Geovisualizing Land Degradation Risk in Southeast Brazil: A Remote Sensing and GIS-Based Assessment

Geovisualizing Land Degradation Risk in Southeast Brazil: A Remote Sensing and GIS-Based Assessment | 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 Geovisualizing Land Degradation Risk in Southeast Brazil: A Remote Sensing and GIS-Based Assessment Mohammad Al Abed, Turkia Almoustafa, Roberson Pimentel, Fábio F. Dias This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8731892/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Land degradation poses a critical threat to ecosystem services and sustainable development, especially in regions experiencing rapid land-use transitions. This study employs an integrated geospatial approach—combining remote sensing, geographic information systems (GIS), and spatial multi-criteria analysis—to assess and visualize land degradation risk in a strategic coastal region of southeastern Brazil (Rio de Janeiro State). Using the United Nations Environment Programme's Priority Actions Programme Regional Activity Centre (UNEP-PAP/RAC) framework applied to recent satellite imagery, we generated spatially explicit maps classifying land into stable and unstable categories. A geospatial prioritization model incorporating biophysical and socio-economic variables was developed to identify conservation hotspots and support decision-making. Results show that 68.4% of the landscape is stable, largely consisting of unmanaged areas with agricultural and forest potential, while 7.8% is unstable, with sheet erosion concentrated at agricultural frontiers. Priority mapping classified 51.7% of the area as Stable Medium Priority, revealing widespread latent vulnerability, and 4.6% as Unstable High Priority, necessitating urgent intervention. Complementary analysis of land-use change (1985–2024) highlighted a 199% urban expansion and a 34% decline in agricultural mosaics, underscoring anthropogenic drivers of degradation. This study not only validates the PAP/RAC framework in a humid tropical coastal setting but also delivers actionable geovisualization outputs and a spatial decision-support tool for targeted land management, contributing to soil conservation and sustainable development policy in Brazil. Geovisualization Spatial multi-criteria analysis Land degradation risk mapping Remote sensing and GIS Soil erosion Conservation prioritization Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Land degradation represents a critical environmental and socio-economic challenge, threatening ecosystem services, agricultural productivity, and sustainable development worldwide (IPBES 2018). In Brazil, a country of vast natural resources and complex biomes, soil erosion driven by anthropogenic activities such as agricultural expansion, deforestation, and urbanization is a persistent issue, particularly in regions undergoing rapid land-use transition (Borrelli et al. 2017). The state of Rio de Janeiro, while known for its coastal metropolises, encompasses diverse hinterlands characterized by mosaics of Atlantic Forest remnants, agricultural lands, and urban-rural interfaces. These areas are susceptible to degradation processes, yet spatially explicit assessments integrating modern geospatial technologies remain essential for informing targeted conservation and land-use policies. This study focuses on a strategically selected area in the southeast of Brazil, situated within the state of Rio de Janeiro. As shown in the location map (Fig. 1 ), the study area is positioned in the eastern part of the state, spanning approximately 3,020 km² between the coordinates 42°48'0"W to 42°30'0"W and 23°00'0"S to 22°30'0"S. The region encompasses a suite of municipalities and districts, including Silva Jardim, Rio Bonito, Armação dos Búzios, São Vicente de Paula, and Cabo Frio, among others. This area represents a quintessential land-use transition zone, featuring interfaces between the Atlantic Forest biome, cultivated lands, coastal systems (including mangroves and sandy areas), and expanding urban peripheries. Such heterogeneity makes it a pertinent model for studying the spatial patterns and drivers of land degradation. Remote sensing (RS) and Geographic Information Systems (GIS) have emerged as indispensable tools for mapping, monitoring, and modeling land degradation at various scales (Visser et al. 2019). The integration of these technologies enables the systematic analysis of land cover change, identification of erosion features, and assessment of degradation risk over large and often inaccessible areas. However, the application of integrated RS/GIS frameworks for detailed risk assessment, particularly those that classify land into stability categories and conservation priorities, remains underexplored for many dynamic regions in southeastern Brazil. To address this, the present study adapts the methodology established by United Nations Environment Program, Priority Actions Program Regional Activity Centre (UNEP, PAP/RAC) methodology. While the PAP/RAC framework is well-established, its application has been largely confined to semi-arid and Mediterranean contexts (e.g., Sadiki et al. 2012; Mesrar et al. 2015; Tahouri et al. 2022; Lhoussaine et al. 2024). Applying this tool to a humid tropical coastal zone characterized by heavy rainfall and steep relief addresses a significant knowledge gap (Author et al. 2025). The objective of this research is to conduct a comprehensive land degradation risk assessment for the study area in Rio de Janeiro State through the integrated use of remote sensing and GIS techniques. Specifically, the study aims to map and quantify the spatial distribution of stable and unstable land classes; analyze the associated erosion processes; and develop a conservation priority map by synthesizing stability status and degradation risk. The findings are intended to provide a scientific basis for land management planning, supporting strategies for soil conservation, ecosystem restoration, and sustainable regional development in one of Brazil's most environmentally and economically significant states. 2. MATERIAL AND METHODS 2.1 Land degradation mapping Land degradation was mapped using a contemporary, very-high-resolution remote sensing approach. The primary data sources consisted of the most recent available very-high-resolution (< 1 m) satellite basemaps to ensure fine-scale accuracy: Airbus imagery (2025) and the Esri World Imagery service. To ensure consistency in spectral analysis and provide a recent medium-resolution baseline, a Landsat image from 2025 was also incorporated. Land cover classification was performed in ArcGIS using the Maximum Likelihood Supervised Classification algorithm. The classification schema was designed according to the established methodological framework of the United Nations Environment Programme's Priority Actions Programme/Regional Activity Centre (UNEP-PAP/RAC) for coastal area management (PAP/RAC 1997; UNEP-MAP/PAP 2000). Training samples for the supervised classification were developed and iteratively refined through visual interpretation of the very-high-resolution Airbus and Esri basemaps. This visual refinement against sub-meter imagery was critical for defining precise spectral signatures in the Landsat data and ensuring high classification accuracy in the absence of concurrent field validation. The classification procedure followed a dual-track logic based on land unit status, distinguishing between stable and unstable areas. Stable land units were defined and characterized based on three primary attributes: (1) the dominant land cover/use type (e.g., sandy areas, unmanaged areas with forest or agricultural potential, managed forest or agricultural areas, mangrove); (2) an inherent instability risk index (0 = no risk, 1 = low, 2 = high, 3 = highest), derived from intrinsic factors; and (3) the primary drivers of potential instability, including topography, geology, vegetation cover, and anthropogenic pressure. Unstable land units were identified and classified based on: (1) the dominant erosion process (sheet, or rill erosion); (2) the spatial extent of the affected area within the unit (localized: 60%); and (3) a qualitative expansion trend index indicating the dynamic state of the erosion (0 = stabilizing, 1 = locally expanding, 2 = regionally expanding, 3 = advancing toward irreversibility). 2.2 Prioritization of Degradation Hotspots To facilitate efficient resource allocation and targeted intervention, a systematic prioritization procedure was applied to identify critical land degradation hotspots. This procedure was developed following the methodological framework established by the United Nations Environment Programme's Priority Actions Programme/Regional Activity Centre (UNEP-PAP/RAC 2004). Fourteen variables were selected based on their direct relevance to local land degradation processes and prevailing socio-economic dynamics (Table 1 ). Each variable was assigned an impact score ranging from 1 (lowest impact) to 3 (highest impact). To ensure the scoring system reflected local conditions, the relative weight and scoring criteria for each variable were determined through structured expert consultation. Table 1 Variables and Scoring Matrix for Land Degradation Intervention Prioritization Variable Description Scoring (1–3) A Physical instability risk (stable areas) 1 = low, 2 = high, 3 = critical B Extent of area affected (unstable areas) 1 = 60% C Expansion trend of degradation (unstable areas) 1 = local, 2 = widespread, 3 = generalized/irreversible D Multiplicator for increased importance (causative agents or degradation process) 1 = none, 2 = increased, 3 = highly increased E Influence on adjacent areas 1 = low, 2 = high, 3 = critical F Overexploitation 1 = insignificant, 2 = significant, 3 = crucial G Rural exodus H Land tenure I Other aggravating socio-economic factors J Value of current land use (local population) 1 = low, 2 = moderate, 3 = high K Value of current land use (national policy) L Potential for forestry M Potential for agriculture N Other land use potentials (recreational, industrial, construction) For each mapped land unit, all fourteen criteria were scored based on the defined matrix. Final composite prioritization scores were then calculated using distinct formulas for stable and unstable areas, reflecting their different underlying risk structures: Stable Areas Priority Score: [(A × D + E) × F × G × H × I] + [(J + K) × L × M × N] Unstable Areas Priority Score: [(B × C × D + E) × F × G × H × I] + [(J + K) × L × M × N] The resulting numerical scores were classified into three distinct intervention priority tiers to guide management actions: High Priority: Score ≥ 60, Medium Priority: Score between 21 and 59, and Low Priority: Score ≤ 20 This quantitative framework translates the multi-criteria assessment into a clear, actionable hierarchy for conservation and restoration planning. 2.3 Spatio-Temporal Land Use and Land Cover Change Detection (1985–2024) Land Use and Land Cover (LULC) data were sourced from the MapBiomas Project, a Brazilian multi-institutional initiative that generates annual, high-resolution land cover maps for Brazil and other regions using Landsat satellite imagery with a 30 m spatial resolution (MapBiomas 2025). This study analyzed multi-temporal LULC changes at ten-year intervals (1985, 1995, 2005, 2015, and 2024). The raster datasets for these five target years were downloaded and processed in ArcGIS 10.8. Each dataset was spatially clipped to the study area's vector boundary to isolate relevant LULC patterns. Area calculations for each land cover class were performed by aggregating pixel counts and applying the sensor’s spatial resolution, with results expressed in square kilometers, hectares, and proportional cover (%) to facilitate cross-temporal and inter-class comparison. The total study area used for all subsequent calculations is 3019.77 km², derived from the official vector boundary. The initial summed area of the clipped MapBiomas raster was approximately 9% larger. This discrepancy is attributed to the inclusion of partial coastal and island pixels during the raster clipping process, a known effect when working with complex shorelines at moderate (30 m) resolution. All final class areas and percentages were calculated based on the definitive vector-based total to ensure geographical accuracy, enabling a quantitative evaluation of land cover transitions over the 40-year period. 3. RESULTS 3.1 Nature and extent of land degradation The land degradation assessment for the study area in Rio de Janeiro State revealed a clear spatial differentiation between stable and unstable land categories, as illustrated in the land degradation map (Fig. 2 ) and summarized quantitatively in Table 2 . The total study area covers approximately 3019.77 km². Spatial analysis indicates that the landscape is predominantly characterized by stable land conditions, constituting approximately 68.4% of the total area. The most extensive stable category is Unmanaged Areas with Agriculture Potential, covering 929.33 km² (30.8% of the study area). This is complemented by significant coverage of Unmanaged Areas with Forest Potential, spanning 541.29 km² (17.9%). Together, these two classes form broad, contiguous land bases in municipalities such as Silva Jardim, Morro Grande, and Rio Bonito. Managed lands also contribute substantially to stable terrain: Managed Areas with Agriculture Use account for 350.45 km² (11.6%), extending mainly across Tamoios and São Vicente de Paula, while Managed Areas with Forest Use cover 198.64 km² (6.6%), predominantly occupying the mountainous regions of Rio Bonito and Silva Jardim. Minor yet ecologically significant stable features include Sandy Areas, occupying 38.71 km² (1.3%), and Mangrove ecosystems, confined to 7.06 km² (0.2%). In contrast, areas exhibiting active soil degradation, classified as unstable, constitute a smaller but notable portion of the landscape, totaling approximately 235 km 2 of the study area. Erosion processes are dominated by sheet erosion, the most widespread form of degradation. Dominant Sheet Erosion affects 112.77 km² (3.7%), followed by Generalized Sheet Erosion across 71.42 km² (2.4%), and Localized Sheet Erosion over 28.25 km² (0.9%). Rill erosion is less extensive but present, with Dominant Rill Erosion occurring over 11.06 km² (0.4%), Localized Rill Erosion over 7.16 km² (0.2%), and Generalized Rill Erosion across 4.79 km² (0.2%). Spatially, unstable areas are not randomly distributed but show a distinct association with specific land use types and vulnerable geomorphological units, often occurring at the interface between managed agricultural zones and natural vegetation. The mapped unstable areas are concentrated within the central and peripheral zones of the study area, encompassing municipalities such as Rio Bonito, Boa Esperança, Silva Jardim, São Vicente de Paula, Iguaba Grande, and São Pedro. Visual assessment suggests that erosion processes are particularly associated with agricultural frontiers and areas of land-use transition. Precise quantification of erosion extent per administrative unit would require further municipal-scale spatial analysis. The remaining surface is composed of anthropogenic and permanent natural features. Urbanized areas account for 411.69 km² (13.6%), while water bodies cover 307.14 km² (10.2%). Table 2 Assessment of Stable and Unstable Land Categories Areas Code Land Category Type Area (km 2 ) Area (hec) Area (%) Stable Areas 0S Sandy Area 38.71 3870.67 1.28 01 Unmanaged Areas with Forest Potential 541.29 54128.97 17.92 02 Unmanaged Areas with Agriculture Potential 929.33 92933.26 30.77 03 Managed Areas with Forest Use 198.64 19864.48 6.58 04 Managed Areas with Agriculture Use 350.45 35045.13 11.61 0M Mangrove 7.06 706.29 0.23 Total Stable Area 2065.48 206548.8 68.39 Unstable Areas Localized Sheet Erosion 28.25 2825.17 0.94 Dominant Sheet Erosion 112.77 11277.45 3.73 Generalized Sheet Erosion 71.42 7141.92 2.37 Localized Rill Erosion 7.16 715.59 0.24 Dominant Rill Erosion 11.06 1105.91 0.37 Generalized Rill Erosion 4.79 478.61 0.16 Total Unstable Area 235.45 23544.65 7.81 Urban Area 411.69 41169.43 13.63 Water 307.14 30714.44 10.17 Total Surface Study Area 3019.77 301977.32 100.00 3.2 Conservation Priority Assessment The land degradation assessment was extended to classify areas based on their conservation priority (Fig. 3 ). The resulting priority map and corresponding areal statistics (Table 3 ) reveal a landscape where the majority of the area is designated for medium-level conservation attention. Stable areas account for 68.37% (2064.65 km²) of the total study area and are subdivided into three priority levels. The largest portion is classified as Stable Medium Priority, encompassing 1560.80 km² or 51.69% of the total landscape. This suggests that over half of the region, while currently stable, possesses characteristics that warrant monitoring or preventive management to maintain its condition. Stable Low Priority areas cover 309.28 km² (10.24%), indicating zones of high resilience or lower degradation risk. In contrast, Stable High Priority areas, though smaller at 194.57 km² (6.44%), highlight specific stable zones that are critically important for conservation, likely due to high ecological value or proximity to vulnerable features. Unstable areas, representing active degradation across 7.78% (235.03 km²) of the study area, are further categorized by intervention urgency. The most critical category is Unstable High Priority, covering 140.17 km² (4.64% of the total area). This signifies that the majority of eroded land requires immediate and targeted intervention to mitigate soil loss. Unstable Medium Priority areas span 77.97 km² (2.58%), while Unstable Low Priority zones are minimal at 16.89 km² (0.56%). The remaining surface is comprised of Urban Area (412.32 km², 13.65%) and Water bodies (307.58 km², 10.19%). Spatially, the priority map (Fig. 3 ) indicates a distinct zonation. High and medium-priority unstable areas are frequently concentrated at the interface between agricultural frontiers and natural vegetation, particularly within the central and southern parts of the study area, overlapping with municipalities such as Rio Bonito, Boa Esperança, Silva Jardim, Sâo Pedro, and São Vicente de Paula. The extensive Stable Medium Priority class forms a contiguous matrix across the region, suggesting a widespread need for sustainable land use planning to prevent future degradation. This priority classification provides a strategic framework for directing conservation resources, emphasizing that preventive measures in medium-priority stable zones are as crucial as rehabilitation efforts in high-priority unstable areas. Table 3 Conservation priority assessment results Area Conservation Priority Area (km 2 ) Area (hec) Area (%) Stable Areas Stable Low Priority 309.28 30927.6 10.24 Stable Medium Priority 1560.80 156080.3 51.69 Stable High Priority 194.57 19456.8 6.44 Total Stable Areas 2064.65 206464.7 68.37 Unstable Areas Unstable Low Priority 16.89 1689.16 0.56 Unstable Medium Priority 77.97 7797.24 2.58 Unstable High Priority 140.17 14016.64 4.64 Total Unstable Areas 235.03 23503.04 7.78 Urban Area 412.32 41231.96 13.65 Water 307.58 30758.2 10.19 Total surface area 3019.58 301957.9 100.00 3.3 Land Use and Land Cover Change Analysis (1985–2024) Between 1985 and 2024, the study area experienced substantial shifts in land cover composition (Fig. 4 ). Urban areas exhibited the most pronounced expansion (Tables 3 and 4 ), increasing by 199.42% (203.50 km²). Pastureland also expanded significantly, growing by 14.74% (167.61 km²), and remained the dominant land use category throughout the period. Mining activities increased dramatically by 6147.73%, albeit from a very small initial extent, while Other Temporary Crops expanded notably by 445.01%. Mangrove areas increased by 53.13% (+ 0.34 km²), suggesting positive restoration or conservation outcomes, and water bodies (River, Lake and Ocean) expanded by 7.38% (+ 23.26 km²). New land use categories emerged during the study period, including Sugar cane (0.64 km² in 2024) and Forest Plantation (0.01 km² in 2024). Table 4 Consolidated Land Use & Land Cover Changes (km 2 ) 1985–2024 ID LULC CLASSES 1985 (km²) 1995 (km²) 2005 (km²) 2015 (km²) 2024 (km²) Net Change (%) Net Change (km²) 3 Forest Formation 555.63 484.60 497.95 517.05 548.61 -1.26 -7.02 5 Mangrove 0.63 0.76 0.88 0.97 0.97 53.13 0.34 9 Forest Plantation 0.00 0.00 0.00 0.01 0.01 100.00 0.01 11 Wetland 36.52 31.09 43.82 40.34 31.67 -13.30 -4.86 12 Grassland 0.46 0.44 0.43 0.44 0.25 -44.81 -0.21 15 Pasture 1137.16 1303.61 1384.17 1453.34 1304.77 14.74 167.61 20 Sugar cane 0.00 0.00 0.00 0.00 0.64 100.00 0.64 21 Mosaic of Uses 1017.59 864.90 710.62 599.09 668.10 -34.35 -349.49 23 Beach, Dune and Sand Spot 13.05 12.81 10.30 11.84 11.26 -13.73 -1.79 24 Urban Area 102.05 166.33 214.70 254.15 305.55 199.42 203.50 25 Other non Vegetated Areas 15.54 14.75 – 22.01 18.84 21.21 3.30 29 Rocky Outcrop 0.12 0.03 0.00 0.01 0.01 -88.37 -0.10 30 Mining 0.04 0.31 19.86 1.08 2.47 6147.73 2.43 31 Aquaculture 49.79 46.12 37.30 20.60 18.23 -63.39 -31.56 32 Hypersaline Tidal Flat 0.09 0.06 0.04 0.00 0.01 -90.91 -0.08 33 River, Lake and Ocean 315.18 325.95 332.32 329.79 338.44 7.38 23.26 39 Soybean 0.00 0.00 0.00 0.05 0.00 0.00 0.00 41 Other Temporary Crops 0.55 5.79 1.03 5.58 2.98 445.01 2.43 49 Wooded Sandbank Vegetation 51.11 37.96 41.40 39.16 42.69 -16.47 -8.42 Conversely, several land cover classes experienced notable declines. Mosaic of Uses decreased by 34.35% (− 349.49 km²), representing the largest absolute reduction. Aquaculture declined substantially by 63.39% (− 31.56 km²), and Grassland diminished by 44.81%. Rocky Outcrop areas were reduced by 88.37%, while Wetlands decreased by 13.30% (− 4.86 km²). Forest Formation showed a modest net decline of 1.26% (− 7.02 km²), though it recovered somewhat after reaching its lowest extent in 1995. Other categories, such as Beach, Dune and Sand Spot and Wooded Sandbank Vegetation, also registered reductions of 13.73% and 16.47%, respectively. 4. DISCUSSION 4.1 Discussion of Land Degradation Patterns The finding that stable land conditions dominate the study area (68.4%) aligns with national-scale analyses of Brazilian land cover, which show that despite intense historical pressure, significant portions of the landscape persist as stable natural vegetation or managed agricultural land (Souza et al. 2020). The most significant stable classes—Unmanaged Areas with Agriculture and Forest Potential, collectively represent nearly half (48.7%) of the total landscape and constitute the region's primary reservoir of productive and ecological capital. However, their classification as "unmanaged" or with specific "potential" indicates a latent vulnerability. These lands represent the very frontiers most susceptible to future conversion, a transition strongly associated with the initiation of soil degradation processes globally (Lambin and Meyfroidt 2011) and identified as a primary driver of ecosystem change worldwide (IPBES 2018). The high proportion of stable area, therefore, should be interpreted not as a lack of threat but as a critical window for implementing preventive land-use planning and sustainable management practices to avert future degradation. Conversely, the mapped unstable areas, though covering a smaller portion of the landscape (7.8%), reveal clear and concerning spatial patterns that are critical for intervention. The predominance of sheet erosion—accounting for the majority (7.04%) of all unstable area—is consistent with global models identifying it as the most extensive form of soil loss by water (Panagos et al. 2018; Borrelli et al. 2017) and a major contributor to global land degradation (IPBES 2018). The spatial correlation of these erosion hotspots with "agricultural frontiers" and interfaces between managed land and natural vegetation underscores a primary driver: land-use transition. This pattern aligns with the classic syndrome of land change where frontier expansion triggers soil degradation (Lambin and Meyfroidt 2011). The resulting soil degradation is a direct consequence of anthropogenic pressure, occurring where natural systems are being modified or where agricultural practices may be exceeding the land's carrying capacity. Similar intense anthropogenic pressures, notably urbanization and land-use change, are documented as critical factors altering both terrestrial and coastal ecosystems in southeastern Brazil (Santos et al. 2022; Souza et al. 2020). The concentration of unstable areas in central and peripheral zones highlights specific localities where current land-use systems warrant urgent review. Targeted soil conservation measures, aligned with and reinforcing instruments such as Brazil's Forest Code (Strassburg et al. 2014), are most needed in these priority intervention zones. 4.2 Discussion of Conservation Priorities and Management Implications The priority classification provides a strategic, multi-tiered framework for directing conservation resources. The finding that over half of the landscape (51.69%) is classified as Stable Medium Priority is significant. This vast area represents lands that are currently stable but possess inherent or contextual vulnerabilities, aligning with the concept of "potential degradation" risk areas that require proactive management to maintain ecosystem services (Visser et al. 2019). Investing in sustainable agricultural practices, agroforestry systems, and compliance with environmental legislation within these zones is a cost-effective strategy to prevent their transition into future degradation hotspots, an approach advocated for in preventative conservation planning (Nkonya et al. 2016). In the Brazilian context, such preventive measures are central to the implementation of the Forest Code, which aims to protect areas of permanent preservation (APPs) and legal reserves to maintain ecological stability on private lands (Soares-Filho et al. 2014; Strassburg et al. 2014). Conversely, the Unstable High Priority areas (4.64% of the total), though smaller in extent, demand immediate and targeted rehabilitation efforts. The concentration of these critical zones at land-use interfaces underscores that degradation is a direct symptom of unsustainable transitions, a pattern well-documented in other global contexts (Lambin and Meyfroidt 2011). The application of the UNEP/PAP/RAC framework in this humid tropical region successfully identified these hotspots, demonstrating its utility beyond the Mediterranean and semi-arid contexts for which it was originally developed (e.g., Sadiki et al. 2012; Lhoussaine et al. 2024). The methodology’s structured integration of biophysical and socio-economic criteria offers a replicable model for translating complex degradation assessments into actionable intervention classes, which is a critical step for operationalizing land degradation assessments (Kust et al. 2017). This dual-focused strategy urgent rehabilitation of high-priority unstable areas (addressing existing degradation) coupled with the safeguarding of medium-priority stable lands (avoiding future degradation) is essential for achieving LDN targets, which require balancing “losses” to degradation against “gains” from improvement (UNCCD GPG v2, Section 1.4). The resulting priority map directly translates a multi-criteria assessment into a practical blueprint for environmental agencies, enabling efficient, spatially explicit allocation of resources. Such prioritization is fundamental for operationalizing LDN and aligning local actions with national LDN targets and SDG 15.3, as it moves beyond merely calculating the proportion of degraded land to informing where and how to act (Sims et al. 2021). By emphasizing that preventive measures in stable areas are as crucial as rehabilitation in degraded ones, this study supports an integrated land management approach aimed at achieving a land degradation-neutral world. 4.3 Discussion of LULC dynamics The observed land-use and land-cover (LULC) transitions in the study area from 1985 to 2024 align closely with broader regional dynamics documented in the Atlantic Forest biome and Rio de Janeiro state, where key drivers include agricultural expansion, urbanization, deforestation, and policy-induced recovery. Urban areas expanded dramatically by 203.50 km² (199.42%), consistent with patterns of metropolitan growth and peri-urbanization across the state, driven by population influx and economic development (Pereira 2019). This expansion often converts agricultural mosaics into built-up land, increasing pressure on surrounding rural landscapes. Concurrently, pasture expanded by 14.74% (167.61 km²) while Mosaic of Uses declined by 34.35% (349.49 km²), indicating a process of agricultural intensification and landscape simplification. This widespread trend in southeastern Brazil is fueled by demand for livestock products, were heterogeneous farming systems transition to more homogeneous pasturelands, often operating below their productive potential (Strassburg et al. 2014).​ Forest Formation showed a modest net loss of 1.26% (7.02 km²) but exhibited recovery after 1995. This pattern mirrors the slow but positive stabilization and regrowth observed in the Atlantic Forest biome, driven by strengthened environmental legislation such as Brazil's Forest Code and reduced deforestation (Rezende et al. 2018; MapBiomas 2025). Divergent coastal ecosystem trends were observed: mangrove areas expanded by 53.13% (0.34 km²), while wetlands contracted by 13.30% (4.86 km²). This highlights differential pressures from coastal development and aquaculture versus inland drainage for agriculture. Mangroves appear to have benefited from protective policies and natural expansion; studies in the Rio de Janeiro coastline document mangroves encroaching landward and expanding over salt marshes such as Sporobolus virginicus . This expansion is often a response to local geomorphological shifts, sedimentation patterns, and relative sea-level rise. Specifically, changes in sedimentary processes in tropical deltaic systems can facilitate the colonization of mangroves over hypersaline flats (apicums) and adjacent vegetation (Santos et al. 2022; Costa Santos et al. 2019). Finally, the emergence of sugarcane (0.64 km² in 2024) and sharp rise in Other Temporary Crops (+ 445.01%, 2.43 km² from a small base) signal nascent agricultural shifts tied to market demands and climate-adapted cropping in southeast Brazil, including Rio de Janeiro, necessitating ongoing monitoring (Maria Guimarães 2015; Souza et al. 2020). 4.4 Limitations and Implications for Management This study provides a robust spatial assessment of land degradation and conservation priorities. However, certain limitations should be acknowledged to contextualize the findings. Firstly, the reliance on remote sensing, while enabling comprehensive coverage, would benefit from systematic ground-truthing for the validation of erosion subtypes (e.g., distinguishing sheet from rill erosion intensity) and for calibrating the spectral signatures of specific land-use classes. Secondly, the conservation prioritization, though based on a structured framework informed by expert criteria, could be enhanced through participatory engagement with local farmers, landowners, and community representatives. Such engagement would capture nuanced socio-economic values, land tenure complexities, and local ecological knowledge, refining the scores for variables related to land use value and socio-economic pressure (Variables J, G, H, I). Despite these limitations, the findings offer clear and actionable implications for land management in the State of Rio de Janeiro. The spatially explicit priority map (Fig. 3 ) serves as a direct decision-support blueprint for environmental agencies at state and municipal levels. It enables the efficient direction of technical assistance, conservation incentives (e.g., payments for ecosystem services), and regulatory enforcement towards the most critical intervention hotspots, particularly the Unstable High Priority areas identified in municipalities such as Rio Bonito and Boa Esperança. Ultimately, the results advocate for an integrated, two-pronged management strategy essential for achieving land degradation neutrality. This strategy must couple the urgent rehabilitation of high-priority unstable areas with robust policies and incentives that safeguard the extensive Stable Medium Priority lands from future degradation. Investing in sustainable agricultural practices, agroforestry, and adherence to the Forest Code within these stable-yet-vulnerable zones represents a cost-effective, preventative approach to conservation. By implementing this dual focus, policymakers and land managers can work towards ensuring the long-term ecological functionality and economic productivity of this environmentally significant region. 5. RECOMMENDATIONS Based on the prioritization of land degradation hotspots in southeastern Brazil, we propose targeted interventions tailored to the socio-environmental context of the Rio de Janeiro study area. Firstly, for the unstable Areas: these areas, encompassing High Priority (140.17 km²) and Medium Priority (77.97 km²) zones, are characterized by active sheet and rill erosion, often at agricultural frontiers and land-use interfaces. Curative and protective interventions include: Soil Conservation Infrastructure: implement contour bunds, terraces, and vegetative barriers in agricultural zones experiencing dominant sheet erosion to reduce runoff velocity and soil loss. Ecological Restoration: reforest critical high-priority zones using native Atlantic Forest species to stabilize degraded slopes and restore hydrological function, particularly in areas adjacent to remaining forest fragments. Governance and Enforcement: strengthen the enforcement of the Brazilian Forest Code, ensuring the protection and restoration of Permanent Preservation Areas (APPs) and Legal Reserves within agricultural properties. Sustainable Land Management Support: provide technical assistance and incentives to farmers in erosion-prone areas for adopting soil conservation practices and integrated crop-livestock-forestry systems. Secondly, for the At-Risk Stable Areas: measures for these currently stable but vulnerable lands, primarily classified as Medium Priority (1560.80 km 2 ) focus on maintaining stability and preventing future degradation: Sustainable Agricultural Intensification: Promote conservation agriculture, agroforestry, and soil cover maintenance in unmanaged areas with agricultural potential to prevent their conversion into future erosion hotspots. Land-Use Zoning and Planning: integrate degradation risk maps into municipal and regional land-use planning (Zoneamento Ecológico-Econômico - ZEE) to guide sustainable development away from high-risk zones and protect areas with high conservation value. Payment for Ecosystem Services (PES): develop and implement PES schemes to incentivize landowners in stable medium-priority areas to maintain forest cover, adopt sustainable practices, and avoid land conversion. To enhance feasibility, these measures should be integrated into existing state and municipal environmental policies and supported by funding mechanisms such as environmental compensation and the Brazilian Fund for Biodiversity (FUNBIO). Future efforts must prioritize participatory validation of the mapped degradation zones with local stakeholders and ground-truthing of erosion processes. By situating the PAP/RAC framework within Brazil’s robust environmental governance structure, this study provides a replicable, spatially explicit model for guiding soil conservation, ecosystem restoration, and sustainable land-use planning in tropical coastal regions. 6. CONCLUSION This study successfully applied an integrated remote sensing and GIS-based UNEP-PAP/RAC framework to assess land degradation risk and prioritize conservation needs in a humid tropical coastal region of southeastern Brazil. The findings reveal a landscape dominated by stable yet vulnerable lands, with significant areas classified as medium priority for preventive conservation. Critically, active degradation hotspots were clearly identified, primarily linked to agricultural frontiers and land-use transitions, underscoring the role of anthropogenic pressure in driving soil erosion. The resulting spatially explicit priority maps provide a science-based, actionable tool for land managers and policymakers, enabling targeted interventions to rehabilitate high-risk areas while safeguarding stable zones from future degradation. Along with the targeted interventions recommended, this approach supports the strategic pursuit of land degradation neutrality and sustainable land management in one of Brazil’s most dynamic and environmentally significant regions. Declarations Competing interests: ”The authors have no relevent financial or non-financial to disclose”. Ethical Statement All authors have read, understood, and have complied as applicable with the statement on ‘Ethical responsibilities of Authors’ as found in the Instructions for Authors. Funding: Author 2 has recieved research support from The British Academy/Cara/Leverhulme Researchers at Risk Research Support Grant. The University of Manchester “The other authors declare that no funds, grants or other support were recived during the prepartion of this manuscript” Author Contribution “All authors have contributed to the study conception design and material preparation, data collection and analysis. The first draft of the manuscript was written by author 1 and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” ACKNOWLEDGEMENT We would like to express our sincere gratitude to the Foundation for Research Support of the State of Rio de Janeiro (Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, FAPERJ) for their support and provision of scientific scholarship (E- 26/200611/2025). Data Availability Data fully available upon request References Author, Saboya M, Santos C, Vargas R and Ferreira Dias F (2025) Land degradation assessment in the Angra dos Reis (Brazil) using remote sensing and GIS. J. Agricultural Science & Technology 2&3(1): 40-56. https://doi.org/10.36108/jast/5202.320.0130 Borrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C, Meusburger K, Modugno S, Schütt B, Ferro V, Bagarello V, Oost KV, Montanarella L and Panagos P (2017) An assessment of the global impact of 21st century land use change on soil erosion. Nature Communications 8(1):1-13. https://doi.org/10.1038/s41467-017-02142-7 Costa Santos CS, Dias FF, Franz B, Santos PRA, Rodrigues T, Vargas R and Américo dos Santos C (2019) Relative Sea Level Rise Effects at the Marambaia Barrier Island and Guaratiba Mangrove: Sepetiba Bay (SE Brazil). Journal of Sedimentary Environments 4 (3): 249–62.https://doi.org/10.12957/jse.2019.44397 IPBES (2018) The IPBES assessment report on land degradation and restoration . Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/zenodo.3237392 Kust G, Andreeva O and Cowie A (2017) Land Degradation Neutrality: Concept development, practical applications and assessment. Journal of Environmental Management 195(Pt 1): 16–24. https://doi.org/10.1016/j.jenvman.2016.10.043 Lambin EF and Meyfroidt P (2011) Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences 108 (9): 3465-3472. https://www.pnas.org/doi/epdf/10.1073/pnas.1100480108 Lhoussaine EM, Meryem M, Moncef B, Mustapha M, Noureddine A, Abdessalam BH, Yousra R and Brahim D (2024) A GIS-based modified PAP/RAC model and Caesium-137 approach for water erosion assessment in the Raouz catchment, Morocco. Environmental Research 251(1): 118-460. https://doi.org/10.1016/j.envres.2024.118460 MapBiomas. 2025. https://Brazil.mapbiomas.org/en/2025/08/13/Brazil-quatro-decadas-de-transformacao-na-cobertura-e-uso-da-terra-revelam-desafios-e-oportunidades/ Maria Guimarães (2015) A new chance for the Atlantic Forest . Revista Pesquisa FAPESP . Issue # 237. https://revistapesquisa.fapesp.br/en/a-new-chance-for-the-atlantic-forest/ Mesrar H, Sadiki A, Navas A, Faleh A, Quijano L and Chaaouan J (2015) Modélisation de l'érosion hydrique et des facteurs causaux: cas de l'Oued Sahla, rif central, Maroc. Zeitschrift für Geomorphologie 59(3): 495 - 514. Nkonya E, Mirzabaev A and von Braun J (Eds.) (2016) Economics of Land Degradation and Improvement – A Global Assessment for Sustainable Development . Springer International Publishing. https://doi.org/10.1007/978-3-319-19168-3 Panagos P, Standardi G, Borrelli P, Lugato E, Montanarella L and Bosello F (2018) Cost of agricultural productivity loss due to soil erosion in the European Union: From direct cost evaluation approaches to the use of macroeconomic models. Land Degradation & Development 29(3): 471-484. https://onlinelibrary.wiley.com/doi/10.1002/ldr.2879 Pereira RHM (2019) Future accessibility impacts of transport policy scenarios: Equity and sensitivity to travel time thresholds for bus rapid transit expansion in Rio de Janeiro. Journal of Transport Geography 74: 321-332 . https://doi.org/10.1016/j.jtrangeo.2018.12.005 Priority Actions Program Regional Activity Centre (PAP/RAC) (1997) Guidelines for mapping and measurement of rainfall-induced erosion processes in the Mediterranean coastal areas . Split, Croatia. Rezende CL, Scarano FR, Assad ED, Joly CA, Metzger JP, Strassburg BBN, Tabarelli M, Fonseca A and Bastos A (2018) From hotspot to hopespot: An opportunity for the Brazilian Atlantic Forest. Perspectives in Ecology and Conservation 16(4): 208-214. https://doi.org/10.1016/j.pecon.2018.10.002 Santos CA, Vargas R, Carvalho VR, Pinheiro VM, Santos PRA and Dias FF (2022) Spatio-temporal evolution of environmental dynamics in Guaratiba State Biological Reserve and its surroundings, Rio de Janeiro, Brazil. Caminhos de Geografia 23(90): 120–138. http://doi.org/10.14393/RCG239061036 Sadiki A, Mesrar H and Faleh A (2012) Modélisation et cartographie des risques de l'érosion hydrique: cas du bassin versant de l’Oued Larbaa, Maroc. Papeles de Geografía 55–56,179–188. Soares-Filho B, Rajão R, Macedo M, Carneiro A, Costa W, Coe M, Rodrigues H and Alencar A (2014) Cracking Brazil's Forest Code. Science, 344(6182): 363–364. https://doi.org/10.1126/science.1246663 Souza CMZ, Shimbo J, Rosa MR, Parente LL, Alencar AA, Rudorff BFT and Azevedo T (2020) Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and earth engine. Remote Sensing 12 ( 17):1-27. https://doi.org/10.3390/rs12172735 Sims NC, Newnham GJ, England JR, Guerschman J, Cox SJD, Roxburgh SH, Viscarra Rossel RA, Fritz S and Wheeler I (2021) Good Practice Guidance. SDG Indicator 15.3.1, Proportion of Land That Is Degraded Over Total Land Area. Version 2.0. United Nations Convention to Combat Desertification, Bonn, Germany. Strassburg BN, Agnieszka E Latawiec, Luis G Barioni, Carlos A Nobre, Vanderley P da Silva, Judson F Valentim hi , Murilo Vianna and Eduardo D. Assad (2014) When enough should be enough: Improving the use of current agricultural lands could meet production demands and spare natural habitats in Brazil. Global Environmental Change 8:84-97. https://doi.org/10.1016/j.gloenvcha.2014.06.001 Tahouri J, Sadiki A, Karrat L, Johnson VC, Chan NW, Fei Z and Kung HT (2022) Using a modified PAP/RAC model and GIS for mapping water erosion and causal risk factors: case study of the Asfalou watershed, Morocco. International Soil and Water Conservation Research 10: 254–272. United Nations Environment Programme (UNEP)/ Mediterranean Action Plan (MAP)/ Priority Actions Programme (PAP). (2004). Improving coastal land degradation monitoring in Lebanon and Syria: Country report Syria . https://wedocs.unep.org/bitstream/handle/20.500.11822/1859/syria.pdf United Nations Environment Programme (UNEP)/Mediterranean Action Plan (MAP)/ Priority Actions Programme (PAP). (2000). Guidelines for erosion and desertification control management with particular reference to Mediterranean coastal areas . Available at: https://iczmplatform.org/storage/documents/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf Visser S, Keesstra S, Maas G and de Cleen M (2019) Soil as a basis to create enabling conditions for transitions towards sustainable land management as a key to achieve the SDGs by 2030. Sustainability 11(23): 1-19. https://doi.org/10.3390/su11236792 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8731892","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582707463,"identity":"9adaad15-2f4a-4749-b74d-bc7757b1eb22","order_by":0,"name":"Mohammad Al Abed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYHACNiCyYWCQIFFLGulaDpOgRbeB+dmDD2XnE/tnNx98wFBjE01Qi9kBNnPDGeduJ864cyzZgOFYWm4DYS0MZtK8bbcTG27kmEkwNhwmRgv7N+m/becS55OghcdMmrHtQOIG4rUc5ik37DmXbLzxRlqyQQJRfjnevu3BjzI72Xk3kg8++FBjQ1gLAzOEcgSrTCCoHAnYk6J4FIyCUTAKRhgAAILWQQFt7wwvAAAAAElFTkSuQmCC","orcid":"","institution":"Fluminense Federal University (UFF)","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Al","lastName":"Abed","suffix":""},{"id":582707465,"identity":"bf5e282c-5459-4811-b8ce-da79d1e52a3a","order_by":1,"name":"Turkia Almoustafa","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Turkia","middleName":"","lastName":"Almoustafa","suffix":""},{"id":582707466,"identity":"292f27f1-4912-475b-a5dd-dd87175fcb86","order_by":2,"name":"Roberson Pimentel","email":"","orcid":"","institution":"Fluminense Federal University (UFF)","correspondingAuthor":false,"prefix":"","firstName":"Roberson","middleName":"","lastName":"Pimentel","suffix":""},{"id":582707467,"identity":"55b36334-bd32-4de9-b5dd-ce55a8ef3989","order_by":3,"name":"Fábio F. Dias","email":"","orcid":"","institution":"Fluminense Federal University (UFF)","correspondingAuthor":false,"prefix":"","firstName":"Fábio","middleName":"F.","lastName":"Dias","suffix":""}],"badges":[],"createdAt":"2026-01-29 13:08:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8731892/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8731892/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101470251,"identity":"af8126bc-ed9a-4c16-8204-df268e2fba72","added_by":"auto","created_at":"2026-01-30 05:15:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":741044,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8731892/v1/3c66496993961545327d41d0.png"},{"id":101470252,"identity":"bd4d2807-66d5-43d0-b8e5-ebbe09427290","added_by":"auto","created_at":"2026-01-30 05:15:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334016,"visible":true,"origin":"","legend":"\u003cp\u003eLand degradation map showing stable areas and unstable areas\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8731892/v1/7a61e53a7b9246a51af4f76d.png"},{"id":101470280,"identity":"a04158fb-2d02-485c-85c6-8baa97771e85","added_by":"auto","created_at":"2026-01-30 05:15:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":297321,"visible":true,"origin":"","legend":"\u003cp\u003eLand degradation conservation priority map\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8731892/v1/b39da3edbb1dd7ba1affb162.png"},{"id":101470254,"identity":"b5573063-d213-4b03-abd3-b6914242339a","added_by":"auto","created_at":"2026-01-30 05:15:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":130844,"visible":true,"origin":"","legend":"\u003cp\u003eLanduse/Landcover Changes between 1985 and 2024\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8731892/v1/f9e2168111bfb38493931406.png"},{"id":102101524,"identity":"d66e6117-1883-429c-a4ef-100bd580b48e","added_by":"auto","created_at":"2026-02-07 10:40:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2678991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8731892/v1/79f5c2a2-3378-496d-bf41-1c43f347eaca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geovisualizing Land Degradation Risk in Southeast Brazil: A Remote Sensing and GIS-Based Assessment","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eLand degradation represents a critical environmental and socio-economic challenge, threatening ecosystem services, agricultural productivity, and sustainable development worldwide (IPBES 2018). In Brazil, a country of vast natural resources and complex biomes, soil erosion driven by anthropogenic activities such as agricultural expansion, deforestation, and urbanization is a persistent issue, particularly in regions undergoing rapid land-use transition (Borrelli et al. 2017). The state of Rio de Janeiro, while known for its coastal metropolises, encompasses diverse hinterlands characterized by mosaics of Atlantic Forest remnants, agricultural lands, and urban-rural interfaces. These areas are susceptible to degradation processes, yet spatially explicit assessments integrating modern geospatial technologies remain essential for informing targeted conservation and land-use policies. This study focuses on a strategically selected area in the southeast of Brazil, situated within the state of Rio de Janeiro. As shown in the location map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the study area is positioned in the eastern part of the state, spanning approximately 3,020 km\u0026sup2; between the coordinates 42\u0026deg;48'0\"W to 42\u0026deg;30'0\"W and 23\u0026deg;00'0\"S to 22\u0026deg;30'0\"S. The region encompasses a suite of municipalities and districts, including Silva Jardim, Rio Bonito, Arma\u0026ccedil;\u0026atilde;o dos B\u0026uacute;zios, S\u0026atilde;o Vicente de Paula, and Cabo Frio, among others. This area represents a quintessential land-use transition zone, featuring interfaces between the Atlantic Forest biome, cultivated lands, coastal systems (including mangroves and sandy areas), and expanding urban peripheries. Such heterogeneity makes it a pertinent model for studying the spatial patterns and drivers of land degradation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRemote sensing (RS) and Geographic Information Systems (GIS) have emerged as indispensable tools for mapping, monitoring, and modeling land degradation at various scales (Visser et al. 2019). The integration of these technologies enables the systematic analysis of land cover change, identification of erosion features, and assessment of degradation risk over large and often inaccessible areas. However, the application of integrated RS/GIS frameworks for detailed risk assessment, particularly those that classify land into stability categories and conservation priorities, remains underexplored for many dynamic regions in southeastern Brazil. To address this, the present study adapts the methodology established by United Nations Environment Program, Priority Actions Program Regional Activity Centre (UNEP, PAP/RAC) methodology. While the PAP/RAC framework is well-established, its application has been largely confined to semi-arid and Mediterranean contexts (e.g., Sadiki et al. 2012; Mesrar et al. 2015; Tahouri et al. 2022; Lhoussaine et al. 2024). Applying this tool to a humid tropical coastal zone characterized by heavy rainfall and steep relief addresses a significant knowledge gap (Author et al. 2025).\u003c/p\u003e \u003cp\u003eThe objective of this research is to conduct a comprehensive land degradation risk assessment for the study area in Rio de Janeiro State through the integrated use of remote sensing and GIS techniques. Specifically, the study aims to map and quantify the spatial distribution of stable and unstable land classes; analyze the associated erosion processes; and develop a conservation priority map by synthesizing stability status and degradation risk. The findings are intended to provide a scientific basis for land management planning, supporting strategies for soil conservation, ecosystem restoration, and sustainable regional development in one of Brazil's most environmentally and economically significant states.\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Land degradation mapping\u003c/h2\u003e \u003cp\u003eLand degradation was mapped using a contemporary, very-high-resolution remote sensing approach. The primary data sources consisted of the most recent available very-high-resolution (\u0026lt;\u0026thinsp;1 m) satellite basemaps to ensure fine-scale accuracy: Airbus imagery (2025) and the Esri World Imagery service. To ensure consistency in spectral analysis and provide a recent medium-resolution baseline, a Landsat image from 2025 was also incorporated. Land cover classification was performed in ArcGIS using the Maximum Likelihood Supervised Classification algorithm. The classification schema was designed according to the established methodological framework of the United Nations Environment Programme's Priority Actions Programme/Regional Activity Centre (UNEP-PAP/RAC) for coastal area management (PAP/RAC 1997; UNEP-MAP/PAP 2000). Training samples for the supervised classification were developed and iteratively refined through visual interpretation of the very-high-resolution Airbus and Esri basemaps. This visual refinement against sub-meter imagery was critical for defining precise spectral signatures in the Landsat data and ensuring high classification accuracy in the absence of concurrent field validation.\u003c/p\u003e \u003cp\u003eThe classification procedure followed a dual-track logic based on land unit status, distinguishing between stable and unstable areas. Stable land units were defined and characterized based on three primary attributes: (1) the dominant land cover/use type (e.g., sandy areas, unmanaged areas with forest or agricultural potential, managed forest or agricultural areas, mangrove); (2) an inherent instability risk index (0\u0026thinsp;=\u0026thinsp;no risk, 1\u0026thinsp;=\u0026thinsp;low, 2\u0026thinsp;=\u0026thinsp;high, 3\u0026thinsp;=\u0026thinsp;highest), derived from intrinsic factors; and (3) the primary drivers of potential instability, including topography, geology, vegetation cover, and anthropogenic pressure. Unstable land units were identified and classified based on: (1) the dominant erosion process (sheet, or rill erosion); (2) the spatial extent of the affected area within the unit (localized: \u0026lt;30%, dominant: 30\u0026ndash;60%, widespread: \u0026gt;60%); and (3) a qualitative expansion trend index indicating the dynamic state of the erosion (0\u0026thinsp;=\u0026thinsp;stabilizing, 1\u0026thinsp;=\u0026thinsp;locally expanding, 2\u0026thinsp;=\u0026thinsp;regionally expanding, 3\u0026thinsp;=\u0026thinsp;advancing toward irreversibility).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Prioritization of Degradation Hotspots\u003c/h2\u003e \u003cp\u003eTo facilitate efficient resource allocation and targeted intervention, a systematic prioritization procedure was applied to identify critical land degradation hotspots. This procedure was developed following the methodological framework established by the United Nations Environment Programme's Priority Actions Programme/Regional Activity Centre (UNEP-PAP/RAC 2004). Fourteen variables were selected based on their direct relevance to local land degradation processes and prevailing socio-economic dynamics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each variable was assigned an impact score ranging from 1 (lowest impact) to 3 (highest impact). To ensure the scoring system reflected local conditions, the relative weight and scoring criteria for each variable were determined through structured expert consultation.\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\u003eVariables and Scoring Matrix for Land Degradation Intervention Prioritization\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\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoring (1\u0026ndash;3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical instability risk (stable areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 2\u0026thinsp;=\u0026thinsp;high, 3\u0026thinsp;=\u0026thinsp;critical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent of area affected (unstable areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;30%, 2\u0026thinsp;=\u0026thinsp;30\u0026ndash;60%, 3\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpansion trend of degradation (unstable areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;local, 2\u0026thinsp;=\u0026thinsp;widespread, 3\u0026thinsp;=\u0026thinsp;generalized/irreversible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiplicator for increased importance (causative agents or degradation process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;none, 2\u0026thinsp;=\u0026thinsp;increased, 3\u0026thinsp;=\u0026thinsp;highly increased\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluence on adjacent areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 2\u0026thinsp;=\u0026thinsp;high, 3\u0026thinsp;=\u0026thinsp;critical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverexploitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;insignificant,\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;significant,\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;crucial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural exodus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand tenure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther aggravating socio-economic factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue of current land use (local population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low,\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;moderate,\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue of current land use (national policy)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential for forestry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential for agriculture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther land use potentials (recreational, industrial, construction)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor each mapped land unit, all fourteen criteria were scored based on the defined matrix. Final composite prioritization scores were then calculated using distinct formulas for stable and unstable areas, reflecting their different underlying risk structures:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStable Areas Priority Score:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e[(A \u0026times; D\u0026thinsp;+\u0026thinsp;E) \u0026times; F \u0026times; G \u0026times; H \u0026times; I] + [(J\u0026thinsp;+\u0026thinsp;K) \u0026times; L \u0026times; M \u0026times; N]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnstable Areas Priority Score:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e[(B \u0026times; C \u0026times; D\u0026thinsp;+\u0026thinsp;E) \u0026times; F \u0026times; G \u0026times; H \u0026times; I] + [(J\u0026thinsp;+\u0026thinsp;K) \u0026times; L \u0026times; M \u0026times; N]\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe resulting numerical scores were classified into three distinct intervention priority tiers to guide management actions: High Priority: Score\u0026thinsp;\u0026ge;\u0026thinsp;60, Medium Priority: Score between 21 and 59, and Low Priority: Score\u0026thinsp;\u0026le;\u0026thinsp;20\u003c/p\u003e \u003cp\u003eThis quantitative framework translates the multi-criteria assessment into a clear, actionable hierarchy for conservation and restoration planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Spatio-Temporal Land Use and Land Cover Change Detection (1985\u0026ndash;2024)\u003c/h2\u003e \u003cp\u003eLand Use and Land Cover (LULC) data were sourced from the MapBiomas Project, a Brazilian multi-institutional initiative that generates annual, high-resolution land cover maps for Brazil and other regions using Landsat satellite imagery with a 30 m spatial resolution (MapBiomas 2025). This study analyzed multi-temporal LULC changes at ten-year intervals (1985, 1995, 2005, 2015, and 2024). The raster datasets for these five target years were downloaded and processed in ArcGIS 10.8. Each dataset was spatially clipped to the study area's vector boundary to isolate relevant LULC patterns. Area calculations for each land cover class were performed by aggregating pixel counts and applying the sensor\u0026rsquo;s spatial resolution, with results expressed in square kilometers, hectares, and proportional cover (%) to facilitate cross-temporal and inter-class comparison. The total study area used for all subsequent calculations is 3019.77 km\u0026sup2;, derived from the official vector boundary. The initial summed area of the clipped MapBiomas raster was approximately 9% larger. This discrepancy is attributed to the inclusion of partial coastal and island pixels during the raster clipping process, a known effect when working with complex shorelines at moderate (30 m) resolution. All final class areas and percentages were calculated based on the definitive vector-based total to ensure geographical accuracy, enabling a quantitative evaluation of land cover transitions over the 40-year period.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Nature and extent of land degradation\u003c/h2\u003e \u003cp\u003eThe land degradation assessment for the study area in Rio de Janeiro State revealed a clear spatial differentiation between stable and unstable land categories, as illustrated in the land degradation map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and summarized quantitatively in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The total study area covers approximately 3019.77 km\u0026sup2;. Spatial analysis indicates that the landscape is predominantly characterized by stable land conditions, constituting approximately 68.4% of the total area. The most extensive stable category is Unmanaged Areas with Agriculture Potential, covering 929.33 km\u0026sup2; (30.8% of the study area). This is complemented by significant coverage of Unmanaged Areas with Forest Potential, spanning 541.29 km\u0026sup2; (17.9%). Together, these two classes form broad, contiguous land bases in municipalities such as Silva Jardim, Morro Grande, and Rio Bonito. Managed lands also contribute substantially to stable terrain: Managed Areas with Agriculture Use account for 350.45 km\u0026sup2; (11.6%), extending mainly across Tamoios and S\u0026atilde;o Vicente de Paula, while Managed Areas with Forest Use cover 198.64 km\u0026sup2; (6.6%), predominantly occupying the mountainous regions of Rio Bonito and Silva Jardim. Minor yet ecologically significant stable features include Sandy Areas, occupying 38.71 km\u0026sup2; (1.3%), and Mangrove ecosystems, confined to 7.06 km\u0026sup2; (0.2%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, areas exhibiting active soil degradation, classified as unstable, constitute a smaller but notable portion of the landscape, totaling approximately 235 km\u003csup\u003e2\u003c/sup\u003e of the study area. Erosion processes are dominated by sheet erosion, the most widespread form of degradation. Dominant Sheet Erosion affects 112.77 km\u0026sup2; (3.7%), followed by Generalized Sheet Erosion across 71.42 km\u0026sup2; (2.4%), and Localized Sheet Erosion over 28.25 km\u0026sup2; (0.9%). Rill erosion is less extensive but present, with Dominant Rill Erosion occurring over 11.06 km\u0026sup2; (0.4%), Localized Rill Erosion over 7.16 km\u0026sup2; (0.2%), and Generalized Rill Erosion across 4.79 km\u0026sup2; (0.2%). Spatially, unstable areas are not randomly distributed but show a distinct association with specific land use types and vulnerable geomorphological units, often occurring at the interface between managed agricultural zones and natural vegetation. The mapped unstable areas are concentrated within the central and peripheral zones of the study area, encompassing municipalities such as Rio Bonito, Boa Esperan\u0026ccedil;a, Silva Jardim, S\u0026atilde;o Vicente de Paula, Iguaba Grande, and S\u0026atilde;o Pedro. Visual assessment suggests that erosion processes are particularly associated with agricultural frontiers and areas of land-use transition. Precise quantification of erosion extent per administrative unit would require further municipal-scale spatial analysis. The remaining surface is composed of anthropogenic and permanent natural features. Urbanized areas account for 411.69 km\u0026sup2; (13.6%), while water bodies cover 307.14 km\u0026sup2; (10.2%).\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\u003eAssessment of Stable and Unstable Land Categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAreas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLand Category Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(hec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eStable Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandy Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3870.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnmanaged Areas with Forest Potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e541.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54128.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnmanaged Areas with Agriculture Potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e929.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92933.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManaged Areas with Forest Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19864.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManaged Areas with Agriculture Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35045.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMangrove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e706.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Stable Area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2065.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e206548.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e68.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eUnstable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocalized Sheet Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2825.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDominant Sheet Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11277.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneralized Sheet Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7141.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocalized Rill Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e715.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDominant Rill Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1105.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneralized Rill Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e478.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Unstable Area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e235.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e23544.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUrban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e411.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41169.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e307.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30714.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Surface Study Area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3019.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e301977.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Conservation Priority Assessment\u003c/h2\u003e \u003cp\u003eThe land degradation assessment was extended to classify areas based on their conservation priority (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The resulting priority map and corresponding areal statistics (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) reveal a landscape where the majority of the area is designated for medium-level conservation attention. Stable areas account for 68.37% (2064.65 km\u0026sup2;) of the total study area and are subdivided into three priority levels. The largest portion is classified as Stable Medium Priority, encompassing 1560.80 km\u0026sup2; or 51.69% of the total landscape. This suggests that over half of the region, while currently stable, possesses characteristics that warrant monitoring or preventive management to maintain its condition. Stable Low Priority areas cover 309.28 km\u0026sup2; (10.24%), indicating zones of high resilience or lower degradation risk. In contrast, Stable High Priority areas, though smaller at 194.57 km\u0026sup2; (6.44%), highlight specific stable zones that are critically important for conservation, likely due to high ecological value or proximity to vulnerable features. Unstable areas, representing active degradation across 7.78% (235.03 km\u0026sup2;) of the study area, are further categorized by intervention urgency. The most critical category is Unstable High Priority, covering 140.17 km\u0026sup2; (4.64% of the total area). This signifies that the majority of eroded land requires immediate and targeted intervention to mitigate soil loss. Unstable Medium Priority areas span 77.97 km\u0026sup2; (2.58%), while Unstable Low Priority zones are minimal at 16.89 km\u0026sup2; (0.56%). The remaining surface is comprised of Urban Area (412.32 km\u0026sup2;, 13.65%) and Water bodies (307.58 km\u0026sup2;, 10.19%). Spatially, the priority map (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicates a distinct zonation. High and medium-priority unstable areas are frequently concentrated at the interface between agricultural frontiers and natural vegetation, particularly within the central and southern parts of the study area, overlapping with municipalities such as Rio Bonito, Boa Esperan\u0026ccedil;a, Silva Jardim, S\u0026acirc;o Pedro, and S\u0026atilde;o Vicente de Paula.\u003c/p\u003e \u003cp\u003eThe extensive Stable Medium Priority class forms a contiguous matrix across the region, suggesting a widespread need for sustainable land use planning to prevent future degradation. This priority classification provides a strategic framework for directing conservation resources, emphasizing that preventive measures in medium-priority stable zones are as crucial as rehabilitation efforts in high-priority unstable areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConservation priority assessment results\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\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConservation Priority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(hec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable Low Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e309.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30927.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable Medium Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1560.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156080.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable High Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19456.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Stable Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2064.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e206464.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e68.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUnstable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstable Low Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1689.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstable Medium Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7797.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstable High Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14016.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Unstable Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e235.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e23503.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUrban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e412.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41231.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30758.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal surface area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3019.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301957.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Land Use and Land Cover Change Analysis (1985\u0026ndash;2024)\u003c/h2\u003e \u003cp\u003eBetween 1985 and 2024, the study area experienced substantial shifts in land cover composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Urban areas exhibited the most pronounced expansion (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), increasing by 199.42% (203.50 km\u0026sup2;). Pastureland also expanded significantly, growing by 14.74% (167.61 km\u0026sup2;), and remained the dominant land use category throughout the period. Mining activities increased dramatically by 6147.73%, albeit from a very small initial extent, while Other Temporary Crops expanded notably by 445.01%. Mangrove areas increased by 53.13% (+\u0026thinsp;0.34 km\u0026sup2;), suggesting positive restoration or conservation outcomes, and water bodies (River, Lake and Ocean) expanded by 7.38% (+\u0026thinsp;23.26 km\u0026sup2;). New land use categories emerged during the study period, including Sugar cane (0.64 km\u0026sup2; in 2024) and Forest Plantation (0.01 km\u0026sup2; in 2024).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsolidated Land Use \u0026amp; Land Cover Changes (km\u003csup\u003e2\u003c/sup\u003e) 1985\u0026ndash;2024\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC CLASSES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1985 (km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003cp\u003e(km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003cp\u003e(km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003cp\u003e(km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003cp\u003e(km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNet Change\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNet Change\u003c/p\u003e \u003cp\u003e(km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest Formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e555.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e484.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e497.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e517.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e548.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-7.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMangrove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest Plantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-13.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-44.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1137.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1303.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1384.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1453.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1304.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e167.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSugar cane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMosaic of Uses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1017.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e864.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e710.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e599.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e668.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-34.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-349.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeach, Dune and Sand Spot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-13.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e254.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e305.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e199.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e203.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther non Vegetated Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRocky Outcrop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-88.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6147.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAquaculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-63.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-31.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypersaline Tidal Flat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-90.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRiver, Lake and Ocean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e332.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e329.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e338.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoybean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Temporary Crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e445.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWooded Sandbank Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-16.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConversely, several land cover classes experienced notable declines. Mosaic of Uses decreased by 34.35% (\u0026minus;\u0026thinsp;349.49 km\u0026sup2;), representing the largest absolute reduction. Aquaculture declined substantially by 63.39% (\u0026minus;\u0026thinsp;31.56 km\u0026sup2;), and Grassland diminished by 44.81%. Rocky Outcrop areas were reduced by 88.37%, while Wetlands decreased by 13.30% (\u0026minus;\u0026thinsp;4.86 km\u0026sup2;). Forest Formation showed a modest net decline of 1.26% (\u0026minus;\u0026thinsp;7.02 km\u0026sup2;), though it recovered somewhat after reaching its lowest extent in 1995. Other categories, such as Beach, Dune and Sand Spot and Wooded Sandbank Vegetation, also registered reductions of 13.73% and 16.47%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Discussion of Land Degradation Patterns\u003c/h2\u003e \u003cp\u003eThe finding that stable land conditions dominate the study area (68.4%) aligns with national-scale analyses of Brazilian land cover, which show that despite intense historical pressure, significant portions of the landscape persist as stable natural vegetation or managed agricultural land (Souza et al. 2020). The most significant stable classes\u0026mdash;Unmanaged Areas with Agriculture and Forest Potential, collectively represent nearly half (48.7%) of the total landscape and constitute the region's primary reservoir of productive and ecological capital. However, their classification as \"unmanaged\" or with specific \"potential\" indicates a latent vulnerability. These lands represent the very frontiers most susceptible to future conversion, a transition strongly associated with the initiation of soil degradation processes globally (Lambin and Meyfroidt 2011) and identified as a primary driver of ecosystem change worldwide (IPBES 2018). The high proportion of stable area, therefore, should be interpreted not as a lack of threat but as a critical window for implementing preventive land-use planning and sustainable management practices to avert future degradation.\u003c/p\u003e \u003cp\u003eConversely, the mapped unstable areas, though covering a smaller portion of the landscape (7.8%), reveal clear and concerning spatial patterns that are critical for intervention. The predominance of sheet erosion\u0026mdash;accounting for the majority (7.04%) of all unstable area\u0026mdash;is consistent with global models identifying it as the most extensive form of soil loss by water (Panagos et al. 2018; Borrelli et al. 2017) and a major contributor to global land degradation (IPBES 2018). The spatial correlation of these erosion hotspots with \"agricultural frontiers\" and interfaces between managed land and natural vegetation underscores a primary driver: land-use transition. This pattern aligns with the classic syndrome of land change where frontier expansion triggers soil degradation (Lambin and Meyfroidt 2011). The resulting soil degradation is a direct consequence of anthropogenic pressure, occurring where natural systems are being modified or where agricultural practices may be exceeding the land's carrying capacity. Similar intense anthropogenic pressures, notably urbanization and land-use change, are documented as critical factors altering both terrestrial and coastal ecosystems in southeastern Brazil (Santos et al. 2022; Souza et al. 2020). The concentration of unstable areas in central and peripheral zones highlights specific localities where current land-use systems warrant urgent review. Targeted soil conservation measures, aligned with and reinforcing instruments such as Brazil's Forest Code (Strassburg et al. 2014), are most needed in these priority intervention zones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Discussion of Conservation Priorities and Management Implications\u003c/h2\u003e \u003cp\u003eThe priority classification provides a strategic, multi-tiered framework for directing conservation resources. The finding that over half of the landscape (51.69%) is classified as Stable Medium Priority is significant. This vast area represents lands that are currently stable but possess inherent or contextual vulnerabilities, aligning with the concept of \"potential degradation\" risk areas that require proactive management to maintain ecosystem services (Visser et al. 2019). Investing in sustainable agricultural practices, agroforestry systems, and compliance with environmental legislation within these zones is a cost-effective strategy to prevent their transition into future degradation hotspots, an approach advocated for in preventative conservation planning (Nkonya et al. 2016). In the Brazilian context, such preventive measures are central to the implementation of the Forest Code, which aims to protect areas of permanent preservation (APPs) and legal reserves to maintain ecological stability on private lands (Soares-Filho et al. 2014; Strassburg et al. 2014).\u003c/p\u003e \u003cp\u003eConversely, the Unstable High Priority areas (4.64% of the total), though smaller in extent, demand immediate and targeted rehabilitation efforts. The concentration of these critical zones at land-use interfaces underscores that degradation is a direct symptom of unsustainable transitions, a pattern well-documented in other global contexts (Lambin and Meyfroidt 2011). The application of the UNEP/PAP/RAC framework in this humid tropical region successfully identified these hotspots, demonstrating its utility beyond the Mediterranean and semi-arid contexts for which it was originally developed (e.g., Sadiki et al. 2012; Lhoussaine et al. 2024). The methodology\u0026rsquo;s structured integration of biophysical and socio-economic criteria offers a replicable model for translating complex degradation assessments into actionable intervention classes, which is a critical step for operationalizing land degradation assessments (Kust et al. 2017).\u003c/p\u003e \u003cp\u003eThis dual-focused strategy urgent rehabilitation of high-priority unstable areas (addressing existing degradation) coupled with the safeguarding of medium-priority stable lands (avoiding future degradation) is essential for achieving LDN targets, which require balancing \u0026ldquo;losses\u0026rdquo; to degradation against \u0026ldquo;gains\u0026rdquo; from improvement (UNCCD GPG v2, Section 1.4). The resulting priority map directly translates a multi-criteria assessment into a practical blueprint for environmental agencies, enabling efficient, spatially explicit allocation of resources. Such prioritization is fundamental for operationalizing LDN and aligning local actions with national LDN targets and SDG 15.3, as it moves beyond merely calculating the proportion of degraded land to informing where and how to act (Sims et al. 2021). By emphasizing that preventive measures in stable areas are as crucial as rehabilitation in degraded ones, this study supports an integrated land management approach aimed at achieving a land degradation-neutral world.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Discussion of LULC dynamics\u003c/h2\u003e \u003cp\u003eThe observed land-use and land-cover (LULC) transitions in the study area from 1985 to 2024 align closely with broader regional dynamics documented in the Atlantic Forest biome and Rio de Janeiro state, where key drivers include agricultural expansion, urbanization, deforestation, and policy-induced recovery. Urban areas expanded dramatically by 203.50 km\u0026sup2; (199.42%), consistent with patterns of metropolitan growth and peri-urbanization across the state, driven by population influx and economic development (Pereira 2019). This expansion often converts agricultural mosaics into built-up land, increasing pressure on surrounding rural landscapes. Concurrently, pasture expanded by 14.74% (167.61 km\u0026sup2;) while Mosaic of Uses declined by 34.35% (349.49 km\u0026sup2;), indicating a process of agricultural intensification and landscape simplification. This widespread trend in southeastern Brazil is fueled by demand for livestock products, were heterogeneous farming systems transition to more homogeneous pasturelands, often operating below their productive potential (Strassburg et al. 2014).​ Forest Formation showed a modest net loss of 1.26% (7.02 km\u0026sup2;) but exhibited recovery after 1995. This pattern mirrors the slow but positive stabilization and regrowth observed in the Atlantic Forest biome, driven by strengthened environmental legislation such as Brazil's Forest Code and reduced deforestation (Rezende et al. 2018; MapBiomas 2025).\u003c/p\u003e \u003cp\u003eDivergent coastal ecosystem trends were observed: mangrove areas expanded by 53.13% (0.34 km\u0026sup2;), while wetlands contracted by 13.30% (4.86 km\u0026sup2;). This highlights differential pressures from coastal development and aquaculture versus inland drainage for agriculture. Mangroves appear to have benefited from protective policies and natural expansion; studies in the Rio de Janeiro coastline document mangroves encroaching landward and expanding over salt marshes such as \u003cem\u003eSporobolus virginicus\u003c/em\u003e. This expansion is often a response to local geomorphological shifts, sedimentation patterns, and relative sea-level rise. Specifically, changes in sedimentary processes in tropical deltaic systems can facilitate the colonization of mangroves over hypersaline flats (apicums) and adjacent vegetation (Santos et al. 2022; Costa Santos et al. 2019).\u003c/p\u003e \u003cp\u003eFinally, the emergence of sugarcane (0.64 km\u0026sup2; in 2024) and sharp rise in Other Temporary Crops (+\u0026thinsp;445.01%, 2.43 km\u0026sup2; from a small base) signal nascent agricultural shifts tied to market demands and climate-adapted cropping in southeast Brazil, including Rio de Janeiro, necessitating ongoing monitoring (Maria Guimar\u0026atilde;es 2015; Souza et al. 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations and Implications for Management\u003c/h2\u003e \u003cp\u003eThis study provides a robust spatial assessment of land degradation and conservation priorities. However, certain limitations should be acknowledged to contextualize the findings. Firstly, the reliance on remote sensing, while enabling comprehensive coverage, would benefit from systematic ground-truthing for the validation of erosion subtypes (e.g., distinguishing sheet from rill erosion intensity) and for calibrating the spectral signatures of specific land-use classes. Secondly, the conservation prioritization, though based on a structured framework informed by expert criteria, could be enhanced through participatory engagement with local farmers, landowners, and community representatives. Such engagement would capture nuanced socio-economic values, land tenure complexities, and local ecological knowledge, refining the scores for variables related to land use value and socio-economic pressure (Variables J, G, H, I).\u003c/p\u003e \u003cp\u003eDespite these limitations, the findings offer clear and actionable implications for land management in the State of Rio de Janeiro. The spatially explicit priority map (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) serves as a direct decision-support blueprint for environmental agencies at state and municipal levels. It enables the efficient direction of technical assistance, conservation incentives (e.g., payments for ecosystem services), and regulatory enforcement towards the most critical intervention hotspots, particularly the Unstable High Priority areas identified in municipalities such as Rio Bonito and Boa Esperan\u0026ccedil;a.\u003c/p\u003e \u003cp\u003eUltimately, the results advocate for an integrated, two-pronged management strategy essential for achieving land degradation neutrality. This strategy must couple the urgent rehabilitation of high-priority unstable areas with robust policies and incentives that safeguard the extensive Stable Medium Priority lands from future degradation. Investing in sustainable agricultural practices, agroforestry, and adherence to the Forest Code within these stable-yet-vulnerable zones represents a cost-effective, preventative approach to conservation. By implementing this dual focus, policymakers and land managers can work towards ensuring the long-term ecological functionality and economic productivity of this environmentally significant region.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. RECOMMENDATIONS","content":"\u003cp\u003eBased on the prioritization of land degradation hotspots in southeastern Brazil, we propose targeted interventions tailored to the socio-environmental context of the Rio de Janeiro study area.\u003c/p\u003e \u003cp\u003eFirstly, for the unstable Areas: these areas, encompassing High Priority (140.17 km\u0026sup2;) and Medium Priority (77.97 km\u0026sup2;) zones, are characterized by active sheet and rill erosion, often at agricultural frontiers and land-use interfaces. Curative and protective interventions include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSoil Conservation Infrastructure: implement contour bunds, terraces, and vegetative barriers in agricultural zones experiencing dominant sheet erosion to reduce runoff velocity and soil loss.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEcological Restoration: reforest critical high-priority zones using native Atlantic Forest species to stabilize degraded slopes and restore hydrological function, particularly in areas adjacent to remaining forest fragments.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGovernance and Enforcement: strengthen the enforcement of the Brazilian Forest Code, ensuring the protection and restoration of Permanent Preservation Areas (APPs) and Legal Reserves within agricultural properties.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustainable Land Management Support: provide technical assistance and incentives to farmers in erosion-prone areas for adopting soil conservation practices and integrated crop-livestock-forestry systems.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSecondly, for the At-Risk Stable Areas: measures for these currently stable but vulnerable lands, primarily classified as Medium Priority (1560.80 km\u003csup\u003e2\u003c/sup\u003e) focus on maintaining stability and preventing future degradation:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSustainable Agricultural Intensification: Promote conservation agriculture, agroforestry, and soil cover maintenance in unmanaged areas with agricultural potential to prevent their conversion into future erosion hotspots.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLand-Use Zoning and Planning: integrate degradation risk maps into municipal and regional land-use planning (Zoneamento Ecol\u0026oacute;gico-Econ\u0026ocirc;mico - ZEE) to guide sustainable development away from high-risk zones and protect areas with high conservation value.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePayment for Ecosystem Services (PES): develop and implement PES schemes to incentivize landowners in stable medium-priority areas to maintain forest cover, adopt sustainable practices, and avoid land conversion.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo enhance feasibility, these measures should be integrated into existing state and municipal environmental policies and supported by funding mechanisms such as environmental compensation and the Brazilian Fund for Biodiversity (FUNBIO). Future efforts must prioritize participatory validation of the mapped degradation zones with local stakeholders and ground-truthing of erosion processes. By situating the PAP/RAC framework within Brazil\u0026rsquo;s robust environmental governance structure, this study provides a replicable, spatially explicit model for guiding soil conservation, ecosystem restoration, and sustainable land-use planning in tropical coastal regions.\u003c/p\u003e"},{"header":"6. CONCLUSION","content":"\u003cp\u003eThis study successfully applied an integrated remote sensing and GIS-based UNEP-PAP/RAC framework to assess land degradation risk and prioritize conservation needs in a humid tropical coastal region of southeastern Brazil. The findings reveal a landscape dominated by stable yet vulnerable lands, with significant areas classified as medium priority for preventive conservation. Critically, active degradation hotspots were clearly identified, primarily linked to agricultural frontiers and land-use transitions, underscoring the role of anthropogenic pressure in driving soil erosion. The resulting spatially explicit priority maps provide a science-based, actionable tool for land managers and policymakers, enabling targeted interventions to rehabilitate high-risk areas while safeguarding stable zones from future degradation. Along with the targeted interventions recommended, this approach supports the strategic pursuit of land degradation neutrality and sustainable land management in one of Brazil\u0026rsquo;s most dynamic and environmentally significant regions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003e\u0026rdquo;The authors have no relevent financial or non-financial to disclose\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026lsquo;Ethical responsibilities of Authors\u0026rsquo; as found in the Instructions for Authors.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eAuthor 2 has recieved research support from The British Academy/Cara/Leverhulme Researchers at Risk Research Support Grant. The University of Manchester\u003c/p\u003e \u003cp\u003e\u0026ldquo;The other authors declare that no funds, grants or other support were recived during the prepartion of this manuscript\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026ldquo;All authors have contributed to the study conception design and material preparation, data collection and analysis. The first draft of the manuscript was written by author 1 and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026rdquo;\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENT\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere gratitude to the Foundation for Research Support of the State of Rio de Janeiro (Funda\u0026ccedil;\u0026atilde;o Carlos Chagas Filho de Amparo \u0026agrave; Pesquisa do Estado do Rio de Janeiro, FAPERJ) for their support and provision of scientific scholarship (E- 26/200611/2025).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData fully available upon request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAuthor, Saboya M, Santos C, Vargas R and Ferreira Dias F (2025) Land degradation assessment in the Angra dos Reis (Brazil) using remote sensing and GIS. \u003cem\u003eJ. Agricultural Science \u0026amp; Technology\u003c/em\u003e 2\u0026amp;3(1): 40-56. https://doi.org/10.36108/jast/5202.320.0130\u003c/li\u003e\n\u003cli\u003eBorrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C, Meusburger K, Modugno S, Sch\u0026uuml;tt B, Ferro V, Bagarello V, Oost KV, Montanarella L and Panagos P (2017) An assessment of the global impact of 21st century land use change on soil erosion. \u003cem\u003eNature Communications \u003c/em\u003e8(1):1-13. https://doi.org/10.1038/s41467-017-02142-7\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCosta Santos CS, Dias FF, Franz B, Santos PRA, Rodrigues T, Vargas R and Am\u0026eacute;rico dos Santos C (2019)\u003c/strong\u003eRelative Sea Level Rise Effects at the Marambaia Barrier Island and Guaratiba Mangrove: Sepetiba Bay (SE Brazil). \u003cem\u003eJournal of Sedimentary Environments 4\u003c/em\u003e(3): 249\u0026ndash;62.https://doi.org/10.12957/jse.2019.44397\u003c/li\u003e\n\u003cli\u003eIPBES (2018) \u003cem\u003eThe IPBES assessment report on land degradation and restoration\u003c/em\u003e. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/zenodo.3237392 \u003c/li\u003e\n\u003cli\u003eKust G, Andreeva O and Cowie A (2017) Land Degradation Neutrality: Concept development, practical applications and assessment. \u003cem\u003eJournal of Environmental Management \u003c/em\u003e195(Pt 1): 16\u0026ndash;24. https://doi.org/10.1016/j.jenvman.2016.10.043\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLambin EF and Meyfroidt P (2011)\u003c/strong\u003e Global land use change, economic globalization, and the looming land scarcity. \u003cem\u003eProceedings of the National Academy of Sciences 108\u003c/em\u003e(9): 3465-3472. https://www.pnas.org/doi/epdf/10.1073/pnas.1100480108 \u003c/li\u003e\n\u003cli\u003eLhoussaine EM, Meryem M, Moncef B, Mustapha M, Noureddine A, Abdessalam BH, Yousra R and Brahim D (2024) A GIS-based modified PAP/RAC model and Caesium-137 approach for water erosion assessment in the Raouz catchment, Morocco. \u003cem\u003eEnvironmental Research\u003c/em\u003e 251(1): 118-460. https://doi.org/10.1016/j.envres.2024.118460 \u003c/li\u003e\n\u003cli\u003eMapBiomas. 2025. https://Brazil.mapbiomas.org/en/2025/08/13/Brazil-quatro-decadas-de-transformacao-na-cobertura-e-uso-da-terra-revelam-desafios-e-oportunidades/ \u003c/li\u003e\n\u003cli\u003eMaria Guimar\u0026atilde;es (2015) A new chance for the Atlantic Forest\u003cem\u003e. \u003c/em\u003e\u003cem\u003eRevista Pesquisa FAPESP\u003c/em\u003e. Issue # 237. https://revistapesquisa.fapesp.br/en/a-new-chance-for-the-atlantic-forest/ \u003c/li\u003e\n\u003cli\u003eMesrar H, Sadiki A, Navas A, Faleh A, Quijano L and Chaaouan J (2015) Mod\u0026eacute;lisation de l\u0026apos;\u0026eacute;rosion hydrique et des facteurs causaux: cas de l\u0026apos;Oued Sahla, rif central, \u003cem\u003eMaroc. Zeitschrift\u003c/em\u003e\u003cem\u003ef\u0026uuml;r Geomorphologie\u003c/em\u003e 59(3): 495 - 514.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNkonya E, Mirzabaev A and von Braun J (Eds.) (2016)\u003c/strong\u003e\u003cem\u003eEconomics of Land Degradation and Improvement \u0026ndash; A Global Assessment for Sustainable Development\u003c/em\u003e. Springer International Publishing. https://doi.org/10.1007/978-3-319-19168-3\u003c/li\u003e\n\u003cli\u003ePanagos P, Standardi G, Borrelli P, Lugato E, Montanarella L and Bosello F (2018) Cost of agricultural productivity loss due to soil erosion in the European Union: From direct cost evaluation approaches to the use of macroeconomic models. \u003cem\u003eLand Degradation \u0026amp; Development \u003c/em\u003e29(3): 471-484. https://onlinelibrary.wiley.com/doi/10.1002/ldr.2879 \u003c/li\u003e\n\u003cli\u003ePereira RHM (2019) Future accessibility impacts of transport policy scenarios: Equity and sensitivity to travel time thresholds for bus rapid transit expansion in Rio de Janeiro. \u003cem\u003eJournal of Transport Geography\u003c/em\u003e74: 321-332\u003cem\u003e.\u003c/em\u003e https://doi.org/10.1016/j.jtrangeo.2018.12.005\u003c/li\u003e\n\u003cli\u003ePriority Actions Program Regional Activity Centre (PAP/RAC) (1997) \u003cem\u003eGuidelines for mapping and\u003c/em\u003e\u003cem\u003emeasurement of rainfall-induced erosion\u003c/em\u003e\u003cem\u003eprocesses in the Mediterranean coastal areas\u003c/em\u003e. Split, Croatia.\u003c/li\u003e\n\u003cli\u003eRezende CL, Scarano FR, Assad ED, Joly CA, Metzger JP, Strassburg BBN, Tabarelli M, Fonseca A and Bastos A (2018) From hotspot to hopespot: An opportunity for the Brazilian Atlantic Forest. \u003cem\u003ePerspectives in Ecology and Conservation\u003c/em\u003e 16(4): 208-214. https://doi.org/10.1016/j.pecon.2018.10.002\u003c/li\u003e\n\u003cli\u003eSantos CA, Vargas R, Carvalho VR, Pinheiro VM, Santos PRA and Dias FF (2022) Spatio-temporal evolution of environmental dynamics in Guaratiba State Biological Reserve and its surroundings, Rio de Janeiro, Brazil. \u003cem\u003eCaminhos de Geografia\u003c/em\u003e 23(90): 120\u0026ndash;138. http://doi.org/10.14393/RCG239061036 \u003c/li\u003e\n\u003cli\u003eSadiki A, Mesrar H and Faleh A (2012) Mod\u0026eacute;lisation et cartographie des risques de l\u0026apos;\u0026eacute;rosion hydrique: cas du bassin versant de l\u0026rsquo;Oued Larbaa, Maroc. \u003cem\u003ePapeles de Geograf\u0026iacute;a\u003c/em\u003e 55\u0026ndash;56,179\u0026ndash;188.\u003c/li\u003e\n\u003cli\u003eSoares-Filho B, Raj\u0026atilde;o R, Macedo M, Carneiro A, Costa W, Coe M, Rodrigues H and Alencar A (2014) Cracking Brazil\u0026apos;s Forest Code. \u003cem\u003eScience,\u003c/em\u003e344(6182): 363\u0026ndash;364. https://doi.org/10.1126/science.1246663\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSouza CMZ, Shimbo J, Rosa MR, Parente LL, Alencar AA, Rudorff BFT and \u003c/strong\u003e\u003cstrong\u003eAzevedo T (2020)\u003c/strong\u003e Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and earth engine. \u003cem\u003eRemote Sensing 12\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e17):1-27. https://doi.org/10.3390/rs12172735\u003c/li\u003e\n\u003cli\u003eSims NC, Newnham GJ, England JR, Guerschman J, Cox SJD, Roxburgh SH, Viscarra Rossel RA, Fritz S and Wheeler I (2021) Good Practice Guidance. SDG Indicator 15.3.1, Proportion of Land That Is Degraded Over Total Land Area. Version 2.0. United Nations Convention to Combat Desertification, Bonn, Germany.\u003c/li\u003e\n\u003cli\u003eStrassburg BN, Agnieszka E Latawiec, Luis G Barioni, Carlos A Nobre, Vanderley P da Silva, Judson F Valentim hi , Murilo Vianna and Eduardo D. Assad (2014) When enough should be enough: Improving the use of current agricultural lands could meet production demands and spare natural habitats in Brazil. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e 8:84-97. https://doi.org/10.1016/j.gloenvcha.2014.06.001\u003c/li\u003e\n\u003cli\u003eTahouri J, Sadiki A, Karrat L, Johnson VC, Chan NW, Fei Z and Kung HT (2022) Using a modified PAP/RAC model and GIS for mapping water erosion and causal risk factors: case study of the Asfalou watershed, Morocco. \u003cem\u003eInternational Soil and Water Conservation \u003c/em\u003e\u003cem\u003eResearch\u003c/em\u003e 10: 254\u0026ndash;272.\u003c/li\u003e\n\u003cli\u003eUnited Nations Environment Programme (UNEP)/ Mediterranean Action Plan (MAP)/ Priority Actions Programme (PAP). (2004). \u003cem\u003eImproving coastal land degradation monitoring in Lebanon and Syria: Country report Syria\u003c/em\u003e. https://wedocs.unep.org/bitstream/handle/20.500.11822/1859/syria.pdf\u003c/li\u003e\n\u003cli\u003eUnited Nations Environment Programme (UNEP)/Mediterranean Action Plan (MAP)/ Priority Actions Programme (PAP). (2000). \u003cem\u003eGuidelines for erosion and desertification control management with particular reference to Mediterranean coastal areas\u003c/em\u003e. Available at: https://iczmplatform.org/storage/documents/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf\u003c/li\u003e\n\u003cli\u003eVisser S, Keesstra S, Maas G and de Cleen M (2019) Soil as a basis to create enabling conditions for transitions towards sustainable land management as a key to achieve the SDGs by 2030. \u003cem\u003eSustainability \u003c/em\u003e11(23): 1-19. https://doi.org/10.3390/su11236792 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geovisualization, Spatial multi-criteria analysis, Land degradation risk mapping, Remote sensing and GIS, Soil erosion, Conservation prioritization","lastPublishedDoi":"10.21203/rs.3.rs-8731892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8731892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand degradation poses a critical threat to ecosystem services and sustainable development, especially in regions experiencing rapid land-use transitions. This study employs an integrated geospatial approach\u0026mdash;combining remote sensing, geographic information systems (GIS), and spatial multi-criteria analysis\u0026mdash;to assess and visualize land degradation risk in a strategic coastal region of southeastern Brazil (Rio de Janeiro State). Using the United Nations Environment Programme's Priority Actions Programme Regional Activity Centre (UNEP-PAP/RAC) framework applied to recent satellite imagery, we generated spatially explicit maps classifying land into stable and unstable categories. A geospatial prioritization model incorporating biophysical and socio-economic variables was developed to identify conservation hotspots and support decision-making. Results show that 68.4% of the landscape is stable, largely consisting of unmanaged areas with agricultural and forest potential, while 7.8% is unstable, with sheet erosion concentrated at agricultural frontiers. Priority mapping classified 51.7% of the area as Stable Medium Priority, revealing widespread latent vulnerability, and 4.6% as Unstable High Priority, necessitating urgent intervention. Complementary analysis of land-use change (1985\u0026ndash;2024) highlighted a 199% urban expansion and a 34% decline in agricultural mosaics, underscoring anthropogenic drivers of degradation. This study not only validates the PAP/RAC framework in a humid tropical coastal setting but also delivers actionable geovisualization outputs and a spatial decision-support tool for targeted land management, contributing to soil conservation and sustainable development policy in Brazil.\u003c/p\u003e","manuscriptTitle":"Geovisualizing Land Degradation Risk in Southeast Brazil: A Remote Sensing and GIS-Based Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 05:15:10","doi":"10.21203/rs.3.rs-8731892/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"796a1f42-5a4b-45b0-a6c7-9b06b19ca9cb","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-08T02:48:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 05:15:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8731892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8731892","identity":"rs-8731892","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}