging forest habitat within the conservancy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;2.3 Habitat Connectivity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 7 illustrates the patch sizes of the three habitat types including grassland, wetlands, and forests between 2010 (2 years before the establishment of the conservancy) and 2025 (15 years after the establishment measures began). Among the three, grasslands had the largest patch sizes, followed by wetlands and then forests. Overall, patch sizes were larger in 2010 than in 2025, indicating a general decline over time. In 2020, patch sizes outside the conservancy were generally larger than those inside; however, the differences were statistically insignificant (Wilcoxon Rank Test: W = 28; p-value = 0.06), except for grasslands, which exhibited greater variation.\u003c/p\u003e\n\u003cp\u003eThe number of patches between 2010 and 2025 showed some variation (Figure 8). Forests had the highest number of patches, followed by wetlands. Overall, there was an increase in patch numbers over the period, particularly outside the conservancy. Among the three habitat types, grasslands remained relatively stable throughout the analysis period.\u003c/p\u003e\n\u003cp\u003eFigure 9 presents the connectivity index from 2010 to 2025. Connectivity remained high inside the conservancy throughout the period. Among the three habitats, wetlands and grasslands were generally more connected than forests. Notably, connectivity for forests and grasslands showed significant improvement over time, while wetlands experienced a slight decline in connectivity.\u003c/p\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eOur findings demonstrate that the SCC has contributed meaningfully to improving habitat integrity and connectivity across the landscape. By comparing land cover changes from 2010 to 2025, we observed distinct differences between areas within and outside the conservancy. Although forest cover declined by 1.1% across the entire study area over the 15-year period, the rate of loss was significantly lower within the conservancy, indicating the positive influence of community-based conservation interventions contingent upon inclusive governance, fair benefit sharing/distribution, and adaptive management [7, 30]. Concurrently, grassland cover expanded particularly within the conservancy, suggesting an ongoing shift toward a grassland-dominated system which suggests a notable shift from forest to grassland over time, suggesting a shift in vegetation cover and possibly land use dynamics over the 15-year period. The landscape metrics further support this trend, showing consistently higher habitat quality and connectivity within the conservancy compared to adjacent, unprotected areas, with disparities becoming more evident by 2025. These results underscore how community conservancies can attain the effectiveness in maintaining ecological function. Furthermore, the results align with previous studies [18] that highlight improved habitat connectivity and integrity in regions where targeted conservation actions are actively implemented and well-managed.\u003c/p\u003e\n\u003cp\u003eThe results of this study underscore the positive impact of the SCC on landscape connectivity within its boundaries. While the establishment of the conservancy has not yet produced measurable effects beyond its borders, connectivity inside the conservancy remains clearly distinct and strong. This suggests that fencing and restricting human activities within conservancy have effectively contributed to maintaining habitat connectivity. However, the comparatively low connectivity levels surrounding the conservancy highlight the urgent need for increased efforts to enhance habitat connectivity in adjacent areas. These trends highlight the stronger anthropogenic pressure and habitat transformation occurring in areas lacking formal protection, highlighting greater land use pressure in unprotected areas. Such efforts may include improved land-use planning, promotion of alternative livelihoods, and fostering changes in local community attitudes toward conservation. The observed expansion of grasslands may indicate habitat recovery in areas formerly used for cropland or settlements. Alternatively, it is possible that rising wildlife populations are suppressing the growth of young vegetation into mature forests, a dynamic that warrants further investigation. These findings identify important ecological and socio-economic factors for future research and planning for conservation priorities.\u003c/p\u003e\n\u003cp\u003eWhile the establishment of the conservancy has brought numerous positive effects on habitat connectivity[31], it has also introduced social complexities that may threaten habitat integrity. Notably, without significant livelihood alternatives outside the conservancy, restricted access to resources within the conservancy has increased pressure on adjacent areas outside its boundaries. This has resulted in intensified grazing, cropland expansion, and greater exploitation of water sources in the surrounding landscape. Such increased resource use adversely affects vegetation and soil quality, leading to further habitat degradation. In contrast, most of the habitat losses, particularly forest and wetland areas, were driven by expansion of the cropland settlements which were predominantly occurring outside the SCC boundaries. These patterns underscore the effectiveness of the conservancy in buffering against land use pressures, while highlighting the need for greater management attention in surrounding areas, indicating greater land use change and anthropogenic pressure in unprotected areas [32]. Consequently, these pressures are driving a decline in habitat connectivity outside the conservancy, creating \u0026lsquo;biological islands\u0026rsquo;, which could ultimately undermine the broader conservation benefits achieved within the community-managed area [33, 34].\u003c/p\u003e\n\u003cp\u003eThis study demonstrated the significant potential of geospatial tools in evaluating the impacts of conservation interventions, particularly the establishment of community conservancies like SCC. The use of open-source data and freely available analytical tools greatly enhances the feasibility of monitoring conservation outcomes, especially in resource-limited settings. By leveraging publicly accessible satellite imagery, such as Landsat and Sentinel data, combined with landscape analysis software like FRAGSTATS and the Landscape Fragmentation Tool [29], we were able to generate detailed land cover maps and quantify changes in habitat connectivity over time. Therefore, technological advancements allow integration of spatio-temporal analysis in the nexus of biodiversity conservation and livelihoods strategies. These approaches offer cost-effective and replicable methods for tracking landscape dynamics and assessing the effectiveness of conservation measures in multi-use landscapes. Importantly, this demonstrates that advanced conservation monitoring does not necessarily require expensive proprietary software or extensive field data collection, making it more accessible to conservation practitioners and community managers. Moving forward, the integration of these geospatial techniques with community knowledge and ground-based observations could further strengthen adaptive management strategies and support evidence-based decision-making in community-led conservation initiatives. Traditional ecological knowledge can play critical role in biodiversity conservation [35]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLandscape connectivity around the SCC is shaped by a complex interplay of socio-economic activities, biophysical characteristics, and climatic conditions [36, 37]. Most of the local population practice small-scale farming, which often requires periodic expansion to meet the needs of a growing population. Biophysically, the area is dominated by sandy soil that has limited agricultural productivity, thereby intensifying the demand for more land and exerting pressure on natural habitats. Additionally, climate change impacts are increasingly evident; for instance, the 2023/24 drought severely curtailed vegetation growth, further stressing the ecosystem and indicating more substantial expansion in unprotected areas and suggesting some localized forest regeneration or restoration. These challenges underscore the urgent need for enhanced mitigation and adaptation strategies that simultaneously promote habitat restoration and improve local livelihoods.\u003c/p\u003e\n\u003cp\u003eThis study makes several important contributions to both scientific literature and practical resource management. It demonstrates a straightforward and cost-effective approach to assessing the role of community conservation initiatives, such as SCC, in enhancing habitat connectivity. The findings highlight both the potential benefits and limitations of conservation efforts within community settings, which are often strongly influenced by traditional norms and practices. The study reveals that increased conservation efforts outside the conservancy boundaries are necessary to strengthen habitat connectivity at the broader KAZA landscape scale and beyond. Expansive connectivity has potential to positively contribute to resolution of socio-economic challenges, including human-wildlife conflicts in the region [8, 33]. Additionally, given that the conservancy model is relatively new in Zambia, this research underscores the need for further studies addressing both management challenges and policy development to optimize community-based conservation outcomes.\u003c/p\u003e\n\u003cp\u003eDespite the promising findings, this study has inherent limitations. First, only two time points (2010 and 2025) were used to assess changes in connectivity and habitat integration, which may overlook gradual or interim landscape dynamics. Conducting analyses at shorter intervals would provide a more nuanced understanding of temporal changes. Second, although the classification maps achieved high overall accuracy, they may not entirely be free from errors. Finally, this study relied on landscape metrics derived from FRAGSTATS and the Landscape Fragmentation Tool; incorporating more advanced spatial modeling techniques could offer deeper insights and enhance the practical relevance of the findings for community conservancy management.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study evaluated the effectiveness of the SCC in enhancing habitat connectivity and integrity in Southern and Western Zambia, using freely available Landsat imagery to compare land cover before its establishment in 2010 and 15 years later in 2025. The analysis, based on land cover classification maps with an accuracy exceeding 80%, revealed contrasting trends inside and outside the conservancy. While habitat loss and fragmentation increased significantly outside the conservancy particularly by 2025 habitats within the conservancy showed greater stability. Notably, there was a shift from forest-dominated landscapes to grasslands within the conservancy, although forest loss remained below 1%, compared to 1.2% outside. Cropland expansion and settlement growth, especially in the eastern region of the conservancy, emerged as key drivers of forest degradation. Despite these anthropogenic pressures, the conservancy demonstrated a positive impact by maintaining higher landscape connectivity and reducing fragmentation within its boundaries. These findings highlight the potential of community-based conservation initiatives to restore and maintain ecological integrity, particularly in supporting species with large home ranges, such as elephants and blue wildebeests. The study provides valuable insights for future conservation planning and wildlife corridor development, emphasizing the importance of community engagement in achieving sustainable land use and biodiversity conservation goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe wish to thank Copperbelt University (CBU) and the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) for providing support during the preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u0026nbsp;\u003c/strong\u003eThis study was conducted under the ethical clearance No.2023/2/13/1 issued by The Copperbelt University, and research permit No. NPW/8/27/1 issued by Department of National Parks and Wildlife (DNPW). The Copperbelt University has a functional ethics committee under the Directorate of Research, which provides oversight on research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Ethics and Accordance:\u0026nbsp;\u003c/strong\u003eThis study involved limited human participation. Nonetheless, all procedures related to human data collection were guided by the ethical clearance No. 2023/2/13/1 issued by The Copperbelt University and research permit No. NPW/8/27/1 issued by the Department of National Parks and Wildlife (DNPW).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material:\u003c/strong\u003e Data will be provided upon reasonable request to the corresponding author. The data is held in Google Drive and a link will be provided.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e: The authors declare that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Funding was provided by Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) under the Enhanced Livelihoods and Natural Resource Management under accelerated Climate Change (ELNAC) project supported by the Federal Ministry of Research, Technology and Space of Germany.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution:\u003c/strong\u003e DCM and GM analysed, DCM and DP drafted the manuscript, while DM, DP, NN, and VRN conceptualized and reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTischendorf, L. and L. 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Gasingirwa, and D. Nsabimana, \u003cem\u003eUnleashing traditional ecological knowledge for biodiversity conservation and resilience to climate change in Rwanda.\u003c/em\u003e African Journal of Science, Technology, Innovation and Development, 2022. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 204-215.\u003c/li\u003e\n\u003cli\u003eFletcher Jr, R.J., \u003cem\u003eMultiple edge effects and their implications in fragmented landscapes.\u003c/em\u003e Journal of Animal Ecology, 2005. \u003cstrong\u003e74\u003c/strong\u003e(2): p. 342-352.\u003c/li\u003e\n\u003cli\u003eLindsey, P.A., et al., \u003cem\u003eUnderperformance of African protected area networks and the case for new conservation models: insights from Zambia.\u003c/em\u003e PLoS one, 2014. \u003cstrong\u003e9\u003c/strong\u003e(5): p. e94109.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Community-Based Conservation, Conservation planning, Habitat fragmentation, Landscape, KAZA - TFCA, Wildlife movement ecology","lastPublishedDoi":"10.21203/rs.3.rs-7506925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7506925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLandscape ecology and conservation are essential for supporting biodiversity, particularly for migratory species. However, increasing habitat fragmentation continues to reduce landscape connectivity, threatening ecological integrity. The Kavango-Zambezi Transfrontier Conservation Area (KAZA TFCA), which hosts key biodiversity, faces such a challenge. This study assessed the impact of a community-based conservation approach specifically the Simalaha Community Conservancy in Western Zambia on improving forest connectivity and habitat integrity. Using freely available Landsat imagery, we analyzed land cover changes before the conservancy\u0026rsquo;s establishment in 2010 and 15 years later in 2025. Image classification was conducted using pixel-based and object-based methods, followed by landscape metrics analysis using FRAGSTATS and the Landscape Fragmentation Tool in ArcMap by focusing on indices, such as Number of Patches (NP), Patch area (PA) and Connectivity Index (CI). The results, based on classification maps with over 80% accuracy, showed increasing habitat loss and fragmentation outside the conservancy, especially in 2025. Within the conservancy, there was a notable shift from forest-dominated to grassland habitat, though forest loss remained below 1%, compared to 1.2% outside. The eastern part of the conservancy was dominated by croplands and settlements, identifying agricultural expansion as a primary driver of forest loss. Overall, the study demonstrates that the community conservancy model has contributed to maintaining and enhancing habitat connectivity and integrity within its boundaries, despite external anthropogenic pressures. These findings provide important insights for conservation planning and wildlife management in transboundary landscapes, especially for migratory species, such as elephants (\u003cem\u003eLoxodonta africana\u003c/em\u003e) and blue wildebeest \u003cem\u003e(Connochaetes taurinus)\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"A Spatial Analysis of Habitat Connectivity in Simalaha Community Conservancy of Zambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 10:05:25","doi":"10.21203/rs.3.rs-7506925/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T08:43:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T00:11:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T07:55:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39287989078251555116327556496182315754","date":"2025-10-17T14:49:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217598366459740962858081662846311762358","date":"2025-10-09T09:12:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-20T17:21:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-20T17:10:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T17:06:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T10:09:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-09-18T07:47:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f024c194-191c-4afd-8f19-4154d7e9ef10","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-01T08:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 10:05:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7506925","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7506925","identity":"rs-7506925","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}