Geoinformatics Application for Land Degradation Mapping in Paraty Municipality Southeast Brazil

Geoinformatics Application for Land Degradation Mapping in Paraty Municipality Southeast Brazil | 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 Geoinformatics Application for Land Degradation Mapping in Paraty Municipality Southeast Brazil Mohammad Al Abed, Fábio Ferreira Dias, Menércia Job Macamo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8872932/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 This study identifies areas affected by water erosion in the municipality of Paraty, Rio de Janeiro, Brazil, where natural and human pressures exacerbate land degradation. To address this issue, detailed land degradation and prioritization maps were produced using the UNEP/Priority Actions Programme methodology, representing its novel application in a tropical coastal environment and integrating multi-satellite imagery with Geographic Information System (GIS) tools. The results show that while most of the municipality is stable (838.34 km²), unstable zones (51.77 km²) persist near expanding urban areas and forest margins. Urban growth and deforestation remain the main drivers of degradation, highlighting the socio-environmental vulnerability of the region. The prioritization analysis identified high-priority zones (16.45 km² unstable and 60.80 km² stable) requiring immediate conservation or restoration measures. Land-use and land-cover changes between 1985 and 2023 revealed increases in both forest (+ 1.91%) and urban areas (+ 0.5%), alongside declines in pasture and mosaic-use areas, illustrating the complex interplay between regeneration and anthropogenic pressure. Beyond technical mapping, this research provides a decision-support tool for municipal land-use planning, environmental licensing, and disaster risk reduction policies under Brazil’s Forest Code. The study demonstrates the value of combining geospatial analysis with socio-environmental approaches to promote sustainable territorial management in fragile coastal systems. Agricultural Engineering Agronomy Forestry Environmental Engineering Geographic Information Systems land degradation soil erosion geoinformatics socio-environmental vulnerability Atlantic Forest Paraty Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The municipality of Paraty is located in the south of the state of Rio de Janeiro (Fig. 1 ). According to the Brazilian Institute of Geography and Statistics (IBGE), it has a population of 45,243 inhabitants, a density of 48.95 inhabitants/km², and an area of 924.3 km² (IBGE, 2022). The region is characterized by a humid subtropical climate and a complex geography shaped by the interaction between the Serra do Mar mountain range and the Atlantic Ocean, which makes the area highly vulnerable to landslides and coastal flooding. The terrain includes mountains, a jagged coastline with numerous islands, and narrow coastal plains. The steep slopes of the Serra do Mar, with altitudes reaching up to 2,088 meters, are particularly prone to landslides during intense rainfall events (Pinheiro et al., 2021). Paraty lies within the Atlantic Forest biome, characterized by dense ombrophilous forests with complex vegetation including epiphytes, ferns, and bromeliads (Joly et al., 2014 ). The soils are generally acidic and of low natural fertility; combined with steep topography, high rainfall, and shallow soil depth, this creates high susceptibility to water erosion and landslides (Guerra et al., 2013 ). From a socio-environmental perspective, Paraty faces multiple challenges that intensify soil erosion and land degradation, such as deforestation, unplanned urbanization, land-use change, and tourism-related infrastructure development. These pressures, together with inadequate trail management, have made soil erosion an increasingly critical environmental problem in several areas of the municipality (Luana et al., 2019 ; Creed et al., 2007 ). Since the 1960s, the region has undergone considerable enthronement due to social and economic development and the rapid increase in urbanization and industrialization (Neto et al. 2017 ; Cordeiro et al. 2020 ; Gallardo et al. 2021 ). While numerous studies have explored the ecological and geomorphological aspects of these transformations (Monteiro et al., 2012 ; Vilela et al., 2014; Tomasella et al., 2018 ), fewer have examined the interactions between environmental degradation and social vulnerability in humid tropical coastal zones. Recent findings indicate that recurrent landslides and erosion events are exacerbated by human occupation in environmentally fragile areas (Vahideh et al., 2025). These trends underscore the urgent need for integrated, geospatially informed approaches that connect environmental diagnostics with policy frameworks and community-level management. In this context, the present study applies the methodology of the United Nations Environment Programme’s Priority Actions Programme Regional Activity Centre (UNEP, PAP/RAC) to map and assess land degradation in Paraty (UNEP, MAP, PAP, 2000; PAPRAC, 1997). This approach has been validated in numerous studies across Mediterranean and semi-arid regions (UNEP, PAP/RAC. 2004, Sadiki et al., 2012 ; Mesrar et al., 2015 ; Tahouri et al., 2019 , 2022 , Lhoussaine et al., 2024, Al Abed et al., 2024 ). but its application in tropical coastal environments remains limited. The study therefore represents a novel adaptation of this framework to the humid Atlantic Forest context of southeastern Brazil. By integrating remote sensing, GIS analysis, and multi-criteria prioritization, it identifies erosion-prone zones and ranks them according to their remediation priority. Beyond its technical dimension, this research contributes to sustainable planning and governance by generating spatial evidence to support the implementation of Brazil’s Forest Code and Plano Diretor , while aligning with the United Nations Sustainable Development Goals (SDG 13: Climate Action and SDG 15: Life on Land). The study aims to evaluate the spatial extent, severity, and dynamics of water erosion in Paraty, identify priority areas for conservation and restoration, and demonstrate how geoinformatics can act as a bridge between scientific knowledge, land management, and environmental policy in tropical coastal systems. 2. Material and Methods 2.1 Data Sources and Methodological Framework To assess land degradation, Landsat 5 TM (1985) and Landsat 8 OLI (2023) satellite images with 30 m spatial resolution were used to analyze temporal land-use and land-cover (LU/LC) changes. High-resolution imagery from Airbus (2024) and Esri’s World Imagery Basemap provided additional support for the detailed delineation of erosion features. All images were obtained from the United States Geological Survey (USGS) and preprocessed in ArcGIS to correct geometry and enhance image quality. The assessment followed the methodology proposed by the United Nations Environment Programme’s Priority Actions Programme Regional Activity Centre (UNEP/PAP-RAC) (PAP/RAC, 1997; UNEP/MAP/PAP, 2000). This framework enables the classification of land into stable and unstable environments based on erosion type, intensity, and trend, allowing for the identification of conservation priorities. The methodology integrates remote sensing, GIS techniques, and multi-criteria evaluation to produce a comprehensive land degradation map. 2.2 Assessment of Stable and Unstable Environments High-resolution imagery from Airbus and Esri was used to identify and classify both stable and unstable environments. For stable areas, six dominant land categories were defined: (0R) rocky areas, (0S) sandy areas, (01) unmanaged areas with forestry potential, (02) unmanaged areas with agricultural potential, (03) managed areas with forest use, and (04) managed areas with agricultural use. Each stable area was assigned a risk grade from 0 (no risk) to 3 (highest risk), considering both topographic and anthropogenic factors. For unstable environments, the main types were sheet erosion (L) and rill erosion (D). Their extent was evaluated on a scale from 1 (localized, 60% affected), and their expansion trend from 0 (stabilized) to 3 (irreversible). Supervised classification was performed using the Maximum Likelihood Classifier (MLC) algorithm. Training samples were generated through detailed visual interpretation of high-resolution imagery, ensuring the accurate identification of both stable and unstable zones. Classification accuracy was validated by cross-checking randomly selected points with reference imagery, providing reliable mapping even in the absence of field data. 2.3 Prioritization of Land Degradation Hotspots for Remediation To identify areas requiring immediate remediation, a prioritization process was applied following PAP/RAC (UNEP, 2004). A total of 14 variables were evaluated and scored based on their physical, environmental, and socio-economic significance. Each variable received a score from 1 (lowest impact) to 3 (highest impact), and weighting factors were defined through expert consultation. These variables included: A. Physical instability risk (for stable areas) B. Extent of the affected area (for unstable areas) C. Expansion trend of the degradation process (for unstable areas) D. Multiplication factors for unfavorable combinations of causative agents E. Influence on adjacent areas F. Overexploitation as an aggravating socio-economic factor G. Rural exodus as an aggravating socio-economic factor H. Land tenure as an aggravating socio-economic factor I. Other aggravating socio-economic factors J. Value of current land use according to the local population K. Value of current land use according to national policies L. Potential for forestry M. Potential for agricultural use N. Other land use potentials After scoring each parameter, the prioritization scores were calculated using two equations: Stable Areas Priority = [(A * D + E) * F * G * H * I] + [(J + K) * L * M * N] Unstable Areas Priority = [(B * C * D + E) * F * G * H * I] + [(J + K) * L * M * N] Finally, these scores were grouped into three priority classes for remediation measures: High priority (scores ≥ 60), Medium priority (scores 21–59), and Low priority (scores ≤ 20). 2.4 Land Use and Land Cover Changes Detection Landuse and landcover (LU/LC) change detection was conducted using Landsat imagery for 1985 and 2023, applying the Brazilian MapBiomas methodology (MapBiomas, 2023 ). The datasets were extracted, clipped, and converted into shapefiles for comparative analysis. Each class’s area and percentage were computed in Excel to quantify the temporal dynamics of land cover. The analysis identified key transformations in forest cover, urban areas, pastures, and mosaics of uses, providing an essential link between land-use change and erosion processes. 3. Results and Discussion 3.1 Nature and Geographic Expansion of Land Degradation A total of 333 sites were identified using high-resolution imagery and classified as either stable or unstable. For stable sites, the type, grade of risk and their causative agents were described. For unstable sites, the type, severity of degradation and the trend of expansion were assessed. The resulting land degradation map (Fig. 2 ) indicates that the majority of the study area consists of stable environments, representing 90.7% (838.34 km²) of the total area, as detailed in Table 1 . The main risks to these stable zones are primarily caused by human activities. Figure 2 shows that the “Managed areas with forest use (03)” class is the predominant form of stable areas, covering 651.48 km² and accounting for about 70.48% of the total area. This category is mainly dominant in the Bocaina Mountain Range National Park, about 60% of the vegetation in this park is composed of native Atlantic Forest, while the remaining area consists of forest that has regenerated (secondary forest) for over 30 years (Wikipedia, Paraty, 2025). These findings align with Guerra et al. ( 2013 ), who described the Serra do Mar range as a topographically rugged and geomorphologically sensitive region with steep slopes prone to erosion and instability, especially where forest cover is disturbed. The (03) class is also prominent in the forested and mountainous areas to the south and east of the study area, reinforcing the link between forest cover and landscape stability. Table 1 Land degradation state with area measures of Paraty Area Type Code Land Category Type Area (km 2 ) Area (hec) Area (%) Stable Areas 0B Beach Areas 0.55 54.91 0.06 0R Rocky Areas 1.32 132.29 0.14 01 Unmanaged Areas with Forest Potential 152.54 15253.65 16.50 02 Unmanaged Areas with Agriculture Potential 27.78 2778.16 3.01 03 Managed Areas with Forest Use 651.48 65148.15 70.48 04 Managed Areas with Agriculture Use 4.67 466.77 0.51 Total 838.34 83833.93 90.7 Unstable Areas L11 L12 Localized Sheet Erosion 2.87 287.05 0.31 L21 L22 Dominant Sheet Erosion 9.21 921.06 1.00 D11 D12 Localized Rill Erosion 2.70 269.58 0.29 D21 D22 Dominant Rill Erosion 36.99 3699.22 4.00 Total 51.77 5176.91 5.6 Urban Areas 31.48 3147.58 3.41 Water Bodies 0.27 27.46 0.03 Total Area 921.86* 92186 99.74 (*To validate spatial calculations, the total mapped area (921.86 km²) was compared with the reference area is 924.29 km² (IBGE, 2022), yielding a minimal relative error of 0.26%. This demonstrates remarkably high accuracy for the geographic area calculation of our municipality, indicating that the method is highly precise). The second most prevalent category is “Unmanaged Areas with Forest Potential (01),” which makes up 16.50% (152.54 km²) of the total area. These zones are most common to the north and south of Paraty and west of the Bocaina Mountain Range National Park. In contrast, agricultural lands (categories 02 and 04) are minimal, occupying only 32.45 km² (3.52%) and located mainly near the coast, southeast of Paraty and close to Patrimônio city. Unstable zones, affected by active sheet and rill erosion, account for 5.6% (51.77 km²) of the total area (Table 1 ). The most significant contributor to this instability is Dominant Rill Erosion (D21, D22), which covers 36.99 km² (4.00%). In comparison, Dominant Sheet Erosion (L12, L22) is less widespread, covering 9.21 km² (1.0%). In general, areas with rill erosion are primarily located west and south of the Serra do Mar Mountain range, bordering São Paulo state, and west of Paraty. In contrast, sheet erosion is concentrated west of Serra do Mar, south of Barra Grande, and north of Vila Oratório. These unstable areas share common characteristics: hilly terrain with sparse vegetation, which exacerbates water erosion. While unstable areas constitute a relatively small fraction of the total land area, their strategic proximity to infrastructure, hillslope roads, and human settlements drastically magnifies the ecological and socio-economic risk. This degradation carries multi-faceted consequences: it poses a direct threat to public safety, causes severe damage to critical infrastructure, and drives up costs for municipal disaster response and mitigation. As noted by Rita et al. (2021), the main causes of this degradation are the conversion of forest to pasture and poorly planned road construction. Our findings align with other studies in the region. Renato et al. (2017) found that a high concentration of landslides, a severe form of degradation, occurred on steep, coastal slopes with heavy rainfall where the stabilizing effect of vegetation was diminished. Similarly, Rita et al. (2021) identified a clear link between unstable hill slopes and a lack of vegetation, a pattern consistent with the rill and sheet erosion hotspots we identified. The use of remote sensing in their work further validates our methodological approach. 3.2 Mapping of Conservation Prioritization The Paraty Land Conservation Priority Map (Fig. 3 ), along with the data in Table 2 , shows that most areas are classified as stable with varying levels of priority. Approximately 376.49 km² were low priority, 399.18 km² were medium priority, and 60.80 km² were high priority. The primary risk to these stable zones is instability caused by human activities, such as deforestation and land-use change. Specifically, the high-priority stable zones (light green in Fig. 3 ) are found near Tarituba, along the coasts of Barra Grande and Paraty, and around Patrimônio. Medium-priority areas are concentrated in southern Paraty, while low-priority areas (dark green in Fig. 3 ) are mostly near the Bocaina Mountain Range National Park. The prioritization procedure identified that a total of 51.77 km² of unstable areas require conservation priority. Of this, 16.45 km² were classified as high priority, 24.29 km² as medium priority, and 11.03 km² as low priority. These unstable areas were identified as locations showing active erosion processes mainly due to localized sheet erosion (L11, L12), dominant sheet erosion (L21, L22), localized rill erosion (D12, D12), and dominant rill erosion (D21, D22). Most of these hotspots are spread near areas characterized by sparse vegetation and complex relief, forming favorable conditions for soil erosion. Our findings align with Luana et al. ( 2019 ), who noted that natural factors (geology, relief) and human pressures (road construction, deforestation) contribute to degradation in the region. Our identified hotspots match this pattern, especially where human activity overlaps with steep terrain. The use of a multi-criteria framework within the applied PAP/RAC methodology also supports the work of Cascini ( 2008 ) and Guzzetti et al. ( 2005 ), reinforcing its effectiveness for identifying risk zones in coastal Atlantic Forest areas. In general, the highest-priority degradation hotspots, shown in red in Fig. 3 , are located primarily west of the Bocaina Mountain Range National Park, south of Barra Grande, and west of Paraty. These areas are suffering from sheet and rill erosion caused by deforestation on hills and mountains. This poses a direct threat to the region's biodiversity and long-term ecological integrity, making these sites the highest priority for remedial action. The medium-priority hotspots, shown in orange, are spread mainly in areas adjacent to São Paulo state. Table 2 Conservation Priority classes with areas in Paraty Conservation Priority Area (km 2 ) Area (hec) Area (%) Stable Areas Beach Areas 0.55 54.91 0.06 Rocky Areas 1.32 132.29 0.14 Stable Low Priority 376.49 37648.64 40.73 Stable Medium Priority 399.18 39917.59 43.19 Stable High Priority 60.80 6080.49 6.58 Total 838.34 83833.92 90.7 Unstable Areas Unstable Low Priority 11.03 1103.28 1.19 Unstable Medium Priority 24.29 2428.70 2.63 Unstable High Priority 16.45 1644.93 1.78 Total 51.77 5176.91 5.6 Water Bodies 0.27 27.46 0.03 Urban Areas 31.48 3147.58 3.41 The successful use of the PAP/RAC methodology in Paraty reinforces its effectiveness. Our findings align with similar studies in other regions, which also found that severe degradation is linked to a combination of steep slopes, low vegetation, high rainfall, and human disturbance (UNEP, PAP/RAC, 2004; Sadiki et al., 2012 ; Mesrar et al., 2015 ; Tahouri et al., 2019 , 2022 ). While our study found a comparatively lower level of active degradation at 4.34%, it still identified critical risk zones. Unlike studies that focused on overgrazing (Tahouri et al., 2022 ), our results highlight the role of urban pressures and deforestation as the main degradation factors, which is consistent with the findings of Al Abed et al. ( 2024 ). Our identified hotspots—near Tarituba, Barra Grande, Paraty, and Patrimônio—are highly vulnerable and require urgent intervention. 3.3 Change detection of Land Use and Cover between (1985–2023) Based on the MapBiomas methodology, we analyzed Landsat images from 1985 and 2023 (Fig. 4 ) to detect changes in land use and land cover. Forest Formation increased by 17.67 km², growing from 840.23 km² in 1985 to 857.9 km² in 2023. Urban areas also saw a significant increase, expanding by 0.5% from 4.6 km² to 10.8 km². This urban growth came at the expense of decreases in wetlands (-1.70%), pasture (-3.44%), and mosaic of uses (-9.70%), as detailed in Table 3 . This loss of vegetative cover, especially its transformation into urban areas, increases the risk of erosion (Welerson et al., 2023 ). Previous studies confirm this link: a lack of vegetation and forest loss are considered key indicators of land degradation (Vieira et al., 2021 ). Felippe et al. ( 2020 ) also found that human expansion in Paraty led to a decrease in native vegetation, directly contributing to soil erosion and degradation. Our findings are consistent with Ana B. et al. (2021), who noted that land degradation in Paraty is a complex issue driven by a combination of natural vulnerability and increasing human pressures from urban growth and tourism. Table 3 Paraty difference in landuse/landcover changes between 1985 & 2023 ID LU/LC Type LU/LC 2023 LU/LC 1985 Diference (2023 − 1985) Area (km 2 ) Area (%) Area (km 2 ) Area (%) Area (km 2 ) Area (hec) Area (%) 3 Forest Formation 857.9 92.82 840.23 90.91 17.67 1767.24 1.91 5 Mangrove 3.49 0.38 3.04 0.33 0.45 45.09 0.05 9 Forest Plantation 0.22 0.02 - - - - - 11 Wetland 0.01 0 0.01 0 0 0 0 15 Pasture 20.1 2.17 21.92 2.37 -1.82 -181.53 -0.2 21 Mosaic of Uses 73.84 7.99 88.83 9.61 -14.99 -1498.77 -1.62 23 Beach, Dune and Sand Spot 0.07 0.01 0.21 0.02 -0.14 -13.5 -0.01 24 Urban Area 10.8 1.17 6.2 0.67 4.6 459.45 0.5 25 Other non Vegetated Areas 0.17 0.02 0.16 0.02 0.01 1.62 0 29 Rocky Outcrop 0.61 0.07 0.77 0.08 -0.16 -16.11 -0.01 32 Hypersaline Tidal Flat - - 0.01 0 33 River, Lake and Ocean 13.91 1.5 16.41 1.77 -2.5 -249.75 -0.27 46 Coffee 0.04 0 0.01 0 0.03 2.52 0 48 Other Perennial Crops - - 0 0 - - - 49 Wooded Sandbank Vegetation 37.81 4.09 41.21 4.46 -3.4 -340.65 -0.37 Similarly, our findings are consistent with Ferreira (2016), who also identified a significant expansion of urban areas in the region over a 30-year period. This urban growth frequently encroaches on steep, unstable hillsides, increasing the risk of landslides to both people and infrastructure. The replacement of permeable soil with impermeable surfaces from urbanization reduces natural infiltration and increases surface runoff, which exacerbates slope instability. We observed this exact pattern in our study near Tarituba, Barra Grande, Paraty, and Patrimônio. While our data shows a slight increase in forest formation (+ 17.67 km²), which may be due to natural regeneration, the simultaneous loss of ecologically vital vegetation like wetlands and sandbanks indicates ongoing landscape degradation. This highlights the crucial point that even with some forest recovery, overall land cover conversion, especially near urban areas, leads to increased runoff and soil instability. 4. Management Recommendations for High-Priority Intervention Areas Effective land degradation control programs require a two-step approach: first, identifying high-priority areas, and second, implementing targeted remedial measures. This includes both unstable and stable areas (UNEP, PAP/RAC, 2004). For high-priority unstable areas measures should be curative and protective. The curative measures should focus on constructing water outlets, providing financial and training support to local communities, and implementing reforestation and forest management programs. The protective measures may include community education is crucial to promote sustainable land use. High-risk communities should receive technical guidance on safe construction techniques. In critical cases, voluntary relocation programs should be enabled, in line with Brazil's National Civil Defense policy (PNPDEC). It is also essential to strengthen the enforcement of zoning regulations and slope-use restrictions as per Brazil’s Forest Code (Law 12.651/2012) and municipal urban plans. While for the stable areas a combination of preventive and curative actions is recommended. Preventive actions should emphasize forest management and reforestation by native Atlantic Forest species (such as Inga spp ., Schinus terebinthifolius , and Euterpe edulis ). This also involves protecting local bushy and shrubby formations and applying sustainable agricultural techniques like contour plowing, terracing, and agroforestry with Gliricidia sepium and Theobroma cacao . Designate legal buffer zones around preserved forests, reinforcing them as Permanent Preservation Areas (APPs), and integrate their mapping into the municipal environmental zoning system (Zoneamento Ecológico-Econômico - ZEE). Curative measures should focus on educating rural communities about the importance of land management and executing reforestation projects. Besides, degraded trails can be restored using bioengineering techniques (Luana et al., 2019 ). 5. Conclusion This study successfully applied the UNEP/PAP-RAC methodology, combined with geospatial and remote sensing tools, to assess land degradation in Paraty Municipality, a coastal Atlantic Forest region under intense socio-environmental pressure. The analysis identified predominantly stable areas (838.34 km²) alongside smaller, high-risk zones of active erosion (51.77 km²), concentrated near urban fronts and steep slopes. The prioritization process revealed that 40.74 km² of unstable zones are of medium to high priority for remediation, underscoring the urgent need for targeted conservation measures. Land-use and land-cover change analysis between 1985 and 2023 indicated that urban areas expanded by + 4.6 km², while native vegetation declined by − 14.99 km², reflecting ongoing human pressure despite partial forest regeneration. These findings emphasize that unregulated land use, deforestation, and construction on steep terrain intensify erosion risks and increase socio-environmental vulnerability. The integration of geoinformatics with the PAP/RAC approach generated spatial evidence that can directly support municipal planning instruments, including the Plano Diretor , zoning, and environmental licensing under Brazil’s Forest Code (Law 12.651/2012). By linking biophysical data with land management and policy frameworks, this research contributes to a more systemic understanding of how urban growth and ecosystem dynamics interact in fragile coastal landscapes. Beyond its technical outcomes, the study highlights the importance of participatory governance, environmental education, and local community engagement for long-term land restoration and disaster risk reduction. Strengthening these connections between science, policy, and society is essential for achieving sustainable territorial management and resilience in coastal municipalities like Paraty. Future research should include field-based validation and socio-economic assessments to further integrate ecological, spatial, and human dimensions in land degradation studies. Declarations Acknowledgments 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). References AL ABED M, SABOYA MTM, SANTOS CA dos;, ASSIS VC, COUTINHO IPO, VARGAS R, DIAS FF (2024) Inventory of land degradation using geoinformatics in Cachoeiras de Macacu’s municipality, Rio de Janeiro, Brazil. Annals of Arid Zone, 63 (4) CASCINI L (2008) Applicability of landslide susceptibility and hazard zoning at different scales. Eng Geol 102(3–4):164–177 CORDEIRO RC, MONTEIRO FF, SANTELLI RE et al (2020) Environmental and anthropic variabilities at Guanabara Bay (Brazil): A comparative perspective of metal depositions in different time scales during the last 5,500 yrs. Chemosphere 267:128895. 10.1016/j.chemosphere.2020.128895 CREED JC, PIRES DO, FIGUEIREDO MA O. (eds) (2007) Marine biodiversity of Ilha Grande Bay. Brasília: Ministry of the Environment, MMA/SBF, Disponível em: https://www.redebim.dphdm.mar.mil.br/vinculos/000010/0000103c.pdf . Acesso em: [Insert Access Date] FELIPPE RT, MENDES LMS, GALDINO S (2020) Anthropic expansion of Paraty in Serra da Bocaina National Park, Mata Atlântica biome. Ambiente & Sociedade, 23 , Disponível em: https://www.scielo.br/j/asoc/a/wVnRNyPRgL5KwhmD5bBfppR . Acesso em: [Insert Access Date] GALLARDO S, FOSSILE T, HERBST DF et al (2021) 150 years of anthropogenic impact on coastal and ocean ecosystems in Brazil revealed by historical newspapers. Ocean Coastal Manage 209:105662. 10.1016/j.ocecoaman.2021.105662 GUERRA AJT, BEZERRA JFR, JORGE MCO, FULLEN MA (2013) The geomorphology of Angra dos Reis and Paraty municipalities, southern Rio de Janeiro State. Revista Geonorte 9(1):1–21 GUZZETTI F, REICHENBACH P, CARRARA A, GALLI M, CIASCA P (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299. 10.1016/j.geomorph.2005.06.002 INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA (IBGE) (2022) Censo Brasileiro, Rio de Janeiro JOLY CA, METZGER JP, TABARELLI M (2014) Experiences from the Brazilian Atlantic Forest: Ecological findings and conservation initiatives. New Phytol 204(3):459–473. 10.1111/nph.12989 LUANA R, JORGE MC, GUERRA A, FULLEN M (2019) Soil erosion and land degradation on trail systems in mountainous areas: two case studies from South-East Brazil. Soil Syst 3(4):56. 10.3390/soilsystems3030056 MAPBIOMAS (2023) Algorithm Theoretical Basis Document (ATBD), Collection 8.0. MapBiomas MESRAR H, SADIKI A, NAVAS A, FALEH A, QUIJANO L, CHAAOUAN J (2015) Modélisation de l’érosion hydrique et des facteurs causaux: Cas de l’Oued Sahla, Rif Central, Maroc. Zeitschrift für Geomorphologie. 10.1127/zfg/2015/0169 MONTEIRO FF, CORDEIRO RC, SANTELLI RE (2012) Sedimentary geochemical record of historical anthropogenic activities affecting Guanabara Bay (Brazil) environmental quality. Environ Earth Sci 65(5):1661–1669. 10.1007/s12665-011-1143-4 NETO JAB, BARRETO CF, VILELA CG et al (2017) Environmental change in Guanabara Bay, SE Brazil, based on microfaunal, pollen and geochemical proxies in sedimentary cores. Ocean Coastal Manage 143:4–15. 10.1016/j.ocecoaman.2016.04.010 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 PINHEIRO AB, SILVA ALC, PEREIRA TG, BAPTISTA NETO JA Coastal erosion and flood susceptibility in Paraty, Southeast Brazil. Revista Geográfica de Chile Terra Australis, 57 (Especial 1): 111–128, 2021. Disponível em: https://www.revistaterraaustralis.cl/index.php/rgch/article/view/103 . Acesso em: [Insert Access Date] MACHADO RENATOGR, CARVALHO W, JÚNIOR O, MONTGOMERY DR, GOMES DR, GREENBERG R, CATALDI H, MENDONÇA M (2017) Determination of areas susceptible to landsliding using spatial patterns of rainfall from Tropical Rainfall Measuring Mission data, Rio de Janeiro, Brazil. ISPRS Int J Geo-Information 6(10):289. 10.3390/ijgi6100289 SADIKI A, MESRAR H, FALEH A (2012) Modélisation et cartographie des risques de l’érosion hydrique: Cas du bassin versant de l’Oued Larbaa. Maroc Papeles de Geografía 56(55–56):179–188 TAHOURI J, SADIKI A, KARRAT L, MESRAR H, JOHNSON VC, ZHANG F, KUNG H (2019) Using PAP/RAC model and GIS tools for mapping and study of water erosion processes in the Mediterranean environment: Case of the Asfalou watershed (Oriental Rif, Morocco). In: Global Symposium on Soil Erosion (GSER19). Rome: FAO TAHOURI J, SADIKI A, KARRAT L, JOHNSON VC, CHAN NW, ZHANG F, 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. Int Soil Water Conserv Res 10(3):254–272. 10.1016/j.iswcr.2022.02.001 TOMASELLA J, VIEIRA RMSP, BARBOSA AA, RODRIGUEZ DA, SANTANA MO, SESTINI MF (2018) Desertification trends in the Northeast of Brazil over the period 2000–2016. Int J Appl Earth Obs Geoinf 73:197–206. 10.1016/j.jag.2018.06.012 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. [Insert Access Date], Acesso em. https://wedocs.unep.org/bitstream/handle/20.500.11822/1859/syria.pdf Disponível em UNITED NATIONS ENVIRONMENT PROGRAMME (UNEP); MEDITERRANEAN ACTION PLAN (MAP); PRIORITY ACTIONS PROGRAMME (PAP). Guidelines for erosion and desertification control management with particular reference to Mediterranean coastal areas (2000) Disponível em: https://iczmplatform.org/storage/documents/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf . Acesso em: [Insert Access Date] MARTINS VAHIDEHFV, FERREIRA M, PEREIRA P, HOHENEGGER E, SAIBRO J, SILVA M, SILVA C, GOMES A, ROCHA H (2025) Impact of paleo rainfall events in the South Atlantic Convergence Zone (SACZ) and human pressures since ~ 1950 in southeastern Brazil: Paraty and Saco de Mamanguá. Environ Earth Sci 84:289. 10.1007/s12665-025-12248-7 VIEIRA RMSP, TOMASELLA J, BARBOSA AA, POLIZEL SP, OMETTO JPHB, SANTOS FC, FERREIRA YC, TOLEDO P (2021) M. Land degradation mapping in the MATOPIBA region (Brazil) using remote sensing data and decision-tree analysis. Sci Total Environ 782:146900. 10.1016/j.scitotenv.2021.146900 VILELA GUTTERRES BO, FIGUEIRA MCM, BAPTISTA NETO JA (2014) Late Holocene evolution and increasing pollution in Guanabara Bay, Rio de Janeiro, SE Brazil. Mar Pollut Bull 79(1–2):175–187. 10.1016/j.marpolbul.2013.12.011 WELERSON CC, BARÃO WN, QUIRELI BA, FARIA VL, PONS NAD, RIONDET-COSTA DRT, MARCONDES A (2023) L. S. Expansão antrópica de Paraty no Parque Nacional Serra da Bocaina. Ambiente Sociedade 26:e045. 10.1590/1809-4422asoc20220045vu2023L3AO Additional Declarations The authors declare no competing interests. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8872932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590956549,"identity":"68a02111-276d-4571-8b8c-b2cc30142ccb","order_by":0,"name":"Mohammad Al Abed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYHACNiCyYWCQIFFLGulaDpOgRbeB+dmDD2XnE/tnNx98wFBjE01Qi9kBNnPDGeduJ864cyzZgOFYWm4DYS0MZtK8bbcTG27kmEkwNhwmRgv7N+m/becS55OghcdMmrHtQOIG4rUc5ik37DmXbLzxRlqyQQJRfjnevu3BjzI72Xk3kg8++FBjQ1gLAzOEcgSrTCCoHAnYk6J4FIyCUTAKRhgAAILWQQFt7wwvAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-3949-959X","institution":"Department of Geoenvironmental Analysis, Fluminense Federal University (UFF), Niterói, Brasil","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Al","lastName":"Abed","suffix":""},{"id":590956550,"identity":"0c65f0ac-b380-4679-b6c9-6c50ace46a2f","order_by":1,"name":"Fábio Ferreira Dias","email":"","orcid":"https://orcid.org/0000-0003-2078-7405","institution":"Department of Geoenvironmental Analysis, Fluminense Federal University (UFF), Niterói, Brasil","correspondingAuthor":false,"prefix":"","firstName":"Fábio","middleName":"Ferreira","lastName":"Dias","suffix":""},{"id":590956551,"identity":"1a8150d6-12fd-4106-9b92-1c5dc68464ac","order_by":2,"name":"Menércia Job Macamo","email":"","orcid":"https://orcid.org/0009-0002-8955-0705","institution":"University Federal Fluminense (UFF), Institute of Geosciences, Niterói, Rio de Janeiro, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Menércia","middleName":"Job","lastName":"Macamo","suffix":""}],"badges":[],"createdAt":"2026-02-13 14:24:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8872932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8872932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102821832,"identity":"c9c748a4-cf97-4749-9c5a-e1e09952083d","added_by":"auto","created_at":"2026-02-17 07:52:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":589482,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of Paraty Municipality\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8872932/v1/efc75e04461a17f4c1cf2be4.png"},{"id":102821835,"identity":"ac12d8f0-1478-412a-9569-4d6e14284f65","added_by":"auto","created_at":"2026-02-17 07:52:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1265274,"visible":true,"origin":"","legend":"\u003cp\u003eThe Stable and Unstable areas Map\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8872932/v1/7070a341f2eba43e8030a879.png"},{"id":102821834,"identity":"ee93771b-ec35-4807-aca3-dde3edd27213","added_by":"auto","created_at":"2026-02-17 07:52:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1399803,"visible":true,"origin":"","legend":"\u003cp\u003eParaty land conservation priority map\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8872932/v1/ffbb6ab1d02bb20a34d3405f.png"},{"id":102821833,"identity":"e232bda7-f9bc-488e-8e43-7a2c8c498eb3","added_by":"auto","created_at":"2026-02-17 07:52:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2545408,"visible":true,"origin":"","legend":"\u003cp\u003eParaty Landuse/Landcover Changes between 1985 and 2023\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8872932/v1/f9188f0cf4c32bab574492da.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGeoinformatics Application for Land Degradation Mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ein Paraty Municipality Southeast Brazil\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe municipality of Paraty is located in the south of the state of Rio de Janeiro (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the Brazilian Institute of Geography and Statistics (IBGE), it has a population of 45,243 inhabitants, a density of 48.95 inhabitants/km\u0026sup2;, and an area of 924.3 km\u0026sup2; (IBGE, 2022). The region is characterized by a humid subtropical climate and a complex geography shaped by the interaction between the Serra do Mar mountain range and the Atlantic Ocean, which makes the area highly vulnerable to landslides and coastal flooding. The terrain includes mountains, a jagged coastline with numerous islands, and narrow coastal plains. The steep slopes of the Serra do Mar, with altitudes reaching up to 2,088 meters, are particularly prone to landslides during intense rainfall events (Pinheiro et al., 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParaty lies within the Atlantic Forest biome, characterized by dense ombrophilous forests with complex vegetation including epiphytes, ferns, and bromeliads (Joly et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The soils are generally acidic and of low natural fertility; combined with steep topography, high rainfall, and shallow soil depth, this creates high susceptibility to water erosion and landslides (Guerra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a socio-environmental perspective, Paraty faces multiple challenges that intensify soil erosion and land degradation, such as deforestation, unplanned urbanization, land-use change, and tourism-related infrastructure development. These pressures, together with inadequate trail management, have made soil erosion an increasingly critical environmental problem in several areas of the municipality (Luana et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Creed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Since the 1960s, the region has undergone considerable enthronement due to social and economic development and the rapid increase in urbanization and industrialization (Neto et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cordeiro et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gallardo et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While numerous studies have explored the ecological and geomorphological aspects of these transformations (Monteiro et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vilela et al., 2014; Tomasella et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), fewer have examined the interactions between environmental degradation and social vulnerability in humid tropical coastal zones. Recent findings indicate that recurrent landslides and erosion events are exacerbated by human occupation in environmentally fragile areas (Vahideh et al., 2025). These trends underscore the urgent need for integrated, geospatially informed approaches that connect environmental diagnostics with policy frameworks and community-level management.\u003c/p\u003e \u003cp\u003eIn this context, the present study applies the methodology of the United Nations Environment Programme\u0026rsquo;s Priority Actions Programme Regional Activity Centre (UNEP, PAP/RAC) to map and assess land degradation in Paraty (UNEP, MAP, PAP, 2000; PAPRAC, 1997). This approach has been validated in numerous studies across Mediterranean and semi-arid regions (UNEP, PAP/RAC. 2004, Sadiki et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mesrar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tahouri et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Lhoussaine et al., 2024, Al Abed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). but its application in tropical coastal environments remains limited. The study therefore represents a novel adaptation of this framework to the humid Atlantic Forest context of southeastern Brazil. By integrating remote sensing, GIS analysis, and multi-criteria prioritization, it identifies erosion-prone zones and ranks them according to their remediation priority.\u003c/p\u003e \u003cp\u003eBeyond its technical dimension, this research contributes to sustainable planning and governance by generating spatial evidence to support the implementation of Brazil\u0026rsquo;s \u003cem\u003eForest Code\u003c/em\u003e and \u003cem\u003ePlano Diretor\u003c/em\u003e, while aligning with the United Nations Sustainable Development Goals (SDG 13: Climate Action and SDG 15: Life on Land). The study aims to evaluate the spatial extent, severity, and dynamics of water erosion in Paraty, identify priority areas for conservation and restoration, and demonstrate how geoinformatics can act as a bridge between scientific knowledge, land management, and environmental policy in tropical coastal systems.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources and Methodological Framework\u003c/h2\u003e \u003cp\u003eTo assess land degradation, Landsat 5 TM (1985) and Landsat 8 OLI (2023) satellite images with 30 m spatial resolution were used to analyze temporal land-use and land-cover (LU/LC) changes. High-resolution imagery from Airbus (2024) and Esri\u0026rsquo;s World Imagery Basemap provided additional support for the detailed delineation of erosion features. All images were obtained from the United States Geological Survey (USGS) and preprocessed in ArcGIS to correct geometry and enhance image quality. The assessment followed the methodology proposed by the United Nations Environment Programme\u0026rsquo;s Priority Actions Programme Regional Activity Centre (UNEP/PAP-RAC) (PAP/RAC, 1997; UNEP/MAP/PAP, 2000). This framework enables the classification of land into stable and unstable environments based on erosion type, intensity, and trend, allowing for the identification of conservation priorities. The methodology integrates remote sensing, GIS techniques, and multi-criteria evaluation to produce a comprehensive land degradation map.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of Stable and Unstable Environments\u003c/h2\u003e \u003cp\u003eHigh-resolution imagery from Airbus and Esri was used to identify and classify both stable and unstable environments. For stable areas, six dominant land categories were defined: (0R) rocky areas, (0S) sandy areas, (01) unmanaged areas with forestry potential, (02) unmanaged areas with agricultural potential, (03) managed areas with forest use, and (04) managed areas with agricultural use. Each stable area was assigned a risk grade from 0 (no risk) to 3 (highest risk), considering both topographic and anthropogenic factors. For unstable environments, the main types were sheet erosion (L) and rill erosion (D). Their extent was evaluated on a scale from 1 (localized, \u0026lt;\u0026thinsp;30% affected) to 3 (generalized, \u0026gt;\u0026thinsp;60% affected), and their expansion trend from 0 (stabilized) to 3 (irreversible). Supervised classification was performed using the Maximum Likelihood Classifier (MLC) algorithm. Training samples were generated through detailed visual interpretation of high-resolution imagery, ensuring the accurate identification of both stable and unstable zones. Classification accuracy was validated by cross-checking randomly selected points with reference imagery, providing reliable mapping even in the absence of field data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Prioritization of Land Degradation Hotspots for Remediation\u003c/h2\u003e \u003cp\u003eTo identify areas requiring immediate remediation, a prioritization process was applied following PAP/RAC (UNEP, 2004). A total of 14 variables were evaluated and scored based on their physical, environmental, and socio-economic significance. Each variable received a score from 1 (lowest impact) to 3 (highest impact), and weighting factors were defined through expert consultation. These variables included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA. Physical instability risk (for stable areas)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eB. Extent of the affected area (for unstable areas)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eC. Expansion trend of the degradation process (for unstable areas)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eD. Multiplication factors for unfavorable combinations of causative agents\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eE. Influence on adjacent areas\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eF. Overexploitation as an aggravating socio-economic factor\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eG. Rural exodus as an aggravating socio-economic factor\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH. Land tenure as an aggravating socio-economic factor\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eI. Other aggravating socio-economic factors\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eJ. Value of current land use according to the local population\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eK. Value of current land use according to national policies\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eL. Potential for forestry\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eM. Potential for agricultural use\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eN. Other land use potentials\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAfter scoring each parameter, the prioritization scores were calculated using two equations:\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStable Areas Priority = [(A * D\u0026thinsp;+\u0026thinsp;E) * F * G * H * I] + [(J\u0026thinsp;+\u0026thinsp;K) * L * M * N]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnstable Areas Priority = [(B * C * D\u0026thinsp;+\u0026thinsp;E) * F * G * H * I] + [(J\u0026thinsp;+\u0026thinsp;K) * L * M * N]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFinally, these scores were grouped into three priority classes for remediation measures: High priority (scores\u0026thinsp;\u0026ge;\u0026thinsp;60), Medium priority (scores 21\u0026ndash;59), and Low priority (scores\u0026thinsp;\u0026le;\u0026thinsp;20).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Land Use and Land Cover Changes Detection\u003c/h2\u003e \u003cp\u003eLanduse and landcover (LU/LC) change detection was conducted using Landsat imagery for 1985 and 2023, applying the Brazilian MapBiomas methodology (MapBiomas, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The datasets were extracted, clipped, and converted into shapefiles for comparative analysis. Each class\u0026rsquo;s area and percentage were computed in Excel to quantify the temporal dynamics of land cover. The analysis identified key transformations in forest cover, urban areas, pastures, and mosaics of uses, providing an essential link between land-use change and erosion processes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Nature and Geographic Expansion of Land Degradation\u003c/h2\u003e \u003cp\u003eA total of 333 sites were identified using high-resolution imagery and classified as either stable or unstable. For stable sites, the type, grade of risk and their causative agents were described. For unstable sites, the type, severity of degradation and the trend of expansion were assessed. The resulting land degradation map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicates that the majority of the study area consists of stable environments, representing 90.7% (838.34 km\u0026sup2;) of the total area, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The main risks to these stable zones are primarily caused by human activities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the \u0026ldquo;Managed areas with forest use (03)\u0026rdquo; class is the predominant form of stable areas, covering 651.48 km\u0026sup2; and accounting for about 70.48% of the total area. This category is mainly dominant in the Bocaina Mountain Range National Park, about 60% of the vegetation in this park is composed of native Atlantic Forest, while the remaining area consists of forest that has regenerated (secondary forest) for over 30 years (Wikipedia, Paraty, 2025). These findings align with Guerra et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who described the Serra do Mar range as a topographically rugged and geomorphologically sensitive region with steep slopes prone to erosion and instability, especially where forest cover is disturbed. The (03) class is also prominent in the forested and mountainous areas to the south and east of the study area, reinforcing the link between forest cover and landscape stability.\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\u003eLand degradation state with area measures of Paraty\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\u003eArea Type\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\u003eStable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeach Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRocky Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\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\u003e152.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15253.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.50\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\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2778.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.01\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\u003e651.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65148.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.48\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\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e466.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\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\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e838.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e83833.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e90.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eUnstable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL11\u003c/p\u003e \u003cp\u003eL12\u003c/p\u003e \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\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL21\u003c/p\u003e \u003cp\u003eL22\u003c/p\u003e \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\u003e9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e921.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD11\u003c/p\u003e \u003cp\u003eD12\u003c/p\u003e \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\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e269.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD21\u003c/p\u003e \u003cp\u003eD22\u003c/p\u003e \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\u003e36.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3699.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.00\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\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e51.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5176.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3147.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater Bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e921.86*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(*To validate spatial calculations, the total mapped area (921.86 km\u0026sup2;) was compared with the reference area is 924.29 km\u0026sup2; (IBGE, 2022), yielding a minimal relative error of 0.26%. This demonstrates remarkably high accuracy for the geographic area calculation of our municipality, indicating that the method is highly precise).\u003c/p\u003e \u003cp\u003eThe second most prevalent category is \u0026ldquo;Unmanaged Areas with Forest Potential (01),\u0026rdquo; which makes up 16.50% (152.54 km\u0026sup2;) of the total area. These zones are most common to the north and south of Paraty and west of the Bocaina Mountain Range National Park. In contrast, agricultural lands (categories 02 and 04) are minimal, occupying only 32.45 km\u0026sup2; (3.52%) and located mainly near the coast, southeast of Paraty and close to Patrim\u0026ocirc;nio city. Unstable zones, affected by active sheet and rill erosion, account for 5.6% (51.77 km\u0026sup2;) of the total area (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most significant contributor to this instability is Dominant Rill Erosion (D21, D22), which covers 36.99 km\u0026sup2; (4.00%). In comparison, Dominant Sheet Erosion (L12, L22) is less widespread, covering 9.21 km\u0026sup2; (1.0%).\u003c/p\u003e \u003cp\u003eIn general, areas with rill erosion are primarily located west and south of the Serra do Mar Mountain range, bordering S\u0026atilde;o Paulo state, and west of Paraty. In contrast, sheet erosion is concentrated west of Serra do Mar, south of Barra Grande, and north of Vila Orat\u0026oacute;rio. These unstable areas share common characteristics: hilly terrain with sparse vegetation, which exacerbates water erosion. While unstable areas constitute a relatively small fraction of the total land area, their strategic proximity to infrastructure, hillslope roads, and human settlements drastically magnifies the ecological and socio-economic risk. This degradation carries multi-faceted consequences: it poses a direct threat to public safety, causes severe damage to critical infrastructure, and drives up costs for municipal disaster response and mitigation. As noted by Rita et al. (2021), the main causes of this degradation are the conversion of forest to pasture and poorly planned road construction. Our findings align with other studies in the region. Renato et al. (2017) found that a high concentration of landslides, a severe form of degradation, occurred on steep, coastal slopes with heavy rainfall where the stabilizing effect of vegetation was diminished. Similarly, Rita et al. (2021) identified a clear link between unstable hill slopes and a lack of vegetation, a pattern consistent with the rill and sheet erosion hotspots we identified. The use of remote sensing in their work further validates our methodological approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mapping of Conservation Prioritization\u003c/h2\u003e \u003cp\u003eThe Paraty Land Conservation Priority Map (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), along with the data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, shows that most areas are classified as stable with varying levels of priority. Approximately 376.49 km\u0026sup2; were low priority, 399.18 km\u0026sup2; were medium priority, and 60.80 km\u0026sup2; were high priority. The primary risk to these stable zones is instability caused by human activities, such as deforestation and land-use change. Specifically, the high-priority stable zones (light green in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) are found near Tarituba, along the coasts of Barra Grande and Paraty, and around Patrim\u0026ocirc;nio. Medium-priority areas are concentrated in southern Paraty, while low-priority areas (dark green in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) are mostly near the Bocaina Mountain Range National Park.\u003c/p\u003e \u003cp\u003eThe prioritization procedure identified that a total of 51.77 km\u0026sup2; of unstable areas require conservation priority. Of this, 16.45 km\u0026sup2; were classified as high priority, 24.29 km\u0026sup2; as medium priority, and 11.03 km\u0026sup2; as low priority. These unstable areas were identified as locations showing active erosion processes mainly due to localized sheet erosion (L11, L12), dominant sheet erosion (L21, L22), localized rill erosion (D12, D12), and dominant rill erosion (D21, D22). Most of these hotspots are spread near areas characterized by sparse vegetation and complex relief, forming favorable conditions for soil erosion. Our findings align with Luana et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who noted that natural factors (geology, relief) and human pressures (road construction, deforestation) contribute to degradation in the region. Our identified hotspots match this pattern, especially where human activity overlaps with steep terrain. The use of a multi-criteria framework within the applied PAP/RAC methodology also supports the work of Cascini (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Guzzetti et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), reinforcing its effectiveness for identifying risk zones in coastal Atlantic Forest areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn general, the highest-priority degradation hotspots, shown in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, are located primarily west of the Bocaina Mountain Range National Park, south of Barra Grande, and west of Paraty. These areas are suffering from sheet and rill erosion caused by deforestation on hills and mountains. This poses a direct threat to the region's biodiversity and long-term ecological integrity, making these sites the highest priority for remedial action. The medium-priority hotspots, shown in orange, are spread mainly in areas adjacent to S\u0026atilde;o Paulo state.\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\u003eConservation Priority classes with areas in Paraty\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\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=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eStable Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeach Areas\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\u003e54.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRocky Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable Low Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e376.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37648.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.73\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\u003e399.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39917.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.19\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\u003e60.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6080.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e838.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e83833.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e90.7\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\u003e\u003cb\u003eUnstable Areas\u003c/b\u003e\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\u003e11.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1103.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\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\u003e24.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2428.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.63\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\u003e16.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1644.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e51.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5176.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Bodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3147.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe successful use of the PAP/RAC methodology in Paraty reinforces its effectiveness. Our findings align with similar studies in other regions, which also found that severe degradation is linked to a combination of steep slopes, low vegetation, high rainfall, and human disturbance (UNEP, PAP/RAC, 2004; Sadiki et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mesrar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tahouri et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While our study found a comparatively lower level of active degradation at 4.34%, it still identified critical risk zones. Unlike studies that focused on overgrazing (Tahouri et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), our results highlight the role of urban pressures and deforestation as the main degradation factors, which is consistent with the findings of Al Abed et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our identified hotspots\u0026mdash;near Tarituba, Barra Grande, Paraty, and Patrim\u0026ocirc;nio\u0026mdash;are highly vulnerable and require urgent intervention.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Change detection of Land Use and Cover between (1985\u0026ndash;2023)\u003c/h2\u003e \u003cp\u003eBased on the MapBiomas methodology, we analyzed Landsat images from 1985 and 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) to detect changes in land use and land cover. Forest Formation increased by 17.67 km\u0026sup2;, growing from 840.23 km\u0026sup2; in 1985 to 857.9 km\u0026sup2; in 2023. Urban areas also saw a significant increase, expanding by 0.5% from 4.6 km\u0026sup2; to 10.8 km\u0026sup2;. This urban growth came at the expense of decreases in wetlands (-1.70%), pasture (-3.44%), and mosaic of uses (-9.70%), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This loss of vegetative cover, especially its transformation into urban areas, increases the risk of erosion (Welerson et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Previous studies confirm this link: a lack of vegetation and forest loss are considered key indicators of land degradation (Vieira et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Felippe et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also found that human expansion in Paraty led to a decrease in native vegetation, directly contributing to soil erosion and degradation. Our findings are consistent with Ana B. et al. (2021), who noted that land degradation in Paraty is a complex issue driven by a combination of natural vulnerability and increasing human pressures from urban growth and tourism.\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\u003eParaty difference in landuse/landcover changes between 1985 \u0026amp; 2023\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLU/LC Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLU/LC\u003c/p\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLU/LC\u003c/p\u003e \u003cp\u003e1985\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eDiference\u003c/p\u003e \u003cp\u003e(2023\u0026thinsp;\u0026minus;\u0026thinsp;1985)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\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 \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(hec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\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\"\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\u003e857.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e840.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1767.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.91\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\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\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\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\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\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\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\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-181.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.2\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\u003e73.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-14.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1498.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.62\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\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.01\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\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e459.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5\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\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\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\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\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\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.01\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e13.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-249.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoffee\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Perennial Crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\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\u003e37.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-340.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, our findings are consistent with Ferreira (2016), who also identified a significant expansion of urban areas in the region over a 30-year period. This urban growth frequently encroaches on steep, unstable hillsides, increasing the risk of landslides to both people and infrastructure. The replacement of permeable soil with impermeable surfaces from urbanization reduces natural infiltration and increases surface runoff, which exacerbates slope instability. We observed this exact pattern in our study near Tarituba, Barra Grande, Paraty, and Patrim\u0026ocirc;nio. While our data shows a slight increase in forest formation (+\u0026thinsp;17.67 km\u0026sup2;), which may be due to natural regeneration, the simultaneous loss of ecologically vital vegetation like wetlands and sandbanks indicates ongoing landscape degradation. This highlights the crucial point that even with some forest recovery, overall land cover conversion, especially near urban areas, leads to increased runoff and soil instability.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Management Recommendations for High-Priority Intervention Areas","content":"\u003cp\u003eEffective land degradation control programs require a two-step approach: first, identifying high-priority areas, and second, implementing targeted remedial measures. This includes both unstable and stable areas (UNEP, PAP/RAC, 2004). For high-priority unstable areas measures should be curative and protective. The curative measures should focus on constructing water outlets, providing financial and training support to local communities, and implementing reforestation and forest management programs. The protective measures may include community education is crucial to promote sustainable land use. High-risk communities should receive technical guidance on safe construction techniques. In critical cases, voluntary relocation programs should be enabled, in line with Brazil's National Civil Defense policy (PNPDEC). It is also essential to strengthen the enforcement of zoning regulations and slope-use restrictions as per Brazil\u0026rsquo;s Forest Code (Law 12.651/2012) and municipal urban plans. While for the stable areas a combination of preventive and curative actions is recommended. Preventive actions should emphasize forest management and reforestation by native Atlantic Forest species (such as \u003cem\u003eInga spp\u003c/em\u003e., \u003cem\u003eSchinus terebinthifolius\u003c/em\u003e, and \u003cem\u003eEuterpe edulis\u003c/em\u003e). This also involves protecting local bushy and shrubby formations and applying sustainable agricultural techniques like contour plowing, terracing, and agroforestry with \u003cem\u003eGliricidia sepium\u003c/em\u003e and \u003cem\u003eTheobroma cacao\u003c/em\u003e. Designate legal buffer zones around preserved forests, reinforcing them as Permanent Preservation Areas (APPs), and integrate their mapping into the municipal environmental zoning system (Zoneamento Ecol\u0026oacute;gico-Econ\u0026ocirc;mico - ZEE). Curative measures should focus on educating rural communities about the importance of land management and executing reforestation projects. Besides, degraded trails can be restored using bioengineering techniques (Luana et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study successfully applied the UNEP/PAP-RAC methodology, combined with geospatial and remote sensing tools, to assess land degradation in Paraty Municipality, a coastal Atlantic Forest region under intense socio-environmental pressure. The analysis identified predominantly stable areas (838.34 km\u0026sup2;) alongside smaller, high-risk zones of active erosion (51.77 km\u0026sup2;), concentrated near urban fronts and steep slopes. The prioritization process revealed that 40.74 km\u0026sup2; of unstable zones are of medium to high priority for remediation, underscoring the urgent need for targeted conservation measures. Land-use and land-cover change analysis between 1985 and 2023 indicated that urban areas expanded by +\u0026thinsp;4.6 km\u0026sup2;, while native vegetation declined by \u0026minus;\u0026thinsp;14.99 km\u0026sup2;, reflecting ongoing human pressure despite partial forest regeneration. These findings emphasize that unregulated land use, deforestation, and construction on steep terrain intensify erosion risks and increase socio-environmental vulnerability. The integration of geoinformatics with the PAP/RAC approach generated spatial evidence that can directly support municipal planning instruments, including the \u003cem\u003ePlano Diretor\u003c/em\u003e, zoning, and environmental licensing under Brazil\u0026rsquo;s Forest Code (Law 12.651/2012). By linking biophysical data with land management and policy frameworks, this research contributes to a more systemic understanding of how urban growth and ecosystem dynamics interact in fragile coastal landscapes.\u003c/p\u003e \u003cp\u003eBeyond its technical outcomes, the study highlights the importance of participatory governance, environmental education, and local community engagement for long-term land restoration and disaster risk reduction. Strengthening these connections between science, policy, and society is essential for achieving sustainable territorial management and resilience in coastal municipalities like Paraty. Future research should include field-based validation and socio-economic assessments to further integrate ecological, spatial, and human dimensions in land degradation studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\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"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAL ABED M, SABOYA MTM, SANTOS CA dos;, ASSIS VC, COUTINHO IPO, VARGAS R, DIAS FF (2024) Inventory of land degradation using geoinformatics in Cachoeiras de Macacu\u0026rsquo;s municipality, Rio de Janeiro, Brazil. Annals of Arid Zone, \u003cem\u003e63\u003c/em\u003e(4)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCASCINI L (2008) Applicability of landslide susceptibility and hazard zoning at different scales. Eng Geol 102(3\u0026ndash;4):164\u0026ndash;177\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCORDEIRO RC, MONTEIRO FF, SANTELLI RE et al (2020) Environmental and anthropic variabilities at Guanabara Bay (Brazil): A comparative perspective of metal depositions in different time scales during the last 5,500 yrs. Chemosphere 267:128895. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chemosphere.2020.128895\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2020.128895\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCREED JC, PIRES DO, FIGUEIREDO MA O. (eds) (2007) Marine biodiversity of Ilha Grande Bay. Bras\u0026iacute;lia: Ministry of the Environment, MMA/SBF, Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.redebim.dphdm.mar.mil.br/vinculos/000010/0000103c.pdf\u003c/span\u003e\u003cspan address=\"https://www.redebim.dphdm.mar.mil.br/vinculos/000010/0000103c.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: [Insert Access Date]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFELIPPE RT, MENDES LMS, GALDINO S (2020) Anthropic expansion of Paraty in Serra da Bocaina National Park, Mata Atl\u0026acirc;ntica biome. Ambiente \u0026amp; Sociedade, \u003cem\u003e23\u003c/em\u003e, Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scielo.br/j/asoc/a/wVnRNyPRgL5KwhmD5bBfppR\u003c/span\u003e\u003cspan address=\"https://www.scielo.br/j/asoc/a/wVnRNyPRgL5KwhmD5bBfppR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: [Insert Access Date]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGALLARDO S, FOSSILE T, HERBST DF et al (2021) 150 years of anthropogenic impact on coastal and ocean ecosystems in Brazil revealed by historical newspapers. Ocean Coastal Manage 209:105662. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ocecoaman.2021.105662\u003c/span\u003e\u003cspan address=\"10.1016/j.ocecoaman.2021.105662\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUERRA AJT, BEZERRA JFR, JORGE MCO, FULLEN MA (2013) The geomorphology of Angra dos Reis and Paraty municipalities, southern Rio de Janeiro State. Revista Geonorte 9(1):1\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUZZETTI F, REICHENBACH P, CARRARA A, GALLI M, CIASCA P (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1\u0026ndash;4):272\u0026ndash;299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.geomorph.2005.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.geomorph.2005.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINSTITUTO BRASILEIRO DE GEOGRAFIA E ESTAT\u0026Iacute;STICA (IBGE) (2022) Censo Brasileiro, Rio de Janeiro\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJOLY CA, METZGER JP, TABARELLI M (2014) Experiences from the Brazilian Atlantic Forest: Ecological findings and conservation initiatives. New Phytol 204(3):459\u0026ndash;473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/nph.12989\u003c/span\u003e\u003cspan address=\"10.1111/nph.12989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLUANA R, JORGE MC, GUERRA A, FULLEN M (2019) Soil erosion and land degradation on trail systems in mountainous areas: two case studies from South-East Brazil. Soil Syst 3(4):56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/soilsystems3030056\u003c/span\u003e\u003cspan address=\"10.3390/soilsystems3030056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMAPBIOMAS (2023) Algorithm Theoretical Basis Document (ATBD), Collection 8.0. MapBiomas\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMESRAR H, SADIKI A, NAVAS A, FALEH A, QUIJANO L, CHAAOUAN J (2015) Mod\u0026eacute;lisation de l\u0026rsquo;\u0026eacute;rosion hydrique et des facteurs causaux: Cas de l\u0026rsquo;Oued Sahla, Rif Central, Maroc. Zeitschrift f\u0026uuml;r Geomorphologie. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1127/zfg/2015/0169\u003c/span\u003e\u003cspan address=\"10.1127/zfg/2015/0169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMONTEIRO FF, CORDEIRO RC, SANTELLI RE (2012) Sedimentary geochemical record of historical anthropogenic activities affecting Guanabara Bay (Brazil) environmental quality. Environ Earth Sci 65(5):1661\u0026ndash;1669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12665-011-1143-4\u003c/span\u003e\u003cspan address=\"10.1007/s12665-011-1143-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNETO JAB, BARRETO CF, VILELA CG et al (2017) Environmental change in Guanabara Bay, SE Brazil, based on microfaunal, pollen and geochemical proxies in sedimentary cores. Ocean Coastal Manage 143:4\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ocecoaman.2016.04.010\u003c/span\u003e\u003cspan address=\"10.1016/j.ocecoaman.2016.04.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePRIORITY 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\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePINHEIRO AB, SILVA ALC, PEREIRA TG, BAPTISTA NETO JA Coastal erosion and flood susceptibility in Paraty, Southeast Brazil. Revista Geogr\u0026aacute;fica de Chile Terra Australis, \u003cem\u003e57\u003c/em\u003e(Especial 1): 111\u0026ndash;128, 2021. Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.revistaterraaustralis.cl/index.php/rgch/article/view/103\u003c/span\u003e\u003cspan address=\"https://www.revistaterraaustralis.cl/index.php/rgch/article/view/103\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: [Insert Access Date]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMACHADO RENATOGR, CARVALHO W, J\u0026Uacute;NIOR O, MONTGOMERY DR, GOMES DR, GREENBERG R, CATALDI H, MENDON\u0026Ccedil;A M (2017) Determination of areas susceptible to landsliding using spatial patterns of rainfall from Tropical Rainfall Measuring Mission data, Rio de Janeiro, Brazil. ISPRS Int J Geo-Information 6(10):289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijgi6100289\u003c/span\u003e\u003cspan address=\"10.3390/ijgi6100289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSADIKI A, MESRAR H, FALEH A (2012) Mod\u0026eacute;lisation et cartographie des risques de l\u0026rsquo;\u0026eacute;rosion hydrique: Cas du bassin versant de l\u0026rsquo;Oued Larbaa. Maroc Papeles de Geograf\u0026iacute;a 56(55\u0026ndash;56):179\u0026ndash;188\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTAHOURI J, SADIKI A, KARRAT L, MESRAR H, JOHNSON VC, ZHANG F, KUNG H (2019) Using PAP/RAC model and GIS tools for mapping and study of water erosion processes in the Mediterranean environment: Case of the Asfalou watershed (Oriental Rif, Morocco). In: Global Symposium on Soil Erosion (GSER19). Rome: FAO\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTAHOURI J, SADIKI A, KARRAT L, JOHNSON VC, CHAN NW, ZHANG F, 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. Int Soil Water Conserv Res 10(3):254\u0026ndash;272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.iswcr.2022.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.iswcr.2022.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTOMASELLA J, VIEIRA RMSP, BARBOSA AA, RODRIGUEZ DA, SANTANA MO, SESTINI MF (2018) Desertification trends in the Northeast of Brazil over the period 2000\u0026ndash;2016. Int J Appl Earth Obs Geoinf 73:197\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jag.2018.06.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jag.2018.06.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNITED 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. [Insert Access Date], Acesso em. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wedocs.unep.org/bitstream/handle/20.500.11822/1859/syria.pdf\u003c/span\u003e\u003cspan address=\"https://wedocs.unep.org/bitstream/handle/20.500.11822/1859/syria.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Dispon\u0026iacute;vel em\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNITED NATIONS ENVIRONMENT PROGRAMME (UNEP); MEDITERRANEAN ACTION PLAN (MAP); PRIORITY ACTIONS PROGRAMME (PAP). Guidelines for erosion and desertification control management with particular reference to Mediterranean coastal areas (2000) Dispon\u0026iacute;vel em: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iczmplatform.org/storage/documents/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf\u003c/span\u003e\u003cspan address=\"https://iczmplatform.org/storage/documents/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Acesso em: [Insert Access Date]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMARTINS VAHIDEHFV, FERREIRA M, PEREIRA P, HOHENEGGER E, SAIBRO J, SILVA M, SILVA C, GOMES A, ROCHA H (2025) Impact of paleo rainfall events in the South Atlantic Convergence Zone (SACZ) and human pressures since ~\u0026thinsp;1950 in southeastern Brazil: Paraty and Saco de Mamangu\u0026aacute;. Environ Earth Sci 84:289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12665-025-12248-7\u003c/span\u003e\u003cspan address=\"10.1007/s12665-025-12248-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVIEIRA RMSP, TOMASELLA J, BARBOSA AA, POLIZEL SP, OMETTO JPHB, SANTOS FC, FERREIRA YC, TOLEDO P (2021) M. Land degradation mapping in the MATOPIBA region (Brazil) using remote sensing data and decision-tree analysis. Sci Total Environ 782:146900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.scitotenv.2021.146900\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2021.146900\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVILELA GUTTERRES BO, FIGUEIRA MCM, BAPTISTA NETO JA (2014) Late Holocene evolution and increasing pollution in Guanabara Bay, Rio de Janeiro, SE Brazil. Mar Pollut Bull 79(1\u0026ndash;2):175\u0026ndash;187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.marpolbul.2013.12.011\u003c/span\u003e\u003cspan address=\"10.1016/j.marpolbul.2013.12.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWELERSON CC, BAR\u0026Atilde;O WN, QUIRELI BA, FARIA VL, PONS NAD, RIONDET-COSTA DRT, MARCONDES A (2023) L. S. Expans\u0026atilde;o antr\u0026oacute;pica de Paraty no Parque Nacional Serra da Bocaina. Ambiente Sociedade 26:e045. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1590/1809-4422asoc20220045vu2023L3AO\u003c/span\u003e\u003cspan address=\"10.1590/1809-4422asoc20220045vu2023L3AO\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"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":"land degradation, soil erosion, geoinformatics, socio-environmental vulnerability, Atlantic Forest, Paraty","lastPublishedDoi":"10.21203/rs.3.rs-8872932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8872932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study identifies areas affected by water erosion in the municipality of Paraty, Rio de Janeiro, Brazil, where natural and human pressures exacerbate land degradation. To address this issue, detailed land degradation and prioritization maps were produced using the UNEP/Priority Actions Programme methodology, representing its novel application in a tropical coastal environment and integrating multi-satellite imagery with Geographic Information System (GIS) tools. The results show that while most of the municipality is stable (838.34 km\u0026sup2;), unstable zones (51.77 km\u0026sup2;) persist near expanding urban areas and forest margins. Urban growth and deforestation remain the main drivers of degradation, highlighting the socio-environmental vulnerability of the region. The prioritization analysis identified high-priority zones (16.45 km\u0026sup2; unstable and 60.80 km\u0026sup2; stable) requiring immediate conservation or restoration measures. Land-use and land-cover changes between 1985 and 2023 revealed increases in both forest (+\u0026thinsp;1.91%) and urban areas (+\u0026thinsp;0.5%), alongside declines in pasture and mosaic-use areas, illustrating the complex interplay between regeneration and anthropogenic pressure. Beyond technical mapping, this research provides a decision-support tool for municipal land-use planning, environmental licensing, and disaster risk reduction policies under Brazil\u0026rsquo;s Forest Code. The study demonstrates the value of combining geospatial analysis with socio-environmental approaches to promote sustainable territorial management in fragile coastal systems.\u003c/p\u003e","manuscriptTitle":"Geoinformatics Application for Land Degradation Mapping\nin Paraty Municipality Southeast Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 07:52:22","doi":"10.21203/rs.3.rs-8872932/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":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62890863,"name":"Agricultural Engineering"},{"id":62890864,"name":"Agronomy"},{"id":62890865,"name":"Forestry"},{"id":62890866,"name":"Environmental Engineering"},{"id":62890867,"name":"Geographic Information Systems"}],"tags":[],"updatedAt":"2026-02-17T07:52:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 07:52:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8872932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8872932","identity":"rs-8872932","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}