Geospatial Prioritization for Land Degradation Restoration: A 37-Year Assessment of the Syrian Coastal Region

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This study addresses a critical research and policy gap by providing a spatially explicit, science-based framework to assess land degradation risk and prioritize conservation actions in the Syrian Coastal Region,a vital socio-ecological zone in the eastern Mediterranean. We applied an integrated geomatics approach, combining the United Nations Environment Programme Priority Actions Programme Regional Activity Centre (UNEP-PAP/RAC) diagnostic framework with multi-temporal Landsat imagery (1985–2022). Biophysical and socio-economic variables were synthesized within a GIS environment and validated through 248 field sites. Analyses included land degradation mapping, conservation priority assessment, land use/land cover (LULC) change detection, and Normalized Difference Vegetation Index (NDVI) trend analysis. Approximately 17.53% (764.76 km²) of the coastal landscape is actively unstable, with sheet erosion dominating degradation processes. Spatial prioritization identified 6.77% of the region as “Unstable High Priority” zones requiring urgent intervention. LULC analysis revealed profound environmental restructuring: a 197.5% expansion of urban areas and a critical 71.2% loss of closed needleleaf forests over 37 years. NDVI trends exhibited an “anthropogenic greening” paradox, with a 44% increase in mean NDVI largely driven by agricultural intensification, masking the ongoing degradation of natural forest ecosystems. The study delivers a replicable, spatially explicit tool for targeting soil conservation and restoration efforts, directly supporting Land Degradation Neutrality (LDN) targets and sustainable land management policies. The findings underscore the necessity of integrating remote sensing diagnostics into municipal master planning and environmental regulation to bridge the gap between scientific assessment and actionable policy. This approach can enhance stakeholder comprehension, guide resource allocation, and foster effective implementation of national and regional environmental strategies in vulnerable Mediterranean coastal regions. land degradation soil erosion conservation priority remote sensing and GIS NDVI Syrian Coast environmental policy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The Mediterranean region is identified as the most susceptible area to soil degradation and desertification. This vulnerability is driven by a combination of accelerating climate change and intensifying human pressures, which are pushing soil natural capital toward critical thresholds (Ferreira et al. 2024 ). The Syrian coastal area, with an area of approximately 4,363.36 km 2 spread over two governorates Lattakia and Tartus, represents one of the most environmentally and economically vital regions in the eastern Mediterranean (Fig. 1 ). Extending across a diverse landscape of fertile coastal plains and steep mountainous terrain, the region is characterized by a typical Mediterranean climate with high annual rainfall, placing it within Syria’s first agro-ecological stability zone. These climatic conditions support high biomass productivity and intensive agricultural activities, dominated by olive groves, citrus orchards, and rapidly expanding greenhouse farming (GORS 2021). Despite this high potential, the region's pronounced spatial heterogeneity where nearly 85% of the land consists of plateaus and mountainsmakes it inherently vulnerable to soil erosion. This physical sensitivity is exacerbated by intense seasonal rainfall and steep gradients, which lead to significant soil displacement and the sedimentation of vital water reservoirs (Barakat 2018 ). Recent decades have seen a profound environmental restructuring, including massive urban expansion at the expense of agricultural land (Cheikh Dib 2020 ) and the depletion of primary forest cover due to recurring fires and improper land management (Dakka et al. 2024 ). Conventional field-based assessments are often insufficient to capture the cumulative effects of these complex processes at a regional scale. Consequently, the integration of remote sensing and Geographic Information Systems (GIS) has become essential for assessing land degradation risk in a spatially explicit and cost-effective manner (Panagos et al. 2015 ; Vrieling 2006 ). The United Nations Environment Programme’s (UNEP-PAP/RAC) methodology provides a standardized framework for identifying landscape stability and prioritizing conservation measures in Mediterranean coastal areas (UNEP/MAP/PAP, 2000). This methodology has proven reliable in diverse Mediterranean environments for assessing land degradation processes (e.g., UNEP, MAP/PAP. 2004; Sadiki et al. 2012 ; Mesrar et al. 2015 ; Tahouri et al. 2022 ; Lhoussaine et al. 2024 ). To address the current research gap, this study aims to apply a comprehensive geomatics approach based on the (UNEP-PAP/RAC) diagnostic criteria. By synthesizing these criteria with multi-temporal Landsat data (1985–2022), the research aims to quantify Land Use/Land Cover (LULC) transitions and vegetation health across the entire Syrian coastal area. The findings provide a scientific framework for prioritizing soil conservation measures and a foundation for the implementation of Syrian environmental laws and municipal master plans, supporting effective resource allocation and ecological restoration in this ecologically critical region. 2. Material and Methods 2.1 Geospatial Assessment of Landscape Stability and Erosion Dynamics Landscape stability and degradation mapping were executed following the consolidated framework for Mapping of Rainfall-Induced Erosion Processes in Mediterranean Coastal Areas , developed by the Priority Actions Programme Regional Activity Centre (PAP/RAC 1997). This methodology, specifically tailored for Mediterranean environments, provides a standardized diagnostic system for identifying, classifying, and evaluating degradation processes driven primarily by water erosion. The assessment was implemented within a Geospatial Information System (GIS) environment using ArcGIS and QGIS software. Spatial analyses adhered to PAP/RAC cartographic standards, utilizing a 1:50,000 scale and integrating Landsat satellite imagery with multi-thematic spatial layers. The GIS database synthesized various datasets, including physiographic unit maps, high-resolution topography, and land use/land cover (LULC) data. The delineation of stable and unstable units was finalized through a cross-analytical approach, validated by extensive field observations from 248 representative sites distributed across the coastal governorates. At each site, data were systematically recorded regarding stability factors, dominant degradation processes, spatial extent, and expansion trends. Following the PAP/RAC criteria (UNEP/MAP/PAP 2000), the landscape was separated into two primary categories: Stable Land Units : These units were evaluated based on their functional category and the main causative factors maintaining their stability. Categories included unmanaged areas (with forestry or agricultural potential) and managed areas (under active forestry or agricultural use). Each unit was assigned an instability risk score ranging from 0 (no risk) to 3 (critical risk), derived from an analysis of slope gradients, lithological characteristics, vegetation density, and anthropogenic pressure. Unstable Land Units : These were classified by the dominant degradation process, primarily focusing on sheet and rill erosion. The spatial footprint of each process was categorized by extent: localized ( 60%). Furthermore, the expansion trend was assessed on a four-level scale (0–3), where 0 represents stabilizing conditions and 3 indicates an advancing trend approaching ecological irreversibility. This integrated geomatics approach ensures that the resulting maps provide a scientifically robust baseline for the Syrian Coastal Area, enabling the transition from descriptive mapping to prescriptive conservation planning. 2.2 Priority Assessment for Land Restoration and Management Effective mitigation of land degradation across the Syrian coastal region requires the optimal allocation of technical and financial resources through the clear identification of priority zones. To achieve this, a spatial prioritization procedure was applied using a multi-criteria scoring system designed to delineate land degradation hotspots requiring urgent management intervention. This framework follows the standardized UNEP/MAP–PAP/RAC methodology (UNEP/MAP/PAP 2004) and integrates biophysical risks with socio-economic drivers influencing landscape stability throughout the study area. The assessment utilized fourteen diagnostic variables selected for their relevance to Mediterranean degradation processes and their significance within the local socio-economic context (Table 1 ). Each variable was assigned a quantitative score ranging from 1 (lowest impact/risk) to 3 (highest impact/risk), reflecting its relative contribution to land stability or vulnerability. The weighting of these variables was established through a structured expert-based evaluation, ensuring the results remained consistent with regional knowledge and extensive field observations conducted across the coastal mountains and plains. Table 1 Variables and Scoring Matrix for Land Degradation Intervention Prioritization Variable Description Scoring (1–3) A Physical instability risk (stable areas) 1 = low, 2 = high, 3 = critical B Extent of area affected (unstable areas) 1 = 60% C Expansion trend of degradation (unstable areas) 1 = local, 2 = widespread, 3 = generalized/irreversible D Multiplicator for increased importance (causative agents or degradation process) 1 = none, 2 = increased, 3 = highly increased E Influence on adjacent areas 1 = low, 2 = high, 3 = critical F Overexploitation 1 = insignificant, 2 = significant, 3 = crucial G Rural exodus H Land tenure I Other aggravating socio-economic factors J Value of current land use (local population) 1 = low, 2 = moderate, 3 = high K Value of current land use (national policy) L Potential for forestry M Potential for agriculture N Other land use potentials (recreational, industrial, construction) After scoring all criteria for each identified area, final prioritization scores were calculated as follows: 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] Final scores were grouped into three priority categories: High (≥ 60), Medium (21–59), and Low (≤ 20). 2.3 Spatio-Temporal Dynamics of Land Use and Land Cover (1985–2022) To evaluate the long-term Land Use and Land Cover (LULC) dynamics across the Syrian coastal area, a multi-temporal analysis was conducted using a series of Landsat satellite missions: Landsat 5 TM (1985), Landsat 7 ETM+ (2000 and 2012), and Landsat 8/9 OLI (2022). To ensure accurate temporal comparisons, all datasets maintained a consistent spatial resolution of 30 meters. Furthermore, imagery was exclusively selected from the dry summer months (June–August) to minimize seasonal variance and phenological differences, thereby ensuring stable and comparable environmental conditions throughout the classification process. Data preprocessing was performed using Google Earth Engine (GEE) and ArcGIS 10.8, involving geometric corrections, projection harmonization to the WGS 84 / UTM zone 37N coordinate system, and atmospheric calibration to surface reflectance. The classification process employed a Supervised Classification approach based on the Maximum Likelihood Classifier (MLC) algorithm. This approach leveraged fieldwork data and extensive local knowledge of the study region to achieve a Level II classification depth. The classification model was initially developed using 2022 reference data and subsequently applied to historical imagery to maintain consistency across the time series. The differentiation of forest types adhered to hierarchical classification principles specifically validated for the Syrian coastal mountains, allowing for the precise separation of deciduous and needleleaf formations (Karmoka 2018 ). This methodology categorized the landscape into nine representative classes reflecting the region's agricultural, urban, and natural characteristics. To enhance thematic map quality, a post-classification majority filter was applied to reduce spectral noise commonly referred to as the "salt-and-pepper" effect and improve class homogeneity. The reliability of the resulting LULC maps was confirmed through a rigorous accuracy assessment. Ground truth reference data, synthesized from field surveys and high-resolution Google Earth imagery, were used to construct a confusion matrix. From this matrix, standard performance metrics, including Overall Accuracy and the Kappa Coefficient, were calculated to statistically validate the mapping results and the subsequent change detection analysis. 2.4 Analysis of Vegetation Dynamics via NDVI Time-Series Vegetation dynamics across the 37-year study period were quantified using the Normalized Difference Vegetation Index (NDVI), a robust proxy for photosynthetic activity and biomass. Data were derived from Landsat Collection 2 Level-2 surface reflectance products sourced from the United States Geological Survey (USGS). To ensure full spatial coverage of the Syrian Coastal Area, a multi-scene mosaic approach was employed, utilizing Path 174, Rows 35 and 36. The analysis focused on four strategic temporal snapshots: 1985, 2000, 2012, and 2022. The dataset integrated imagery from Landsat 5 TM (for 1985 and 2000) using Band 3 (Red) and Band 4 (NIR), and Landsat 8/9 OLI (for 2012 and 2022) using Band 4 (Red) and Band 5 (NIR). NDVI was selected as the primary indicator due to its established high correlation with forest growth parameters and biomass density specifically within Syrian coastal environments (Karmoka 2018 ). The utilization of Level-2 products ensured radiometric consistency and standardized atmospheric correction across the time series. Preprocessing was executed within a GIS environment, where the dual scenes (174/35 and 174/36) were mosaicked and subsequently clipped to the administrative boundaries of the Syrian Coastal Area. For each reference year, NDVI Max and NDVI Mean values were extracted to provide a comprehensive quantitative assessment of both peak vegetation health and overall landscape biomass. This systematic workflow produced spatially consistent rasters, georeferenced to the WGS 84 / UTM Zone 37N coordinate system. These outputs provided the statistical foundation necessary to analyze the "anthropogenic greening" trends and forest fragmentation patterns discussed in the results of this study. 3. Results 3.1 Coastal Land Degradation Analysis The geospatial assessment of the Syrian coastal area identifies a landscape predominantly characterized by stability, yet significantly impacted by active degradation processes. As detailed in Table 2 , the region is categorized into stable areas, unstable areas, and non-relevant units (comprising urban and waterbodies). Stable land units encompass the vast majority of the study area, totaling 3243.39 km², or 74.34% of the coastal territory (Table 2 ). The spatial distribution of these stable classes shows distinct geographic patterns: Unmanaged Areas with Agricultural Potential represent the most extensive stable land type, covering 1121.68 km² (25.71%). These areas are predominantly located in the southern and eastern plains of the Tartus governorate and the hinterlands of Lattakia (Fig. 2 ). Managed Areas with Agricultural Use account for 945.99 km² (21.68%), forming a contiguous belt across the fertile coastal plains of both governorates, particularly surrounding urban centers. Unmanaged Areas with Forest Potential cover 750.68 km² (17.21%), primarily occupying the mid-slope zones of the coastal mountain ranges. Managed Areas with Forest Use, covering 425.04 km² (9.74%), are almost exclusively concentrated in the northern part of the study area within Lattakia governorate, corresponding to the denser forest reserves of the region. Table 2 Areas of degradation and stability patterns in the Syrian coastal area Area Code Land Category Type Latakia Area (km 2 ) Tartus Area (km 2 ) Costal Area (km 2 ) Costal Area (%) Stable Area 01 Unmanaged Areas with Forest Potential 403.67 347.01 750.68 17.21 02 Unmanaged Areas with Agriculture Potential 264.37 857.31 1121.68 25.71 03 Managed Areas with Forest Use 416.07 8.97 425.04 9.74 04 Managed Areas with Agriculture Use 620.93 325.06 945.99 21.68 Total Stable area 1705.04 1538.35 3243.39 74.34 Unstable Area L Sheet Erosion 533.01 179.26 712.27 16.33 D Rill Erosion 38.55 8.11 46.66 1.07 C Gully Erosion 4.71 0.00 4.71 0.11 M Mass Earth Movement 1.12 0.00 1.12 0.03 Total Unstable area 577.39 187.37 764.76 17.53 -- Not Relevant (Urban, Water) 170.55 184.66 355.21 8.14 Total costal area 2452.99 1910.37 4362.76 100 In contrast, areas exhibiting active soil degradation classified as unstable cover a significant 764.76 km², representing 17.53% of the coastal region. As visually prominent in Fig. 2 , sheet erosion (shown in red) is the overwhelmingly dominant degradation mechanism, affecting 712.27 km² or 16.33% of the total area. This process is not uniformly distributed but is intensely concentrated in the central and northern mountainous sectors of Lattakia governorate. Major hotspots of sheet erosion are evident in the districts of Haffa, Kurdaha, and Jabla, where steep slopes and intensive land use interact. Other erosion processes are spatially limited: Rill Erosion affects 46.66 km² (1.07%), while Gully Erosion and Mass Earth Movements collectively impact less than 0.15% of the territory. The distribution between governorates is highly asymmetrical (Table 2 ). Lattakia contains 577.39 km² of unstable land, compared to 187.37 km² in Tartus. This disparity underscores the heightened physical vulnerability of Lattakia's steeper terrain to rainfall-induced erosion. The remaining 8.14% (355.21 km²) of the study area is classified as "Not Relevant," consisting of urban centers and water bodies that fall outside the PAP/RAC soil degradation diagnostic criteria. 3.2. Coastal Land Degradation Conservation Priority The spatial prioritization of conservation efforts, determined through the multi-criteria scoring of biophysical and socio-economic variables, provides a strategic framework for land management across the Syrian coastal region. As detailed in Table 3 , the landscape is categorized into three hierarchical priority levels for both stable and unstable units. Table 3 Spatial extent of conservation priority zones in the Syrian coastal area Area Conservation Priority Latakia Area (km²) Tartus Area (km²) Coastal Area (km²) Coastal Area (%) Stable Areas Stable Low Priority 524.56 15.20 539.76 12.37 Stable Medium Priority 1147.67 1461.53 2609.20 59.79 Stable High Priority 33.44 58.06 91.50 2.10 Total Stable Areas 1705.67 1534.79 3240.46 74.25 Unstable Areas Unstable Low Priority 145.62 0.97 146.59 3.36 Unstable Medium Priority 243.11 80.20 323.31 7.41 Unstable High Priority 186.49 108.77 295.26 6.77 Total Unstable Areas 575.22 189.95 765.17 17.53 Not Relevant (Urban, Water) 172.54 185.80 358.34 8.21 Total Areas 2453.42 1910.55 4363.97 100 Stable areas, constituting 74.25% (3240.46 km²) of the region, are largely designated for preventive management. The Stable Medium Priority class forms the most extensive contiguous zone, covering 2609.20 km² (59.79%). As shown in Fig. 3 , this class dominates the central and southern agricultural plains of Tartus and the mid-elevation slopes of Lattakia, representing lands that are currently stable but require consistent monitoring and sustainable practices to prevent future degradation. Stable Low Priority areas, covering 539.76 km² (12.37%), are primarily located in the most resilient coastal lowlands and some managed forest patches. Conversely, Stable High Priority zones, though limited to 91.50 km² (2.10%), are critically important. These areas are spatially concentrated in the remaining primary forest fragments of Lattakia’s northern mountains, as visible in Fig. 3 , and require strict protective measures to maintain their vital role as ecological stabilizers. Unstable areas, representing active degradation across 17.53% (765.17 km²) of the territory, are the primary targets for restorative intervention. The most critical category, Unstable High Priority, covers 295.26 km² (6.77%). Figure 3 shows these zones as pronounced hotspots, intensely concentrated in the steep, erosion-prone mountainous interiors of Lattakia governorate, particularly within the districts of Haffa, Kurdaha, and Jabla. These areas demand urgent curative actions, such as terracing and reforestation. Unstable Medium Priority areas, spanning 323.31 km² (7.41%), often form a transitional belt surrounding the high-priority cores or appear in the foothills of Tartus. Unstable Low Priority zones are minimal, covering 146.59 km² (3.36%), and are typically found in areas of less severe or stabilizing erosion. A clear geographic disparity is evident, Lattakia contains a significantly larger extent of Unstable High Priority land (186.49 km²) compared to Tartus (108.77 km²), reflecting its greater physiographic vulnerability. Consequently, while management in Tartus can emphasize the protection of stable agricultural plains, resource allocation in Lattakia must be heavily weighted toward urgent soil stabilization in its mountainous interior. 3.3 Coastal Land use and Land cover change analysis (1985–2022) The multi-temporal analysis of the Syrian Coastal Area reveals a landscape undergoing profound environmental restructuring between 1985 and 2022 (Fig. 4 ). The most significant transformation is the aggressive expansion of Urban Areas (Table 4 ), which grew from 35.58 km 2 to 105.86 km 2 , representing a net increase of 197.5% (Cheikh Dib, 2020 ). This rapid urbanization has primarily occurred at the expense of agricultural and natural landscapes, reflecting intensified human pressure in the region. Significant shifts were also observed in natural vegetation and forest ecosystems. Tree Cover – Closed Needleleaf Forest suffered the most drastic decline, plummeting from 306.46 km² in 1985 to just 88.39 km² in 2022, a loss of 71.2%. Similarly, Tree Cover – Closed Broadleaf Deciduous Forest decreased by 50.6%, losing approximately 25.44 km² over the study period. These losses indicate severe ecosystem fragmentation, likely driven by recurring forest fires and improper land management (Dakka et al. 2024 ; GFW 2025). Conversely, Tree Cover – Open Mixed Leaf Forest showed a notable increase of 24.9% (90.73 km²), suggesting a shift in forest structure or a transition from closed to more open canopy formations. Table 4 Total Syrian Coastal Area LULC Change (1985–2022) Landuse & Landcover Category 1985 (km 2 ) 2000 (km 2 ) 2012 (km 2 ) 2022 (km 2 ) Change (km²) Change (%) Mixed Crop & Natural Vegetation 879.01 1373.47 1388.10 1017.88 + 138.87 + 15.8 Urban Areas 35.58 41.72 82.35 105.86 + 70.28 + 197.5 Agricultural Area 2431.56 2017.31 2021.36 2420.65 -10.91 -0.4 Waterbodies 24.08 23.12 23.18 25.98 + 1.90 + 7.9 Bare Land 245.03 247.48 210.65 200.16 -44.87 -18.3 Shrubland 27.18 98.52 100.06 25.15 -2.03 -7.5 Tree Cover - Closed Broadleaf Deciduous Forest 50.27 58.95 42.96 24.83 -25.44 -50.6 Tree Cover - Closed Needleleaf Forest 306.46 219.47 164.69 88.39 -218.07 -71.2 Tree Cover - Open Mixed Leaf Forest 363.72 282.85 330.01 454.45 + 90.73 + 24.9 Sparse Vegetation (Tree. Shrub. Herbaceous) 0.47 0.48 0 0 -0.47 -100 Total Area 4363.36 4363.36 4363.36 4363.36 0.00 0.0 Agricultural dynamics show a complex pattern of stability and expansion: Mixed Crop & Natural Vegetation expanded by 15.8% (+ 138.87 km 2 ), indicating an increase in heterogeneous agricultural landscapes. The primary Agricultural Area remained relatively stable in terms of total extent, showing only a marginal decrease of 0.4% (10.91 km 2 ) by 2022, despite fluctuations in the intervening years (Saqer et al. 2024 ). Sparse Vegetation essentially disappeared from the study area, recording a 100% loss from its initial 0.47 km 2 in 1985. The reduction in Bare Land by 18.3% (44.87 km 2 ) and the slight increase in Waterbodies (+ 7.9%) further illustrate the dynamic nature of the coastal environment. Overall, these LULC transitions highlight a clear trend: the expansion of the built environment and certain agricultural types is occurring alongside a critical depletion of the region's primary natural forest cover. This restructuring of the landscape serves as a fundamental driver for the land degradation patterns and erosion risks identified in the study. 3.4 Coastal NDVI trend analysis (1985–2022) The analysis of the Normalized Difference Vegetation Index (NDVI) across the Syrian Coastal Area provides a quantitative measure of vegetation health and biomass dynamics over the 37-year study period (Fig. 5 ). As shown in Table 5 , the regional vegetation trends exhibit a consistent overall improvement in greenness, despite localized pressures. Table 5 NDVI Values for the Syrian costal area (1985–2022) Year NDVI Max NDVI Mean 1985 0.773 0.343 2000 0.833 0.399 2012 0.869 0.389 2022 0.857 0.494 In 1985, the region recorded an NDVI Mean of 0.343 and an NDVI Max of 0.773. By the year 2000, these values rose significantly to a mean of 0.399 and a maximum of 0.833, indicating a period of robust vegetation growth and potentially favorable climatic conditions or agricultural expansion. A critical observation occurs in the 2000–2012 interval. While the NDVI Max increased further to 0.869, the NDVI Mean experienced a slight decrease, dropping to 0.389. This "2012 dip" aligns with the land cover findings regarding the depletion of primary forest cover and the impacts of recurring forest fires noted in the region (Najjar 2019 ; GFW 2025). The stability in peak NDVI alongside a declining mean suggests that while high-biomass areas remained productive, the overall landscape experienced a degree of degradation or biomass loss, particularly in natural vegetation. By 2022, however, the region showed a strong recovery, with the NDVI Mean reaching its highest recorded value of 0.494 and the NDVI Max slightly declining to 0.857. This substantial increase in mean value (representing a nearly 44% growth since 1985) likely reflects the intensification of irrigated agriculture and the expansion of greenhouse farming and mixed crop landscapes identified in the LULC analysis. Overall, the NDVI results confirm that the Syrian Coastal Area is undergoing an "anthropogenic greening" process. While the rising mean values indicate an increase in total biomass, the decoupling between maximum and mean trends in 2012 as well as the overall loss of natural forest cover suggests that this regional greening is largely driven by agricultural productivity rather than the recovery of natural ecosystems. 4. Discussion 4.1 Dynamics of Coastal Land Degradation The spatial assessment of the Syrian Coastal Area reveals a landscape where stability is the dominant state, yet significant pockets of active degradation pose a long-term threat to soil security. Approximately 74.34% of the region is classified as stable, largely supported by managed agricultural systems and remaining forest fragments. However, the identification of 764.76 km² as unstable (17.53% of the total area) highlights a critical environmental transition. The prevalence of sheet erosion, affecting 16.33% of the coastal territory, suggests that surface-level soil displacement is the primary degradation mechanism. This process is particularly intense in the Lattakia governorate, which contains over three times the extent of unstable land found in Tartus. This heightened vulnerability in Lattakia is primarily attributed to its steeper mountainous topography and higher susceptibility to intense rainfall-induced erosion (Barakat 2018 ). The concentration of sheet erosion in Lattakia’s mountainous areas aligns with earlier findings in Tartus, where steep slopes and sparse vegetation were identified as key drivers of high soil loss rates (AlAbed et al. 2018 ). The findings align with Barakat ( 2018 ), who noted that seasonal rainfall patterns in the region lead to significant soil displacement and reservoir sedimentation. The minimal presence of gully erosion (0.11%) and mass earth movements (0.03%) suggests that while the degradation is widespread, it remains at a stage where surface-level conservation measures could be highly effective if implemented immediately. Overall, these results underscore that while the coastal landscape is largely stable, the concentrated areas of sheet erosion in Lattakia represent a significant environmental challenge requiring targeted conservation efforts. 4.2 Conservation Planning and Resource Allocation By applying the UNEP-PAP/RAC multi-criteria scoring system, this study provides a clear roadmap for environmental intervention. The results indicate that while 59.79% of the coastal area is a “Stable Medium Priority,” a significant 6.77% (295.26 km²) falls into the Unstable High Priority category. These high-priority zones represent “degradation hotspots” where the convergence of steep slopes, overexploitation, and fragile lithology has pushed the landscape toward potential irreversibility a pattern observed in similar fragile Mediterranean environments (García-Ruiz et al. 2013; Tahouri et al. 2022 ). In these areas, curative soil conservation such as the construction of terraces or the restoration of protective vegetation is no longer optional but essential to prevent the total loss of the soil’s natural capital, aligning with recommended erosion control practices in mountainous regions (Mesrar et al. 2015 ; Jucker Riva et al. 2017 ). Conversely, the 91.50 km² identified as Stable High Priority represents the region’s most valuable natural assets, including primary forest fragments that require strict preventive protection to maintain their role as landscape stabilizers (Karmoka 2018 ; GFW 2025). This tiered prioritization allows municipal master plans to allocate limited resources effectively, focusing on curative actions in the mountains of Lattakia and preventive management in the agricultural plains of Tartus, supporting integrated land-use planning in vulnerable socio-ecological systems (Ferreira et al. 2024 ). 4.3 Spatio-Temporal Evolution of Land Cover (1985–2022) The multi-temporal analysis reveals a profound environmental restructuring of the Syrian Coastal Area, characterized by the aggressive expansion of the built environment at the expense of vital natural ecosystems. The 197.5% increase in Urban Areas (growing from 35.58 km² to 105.86 km²) highlights the intensity of anthropogenic pressure, which is a primary driver of the land degradation patterns identified in this study. This urbanization trend often targets fertile coastal plains, leading to the fragmentation of agricultural landscapes (Cheikh Dib 2020 ). Similar to findings in Tartus district, where high erosion rates were linked to land-use intensification and topographic vulnerability (AlAbed et al. 2018 ), our study underscores the interaction between anthropogenic pressure and biophysical susceptibility. The most alarming ecological shift is the systematic depletion of primary forest cover. The 71.2% loss of Closed Needleleaf Forest and the 50.6% decline in Closed Broadleaf Deciduous Forest represent a critical reduction in the region's natural protective barriers. These findings are corroborated by external land cover statistics from Global Forest Watch (GFW), which indicate that by 2020, natural forests constituted only 22% of land cover in Lattakia and 13% in Tartus. Furthermore, GFW data shows that tree cover with a canopy density > 30% a key indicator of healthy, dense forest declined from 23% in Lattakia and 6.0% in Tartus in the year 2000. This external benchmark confirms the severity and regional consistency of the forest degradation process documented in our LULC analysis. The observed trends suggest that the coastal mountains are losing the dense biological structures essential for regulating soil stability and hydrology, likely due to a combination of recurring forest fires, land conversion for agriculture, and improper land management (Dakka et al. 2024 ; GFW 2025). The simultaneous 24.9% increase in Open Mixed Leaf Forest is indicative of a landscape transition from dense, stable forest canopies to more open, degraded, or secondary formations. This shift aligns with GFW data pointing to the significant extent of “unknown” plantation types in both governorates, suggesting a replacement of natural forest ecosystems with managed or successional tree cover that offers diminished erosion control and ecological value. Our findings align with the broader Mediterranean context where land-use change, particularly farmland abandonment and agricultural intensification serves as a primary driver of erosional response. García-Ruiz et al (2013) highlighted that the co-existence of land abandonment in mountainous areas and the expansion of subsidized crops into marginal lands creates a fragmented landscape where erosion processes are spatially and temporally variable. This duality of intensification and extensification helps explain the contrasting sediment yields observed in our study catchments. 4.4 Biomass Trends and Vegetation Health Indicators The NDVI analysis provides a critical, yet complex, perspective on regional environmental health, revealing what can be termed an “anthropogenic greening paradox.” While the NDVI Mean increased by nearly 44% (from 0.343 to 0.494) between 1985 and 2022, this improvement in aggregate “greenness” does not signify ecological restoration. Instead, this trend is decisively linked to the intensification of irrigated agriculture and the rapid expansion of greenhouse farming captured in the LULC analysis. A significant diagnostic signal is the 2000–2012 interval, where the NDVI Mean experienced a slight decline to 0.389. This ‘2012 dip’ aligns with documented depletion of primary forest cover and recurring fire impacts in the region (Najjar 2019 ; Dakka et al. 2024 ; GFW 2025). Notably, the NDVI Max remained high (0.869 in 2012), indicating sustained productivity in intensive agricultural zones, while the mean dropped. This decoupling underscores that agricultural intensification can maintain or elevate peak vegetation indices even as native forest cover which contributes significantly to the landscape's average greenness is being reduced. The substantial rise in mean NDVI by 2022 must therefore be interpreted with caution. The external GFW data, showing low percentages of natural forest cover (22% in Lattakia, 13% in Tartus) and minimal area with high-density tree cover by 2020, provides crucial context. It confirms that the high NDVI values are not driven by a recovery of dense, natural forests but by the proliferation of agriculturally driven vegetation. This creates a paradox where the landscape appears “greener” and more productive according to spectral indices, while simultaneously suffering a net loss in the very native forest ecosystems that are most critical for long-term soil stability, biodiversity, and climate resilience. These results highlight a vital methodological insight: high NDVI values can mask underlying land degradation risks. A landscape may appear “greener” due to agricultural expansion while simultaneously losing the ecological quality, structure, and protective functions of its native forest cover. This reinforces the imperative of supplementing NDVI analysis with detailed land cover classification and degradation mapping as done in this study to accurately diagnose environmental health. In line with Jucker Riva et al ( 2017 ), who emphasized that NDVI-based assessments must be contextualized within landscape units (considering land cover, slope, and aspect) to disentangle anthropogenic degradation from natural variability, our integrated approach reveals that what appears as greening in the Syrian Coastal Area is, in fact, a signal of land-use intensification rather than ecological recovery. Such nuanced interpretation is essential for informing sustainable land management in regions undergoing rapid anthropogenic transformation. 5. Conclusion and Recommendations 5.1 Conclusion This study provides a comprehensive geospatial assessment of land degradation and landscape dynamics in the Syrian Coastal Area from 1985 to 2022. By integrating the PAP/RAC diagnostic framework with multi-temporal remote sensing data, several critical environmental transitions were identified: Landscape Stability : While the majority of the region (74.34%) remains stable, approximately 17.53% (764.76 km²) is actively unstable, with sheet erosion being the primary degradation process, especially in the steep mountainous terrain of Lattakia. Ecological Restructuring : Over the 37-year study period, the region has undergone a profound transformation characterized by a 197.5% increase in urban areas and a catastrophic 71.2% loss of closed needleleaf forests. The Greening Paradox : NDVI analysis revealed a 44% increase in mean vegetation greenness (from 0.343 to 0.494). However, this “anthropogenic greening” is largely driven by agricultural intensification and greenhouse expansion, which is underscored by the decoupling between rising mean NDVI and stable or declining natural forest biomass. Peak NDVI values remained high even increasing to 0.869 in 2012 reflecting agricultural productivity, yet the concurrent decline in natural forest cover signals underlying ecosystem degradation. Conservation Priority : Spatial prioritization identified 295.26 km² (6.77%) of the study area as Unstable High Priority zones. These areas represent critical “degradation hotspots” where immediate intervention is required to prevent irreversible environmental damage. 5.2 Recommendations To mitigate further land degradation and support Land Degradation Neutrality (LDN) in this vital Mediterranean region, the following actions are recommended: Targeted Restoration : Urgent curative measures, including reforestation and the construction of traditional soil-retaining terraces, should be prioritized in the Unstable High Priority zones identified in the Lattakia and Tartus mountains. Protective Management : Strict preventive protection must be enforced for the remaining Stable High Priority areas (91.50 km²), which consist of primary forest fragments essential for regional landscape stability. Urban Planning : Municipal master plans should be updated to direct urban expansion away from fertile agricultural plains and vulnerable mountainous slopes to prevent further habitat fragmentation and soil sealing. Continuous Monitoring : A regional environmental observatory using high-resolution geomatics should be established to monitor NDVI trends and LULC changes continuously, allowing for rapid response to new degradation signals or forest fire events. Policy Integration : The findings of this study should serve as a scientific baseline for implementing Syrian environmental laws, ensuring that national resource allocation is directed toward the most vulnerable ecological zones. Declarations Competing interests : The authors have no competing interests to declare that are relevant to the content of this article. Ethical Statement All authors have read, understood, and have complied as applicable with the statement on ‘Ethical responsibilities of Authors’ as found in the Instructions for Authors. Funding: Author 4 has received research support from The British Academy/Cara/Leverhulme Researchers at Risk Research Support Grant. The University of Manchester.” The other authors declare that no funds, grants or other support were received during the preparation of this manuscript. Author Contribution “All authors have contributed to the study conception design and material preparation, data collection and analysis. The first draft of the manuscript was written by author 1 and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” Acknowledgement Authors sincerely thank the ICALD project team “Inventory of Coastal Area Land Degradation Using Remote Sensing and GIS Techniques” for providing the field data essential to creating the land degradation and priority conservation maps presented in this article. Data Availability Data fully available upon request References AlAbed M, Salhab J, Hani E and Dweri S (2018) Quantitative Estimation of Annual Soil Loss by Integration of Remote Sensing, GIS, and Universal Soil Loss Equation (Case Study: Tartus District, Syria). Annals of Arid Zone. 57(3&4):129–134. Barakat M (2018) Prediction of the soil lost amount by water erosion in the Hawiz dam basin region using the Revised Universal Soil Equation (RUSLE) and GIS techniques. Tishreen Univ J Res Sci Stud Biol Sci Ser . 40(5):11–29 Cheikh Dib S (2020) The impact of urban expansion on agricultural lands in Lattakia city. Tishreen Univ J Res Sci Stud Eng Sci Ser . 24(5): 3–45. Dakka M, Idris Y, Al-Ghammaz F (2024) Studying the impact of forest fires on soil degradation using GIS and Remote Sensing in the Al-Bayer and Al-Basit region. Tishreen Univ J Res Sci Stud Biol Sci Ser . 46(1): 205–227. Ferreira C S, Keesstra S, Destouni G, Solomun M K, and Kalantari Z (2024) Soil Degradation in the Mediterranean Region: Drivers and Future Trends. In Environmental Sustainability in the Mediterranean Region (P. 81–112). Springer Nature. General Organization of Remote Sensing (GORS), Ministry of Agriculture & Agrarian Reform, Ministry of Local Administration & Environment. (2021). Inventory of coastal area land degradation using remote sensing and GIS techniques . Global Forest Watch (GFW). (2025). Interactive map . World Resources Institute. Retrieved January 30, 2026, from https://www.globalforestwatch.org/ García R, Estela N R, Noemí L R, Santiago B (2013) Erosion in Mediterranean landscapes: Changes and future challenges. Geomorphology. 198: 20–36. https://doi.org/10.1016/j.geomorph.2013.05.023 Jucker Riva M, Daliakopoulos I N, Eckert S, Hodel E and Liniger H (2017) Assessment of land degradation in Mediterranean forests and grazing lands using a landscape unit approach and the normalized difference vegetation index. Applied Geography , 86: 8–21. http://dx.doi.org/10.1016/j.apgeog.2017.06.017 Karmoka, R F (2018) Using remote sensing and Geographic Information System to evaluate some growth indicators in Lattakia forests. [Ph.D. Thesis]. Faculty of Agriculture, Damascus University, Syria. Lhoussaine E M, Meryem M, Moncef B, Mustapha M, Noureddine A, Abdessalam B H, Yousra R and Brahim D (2024) A GIS-based modified PAP/RAC model and Caesium-137 approach for water erosion assessment in the Raouz catchment, Morocco. Environmental Research . 251(1): 118–460. https://doi.org/10.1016/j.envres.2024.118460 Mesrar H, Sadiki A, Navas A, Faleh A, Quijano L and Chaaouan J (2015) Modélisation de l'érosion hydrique et des facteurs causaux: cas de l'Oued Sahla, rif central, Maroc. Zeitschrift für Geomorphologie . Najjar D (2019) Mapping forest fire risk in Tartus region using remote sensing and GIS technologies . [Master’s Dissertation]. Faculty of Agriculture, Aleppo University, Syria. Panagos P, Pasquale B, Jean P, Cristiano B, Emanuele L, Katrin M, Luca M, Christine A (2015) The new assessment of soil loss by water erosion in Europe. Environmental Science & Policy. 54: 438–447. https://doi.org/10.1016/j.envsci.2015.08.012 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. Sadiki A, Mesrar H and Faleh A (2012) Modélisation et cartographie des risques de l'érosion hydrique: cas du bassin versant de l’Oued Larbaa, Maroc. Papeles de Geografía , 55–56:179–188. Saqer I, Ahmed A and Arhaya A (2024) Land use analysis in Tartus governorate. Tishreen University Journal for Research and Scientific Studies - Biological Sciences Series , 46(4): 139–151. Tahouri J, Sadiki A, Karrat L, Johnson V C, Chan N W, Fei Z and Kung H T (2022) Using a modified PAP/RAC model and GIS for mapping water erosion and causal risk factors: case study of the Asfalou watershed, Morocco. International Soil and Water Conservation Research . 10: 254–272. United Nations Environment Programme (UNEP)/Mediterranean Action Plan (MAP)/ Priority Actions Programme. (2000). Guidelines for erosion and desertification control management with particular reference to Mediterranean coastal areas . Avalable at: https://iczmplatform.org/storage/documents/Vn1Imo6Q5bY 3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf 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 . Available at: https://wedocs.unep.org/bitstream/handle/20.500.11822/1859/syria.pdf Vrieling A (2006) Satellite remote sensing for water erosion assessment: A review. CATENA . 65 (1):2–18. https://doi.org/10.1016/j.catena.2005.10.005 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 02 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8769228","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588154508,"identity":"47b70257-0a2c-4f29-ac1c-239e82834dda","order_by":0,"name":"Mohammad AlAbed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYHACNiCyYWCQIFFLGulaDpOgRbeB+dmDD2XnE/tnNx98wFBjE01Qi9kBNnPDGeduJ864cyzZgOFYWm4DYS0MZtK8bbcTG27kmEkwNhwmRgv7N+m/becS55OghcdMmrHtQOIG4rUc5ik37DmXbLzxRlqyQQJRfjnevu3BjzI72Xk3kg8++FBjQ1gLAzOEcgSrTCCoHAnYk6J4FIyCUTAKRhgAAILWQQFt7wwvAAAAAElFTkSuQmCC","orcid":"","institution":"Fluminense Federal University (UFF)","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"AlAbed","suffix":""},{"id":588154509,"identity":"de1f22f0-7418-4e14-ba7f-ce641fd0c104","order_by":1,"name":"Rosa Karmoka","email":"","orcid":"","institution":"General Organization of Remote Sensing","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"","lastName":"Karmoka","suffix":""},{"id":588154510,"identity":"b41c570e-fc24-4e35-be4c-b5cbb69e3c34","order_by":2,"name":"Silva Loulou","email":"","orcid":"","institution":"Damascus University","correspondingAuthor":false,"prefix":"","firstName":"Silva","middleName":"","lastName":"Loulou","suffix":""},{"id":588154511,"identity":"6bc364ad-bdb8-4f4c-b249-b96a247141be","order_by":3,"name":"Turkia Almoustafa","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Turkia","middleName":"","lastName":"Almoustafa","suffix":""}],"badges":[],"createdAt":"2026-02-02 22:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8769228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8769228/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102307192,"identity":"ca0d0b40-eb7d-425d-8d9e-b6d63ccc85d4","added_by":"auto","created_at":"2026-02-10 11:44:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":617800,"visible":true,"origin":"","legend":"\u003cp\u003eSyrian coastal area location map\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8769228/v1/b79538295be629e292b1cb73.png"},{"id":102307183,"identity":"33c4be02-e655-4083-be41-fc1227ad7c4e","added_by":"auto","created_at":"2026-02-10 11:44:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":585738,"visible":true,"origin":"","legend":"\u003cp\u003eSyrian coastal area land degradation map\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8769228/v1/8a0a768123c34722e7ef1bdc.png"},{"id":102309549,"identity":"4155713f-e5d7-4150-8ae2-0653d6ebdd7a","added_by":"auto","created_at":"2026-02-10 11:51:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":660535,"visible":true,"origin":"","legend":"\u003cp\u003eSyrian coastal area land degradation conservation priority map\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8769228/v1/93c9294acdb951d3406c8ff5.png"},{"id":102308303,"identity":"ba6d8642-0bdb-49f8-a5f4-31a40f9a1c2c","added_by":"auto","created_at":"2026-02-10 11:48:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97202,"visible":true,"origin":"","legend":"\u003cp\u003eLand use \u0026amp; Land cover Changes (1985-2022)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8769228/v1/b9d0a4b9e259edd0c37f8983.png"},{"id":102307181,"identity":"41a8b202-0abb-4044-98db-dd814206de77","added_by":"auto","created_at":"2026-02-10 11:44:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":663407,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal Dynamics of NDVI (1985-2022)\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8769228/v1/9fee9bdea6c8e3e010da96a2.png"},{"id":102397153,"identity":"415cb1af-ddc1-42a9-8bdb-fc53da17d620","added_by":"auto","created_at":"2026-02-11 10:04:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3774102,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8769228/v1/f55da490-f8ee-4713-8e10-7becd7dc176d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geospatial Prioritization for Land Degradation Restoration: A 37-Year Assessment of the Syrian Coastal Region","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Mediterranean region is identified as the most susceptible area to soil degradation and desertification. This vulnerability is driven by a combination of accelerating climate change and intensifying human pressures, which are pushing soil natural capital toward critical thresholds (Ferreira et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Syrian coastal area, with an area of approximately 4,363.36 km\u003csup\u003e2\u003c/sup\u003e spread over two governorates Lattakia and Tartus, represents one of the most environmentally and economically vital regions in the eastern Mediterranean (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Extending across a diverse landscape of fertile coastal plains and steep mountainous terrain, the region is characterized by a typical Mediterranean climate with high annual rainfall, placing it within Syria\u0026rsquo;s first agro-ecological stability zone. These climatic conditions support high biomass productivity and intensive agricultural activities, dominated by olive groves, citrus orchards, and rapidly expanding greenhouse farming (GORS 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite this high potential, the region's pronounced spatial heterogeneity where nearly 85% of the land consists of plateaus and mountainsmakes it inherently vulnerable to soil erosion. This physical sensitivity is exacerbated by intense seasonal rainfall and steep gradients, which lead to significant soil displacement and the sedimentation of vital water reservoirs (Barakat \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent decades have seen a profound environmental restructuring, including massive urban expansion at the expense of agricultural land (Cheikh Dib \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the depletion of primary forest cover due to recurring fires and improper land management (Dakka et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conventional field-based assessments are often insufficient to capture the cumulative effects of these complex processes at a regional scale. Consequently, the integration of remote sensing and Geographic Information Systems (GIS) has become essential for assessing land degradation risk in a spatially explicit and cost-effective manner (Panagos et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vrieling \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The United Nations Environment Programme\u0026rsquo;s (UNEP-PAP/RAC) methodology provides a standardized framework for identifying landscape stability and prioritizing conservation measures in Mediterranean coastal areas (UNEP/MAP/PAP, 2000). This methodology has proven reliable in diverse Mediterranean environments for assessing land degradation processes (e.g., UNEP, MAP/PAP. 2004; Sadiki et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mesrar et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tahouri et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lhoussaine et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address the current research gap, this study aims to apply a comprehensive geomatics approach based on the (UNEP-PAP/RAC) diagnostic criteria. By synthesizing these criteria with multi-temporal Landsat data (1985\u0026ndash;2022), the research aims to quantify Land Use/Land Cover (LULC) transitions and vegetation health across the entire Syrian coastal area. The findings provide a scientific framework for prioritizing soil conservation measures and a foundation for the implementation of Syrian environmental laws and municipal master plans, supporting effective resource allocation and ecological restoration in this ecologically critical region.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geospatial Assessment of Landscape Stability and Erosion Dynamics\u003c/h2\u003e \u003cp\u003eLandscape stability and degradation mapping were executed following the consolidated framework for \u003cem\u003eMapping of Rainfall-Induced Erosion Processes in Mediterranean Coastal Areas\u003c/em\u003e, developed by the Priority Actions Programme Regional Activity Centre (PAP/RAC 1997). This methodology, specifically tailored for Mediterranean environments, provides a standardized diagnostic system for identifying, classifying, and evaluating degradation processes driven primarily by water erosion. The assessment was implemented within a Geospatial Information System (GIS) environment using ArcGIS and QGIS software. Spatial analyses adhered to PAP/RAC cartographic standards, utilizing a 1:50,000 scale and integrating Landsat satellite imagery with multi-thematic spatial layers. The GIS database synthesized various datasets, including physiographic unit maps, high-resolution topography, and land use/land cover (LULC) data. The delineation of stable and unstable units was finalized through a cross-analytical approach, validated by extensive field observations from 248 representative sites distributed across the coastal governorates. At each site, data were systematically recorded regarding stability factors, dominant degradation processes, spatial extent, and expansion trends. Following the PAP/RAC criteria (UNEP/MAP/PAP 2000), the landscape was separated into two primary categories:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStable Land Units\u003c/b\u003e: These units were evaluated based on their functional category and the main causative factors maintaining their stability. Categories included unmanaged areas (with forestry or agricultural potential) and managed areas (under active forestry or agricultural use). Each unit was assigned an instability risk score ranging from 0 (no risk) to 3 (critical risk), derived from an analysis of slope gradients, lithological characteristics, vegetation density, and anthropogenic pressure.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eUnstable Land Units\u003c/b\u003e: These were classified by the dominant degradation process, primarily focusing on sheet and rill erosion. The spatial footprint of each process was categorized by extent: localized (\u0026lt;\u0026thinsp;30%), dominant (30\u0026ndash;60%), or widespread (\u0026gt;\u0026thinsp;60%). Furthermore, the expansion trend was assessed on a four-level scale (0\u0026ndash;3), where 0 represents stabilizing conditions and 3 indicates an advancing trend approaching ecological irreversibility.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis integrated geomatics approach ensures that the resulting maps provide a scientifically robust baseline for the Syrian Coastal Area, enabling the transition from descriptive mapping to prescriptive conservation planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cb\u003ePriority Assessment for Land Restoration and Management\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eEffective mitigation of land degradation across the Syrian coastal region requires the optimal allocation of technical and financial resources through the clear identification of priority zones. To achieve this, a spatial prioritization procedure was applied using a multi-criteria scoring system designed to delineate land degradation hotspots requiring urgent management intervention. This framework follows the standardized UNEP/MAP\u0026ndash;PAP/RAC methodology (UNEP/MAP/PAP 2004) and integrates biophysical risks with socio-economic drivers influencing landscape stability throughout the study area. The assessment utilized fourteen diagnostic variables selected for their relevance to Mediterranean degradation processes and their significance within the local socio-economic context (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each variable was assigned a quantitative score ranging from 1 (lowest impact/risk) to 3 (highest impact/risk), reflecting its relative contribution to land stability or vulnerability. The weighting of these variables was established through a structured expert-based evaluation, ensuring the results remained consistent with regional knowledge and extensive field observations conducted across the coastal mountains and plains.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables and Scoring Matrix for Land Degradation Intervention Prioritization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoring (1\u0026ndash;3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical instability risk (stable areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 2\u0026thinsp;=\u0026thinsp;high, 3\u0026thinsp;=\u0026thinsp;critical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtent of area affected (unstable areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;30%, 2\u0026thinsp;=\u0026thinsp;30\u0026ndash;60%, 3\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpansion trend of degradation (unstable areas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;local, 2\u0026thinsp;=\u0026thinsp;widespread, 3\u0026thinsp;=\u0026thinsp;generalized/irreversible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiplicator for increased importance (causative agents or degradation process)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;none, 2\u0026thinsp;=\u0026thinsp;increased, 3\u0026thinsp;=\u0026thinsp;highly increased\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluence on adjacent areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low, 2\u0026thinsp;=\u0026thinsp;high, 3\u0026thinsp;=\u0026thinsp;critical\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverexploitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;insignificant,\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;significant,\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;crucial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural exodus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand tenure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther aggravating socio-economic factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eJ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue of current land use (local population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;low,\u003c/p\u003e \u003cp\u003e2\u0026thinsp;=\u0026thinsp;moderate,\u003c/p\u003e \u003cp\u003e3\u0026thinsp;=\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue of current land use (national policy)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential for forestry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential for agriculture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther land use potentials (recreational, industrial, construction)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter scoring all criteria for each identified area, final prioritization scores were calculated as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStable Areas Priority = [(A \u0026times; D\u0026thinsp;+\u0026thinsp;E) \u0026times; F \u0026times; G \u0026times; H \u0026times; I] + [(J\u0026thinsp;+\u0026thinsp;K) \u0026times; L \u0026times; M \u0026times; N]\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnstable Areas Priority = [(B \u0026times; C \u0026times; D\u0026thinsp;+\u0026thinsp;E) \u0026times; F \u0026times; G \u0026times; H \u0026times; I] + [(J\u0026thinsp;+\u0026thinsp;K) \u0026times; L \u0026times; M \u0026times; N]\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFinal scores were grouped into three priority categories: High (\u0026ge;\u0026thinsp;60), Medium (21\u0026ndash;59), and Low (\u0026le;\u0026thinsp;20).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Spatio-Temporal Dynamics of Land Use and Land Cover (1985\u0026ndash;2022)\u003c/h2\u003e \u003cp\u003eTo evaluate the long-term Land Use and Land Cover (LULC) dynamics across the Syrian coastal area, a multi-temporal analysis was conducted using a series of Landsat satellite missions: Landsat 5 TM (1985), Landsat 7 ETM+ (2000 and 2012), and Landsat 8/9 OLI (2022). To ensure accurate temporal comparisons, all datasets maintained a consistent spatial resolution of 30 meters. Furthermore, imagery was exclusively selected from the dry summer months (June\u0026ndash;August) to minimize seasonal variance and phenological differences, thereby ensuring stable and comparable environmental conditions throughout the classification process.\u003c/p\u003e \u003cp\u003eData preprocessing was performed using Google Earth Engine (GEE) and ArcGIS 10.8, involving geometric corrections, projection harmonization to the WGS 84 / UTM zone 37N coordinate system, and atmospheric calibration to surface reflectance. The classification process employed a Supervised Classification approach based on the Maximum Likelihood Classifier (MLC) algorithm. This approach leveraged fieldwork data and extensive local knowledge of the study region to achieve a Level II classification depth. The classification model was initially developed using 2022 reference data and subsequently applied to historical imagery to maintain consistency across the time series.\u003c/p\u003e \u003cp\u003eThe differentiation of forest types adhered to hierarchical classification principles specifically validated for the Syrian coastal mountains, allowing for the precise separation of deciduous and needleleaf formations (Karmoka \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This methodology categorized the landscape into nine representative classes reflecting the region's agricultural, urban, and natural characteristics. To enhance thematic map quality, a post-classification majority filter was applied to reduce spectral noise commonly referred to as the \"salt-and-pepper\" effect and improve class homogeneity.\u003c/p\u003e \u003cp\u003eThe reliability of the resulting LULC maps was confirmed through a rigorous accuracy assessment. Ground truth reference data, synthesized from field surveys and high-resolution Google Earth imagery, were used to construct a confusion matrix. From this matrix, standard performance metrics, including Overall Accuracy and the Kappa Coefficient, were calculated to statistically validate the mapping results and the subsequent change detection analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of Vegetation Dynamics via NDVI Time-Series\u003c/h2\u003e \u003cp\u003eVegetation dynamics across the 37-year study period were quantified using the Normalized Difference Vegetation Index (NDVI), a robust proxy for photosynthetic activity and biomass. Data were derived from Landsat Collection 2 Level-2 surface reflectance products sourced from the United States Geological Survey (USGS). To ensure full spatial coverage of the Syrian Coastal Area, a multi-scene mosaic approach was employed, utilizing Path 174, Rows 35 and 36. The analysis focused on four strategic temporal snapshots: 1985, 2000, 2012, and 2022. The dataset integrated imagery from Landsat 5 TM (for 1985 and 2000) using Band 3 (Red) and Band 4 (NIR), and Landsat 8/9 OLI (for 2012 and 2022) using Band 4 (Red) and Band 5 (NIR). NDVI was selected as the primary indicator due to its established high correlation with forest growth parameters and biomass density specifically within Syrian coastal environments (Karmoka \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe utilization of Level-2 products ensured radiometric consistency and standardized atmospheric correction across the time series. Preprocessing was executed within a GIS environment, where the dual scenes (174/35 and 174/36) were mosaicked and subsequently clipped to the administrative boundaries of the Syrian Coastal Area. For each reference year, NDVI Max and NDVI Mean values were extracted to provide a comprehensive quantitative assessment of both peak vegetation health and overall landscape biomass. This systematic workflow produced spatially consistent rasters, georeferenced to the WGS 84 / UTM Zone 37N coordinate system. These outputs provided the statistical foundation necessary to analyze the \"anthropogenic greening\" trends and forest fragmentation patterns discussed in the results of this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Coastal Land Degradation Analysis\u003c/h2\u003e \u003cp\u003eThe geospatial assessment of the Syrian coastal area identifies a landscape predominantly characterized by stability, yet significantly impacted by active degradation processes. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the region is categorized into stable areas, unstable areas, and non-relevant units (comprising urban and waterbodies).\u003c/p\u003e \u003cp\u003eStable land units encompass the vast majority of the study area, totaling 3243.39 km\u0026sup2;, or 74.34% of the coastal territory (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The spatial distribution of these stable classes shows distinct geographic patterns:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUnmanaged Areas with Agricultural Potential represent the most extensive stable land type, covering 1121.68 km\u0026sup2; (25.71%). These areas are predominantly located in the southern and eastern plains of the Tartus governorate and the hinterlands of Lattakia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eManaged Areas with Agricultural Use account for 945.99 km\u0026sup2; (21.68%), forming a contiguous belt across the fertile coastal plains of both governorates, particularly surrounding urban centers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnmanaged Areas with Forest Potential cover 750.68 km\u0026sup2; (17.21%), primarily occupying the mid-slope zones of the coastal mountain ranges.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eManaged Areas with Forest Use, covering 425.04 km\u0026sup2; (9.74%), are almost exclusively concentrated in the northern part of the study area within Lattakia governorate, corresponding to the denser forest reserves of the region.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \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\u003eAreas of degradation and stability patterns in the Syrian coastal area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\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\u003eLatakia Area\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTartus Area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCostal Area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCostal Area\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=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStable Area\u003c/p\u003e \u003c/td\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\u003e403.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e347.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e750.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.21\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\u003e264.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e857.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1121.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.71\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\u003e416.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e425.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.74\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\u003e620.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e325.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e945.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Stable area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1705.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1538.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3243.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e74.34\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 Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSheet Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e533.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e179.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e712.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRill Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGully Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMass Earth Movement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Unstable area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e577.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e187.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e764.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17.53\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\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Relevant (Urban, Water)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e184.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e355.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal costal area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2452.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1910.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4362.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, areas exhibiting active soil degradation classified as unstable cover a significant 764.76 km\u0026sup2;, representing 17.53% of the coastal region. As visually prominent in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, sheet erosion (shown in red) is the overwhelmingly dominant degradation mechanism, affecting 712.27 km\u0026sup2; or 16.33% of the total area. This process is not uniformly distributed but is intensely concentrated in the central and northern mountainous sectors of Lattakia governorate. Major hotspots of sheet erosion are evident in the districts of Haffa, Kurdaha, and Jabla, where steep slopes and intensive land use interact. Other erosion processes are spatially limited: Rill Erosion affects 46.66 km\u0026sup2; (1.07%), while Gully Erosion and Mass Earth Movements collectively impact less than 0.15% of the territory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution between governorates is highly asymmetrical (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Lattakia contains 577.39 km\u0026sup2; of unstable land, compared to 187.37 km\u0026sup2; in Tartus. This disparity underscores the heightened physical vulnerability of Lattakia's steeper terrain to rainfall-induced erosion. The remaining 8.14% (355.21 km\u0026sup2;) of the study area is classified as \"Not Relevant,\" consisting of urban centers and water bodies that fall outside the PAP/RAC soil degradation diagnostic criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Coastal Land Degradation Conservation Priority\u003c/h2\u003e \u003cp\u003eThe spatial prioritization of conservation efforts, determined through the multi-criteria scoring of biophysical and socio-economic variables, provides a strategic framework for land management across the Syrian coastal region. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the landscape is categorized into three hierarchical priority levels for both stable and unstable units.\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\u003eSpatial extent of conservation priority zones in the Syrian coastal area\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\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConservation Priority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatakia Area\u003c/p\u003e \u003cp\u003e(km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTartus Area\u003c/p\u003e \u003cp\u003e(km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoastal Area (km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoastal Area (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003cp\u003eAreas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable Low Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e524.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e539.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.37\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\u003e1147.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1461.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2609.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.79\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\u003e33.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Stable Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1705.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1534.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3240.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e74.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUnstable\u003c/p\u003e \u003cp\u003eAreas\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\u003e145.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.36\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\u003e243.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e323.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.41\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\u003e186.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e295.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal Unstable Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e575.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e189.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e765.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e17.53\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\u003eNot Relevant (Urban, Water)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e358.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.21\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\u003e\u003cb\u003eTotal Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2453.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1910.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4363.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eStable areas, constituting 74.25% (3240.46 km\u0026sup2;) of the region, are largely designated for preventive management. The Stable Medium Priority class forms the most extensive contiguous zone, covering 2609.20 km\u0026sup2; (59.79%). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, this class dominates the central and southern agricultural plains of Tartus and the mid-elevation slopes of Lattakia, representing lands that are currently stable but require consistent monitoring and sustainable practices to prevent future degradation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStable Low Priority areas, covering 539.76 km\u0026sup2; (12.37%), are primarily located in the most resilient coastal lowlands and some managed forest patches. Conversely, Stable High Priority zones, though limited to 91.50 km\u0026sup2; (2.10%), are critically important. These areas are spatially concentrated in the remaining primary forest fragments of Lattakia\u0026rsquo;s northern mountains, as visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and require strict protective measures to maintain their vital role as ecological stabilizers. Unstable areas, representing active degradation across 17.53% (765.17 km\u0026sup2;) of the territory, are the primary targets for restorative intervention. The most critical category, Unstable High Priority, covers 295.26 km\u0026sup2; (6.77%). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows these zones as pronounced hotspots, intensely concentrated in the steep, erosion-prone mountainous interiors of Lattakia governorate, particularly within the districts of Haffa, Kurdaha, and Jabla. These areas demand urgent curative actions, such as terracing and reforestation. Unstable Medium Priority areas, spanning 323.31 km\u0026sup2; (7.41%), often form a transitional belt surrounding the high-priority cores or appear in the foothills of Tartus. Unstable Low Priority zones are minimal, covering 146.59 km\u0026sup2; (3.36%), and are typically found in areas of less severe or stabilizing erosion.\u003c/p\u003e \u003cp\u003eA clear geographic disparity is evident, Lattakia contains a significantly larger extent of Unstable High Priority land (186.49 km\u0026sup2;) compared to Tartus (108.77 km\u0026sup2;), reflecting its greater physiographic vulnerability. Consequently, while management in Tartus can emphasize the protection of stable agricultural plains, resource allocation in Lattakia must be heavily weighted toward urgent soil stabilization in its mountainous interior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Coastal Land use and Land cover change analysis (1985\u0026ndash;2022)\u003c/h2\u003e \u003cp\u003eThe multi-temporal analysis of the Syrian Coastal Area reveals a landscape undergoing profound environmental restructuring between 1985 and 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The most significant transformation is the aggressive expansion of Urban Areas (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which grew from 35.58 km\u003csup\u003e2\u003c/sup\u003e to 105.86 km\u003csup\u003e2\u003c/sup\u003e, representing a net increase of 197.5% (Cheikh Dib, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This rapid urbanization has primarily occurred at the expense of agricultural and natural landscapes, reflecting intensified human pressure in the region.\u003c/p\u003e \u003cp\u003eSignificant shifts were also observed in natural vegetation and forest ecosystems. Tree Cover \u0026ndash; Closed Needleleaf Forest suffered the most drastic decline, plummeting from 306.46 km\u0026sup2; in 1985 to just 88.39 km\u0026sup2; in 2022, a loss of 71.2%. Similarly, Tree Cover \u0026ndash; Closed Broadleaf Deciduous Forest decreased by 50.6%, losing approximately 25.44 km\u0026sup2; over the study period. These losses indicate severe ecosystem fragmentation, likely driven by recurring forest fires and improper land management (Dakka et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; GFW 2025). Conversely, Tree Cover \u0026ndash; Open Mixed Leaf Forest showed a notable increase of 24.9% (90.73 km\u0026sup2;), suggesting a shift in forest structure or a transition from closed to more open canopy formations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal Syrian Coastal Area LULC Change (1985\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanduse \u0026amp; Landcover Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1985 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2012 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2022 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChange (km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChange (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed Crop \u0026amp; Natural Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e879.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1373.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1388.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1017.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;138.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;70.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;197.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2431.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2017.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2420.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterbodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-44.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShrubland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree Cover - Closed Broadleaf Deciduous Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-50.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree Cover - Closed Needleleaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-218.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-71.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree Cover - Open Mixed Leaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e363.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e454.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;90.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;24.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSparse Vegetation (Tree. Shrub. Herbaceous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4363.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4363.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4363.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4363.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0\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\u003eAgricultural dynamics show a complex pattern of stability and expansion:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMixed Crop \u0026amp; Natural Vegetation expanded by 15.8% (+\u0026thinsp;138.87 km\u003csup\u003e2\u003c/sup\u003e), indicating an increase in heterogeneous agricultural landscapes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe primary Agricultural Area remained relatively stable in terms of total extent, showing only a marginal decrease of 0.4% (10.91 km\u003csup\u003e2\u003c/sup\u003e) by 2022, despite fluctuations in the intervening years (Saqer et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSparse Vegetation essentially disappeared from the study area, recording a 100% loss from its initial 0.47 km\u003csup\u003e2\u003c/sup\u003e in 1985.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe reduction in Bare Land by 18.3% (44.87 km\u003csup\u003e2\u003c/sup\u003e) and the slight increase in Waterbodies (+\u0026thinsp;7.9%) further illustrate the dynamic nature of the coastal environment. Overall, these LULC transitions highlight a clear trend: the expansion of the built environment and certain agricultural types is occurring alongside a critical depletion of the region's primary natural forest cover. This restructuring of the landscape serves as a fundamental driver for the land degradation patterns and erosion risks identified in the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Coastal NDVI trend analysis (1985\u0026ndash;2022)\u003c/h2\u003e \u003cp\u003eThe analysis of the Normalized Difference Vegetation Index (NDVI) across the Syrian Coastal Area provides a quantitative measure of vegetation health and biomass dynamics over the 37-year study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the regional vegetation trends exhibit a consistent overall improvement in greenness, despite localized pressures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNDVI Values for the Syrian costal area (1985\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDVI Max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDVI Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.494\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\u003eIn 1985, the region recorded an NDVI Mean of 0.343 and an NDVI Max of 0.773. By the year 2000, these values rose significantly to a mean of 0.399 and a maximum of 0.833, indicating a period of robust vegetation growth and potentially favorable climatic conditions or agricultural expansion.\u003c/p\u003e \u003cp\u003eA critical observation occurs in the 2000\u0026ndash;2012 interval. While the NDVI Max increased further to 0.869, the NDVI Mean experienced a slight decrease, dropping to 0.389. This \"2012 dip\" aligns with the land cover findings regarding the depletion of primary forest cover and the impacts of recurring forest fires noted in the region (Najjar \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; GFW 2025). The stability in peak NDVI alongside a declining mean suggests that while high-biomass areas remained productive, the overall landscape experienced a degree of degradation or biomass loss, particularly in natural vegetation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy 2022, however, the region showed a strong recovery, with the NDVI Mean reaching its highest recorded value of 0.494 and the NDVI Max slightly declining to 0.857. This substantial increase in mean value (representing a nearly 44% growth since 1985) likely reflects the intensification of irrigated agriculture and the expansion of greenhouse farming and mixed crop landscapes identified in the LULC analysis. Overall, the NDVI results confirm that the Syrian Coastal Area is undergoing an \"anthropogenic greening\" process. While the rising mean values indicate an increase in total biomass, the decoupling between maximum and mean trends in 2012 as well as the overall loss of natural forest cover suggests that this regional greening is largely driven by agricultural productivity rather than the recovery of natural ecosystems.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Dynamics of Coastal Land Degradation\u003c/h2\u003e \u003cp\u003eThe spatial assessment of the Syrian Coastal Area reveals a landscape where stability is the dominant state, yet significant pockets of active degradation pose a long-term threat to soil security. Approximately 74.34% of the region is classified as stable, largely supported by managed agricultural systems and remaining forest fragments. However, the identification of 764.76 km\u0026sup2; as unstable (17.53% of the total area) highlights a critical environmental transition.\u003c/p\u003e \u003cp\u003eThe prevalence of sheet erosion, affecting 16.33% of the coastal territory, suggests that surface-level soil displacement is the primary degradation mechanism. This process is particularly intense in the Lattakia governorate, which contains over three times the extent of unstable land found in Tartus. This heightened vulnerability in Lattakia is primarily attributed to its steeper mountainous topography and higher susceptibility to intense rainfall-induced erosion (Barakat \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The concentration of sheet erosion in Lattakia\u0026rsquo;s mountainous areas aligns with earlier findings in Tartus, where steep slopes and sparse vegetation were identified as key drivers of high soil loss rates (AlAbed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The findings align with Barakat (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who noted that seasonal rainfall patterns in the region lead to significant soil displacement and reservoir sedimentation. The minimal presence of gully erosion (0.11%) and mass earth movements (0.03%) suggests that while the degradation is widespread, it remains at a stage where surface-level conservation measures could be highly effective if implemented immediately. Overall, these results underscore that while the coastal landscape is largely stable, the concentrated areas of sheet erosion in Lattakia represent a significant environmental challenge requiring targeted conservation efforts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Conservation Planning and Resource Allocation\u003c/h2\u003e \u003cp\u003eBy applying the UNEP-PAP/RAC multi-criteria scoring system, this study provides a clear roadmap for environmental intervention. The results indicate that while 59.79% of the coastal area is a \u0026ldquo;Stable Medium Priority,\u0026rdquo; a significant 6.77% (295.26 km\u0026sup2;) falls into the Unstable High Priority category. These high-priority zones represent \u0026ldquo;degradation hotspots\u0026rdquo; where the convergence of steep slopes, overexploitation, and fragile lithology has pushed the landscape toward potential irreversibility a pattern observed in similar fragile Mediterranean environments (Garc\u0026iacute;a-Ruiz et al. 2013; Tahouri et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In these areas, curative soil conservation such as the construction of terraces or the restoration of protective vegetation is no longer optional but essential to prevent the total loss of the soil\u0026rsquo;s natural capital, aligning with recommended erosion control practices in mountainous regions (Mesrar et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jucker Riva et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Conversely, the 91.50 km\u0026sup2; identified as Stable High Priority represents the region\u0026rsquo;s most valuable natural assets, including primary forest fragments that require strict preventive protection to maintain their role as landscape stabilizers (Karmoka \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; GFW 2025). This tiered prioritization allows municipal master plans to allocate limited resources effectively, focusing on curative actions in the mountains of Lattakia and preventive management in the agricultural plains of Tartus, supporting integrated land-use planning in vulnerable socio-ecological systems (Ferreira et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Spatio-Temporal Evolution of Land Cover (1985\u0026ndash;2022)\u003c/h2\u003e \u003cp\u003eThe multi-temporal analysis reveals a profound environmental restructuring of the Syrian Coastal Area, characterized by the aggressive expansion of the built environment at the expense of vital natural ecosystems. The 197.5% increase in Urban Areas (growing from 35.58 km\u0026sup2; to 105.86 km\u0026sup2;) highlights the intensity of anthropogenic pressure, which is a primary driver of the land degradation patterns identified in this study. This urbanization trend often targets fertile coastal plains, leading to the fragmentation of agricultural landscapes (Cheikh Dib \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar to findings in Tartus district, where high erosion rates were linked to land-use intensification and topographic vulnerability (AlAbed et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), our study underscores the interaction between anthropogenic pressure and biophysical susceptibility. The most alarming ecological shift is the systematic depletion of primary forest cover. The 71.2% loss of Closed Needleleaf Forest and the 50.6% decline in Closed Broadleaf Deciduous Forest represent a critical reduction in the region's natural protective barriers. These findings are corroborated by external land cover statistics from Global Forest Watch (GFW), which indicate that by 2020, natural forests constituted only 22% of land cover in Lattakia and 13% in Tartus. Furthermore, GFW data shows that tree cover with a canopy density\u0026thinsp;\u0026gt;\u0026thinsp;30% a key indicator of healthy, dense forest declined from 23% in Lattakia and 6.0% in Tartus in the year 2000. This external benchmark confirms the severity and regional consistency of the forest degradation process documented in our LULC analysis.\u003c/p\u003e \u003cp\u003eThe observed trends suggest that the coastal mountains are losing the dense biological structures essential for regulating soil stability and hydrology, likely due to a combination of recurring forest fires, land conversion for agriculture, and improper land management (Dakka et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; GFW 2025). The simultaneous 24.9% increase in Open Mixed Leaf Forest is indicative of a landscape transition from dense, stable forest canopies to more open, degraded, or secondary formations. This shift aligns with GFW data pointing to the significant extent of \u0026ldquo;unknown\u0026rdquo; plantation types in both governorates, suggesting a replacement of natural forest ecosystems with managed or successional tree cover that offers diminished erosion control and ecological value. Our findings align with the broader Mediterranean context where land-use change, particularly farmland abandonment and agricultural intensification serves as a primary driver of erosional response. Garc\u0026iacute;a-Ruiz et al (2013) highlighted that the co-existence of land abandonment in mountainous areas and the expansion of subsidized crops into marginal lands creates a fragmented landscape where erosion processes are spatially and temporally variable. This duality of intensification and extensification helps explain the contrasting sediment yields observed in our study catchments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Biomass Trends and Vegetation Health Indicators\u003c/h2\u003e \u003cp\u003eThe NDVI analysis provides a critical, yet complex, perspective on regional environmental health, revealing what can be termed an \u0026ldquo;anthropogenic greening paradox.\u0026rdquo; While the NDVI Mean increased by nearly 44% (from 0.343 to 0.494) between 1985 and 2022, this improvement in aggregate \u0026ldquo;greenness\u0026rdquo; does not signify ecological restoration. Instead, this trend is decisively linked to the intensification of irrigated agriculture and the rapid expansion of greenhouse farming captured in the LULC analysis.\u003c/p\u003e \u003cp\u003eA significant diagnostic signal is the 2000\u0026ndash;2012 interval, where the NDVI Mean experienced a slight decline to 0.389. This \u0026lsquo;2012 dip\u0026rsquo; aligns with documented depletion of primary forest cover and recurring fire impacts in the region (Najjar \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dakka et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; GFW 2025). Notably, the NDVI Max remained high (0.869 in 2012), indicating sustained productivity in intensive agricultural zones, while the mean dropped. This decoupling underscores that agricultural intensification can maintain or elevate peak vegetation indices even as native forest cover which contributes significantly to the landscape's average greenness is being reduced.\u003c/p\u003e \u003cp\u003eThe substantial rise in mean NDVI by 2022 must therefore be interpreted with caution. The external GFW data, showing low percentages of natural forest cover (22% in Lattakia, 13% in Tartus) and minimal area with high-density tree cover by 2020, provides crucial context. It confirms that the high NDVI values are not driven by a recovery of dense, natural forests but by the proliferation of agriculturally driven vegetation. This creates a paradox where the landscape appears \u0026ldquo;greener\u0026rdquo; and more productive according to spectral indices, while simultaneously suffering a net loss in the very native forest ecosystems that are most critical for long-term soil stability, biodiversity, and climate resilience.\u003c/p\u003e \u003cp\u003eThese results highlight a vital methodological insight: high NDVI values can mask underlying land degradation risks. A landscape may appear \u0026ldquo;greener\u0026rdquo; due to agricultural expansion while simultaneously losing the ecological quality, structure, and protective functions of its native forest cover. This reinforces the imperative of supplementing NDVI analysis with detailed land cover classification and degradation mapping as done in this study to accurately diagnose environmental health. In line with Jucker Riva et al (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who emphasized that NDVI-based assessments must be contextualized within landscape units (considering land cover, slope, and aspect) to disentangle anthropogenic degradation from natural variability, our integrated approach reveals that what appears as greening in the Syrian Coastal Area is, in fact, a signal of land-use intensification rather than ecological recovery. Such nuanced interpretation is essential for informing sustainable land management in regions undergoing rapid anthropogenic transformation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study provides a comprehensive geospatial assessment of land degradation and landscape dynamics in the Syrian Coastal Area from 1985 to 2022. By integrating the PAP/RAC diagnostic framework with multi-temporal remote sensing data, several critical environmental transitions were identified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLandscape Stability\u003c/b\u003e: While the majority of the region (74.34%) remains stable, approximately 17.53% (764.76 km\u0026sup2;) is actively unstable, with sheet erosion being the primary degradation process, especially in the steep mountainous terrain of Lattakia.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEcological Restructuring\u003c/b\u003e: Over the 37-year study period, the region has undergone a profound transformation characterized by a 197.5% increase in urban areas and a catastrophic 71.2% loss of closed needleleaf forests.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Greening Paradox\u003c/b\u003e: NDVI analysis revealed a 44% increase in mean vegetation greenness (from 0.343 to 0.494). However, this \u0026ldquo;anthropogenic greening\u0026rdquo; is largely driven by agricultural intensification and greenhouse expansion, which is underscored by the decoupling between rising mean NDVI and stable or declining natural forest biomass. Peak NDVI values remained high even increasing to 0.869 in 2012 reflecting agricultural productivity, yet the concurrent decline in natural forest cover signals underlying ecosystem degradation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eConservation Priority\u003c/b\u003e: Spatial prioritization identified 295.26 km\u0026sup2; (6.77%) of the study area as Unstable High Priority zones. These areas represent critical \u0026ldquo;degradation hotspots\u0026rdquo; where immediate intervention is required to prevent irreversible environmental damage.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Recommendations\u003c/h2\u003e \u003cp\u003eTo mitigate further land degradation and support Land Degradation Neutrality (LDN) in this vital Mediterranean region, the following actions are recommended:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTargeted Restoration\u003c/b\u003e: Urgent curative measures, including reforestation and the construction of traditional soil-retaining terraces, should be prioritized in the Unstable High Priority zones identified in the Lattakia and Tartus mountains.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProtective Management\u003c/b\u003e: Strict preventive protection must be enforced for the remaining Stable High Priority areas (91.50 km\u0026sup2;), which consist of primary forest fragments essential for regional landscape stability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eUrban Planning\u003c/b\u003e: Municipal master plans should be updated to direct urban expansion away from fertile agricultural plains and vulnerable mountainous slopes to prevent further habitat fragmentation and soil sealing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eContinuous Monitoring\u003c/b\u003e: A regional environmental observatory using high-resolution geomatics should be established to monitor NDVI trends and LULC changes continuously, allowing for rapid response to new degradation signals or forest fire events.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePolicy Integration\u003c/b\u003e: The findings of this study should serve as a scientific baseline for implementing Syrian environmental laws, ensuring that national resource allocation is directed toward the most vulnerable ecological zones.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Statement\u003c/h2\u003e \u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026lsquo;Ethical responsibilities of Authors\u0026rsquo; as found in the Instructions for Authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eAuthor 4 has received research support from The British Academy/Cara/Leverhulme Researchers at Risk Research Support Grant. The University of Manchester.\u0026rdquo; The other authors declare that no funds, grants or other support were received during the preparation of this manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\u0026ldquo;All authors have contributed to the study conception design and material preparation, data collection and analysis. The first draft of the manuscript was written by author 1 and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eAuthors sincerely thank the ICALD project team \u0026ldquo;Inventory of Coastal Area Land Degradation Using Remote Sensing and GIS Techniques\u0026rdquo; for providing the field data essential to creating the land degradation and priority conservation maps presented in this article.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData fully available upon request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlAbed M, Salhab J, Hani E and Dweri S (2018) Quantitative Estimation of Annual Soil Loss by Integration of Remote Sensing, GIS, and Universal Soil Loss Equation (Case Study: Tartus District, Syria). \u003cem\u003eAnnals of Arid Zone.\u003c/em\u003e 57(3\u0026amp;4):129\u0026ndash;134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarakat M (2018) Prediction of the soil lost amount by water erosion in the Hawiz dam basin region using the Revised Universal Soil Equation (RUSLE) and GIS techniques. \u003cem\u003eTishreen Univ J Res Sci Stud Biol Sci Ser\u003c/em\u003e. 40(5):11\u0026ndash;29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheikh Dib S (2020) The impact of urban expansion on agricultural lands in Lattakia city. \u003cem\u003eTishreen Univ J Res Sci Stud Eng Sci Ser\u003c/em\u003e. 24(5): 3\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDakka M, Idris Y, Al-Ghammaz F (2024) Studying the impact of forest fires on soil degradation using GIS and Remote Sensing in the Al-Bayer and Al-Basit region. \u003cem\u003eTishreen Univ J Res Sci Stud Biol Sci Ser\u003c/em\u003e. 46(1): 205\u0026ndash;227.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira C S, Keesstra S, Destouni G, Solomun M K, and Kalantari Z (2024) Soil Degradation in the Mediterranean Region: Drivers and Future Trends. 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Available at: \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\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVrieling A (2006) Satellite remote sensing for water erosion assessment: A review. \u003cem\u003eCATENA\u003c/em\u003e. 65 (1):2\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.catena.2005.10.005\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2005.10.005\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-sciences-europe","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eseu","sideBox":"Learn more about [Environmental Sciences Europe](http://enveurope.springeropen.com)","snPcode":"12302","submissionUrl":"https://submission.nature.com/new-submission/12302/3","title":"Environmental Sciences Europe","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"land degradation, soil erosion, conservation priority, remote sensing and GIS, NDVI, Syrian Coast, environmental policy","lastPublishedDoi":"10.21203/rs.3.rs-8769228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8769228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Mediterranean region, a global hotspot for soil degradation, faces intensifying pressures from climate change and anthropogenic activities. This study addresses a critical research and policy gap by providing a spatially explicit, science-based framework to assess land degradation risk and prioritize conservation actions in the Syrian Coastal Region,a vital socio-ecological zone in the eastern Mediterranean. We applied an integrated geomatics approach, combining the United Nations Environment Programme Priority Actions Programme Regional Activity Centre (UNEP-PAP/RAC) diagnostic framework with multi-temporal Landsat imagery (1985\u0026ndash;2022). Biophysical and socio-economic variables were synthesized within a GIS environment and validated through 248 field sites. Analyses included land degradation mapping, conservation priority assessment, land use/land cover (LULC) change detection, and Normalized Difference Vegetation Index (NDVI) trend analysis. Approximately 17.53% (764.76 km\u0026sup2;) of the coastal landscape is actively unstable, with sheet erosion dominating degradation processes. Spatial prioritization identified 6.77% of the region as \u0026ldquo;Unstable High Priority\u0026rdquo; zones requiring urgent intervention. LULC analysis revealed profound environmental restructuring: a 197.5% expansion of urban areas and a critical 71.2% loss of closed needleleaf forests over 37 years. NDVI trends exhibited an \u0026ldquo;anthropogenic greening\u0026rdquo; paradox, with a 44% increase in mean NDVI largely driven by agricultural intensification, masking the ongoing degradation of natural forest ecosystems. The study delivers a replicable, spatially explicit tool for targeting soil conservation and restoration efforts, directly supporting Land Degradation Neutrality (LDN) targets and sustainable land management policies. The findings underscore the necessity of integrating remote sensing diagnostics into municipal master planning and environmental regulation to bridge the gap between scientific assessment and actionable policy. This approach can enhance stakeholder comprehension, guide resource allocation, and foster effective implementation of national and regional environmental strategies in vulnerable Mediterranean coastal regions.\u003c/p\u003e","manuscriptTitle":"Geospatial Prioritization for Land Degradation Restoration: A 37-Year Assessment of the Syrian Coastal Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:17:44","doi":"10.21203/rs.3.rs-8769228/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-28T17:46:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T08:27:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T01:14:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T19:34:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137041116448081916120778486979395206492","date":"2026-02-14T10:07:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18979110452184396500385950794663161411","date":"2026-02-12T10:43:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303564618370479874651440563383036105613","date":"2026-02-09T09:50:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115257982994959158771460405865707251581","date":"2026-02-08T23:50:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303236161617473099121416739971578896593","date":"2026-02-08T08:41:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T08:38:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T12:03:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T12:02:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Sciences Europe","date":"2026-02-02T22:22:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-sciences-europe","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"eseu","sideBox":"Learn more about [Environmental Sciences Europe](http://enveurope.springeropen.com)","snPcode":"12302","submissionUrl":"https://submission.nature.com/new-submission/12302/3","title":"Environmental Sciences Europe","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"796a1f42-5a4b-45b0-a6c7-9b06b19ca9cb","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T18:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 11:17:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8769228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8769228","identity":"rs-8769228","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}