Evaluating Multi-Decadal Land Degradation and Anthropogenic Greening in Mediterranean Coastal Ecosystems: A Geospatial-Based Assessment of Tartus, Syria

Evaluating Multi-Decadal Land Degradation and Anthropogenic Greening in Mediterranean Coastal Ecosystems: A Geospatial-Based Assessment of Tartus, Syria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating Multi-Decadal Land Degradation and Anthropogenic Greening in Mediterranean Coastal Ecosystems: A Geospatial-Based Assessment of Tartus, Syria Mohammad AlAbed, Rosa Karmoka, Silva Loulou, Turkia Almoustafa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8870510/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Land degradation remains a critical environmental challenge in the Mediterranean basin, exacerbated by rapid land-use transitions, climate variability, and anthropogenic pressure. This study assesses the land degradation risk and environmental dynamics in the Tartus Governorate, Syria, over a comprehensive 37-year period (1985–2022). Utilizing the United Nations Environment Programme (UNEP) Priority Actions Programme/Regional Activity Centre (PAP/RAC) methodology, we integrated multi-temporal Landsat imagery (5 TM, 8 OLI, and 9 OLI) with Geographic Information Systems (GIS) to map Land Use/Land Cover (LULC) changes and vegetation health via the Normalized Difference Vegetation Index (NDVI). Our spatial analysis incorporated 14 diverse socio-economic and biophysical variables to determine the land degradation risk according to the PAP/RAC consolidated methodology. Results indicate a profound environmental restructuring of the governorate. While Agricultural Area remained the dominant land cover (64.54% in 1985 to 62.78% in 2022), Urban Areas experienced a massive expansion of 273.61%, largely at the expense of primary Closed Broadleaf Deciduous forests, which were almost entirely depleted. Concurrently, a significant "anthropogenic greening" trend was observed, with the Mean NDVI rising from 0.313 to 0.459, primarily driven by the intensification of irrigated agricultural practices and greenhouse farming. Despite this apparent greening, the PAP/RAC model identifies that 80.34% of the landscape is stable, yet approximately 108.77 km² (5.71% of the territory) is classified as a high-priority unstable zone. These hotspots are predominantly characterized by severe sheet and rill erosion in the mountainous eastern and northern districts. These findings emphasize that vegetation density alone does not equate to land stability. The study concludes that immediate curative interventions and integrated coastal zone management are essential to mitigate irreversible soil loss in these high-priority unstable zones, providing a scalable model for Mediterranean environmental recovery. Agronomy Agroecology Forestry Soil erosion land degradation landuse change detection remote sensing GIS NDVI Tartus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The Tartus Governorate is situated along the eastern Mediterranean coast of western Syria with an area of about 189,620 ha, extending approximately between latitudes 34° 30' to 35° 20' N and longitudes 35° 40' to 36° 20' E (Fig. 1 ). According to the Central Bureau of Statistics (CBS), the population was estimated at 458,327 in 2023. The region experiences a typical Mediterranean climate characterized by mild, wet winters and hot, humid summers, with annual rainfall ranging from approximately 896.6 to 1380.7 mm. These conditions place the governorate within Syria’s first agro-ecological stability zone. While such climatic conditions support relatively high biomass productivity, they also enhance rainfall erosivity, particularly on sloping terrain (GORS 2021). Forest formations are found on mountainous terrain; however, agriculture represents the dominant land-use activity in the study area. It is characterized by extensive cultivation of fruit trees, particularly olives and citrus. Field crops, including wheat, peanuts, and vegetables, are also widely cultivated, while greenhouse farming has expanded rapidly for protected vegetable and floriculture production. Agricultural systems in the governorate rely on both rainfed practices and irrigation from wells, rivers, and dams. Soils are traditionally classified into six main groups, including deep alluvial soils along the coastal strip, moderately deep hill and plateau soils developed on calcareous and basaltic substrates, shallow slope and mountain soils, and valley soils formed through fluvial deposition. Many of these soils, particularly those on slopes and mountainous terrain are highly vulnerable to erosion due to shallow depth, stoniness, and cultivation on steep gradients (Mahmoud et al. 2019). Physiographically, the governorate exhibits pronounced spatial heterogeneity. Coastal plains account for approximately 15% of the total area, while the remaining 85% consists of plateaus, hills, and mountainous terrain. These upland systems have been identified as highly sensitive to land degradation due to the combination of steep slopes, shallow soils, and intense seasonal rainfall (García-Ruiz et al. 2013 ; Mahmoud et al. 2019). Furthermore, in comparable Mediterranean regions, agricultural systems have been shown to exert significant pressure on these fragile soil resources. When conservation measures are inadequate, this human-induced pressure often results in accelerated soil erosion and severe land degradation (Kosmas et al. 2014 ). In the Syrian coastal region, despite the apparent agricultural productivity of Tartus Governorate, increasing land-use intensity, deforestation, expansion of cultivation into marginal areas, and insufficient soil conservation practices have heightened the risk of land degradation (GORS 2021). Localized studies, such as those conducted in the Al-Abrash river basin, have highlighted how high rainfall intensities lead to the displacement of tons of soil, which eventually accumulate in dam reservoirs like the Al-Basel Dam, with an estimated 74,995 tons of sediment arriving annually (Jouhra 2021 ). Conventional field-based assessments alone are often insufficient to capture the spatial variability and cumulative effects of these processes at the regional scale. Consequently, the integration of remote sensing and Geographic Information Systems (GIS) has become an essential approach for assessing land degradation risk in a spatially explicit, consistent, and cost-effective manner (AlAbed et al. 2018). Remote sensing provides multi-temporal observations of land surface conditions, vegetation dynamics, and indicators related to soil erosion and degradation, while GIS enables the integration of environmental factors such as topography, soils, land use, and climate (Vrieling 2006; AbdelRahman 2023 ). These techniques have been successfully applied worldwide for mapping erosion risk, monitoring land degradation, and supporting conservation planning, particularly in Mediterranean and semi-arid regions (Panagos et al. 2015; Borrelli et al. 2017 ). However, their application in coastal Syrian environments remains limited. To address this gap, the present study applies an integrated geomatics approach based on the methodology developed by the United Nations Environment Programme, Priority Actions Programme Regional Activity Centre (UNEP, PAP/RAC). 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 ). Therefore, the main objective of this study is to integrate remote sensing and GIS techniques to assess land degradation risk in Tartus Governorate by identifying spatial patterns of stable and unstable land conditions and analyzing their relationships with environmental and land-use factors. The results aim to support land-use planning, prioritize soil conservation measures, and contribute to sustainable land management strategies in one of Syria’s most environmentally and agriculturally important coastal regions. 2. Material and Methods 2 .1 Landscape Stability and Degradation Mapping Land degradation mapping in the study area was carried out following the consolidated methodology for the Mapping of Rainfall-Induced Erosion Processes in Mediterranean Coastal Areas developed by the Priority Actions Programme Regional Activity Centre (PAP/RAC 1997). This methodology is specifically designed for Mediterranean environments and provides a standardized framework for identifying, classifying, and evaluating land degradation processes driven primarily by water erosion. The assessment was implemented within a (GIS) environment using ArcGIS and QGIS software. Spatial analyses were conducted in accordance with the PAP/RAC criteria and cartographic standards, employing Landsat satellite imagery and thematic maps at a scale of 1:50,000. The GIS database integrated multiple spatial layers, including land use/land cover maps, physiographic unit maps, topography, and supporting field observations. The delineation of stable and unstable land units was achieved through the cross-analysis of land use/land cover data, physiographic characteristics, and field survey information collected from approximately 92 representative sites across the study area. For each field site, the type of stable land, the dominant factors influencing its stability, and the associated degree of erosion risk were identified. Similarly, for unstable land units, the type of degradation process, its spatial extent, and its expansion trend were systematically recorded. This integrated approach enabled the classification of the landscape into two primary categories—stable and unstable areas—consistent with the definitions and criteria established by the PAP/RAC methodology (PAP/RAC 1997; UNEP/MAP/PAP 2000). Stable land units were evaluated based on their functional category, erosion risk level, and the main causative factors controlling their stability. The dominant stable land types identified include unmanaged areas (with forestry or agricultural potential), managed areas (under forestry or agricultural use). Each stable land unit was assigned an instability risk score on a scale from 0 to 3, where 0 represents no risk and 3 indicates critically unstable conditions. This ranking considered key degradation drivers, including slope steepness, geological and lithological characteristics, vegetation cover, and anthropogenic land use intensity. Unstable land units were classified according to the dominant type of degradation process in the area, including sheet erosion and rill erosion. The spatial extent of each degradation type was categorized as localized (60%). In addition, the expansion trend of erosion was evaluated using a four-level scale ranging from 0 to 3, where 0 denotes stabilizing conditions, 1 indicates locally expanding degradation, 2 represents regionally expanding processes, and 3 corresponds to an advancing trend approaching irreversibility. 2.2 Multi-Criteria Prioritization and Risk Categorization Effective land degradation mitigation requires the optimal allocation of limited resources and the clear identification of priority areas for intervention. To achieve this, a spatial prioritization procedure was applied using a multi-criteria scoring system designed to identify land degradation hotspots requiring urgent management action. The procedure follows the framework proposed by the UNEP/MAP–PAP/RAC methodology (UNEP/MAP/PAP 2004) and integrates biophysical and socio-economic factors influencing land stability and degradation dynamics. A total of fourteen variables were selected based on their relevance to land degradation processes and their significance within the local socio-economic context. Each variable was assigned a score ranging from 1 (lowest impact or risk) to 3 (highest impact or risk), reflecting its relative contribution to land stability or instability. The weighting of variables was established through a structured expert-based evaluation, ensuring consistency with regional knowledge and field observations. The variables used in the prioritization procedure include: A. Physical instability risk (for stable areas) B. Extent of the affected area (for unstable areas) C. Expansion trend of the degradation process (for unstable areas) D. Multiplication factors for unfavorable combinations of causative agents E. Influence on adjacent areas F. Overexploitation as an aggravating socio-economic factor G. Rural exodus as an aggravating socio-economic factor H. Land tenure as an aggravating socio-economic factor I. Other aggravating socio-economic factors J. Value of current land use according to the local population K. Value of current land use according to national policies L. Potential for forestry M. Potential for agricultural use N. Other land use potentials After scoring 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 Land Use and Land Cover change Analysis (1985–2022) To evaluate Land Use and Land Cover (LULC) dynamics across the Tartus Governorate, multi-temporal satellite imagery was utilized, specifically employing Landsat 5 TM (1985), Landsat 7 ETM+ (2000/2012), and Landsat 8/9 OLI (2022). All datasets maintained a consistent 30m spatial resolution to facilitate accurate temporal comparisons across the study intervals. To minimize seasonal variance and phenological differences, imagery was selected from the dry summer months (June–August), ensuring stable and comparable environmental conditions for the classification process. Preprocessing was implemented using Google Earth Engine (GEE) and ArcGIS 10.8, including geometric corrections, projection harmonization to the WGS 84 / UTM zone 37N coordinate system, and atmospheric calibration to surface reflectance. The classification was executed using a Supervised Classification approach based on the Maximum Likelihood Classifier (MLC) algorithm, depending on the local knowledge of the study area and falls within the second level of classification. The differentiation of forest types followed hierarchical classification principles previously validated in the Syrian coastal mountains, ensuring the accurate separation of deciduous and needleleaf formations (Karmoka 2018). This method categorized the landscape into nine representative classes, reflecting the region's agricultural, urban, and natural features. Additionally, a post-classification spatial filter (majority filter) was applied to the outputs to reduce spectral noise ("salt-and-pepper" effect) and enhance class homogeneity. The reliability of the LULC maps was validated through a rigorous accuracy assessment. Ground truth reference data were compiled from field surveys and high-resolution Google Earth imagery to construct a confusion matrix. From this matrix, standard performance metrics were derived, specifically Overall Accuracy and the Kappa Coefficient, to confirm the statistical validity of the mapping and subsequent change detection analysis. 2.4 NDVI Computation and Preprocessing Vegetation dynamics were analyzed using the Normalized Difference Vegetation Index (NDVI) derived from Landsat Collection 2 Level-2 surface reflectance products (Path 174, Rows 36) obtained from the USGS. The dataset included imagery from Landsat 4/5 TM (1985 and 2000) using Band 3 (Red) and Band 4 (NIR), and Landsat 8/9 OLI (2012 and 2022) using Band 4 (Red) and Band 5 (NIR). NDVI was selected as a primary indicator for vegetation dynamics due to its high correlation with forest growth parameters and biomass in Syrian coastal environments (Karmoka 2018). The use of Level-2 products ensured radiometric consistency and atmospheric correction across the multitemporal series, supporting a robust long-term analysis. Preprocessing was conducted within a GIS environment, where individual scenes were mosaicked to ensure full spatial coverage and clipped to the study area boundary. NDVI layers were generated for each reference year using the standard normalized difference ratio of the Red and NIR bands via the Raster Calculator tool. This systematic approach provided spatially consistent rasters (WGS_1984_UTM_Zone_37N) for the quantitative assessment of vegetation cover changes across the 37-year study period. 3. Results and Discussion 3.1 Analysis of Land Degradation in Tartus The land cover of the Tartus Governorate is categorized into three primary states: Stable Areas, Unstable Areas (undergoing active degradation), and Not Relevant zones as shown in Fig. 2 . The Stable areas represent the vast majority of the region, encompassing 80.36% (Table 1 ) of the total study area (1538.35 km 2 ). This classification is further subdivided based on management status and natural potential: Unmanaged Areas with Agricultural Potential (44.76%): Covering 857.31 km 2 , this is the most dominant land class. Spatially, it occupies the central and northern interior, including Sheick Bader and parts of Dreikish. The prevalence of this class suggests significant untapped capacity for agricultural expansion, as these lands are currently underutilized or restricted to subsistence use. Unmanaged Areas with Forest Potential (18.11%): This category accounts for 347.01km 2 . Located primarily in mid-altitude zones, these areas serve as a vital ecological buffer between the coastal lowlands and steep mountain ridges. While naturally suited for forestry, they currently lack active management. Managed Areas with Agricultural Use (17.02%): Representing the region’s economic core, this class covers 325.06 km 2 . It is concentrated along the coastal plains and southern borders, characterized by intensive greenhouse farming and citrus orchards. Managed Areas with Forest Use (0.47%): The smallest stable category, totaling only 8.97 km 2 . These managed forests appear in small, concentrated patches within the transitional hills of Safita and Dreikish. In contrast, the unstable areas cover 9.78% (187.337 km 2 ) of the region mainly due to sheet and rill erosion. The Sheet Erosion (9.36%) is the primary driver of degradation in Tartus, affecting 179.26 km 2 . It is most critical along the eastern and northeastern mountainous fringes, such as Qadmous. The distribution follows steep topographical gradients, indicating that deforestation and improper tillage on slopes are likely contributing to this widespread removal of thin topsoil layers. The Rill Erosion (0.42%) a more localized but advanced stage of water erosion, covering 8.11 km 2 . It appears in isolated pockets, particularly near Banyas, where runoff has carved visible channels into the landscape. For the areas classified as "Not Relevant," which include built-up urban environments and water bodies, account for 9.66% (184.66 km 2 ) of the total area. Table 1 Areas of Degradation and Stability Patterns in Tartus Areas Code Land Category Type Area (hec) Area (km 2 ) Area (%) Stable Areas 01 Unmanaged Areas with Forest Potential 34701.44 347.01 18.11 02 Unmanaged Areas with Agriculture Potential 85730.60 857.31 44.76 03 Managed Areas with Forest Use 897.35 8.97 0.47 04 Managed Areas with Agriculture Use 32505.96 325.06 17.02 Total Stable Areas 153835.35 1538.35 80.36 Unstable Areas L11 L21 L22 Sheet Erosion 17925.51 179.26 9.36 D11 D21 D22 Rill Erosion 810.75 8.11 0.42 Total Unstable Areas 18736.26 187.37 9.78 Not Relevant (Urban, Water) 18465.50 184.66 9.66 Total Area 191037.11 1910.37 99.63 The analysis of land degradation risk in the Tartus Governorate reveals a landscape under varying degrees of environmental pressure. While a large portion of the study area is classified as stable, the high-priority zones characterized by active rill and sheet erosion reflect the inherent vulnerability of the region’s topography. These findings are supported by quantitative assessments in the Tartus District using the USLE model, which estimated that soil loss can reach as high as 150 t/ha/yr in hilly terrains where vegetation is sparse or absent (AlAbed et al. 2018). These severe erosion rates are consistent with other regional studies, such as the RUSLE analysis in the Basel al-Assad Basin, which identified very high erosion risk across 16% of the area (Barakat et al. 2017 ), and the CORINE-based assessment of the Al-Abrash River Basin, where 4.81% of the land faces high erosion risk (Barakat et al. 2020 ). 3.2 Distribution of Conservation Priority Classes The conservation priority map of the Tartus study area reveals a clear dominance of stable land units (as shown in Fig. 3 ), which together account for 80.34% of the total area (1534.79 km²), as in Table 2 . Among these, stable medium-priority areas represent by far the largest class, covering 1461.53 km², equivalent to 76.50% of the study area, and are widely distributed across all districts within the study area. This extensive coverage indicates that most of the landscape exhibits moderate conservation value, requiring balanced management strategies that combine sustainable land use with preventive conservation measures. In contrast, stable high-priority areas occupy a relatively limited extent (58.06 km²; 3.04%), reflecting localized zones of elevated ecological or environmental importance that may demand stricter protection, primarily concentrated in the high-altitude northern regions of Al-Qadmus and the eastern hilly terrains of the Tartus center district. While, the stable low-priority areas are marginal, covering only 15.20 km² (0.80%), suggesting that areas with minimal conservation concern are scarce within the region, and predominantly found along the western boundary between the Dreikish and Tartus districts. Unstable areas constitute 9.94% of the total study area (189.95 km²), highlighting zones potentially affected by land degradation processes or environmental instability. Within this category, unstable high-priority areas account for 108.77 km² (5.69%), representing the most critical zones where immediate conservation or rehabilitation interventions may be required, occupy the eastern hilly parts of Safita and northern Banyas. Unstable medium-priority areas cover 80.20 km² (4.20%), indicating areas at moderate risk that warrant monitoring and targeted management, mainly spread in hilly areas in Banyas, Qadmos, and Sheick Bader. Unstable low-priority areas are negligible, comprising only 0.97 km² (0.05%), which suggests that most unstable zones are associated with moderate to high conservation concern. Overall, the spatial distribution of conservation priority classes underscores the predominance of stable landscapes in Tartus, while also emphasizing the presence of critical unstable high-priority zones that require focused management efforts to mitigate environmental risks and support sustainable land-use planning. Table 2 Spatial extent of conservation priority zones in Tartus Areas Conservation Priority Area (hec) Area (km 2 ) Area (%) Stable Areas Stable Low Priority 1520.64 15.20 0.80 Stable Medium Priority 146153 1461.53 76.50 Stable High Priority 5806.08 58.06 3.04 Total Stable Areas 153479.72 1534.79 80.34 Unstable Areas Unstable Low Priority 97.28 0.97 0.05 Unstable Medium Priority 8020.48 80.20 4.20 Unstable High Priority 10877.44 108.77 5.69 Total Unstable Areas 18995.20 189.95 9.94 Not Relevant 18580.48 185.80 9.73 Total Areas 191055.4 1910.55 100.00 The spatial distribution of conservation priority classes highlights the dominance of stable medium-priority units, which require sustainable management to prevent future degradation. However, specific geographic "hotspots" require immediate attention. Previous research pinpointed areas surrounding towns such as Sheikh Bader, Hamam Wasel, and Safasif as being particularly threatened by water erosion due to the combination of steep slopes and high rainfall intensity (AlAbed et al., 2018). Furthermore, in the mountainous regions of Al-Qadmus, the degradation of stable high-priority zones is often accelerated by anthropogenic pressures. A study of the Acha'ra Acharquieh Reserved documented the deterioration of natural forests due to overgrazing and unmanaged cutting (Dayoub and Abbas 2009 ). Consequently, the stable high-priority classes identified in this study (3.04%) demand integrated forest management to prevent these critical ecosystems from transitioning into active degradation categories. The identification of mountainous eastern districts as high-priority zones for intervention is consistent with localized findings in the Al-Abrash basin, where the steepest slopes were found to be the most susceptible to severe water erosion (Jouhra 2021 ). 3.3 Land use and land cover change mapping and detection The analysis of Land Use/Land Cover (LULC) dynamics in the Tartus Governorate between 1985 and 2022 reveals a period of significant environmental restructuring (Fig. 4 ). In the 1985 baseline, the landscape was heavily dominated by "Agricultural Area," which accounted for 1232.89 km 2 , representing 64.54% of the total study area (Table 3 ). By 2022, while agriculture remained the primary land use at 1199.36 km 2 (62.78%), the emergence and fluctuation of forest and urban classes indicate a highly dynamic anthropogenic environment. A notable feature of this transformation is the shifting classification of tree cover. While the "Tree Cover - Open Mixed Leaf Forest" class was not recorded in 1985, it emerged in subsequent years, peaking around 2012 before settling at 22.15 km 2 (1.16%) in 2022. Conversely, original forest types present in 1985, such as "Closed Broadleaf Deciduous Forest" and "Sparse Vegetation," faced near-total depletion by the end of the study period, reflecting a loss of primary natural cover. Urban Areas experienced the most aggressive expansion, growing from 15.84 km 2 (0.83%) in 1985 to 59.18 km 2 (3.10%) in 2022. This represents a massive net increase of 273.61%, signaling rapid infrastructure development and population pressure across the governorate. Other changes observed include: Bare Land: Decreased by 30.37% (from 93.68 km 2 to 65.23 km 2 ), suggesting conversion into built-up areas or more intensive cultivation. Shrubland: Declined by 14.85%, falling to 19.84 km 2 . Mixed Crop & Natural Vegetation: Remained the most stable category, maintaining a consistent presence of approximately 535.77 km 2 (28.05%) by 2022. Collectively, these shifts highlight a transition toward a human-dominated landscape. The reduction in natural vegetation density and the expansion of urban surfaces suggest an increased vulnerability to land degradation and soil erosion, particularly on the region's sloping terrains. Table 3 Landuse/Landcover Change Analysis in Tartus (1985–2022) Year LULC Area (ha) Area (Km 2 ) Area (%) 1985 Tree Cover - Closed Broadleaf Deciduous Forest 176.06 1.76 0.09 Tree Cover - Closed Needleleaf Forest 314.51 3.15 0.16 Agricultural Area 123288.53 1232.89 64.54 Srubland 2329.97 23.30 1.22 Sparse Vegetation (Tree, Shrub, Herbaceous) 47.07 0.47 0.02 Mixed Crop & Natural Vegetation 53233.92 532.34 27.87 Bare Land 9368.33 93.68 4.90 Waterbodies 695.21 6.95 0.36 Urban Areas 1583.50 15.84 0.83 2000 Tree Cover - Closed Broadleaf Deciduous Forest 262.72 2.63 0.14 Tree Cover - Closed Needleleaf Forest 286.66 2.87 0.15 Tree Cover - Open Mixed Leaf Forest 1729.56 17.30 0.91 Srubland 9379.00 93.79 4.91 Sparse Vegetation (Tree, Shrub, Herbaceous) 47.73 0.48 0.02 Mixed Crop & Natural Vegetation 80216.48 802.16 41.99 Agricultural Area 87713.36 877.13 45.91 Bare Land 8940.98 89.41 4.68 Waterbodies 597.56 5.98 0.31 Urban Areas 1863.07 18.63 0.98 2012 Tree Cover - Closed Broadleaf Deciduous Forest 260.51 2.61 0.14 Tree Cover - Open Mixed Leaf Forest 1746.64 17.47 0.91 Mixed Crop & Natural Vegetation 79929.87 799.30 41.84 Srubland 9500.42 95.00 4.97 Agricultural Area 87998.49 879.98 46.06 Bare Land 6509.57 65.10 3.41 Waterbodies 605.51 6.06 0.32 Urban Areas 4486.09 44.86 2.35 2022 Tree Cover - Closed Needleleaf Forest 102.95 1.03 0.05 Tree Cover - Open Mixed Leaf Forest 2214.62 22.15 1.16 Srubland 1984.36 19.84 1.04 Mixed Crop & Natural Vegetation 53576.93 535.77 28.05 Agricultural Area 119936.20 1199.36 62.78 Bare Land 6523.46 65.23 3.41 Waterbodies 780.93 7.81 0.41 Urban Areas 5917.66 59.18 3.10 The cumulative changes in land cover across the Tartus Governorate are summarized in Table 4 , which highlights the net gains and losses over the 37-year study period. The data reveals a landscape in transition, where human-driven expansion is the primary catalyst for change. The most striking transformation is found in Urban Areas, which underwent a massive expansion of 273.61%, reflecting the region's rapid demographic and infrastructural growth. While Agricultural Area remained the dominant land use, it experienced a subtle net contraction of 2.72% (-33.53 km 2 ), likely due to the conversion of peri-urban farmland into built-up environments. In terms of natural cover, the region witnessed a replacement of original 1985 forest types—specifically the "Closed Broadleaf" and "Sparse Vegetation" classes—with the emergence of "Open Mixed Leaf Forest." However, the overall decline in Bare Land (-30.37%) and Shrubland (-14.85%) further underscores the intensifying pressure of anthropogenic activities on the remaining natural spaces. Table 4 Landuse/Landcover Net Change Analysis in Tartus between 1985 and 2022 Landuse/Landcover LULC Area (Km 2 ) 1985 Area (Km 2 ) 2022 Net Change (Km 2 ) Net Percentag Change (%) Agricultural Area 1232.89 1199.36 -33.53 -2.72% Bare Land 93.68 65.23 -28.45 -30.37% Shrubland 23.30 19.84 -3.46 -14.85% Tree Cover - Closed Needleleaf Forest 3.15 1.03 -2.12 -67.30% Waterbodies 6.95 7.81 + 0.86 + 12.37% Mixed Crop & Natural Vegetation 532.34 535.77 + 3.43 + 0.64% Urban Areas 15.84 59.18 + 43.34 + 273.61% Tree Cover - Closed Broadleaf Deciduous Forest 1.76 - -1.76 Lost Class (1985) Sparse Vegetation (Tree, Shrub, Herbaceous) 0.47 - -0.47 Lost Class (1985) Tree Cover - Open Mixed Leaf Forest - 22.15 + 22.15 Gain Class (2022) These observed fluctuations in agricultural land are corroborated by recent statistical analyses of land use in the governorate. Saqer et al. ( 2024 ) noted an instability in arable land area between 2001 and 2022, finding a negligible annual growth rate of only 0.07%, which fails to meet the growing food demands of the local population. Furthermore, their study highlighted a statistically significant decrease in non-arable land at an annual rate of 0.17%, reflecting ongoing land reclamation efforts to bring marginal lands into agricultural production (Saqer et al. 2024 ). The observed transition in forest classes specifically the near-total depletion of Closed Broadleaf Deciduous Forest is clarified by high-resolution disturbance data. According to Global Forest Watch (2024), the Tartus Governorate lost approximately 1.3 kha of tree cover between 2001 and 2024, representing a 13% reduction of the forest extent present in 2000. A significant catalyst for this restructuring has been forest fires, which accounted for 550 ha of the total loss during this period. The impact of fire was most devastating in 2021, when 230 ha were lost to fires alone, constituting 60% of all tree cover loss for that year. Geographically, these losses are not uniform; the Baniyas district emerged as a primary hotspot, accounting for 560 ha of tree cover loss nearly double the governorate average. These disturbances explain why primary forest types present in the 1985 baseline have struggled to persist, leading to the modified forest structures identified in this study (GFW 2026). The analysis of Land Use/Land Cover (LULC) dynamics reveals a period of profound environmental restructuring within the Tartus Governorate. In the 1985 baseline, the landscape was heavily dominated by 'Agricultural Area,' representing 64.54% of the total study area. Over the study period, original primary forest types, such as 'Closed Broadleaf Deciduous Forest,' faced near-total depletion. The emergence of 'Tree Cover - Open Mixed Leaf Forest' in later years reflects a regional trend in the coastal range where forest structural parameters have shifted following historical disturbance events (Karmoka 2018 ). These structural changes are closely linked to the high susceptibility of the region's forests to fire. Najjar ( 2019 ) developed forest fire risk maps for the Tartus region, identifying very high-risk areas primarily in the Center and North of the governorate, while high-risk zones were concentrated in the Southeast. These recurring fire hazards act as a primary driver for the degradation of dense natural forests and their subsequent transition into more open or modified forest classifications (Najjar 2019 ; Karmoka 2018 ). 3.4 Vegetation Dynamics and NDVI Trend Analysis The temporal analysis of the Normalized Difference Vegetation Index (NDVI) for the Tartus Governorate between 1985 and 2022 indicates a progressive increase in both peak and average photosynthetic activity (Fig. 5 ). As shown in Table 5 , the Mean NDVI rose from 0.313 in 1985 to 0.459 by 2022, representing a notable enhancement in overall greenness across the study area. Similarly, the Maximum NDVI values reached 0.857 in 2022, up from 0.692 in 1985, signaling higher peak biomass productivity in specific locations. While the LULC data shows that Agricultural Area has remained the dominant land cover (staying above 62% throughout the study period), the significant upward trend in NDVI reflects a radical intensification of land use within those areas. The shift from traditional farming to managed, high-biomass systems, such as irrigated citrus orchards, olive groves, and intensive greenhouse farming has contributed to higher and more consistent vegetation indices compared to the 1985 baseline. Furthermore, the emergence of the "Tree Cover - Open Mixed Leaf Forest" class and the relative stability of the "Mixed Crop & Natural Vegetation" category (which covered 28.05% by 2022) have provided additional layers of chlorophyll activity. Despite slight fluctuations between 2000 and 2012 likely due to localized climatic variations, the overall trajectory suggests that while primary forest types (e.g., Closed Broadleaf) have been lost, the "greenness" of the governorate has been anthropogenically enhanced through agricultural modernization and a shift toward modified vegetation structures. Table 5 Comprehensive NDVI Trend Analysis for Tartus Area (1985–2022) Year Max NDVI Mean NDVI Trend Summary 1985 0.692 0.313 Baseline vegetation period 2000 0.781 0.360 Initial increase in productivity 2012 0.780 0.351 Minor fluctuation/stabilization 2022 0.857 0.459 Peak productivity driven by agriculture The increased Mean NDVI (from 0.313 to 0.459) is not only a result of agricultural intensification but also reflects significant tree cover gains in specific managed or regenerating areas. While Tartus faced tree cover losses, Global Forest Watch records a simultaneous gain of 1.7 kha of tree cover between 2000 and 2020. Remarkably, this represents 30% of all tree cover gains in Syria during this period. When accounting for both losses and gains, the region experienced a net tree cover increase of 970 ha (4.0%). This net gain, coupled with the emergence of the "Tree Cover - Open Mixed Leaf Forest" class in our LULC analysis, provides a structural explanation for the peak NDVI productivity recorded in 2022. This suggests that while primary natural forests have been degraded by fire and other drivers, the "greenness" of the governorate is being maintained and even enhanced by a combination of agricultural modernization and the growth of secondary or managed tree cover (GFW 2026). The rising Mean NDVI (from 0.313 to 0.459) reflects an "anthropogenic greening" driven by agricultural intensification and forest regeneration. However, this upward trend is often interrupted by localized disturbances that impact biomass productivity. The fluctuations observed in the NDVI trend, particularly the minor stabilization between 2000 and 2012 can be attributed to the impact of forest fires on vegetation health. According to fire risk assessments conducted in the region, fire events significantly alter the spectral signatures of vegetation by reducing chlorophyll activity and biomass in high-risk zones (Najjar 2019 ). These disturbances are further supported by Global Forest Watch data, which noted that in 2021 alone, fire was responsible for 60% of all tree cover loss in Tartus. Despite these periodic losses, the overall net gain in tree cover (1.7 kha between 2000–2020) and agricultural modernization continue to drive the long-term increase in the region’s vegetation index. 4. Recommendations for Land Degradation Control To address identified land degradation risks and promote sustainable land management in Tartus, the following strategic actions are recommended based on the PAP/RAC priority mapping and observed LULC dynamics: 4.1. Curative Interventions for High-Priority Unstable Areas Targeted Erosion Control: Immediate technical interventions are required in the 108.77 km² identified as high-priority unstable zones, particularly in the eastern mountainous fringes of Safita and northern Baniyas. Measures should include the construction of check dams and mechanical runoff barriers to stabilize rill and sheet erosion. Agro-Ecological Restoration: In areas where primary forest cover has been depleted, reforestation should utilize indigenous species such as Quercus and Pinus . Promoting agro-forestry—integrating fruit trees with protective vegetation—can help restore soil organic matter while maintaining agricultural productivity. 4.2. Preventive Measures for Stable Agricultural Zones Adoption of Conservation Agriculture: Given that agriculture occupies 62.78% of the governorate, farmers should be encouraged to adopt "zero-tillage" or "contour plowing" to minimize topsoil disturbance, especially on the mid-altitude plateaus of Sheick Bader and Dreikish. Sustainable Intensification Management: As rising NDVI values indicate higher biomass productivity through intensification, it is critical to implement balanced fertilization and irrigation to prevent soil salinization and long-term depletion. Greenhouse Runoff Management: To mitigate the impact of rapid urban and greenhouse expansion (+ 273.6%), localized drainage systems should be implemented around greenhouse clusters to prevent concentrated runoff from causing erosion on adjacent lower-lying lands. 4.3. Institutional and Policy Frameworks Geomatics-Based Planning: The 857.31 km² of "Unmanaged Areas with Agricultural Potential" should be developed under a strict land-use plan that prohibits the clearing of remaining natural forest patches for new agricultural plots. Continuous Monitoring: A permanent GIS-based monitoring unit should be established to track NDVI and LULC changes biennially to identify new degradation "hotspots" before they reach an irreversible stage 5. Conclusion This study successfully integrated Remote Sensing and GIS techniques to assess land degradation risk in the Tartus Governorate, Syria, using the UNEP PAP/RAC methodology. The results reveal a landscape in transition; while 80.36% of the area remains classified as stable, significant portions are under increasing environmental pressure. The analysis of LULC dynamics between 1985 and 2022 highlights a fundamental restructuring of the region. While Agricultural Area remained the dominant land use at 62.78%, the period was marked by a massive 273.61% expansion of Urban Areas and a significant shift in forest composition. Specifically, original 1985 forest types—Closed Broadleaf and Sparse Vegetation—faced near-total depletion, partially replaced by the emergence of the Open Mixed Leaf Forest class by 2022. Despite the loss of primary natural cover, the Mean NDVI rose from 0.313 to 0.459 over the study period. This "anthropogenic greening" does not signify natural recovery, but rather the radical intensification of the agricultural sector, characterized by high-biomass irrigated orchards and greenhouse farming. However, this productivity remains vulnerable, as 9.78% of the region is undergoing active degradation through sheet and rill erosion, particularly on steep topographical gradients. The conservation priority mapping identifies 108.77 km² as high-priority unstable areas requiring Simmediate curative intervention. Ultimately, this research provides a vital geomatics framework for decision-makers to balance necessary agricultural productivity with essential soil conservation, ensuring the long-term environmental sustainability of Syria’s coastal ecosystems. Declarations Funding : Author 4 has recieved research support from The British Academy/Cara/Leverhulme Researchers at Risk Research Support Grant. The University of Manchester “The other authors declare that no funds, grants or other support were recived during the prepartion of this manuscript” Competing interests : ”The authors have no relevent financial or non-financial to disclose”. Availability of data and material : Data fully available upon request Code availability : Not applicable 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. Authors' contributions : “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 Mohammad AlAbed and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.” References AbdelRahman M (2023) An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications. Rend Lincei – Scienze Fis e Naturali 34:767–808. https://doi.org/10.1007/s12210-023-01155-3 AlAbed Mohammad J, Salhab, Hani Ebrahim and Safa Dweri (2018) Quantitative Estimation of Annual Soil Loss by Integration of Remote Sensing, GIS, and Universal Soil Loss Equation (Case Study: Tartus District, Syria). Ann Arid Zone 57(34):129–134 Barakat M, Mahfoud I, Jouhra A (2017) Assessment of Soil Erosion Risk within Basel al-Assad Basin Area Using GIS and RUSLE. Tishreen Univ J Res Sci Stud - Biol Sci Ser 39(4):165–184 Barakat MA, Al-Abed M, Hasan E, Jouhra A (2020) Preparation of Water Erosion Risk Map for AL-Abrash River Basin Soil in Tartus Using CORINE Model and GIS. Tishreen Univ J Res Sci Stud - Biol Sci Ser 42(5):139–160 Borrelli P, Robinson DA, Fleischer LR et al (2017) An assessment of the global impact of 21st century land use change on soil erosion. Nature Communcations. 8, 2013. https://doi.org/10.1038/s41467-017-02142-7 Central Bureau of Statistics (CBS). Statistical abstract (2018) Available from: https://cbssyr.sy/index-EN.htm Dayoub F, Abbas H (2009) Ecological and Phytosociological Study of Acha'ra Acharquieh Reserved. Tishreen Univ J Res Sci Stud - Biol Sci Ser 31(2):123–145 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) (2024) Interactive Forest Dashboard: Syria, Tartus. World Resources Institute. Available at: https://www.globalforestwatch.org (Accessed: January 18, 2026) García-Ruiz E, Nadal-Romero Noemí, Lana-Renault, Santiago, Beguería (2013) Erosion in Mediterranean landscapes: Changes and future challenges. Geomorphology 198:20–36 Jouhra AS (2021) Estimating of Soil Erosion Quantity and Sediments in the AL-Abrash river basin using Remote Sensing, GIS and Mathematical models. Ph.D. Thesis. Faculty of Agriculture, Tishreen University, Syria Karmoka RF (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 Kosmas C, Kairis O, Karavitis C et al (2014) Evaluation and selection of indicators for land degradation and desertification Monitoring: Methodological approach. Environ Manage 54:951–970. https://doi.org/10.1007/s00267-013-0109-6 Lhoussaine EM, Meryem M, Moncef B, Mustapha M, Noureddine A, Abdessalam BH, Yousra R, Brahim D (2024) A GIS-based modified PAP/RAC model and Caesium-137 approach for water erosion assessment in the Raouz catchment, Morocco. Environ Res 251(1):118460. https://doi.org/10.1016/j.envres.2024.118460 Mahmoud Daoud F, Al-Ghamaz Y, Ahmed, Balull M (2019) Classification Guide for Tartus Governorate Soils. Ministry of Agriculture and Agrarian Reform. Syria Mesrar H, Sadiki A, Navas A, Faleh A, Quijano L, Chaaouan J (2015) Modélisation de l'érosion hydrique et des facteurs causaux: cas de l'Oued Sahla, rif central, Maroc. Zeitschrift für Geomorphologie 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 Panos P, Borrelli J, Poesen C, Ballabio E, Lugato K, Meusburger L, Montanarella, Alewell C (2015) The new assessment of soil loss by water erosion in Europe. Environ Sci 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, 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, Arhaya A (2024) Land use analysis in Tartus governorate. Tishreen Univ J Res Sci Stud - Biol Sci Ser, 46(4) Tahouri J, Sadiki A, Karrat L, Johnson VC, Chan NW, Fei Z, Kung HT (2022) Using a modified PAP/RAC model and GIS for mapping water erosion and causal risk factors: case study of the Asfalou watershed, Morocco. Int Soil Water Conserv Res 10:254–272 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/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.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 United States Geological Survey (USGS). Landsat Collection 2 Level-2 Science Products (2021) Available from: https://www.usgs.gov/landsat-missions Vrieling Anton (2006) Satellite remote sensing for water erosion assessment: A review. CATENA . Volume 65, Issue 1. 2–18. https://doi.org/10.1016/j.catena.2005.10.005 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8870510","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590802101,"identity":"b5a20927-e730-4f27-94d5-d99f762baa3e","order_by":0,"name":"Mohammad AlAbed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYHACNiCyYWCQIFFLGulaDpOgRbeB+dmDD2XnE/tnNx98wFBjE01Qi9kBNnPDGeduJ864cyzZgOFYWm4DYS0MZtK8bbcTG27kmEkwNhwmRgv7N+m/becS55OghcdMmrHtQOIG4rUc5ik37DmXbLzxRlqyQQJRfjnevu3BjzI72Xk3kg8++FBjQ1gLAzOEcgSrTCCoHAnYk6J4FIyCUTAKRhgAAILWQQFt7wwvAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-3949-959X","institution":"1Department of Geoenvironmental Analysis, Fluminense Federal University (UFF), Niterói, Brasil","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"AlAbed","suffix":""},{"id":590811666,"identity":"7d37f9f7-7aae-4732-aa3b-fad1350876e0","order_by":1,"name":"Rosa Karmoka","email":"","orcid":"https://orcid.org/0000-0003-4024-3755","institution":"General Organization of Remote Sensing, Department of Research Center, Syria","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"","lastName":"Karmoka","suffix":""},{"id":590811667,"identity":"4dc51bdf-6ab0-42bc-939f-d873672793c3","order_by":2,"name":"Silva Loulou","email":"","orcid":"https://orcid.org/0009-0006-3265-1811","institution":"Geography Faculty, Damascus University, Syria","correspondingAuthor":false,"prefix":"","firstName":"Silva","middleName":"","lastName":"Loulou","suffix":""},{"id":590811668,"identity":"0a64a908-0c32-403a-9757-19aad8dc2a56","order_by":3,"name":"Turkia Almoustafa","email":"","orcid":"","institution":"Department of Geography, University of Manchester, Manchester, UK","correspondingAuthor":false,"prefix":"","firstName":"Turkia","middleName":"","lastName":"Almoustafa","suffix":""},{"id":590811669,"identity":"8d9695da-0f6d-4921-9964-defe51646da2","order_by":4,"name":"Diyaa Najjar","email":"","orcid":"","institution":"Northern Arctic Federal University, Dep. of Forestry and Forest Management, Arkhangelsk, Russia","correspondingAuthor":false,"prefix":"","firstName":"Diyaa","middleName":"","lastName":"Najjar","suffix":""}],"badges":[],"createdAt":"2026-02-13 10:13:40","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8870510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8870510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102729708,"identity":"644ff776-def1-466e-bf92-77e84096eaf7","added_by":"auto","created_at":"2026-02-16 04:19:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":599847,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area location: Tartus Governorate, Syria.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8870510/v1/ba7c67a8516f07d3363566e7.png"},{"id":102729709,"identity":"7ae4ca08-272a-439c-9429-71647d3a77f3","added_by":"auto","created_at":"2026-02-16 04:19:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":395625,"visible":true,"origin":"","legend":"\u003cp\u003eLand degradation map showing stable areas and unstable erosion hotspots\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8870510/v1/e08212ed5069711044e26dd5.png"},{"id":102729711,"identity":"87d8e9d1-91c1-488f-90c8-b914ad48b517","added_by":"auto","created_at":"2026-02-16 04:19:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":703883,"visible":true,"origin":"","legend":"\u003cp\u003eLand conservation priority zones for curative and preventive action\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8870510/v1/5423a3f202c049d6d21a1384.png"},{"id":102729712,"identity":"d000f835-edf7-4eba-80a6-e8df5a1b0444","added_by":"auto","created_at":"2026-02-16 04:19:26","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":771429,"visible":true,"origin":"","legend":"\u003cp\u003eTartus Landuse/Landcover Changes between 1985 and 2022\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8870510/v1/d40e108a61598a31fd056241.jpeg"},{"id":102729710,"identity":"24ac592c-1077-4820-af9f-babeec5d2364","added_by":"auto","created_at":"2026-02-16 04:19:26","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":834800,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal Evolution of NDVI (1985 - 2022)\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8870510/v1/2279ba37456505816f26009e.jpeg"},{"id":102749034,"identity":"632f5316-0db8-44a1-923f-9b0b50e9439e","added_by":"auto","created_at":"2026-02-16 09:11:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4267887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8870510/v1/f24744f8-d931-4007-8c08-bc5daac267c1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEvaluating Multi-Decadal Land Degradation and Anthropogenic Greening in Mediterranean Coastal Ecosystems: A Geospatial-Based Assessment of Tartus, Syria\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Tartus Governorate is situated along the eastern Mediterranean coast of western Syria with an area of about 189,620 ha, extending approximately between latitudes 34\u0026deg; 30' to 35\u0026deg; 20' N and longitudes 35\u0026deg; 40' to 36\u0026deg; 20' E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the Central Bureau of Statistics (CBS), the population was estimated at 458,327 in 2023. The region experiences a typical Mediterranean climate characterized by mild, wet winters and hot, humid summers, with annual rainfall ranging from approximately 896.6 to 1380.7 mm. These conditions place the governorate within Syria\u0026rsquo;s first agro-ecological stability zone. While such climatic conditions support relatively high biomass productivity, they also enhance rainfall erosivity, particularly on sloping terrain (GORS 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eForest formations are found on mountainous terrain; however, agriculture represents the dominant land-use activity in the study area. It is characterized by extensive cultivation of fruit trees, particularly olives and citrus. Field crops, including wheat, peanuts, and vegetables, are also widely cultivated, while greenhouse farming has expanded rapidly for protected vegetable and floriculture production. Agricultural systems in the governorate rely on both rainfed practices and irrigation from wells, rivers, and dams. Soils are traditionally classified into six main groups, including deep alluvial soils along the coastal strip, moderately deep hill and plateau soils developed on calcareous and basaltic substrates, shallow slope and mountain soils, and valley soils formed through fluvial deposition. Many of these soils, particularly those on slopes and mountainous terrain are highly vulnerable to erosion due to shallow depth, stoniness, and cultivation on steep gradients (Mahmoud et al. 2019).\u003c/p\u003e \u003cp\u003ePhysiographically, the governorate exhibits pronounced spatial heterogeneity. Coastal plains account for approximately 15% of the total area, while the remaining 85% consists of plateaus, hills, and mountainous terrain. These upland systems have been identified as highly sensitive to land degradation due to the combination of steep slopes, shallow soils, and intense seasonal rainfall (Garc\u0026iacute;a-Ruiz et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mahmoud et al. 2019). Furthermore, in comparable Mediterranean regions, agricultural systems have been shown to exert significant pressure on these fragile soil resources. When conservation measures are inadequate, this human-induced pressure often results in accelerated soil erosion and severe land degradation (Kosmas et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Syrian coastal region, despite the apparent agricultural productivity of Tartus Governorate, increasing land-use intensity, deforestation, expansion of cultivation into marginal areas, and insufficient soil conservation practices have heightened the risk of land degradation (GORS 2021). Localized studies, such as those conducted in the Al-Abrash river basin, have highlighted how high rainfall intensities lead to the displacement of tons of soil, which eventually accumulate in dam reservoirs like the Al-Basel Dam, with an estimated 74,995 tons of sediment arriving annually (Jouhra \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conventional field-based assessments alone are often insufficient to capture the spatial variability and cumulative effects of these processes at the regional scale. Consequently, the integration of remote sensing and Geographic Information Systems (GIS) has become an essential approach for assessing land degradation risk in a spatially explicit, consistent, and cost-effective manner (AlAbed et al. 2018). Remote sensing provides multi-temporal observations of land surface conditions, vegetation dynamics, and indicators related to soil erosion and degradation, while GIS enables the integration of environmental factors such as topography, soils, land use, and climate (Vrieling 2006; AbdelRahman \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These techniques have been successfully applied worldwide for mapping erosion risk, monitoring land degradation, and supporting conservation planning, particularly in Mediterranean and semi-arid regions (Panagos et al. 2015; Borrelli et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, their application in coastal Syrian environments remains limited.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study applies an integrated geomatics approach based on the methodology developed by the United Nations Environment Programme, Priority Actions Programme Regional Activity Centre (UNEP, PAP/RAC). 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=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mesrar et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tahouri et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lhoussaine et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the main objective of this study is to integrate remote sensing and GIS techniques to assess land degradation risk in Tartus Governorate by identifying spatial patterns of stable and unstable land conditions and analyzing their relationships with environmental and land-use factors. The results aim to support land-use planning, prioritize soil conservation measures, and contribute to sustainable land management strategies in one of Syria\u0026rsquo;s most environmentally and agriculturally important coastal regions.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.1 Landscape Stability and Degradation Mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLand degradation mapping in the study area was carried out following the consolidated methodology for the Mapping of Rainfall-Induced Erosion Processes in Mediterranean Coastal Areas developed by the Priority Actions Programme Regional Activity Centre (PAP/RAC 1997). This methodology is specifically designed for Mediterranean environments and provides a standardized framework for identifying, classifying, and evaluating land degradation processes driven primarily by water erosion. The assessment was implemented within a (GIS) environment using ArcGIS and QGIS software. Spatial analyses were conducted in accordance with the PAP/RAC criteria and cartographic standards, employing Landsat satellite imagery and thematic maps at a scale of 1:50,000. The GIS database integrated multiple spatial layers, including land use/land cover maps, physiographic unit maps, topography, and supporting field observations. The delineation of stable and unstable land units was achieved through the cross-analysis of land use/land cover data, physiographic characteristics, and field survey information collected from approximately 92 representative sites across the study area. For each field site, the type of stable land, the dominant factors influencing its stability, and the associated degree of erosion risk were identified. Similarly, for unstable land units, the type of degradation process, its spatial extent, and its expansion trend were systematically recorded. This integrated approach enabled the classification of the landscape into two primary categories—stable and unstable areas—consistent with the definitions and criteria established by the PAP/RAC methodology (PAP/RAC 1997; UNEP/MAP/PAP 2000).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStable land units were evaluated based on their functional category, erosion risk level, and the main causative factors controlling their stability. The dominant stable land types identified include unmanaged areas (with forestry or agricultural potential), managed areas (under forestry or agricultural use). Each stable land unit was assigned an instability risk score on a scale from 0 to 3, where 0 represents no risk and 3 indicates critically unstable conditions. This ranking considered key degradation drivers, including slope steepness, geological and lithological characteristics, vegetation cover, and anthropogenic land use intensity. Unstable land units were classified according to the dominant type of degradation process in the area, including sheet erosion and rill erosion. The spatial extent of each degradation type was categorized as localized (\u0026lt;30% of the unit), dominant (30–60%), or widespread (\u0026gt;60%). In addition, the expansion trend of erosion was evaluated using a four-level scale ranging from 0 to 3, where 0 denotes stabilizing conditions, 1 indicates locally expanding degradation, 2 represents regionally expanding processes, and 3 corresponds to an advancing trend approaching irreversibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Multi-Criteria Prioritization and Risk Categorization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEffective land degradation mitigation requires the optimal allocation of limited resources and the clear identification of priority areas for intervention. To achieve this, a spatial prioritization procedure was applied using a multi-criteria scoring system designed to identify land degradation hotspots requiring urgent management action. The procedure follows the framework proposed by the UNEP/MAP–PAP/RAC methodology (UNEP/MAP/PAP 2004) and integrates biophysical and socio-economic factors influencing land stability and degradation dynamics.\u003c/p\u003e\n\u003cp\u003eA total of fourteen variables were selected based on their relevance to land degradation processes and their significance within the local socio-economic context. Each variable was assigned a score ranging from 1 (lowest impact or risk) to 3 (highest impact or risk), reflecting its relative contribution to land stability or instability. The weighting of variables was established through a structured expert-based evaluation, ensuring consistency with regional knowledge and field observations.\u003c/p\u003e\n\u003cp\u003eThe variables used in the prioritization procedure include:\u003c/p\u003e\n\u003cp\u003eA. Physical instability risk (for stable areas)\u003c/p\u003e\n\u003cp\u003eB. Extent of the affected area (for unstable areas)\u003c/p\u003e\n\u003cp\u003eC. Expansion trend of the degradation process (for unstable areas)\u003c/p\u003e\n\u003cp\u003eD. Multiplication factors for unfavorable combinations of causative agents\u003c/p\u003e\n\u003cp\u003eE. Influence on adjacent areas\u003c/p\u003e\n\u003cp\u003eF. Overexploitation as an aggravating socio-economic factor\u003c/p\u003e\n\u003cp\u003eG. Rural exodus as an aggravating socio-economic factor\u003c/p\u003e\n\u003cp\u003eH. Land tenure as an aggravating socio-economic factor\u003c/p\u003e\n\u003cp\u003eI. Other aggravating socio-economic factors\u003c/p\u003e\n\u003cp\u003eJ. Value of current land use according to the local population\u003c/p\u003e\n\u003cp\u003eK. Value of current land use according to national policies\u003c/p\u003e\n\u003cp\u003eL. Potential for forestry\u003c/p\u003e\n\u003cp\u003eM. Potential for agricultural use\u003c/p\u003e\n\u003cp\u003eN. Other land use potentials\u003c/p\u003e\n\u003cp\u003eAfter scoring all criteria for each identified area, final prioritization scores were calculated as follows:\u003c/p\u003e\n\u003cp\u003e- Stable Areas Priority = [(A × D + E) × F × G × H × I] + [(J + K) × L × M × N]\u003c/p\u003e\n\u003cp\u003e- Unstable Areas Priority = [(B × C × D + E) × F × G × H × I] + [(J + K) × L × M × N]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinal scores were grouped into three priority categories: High (≥ 60), Medium (21–59), and Low (≤ 20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Land Use and Land Cover change Analysis (1985–2022)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate Land Use and Land Cover (LULC) dynamics across the Tartus Governorate, multi-temporal satellite imagery was utilized, specifically employing Landsat 5 TM (1985), Landsat 7 ETM+ (2000/2012), and Landsat 8/9 OLI (2022). All datasets maintained a consistent 30m spatial resolution to facilitate accurate temporal comparisons across the study intervals. To minimize seasonal variance and phenological differences, imagery was selected from the dry summer months (June–August), ensuring stable and comparable environmental conditions for the classification process. Preprocessing was implemented using Google Earth Engine (GEE) and ArcGIS 10.8, including geometric corrections, projection harmonization to the WGS 84 / UTM zone 37N coordinate system, and atmospheric calibration to surface reflectance. The classification was executed using a Supervised Classification approach based on the Maximum Likelihood Classifier (MLC) algorithm, depending on the local knowledge of the study area and falls within the second level of classification. The differentiation of forest types followed hierarchical classification principles previously validated in the Syrian coastal mountains, ensuring the accurate separation of deciduous and needleleaf formations (Karmoka 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis method categorized the landscape into nine representative classes, reflecting the region's agricultural, urban, and natural features. Additionally, a post-classification spatial filter (majority filter) was applied to the outputs to reduce spectral noise (\"salt-and-pepper\" effect) and enhance class homogeneity. The reliability of the LULC maps was validated through a rigorous accuracy assessment. Ground truth reference data were compiled from field surveys and high-resolution Google Earth imagery to construct a confusion matrix. From this matrix, standard performance metrics were derived, specifically Overall Accuracy and the Kappa Coefficient, to confirm the statistical validity of the mapping and subsequent change detection analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 NDVI Computation and Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVegetation dynamics were analyzed using the Normalized Difference Vegetation Index (NDVI) derived from Landsat Collection 2 Level-2 surface reflectance products (Path 174, Rows 36) obtained from the USGS. The dataset included imagery from Landsat 4/5 TM (1985 and 2000) using Band 3 (Red) and Band 4 (NIR), and Landsat 8/9 OLI (2012 and 2022) using Band 4 (Red) and Band 5 (NIR). NDVI was selected as a primary indicator for vegetation dynamics due to its high correlation with forest growth parameters and biomass in Syrian coastal environments (Karmoka 2018). The use of Level-2 products ensured radiometric consistency and atmospheric correction across the multitemporal series, supporting a robust long-term analysis. Preprocessing was conducted within a GIS environment, where individual scenes were mosaicked to ensure full spatial coverage and clipped to the study area boundary. NDVI layers were generated for each reference year using the standard normalized difference ratio of the Red and NIR bands via the Raster Calculator tool. This systematic approach provided spatially consistent rasters (WGS_1984_UTM_Zone_37N) for the quantitative assessment of vegetation cover changes across the 37-year study period.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of Land Degradation in Tartus\u003c/h2\u003e \u003cp\u003eThe land cover of the Tartus Governorate is categorized into three primary states: Stable Areas, Unstable Areas (undergoing active degradation), and Not Relevant zones as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Stable areas represent the vast majority of the region, encompassing 80.36% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of the total study area (1538.35 km\u003csup\u003e2\u003c/sup\u003e). This classification is further subdivided based on management status and natural potential:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUnmanaged Areas with Agricultural Potential (44.76%): Covering 857.31 km\u003csup\u003e2\u003c/sup\u003e, this is the most dominant land class. Spatially, it occupies the central and northern interior, including Sheick Bader and parts of Dreikish. The prevalence of this class suggests significant untapped capacity for agricultural expansion, as these lands are currently underutilized or restricted to subsistence use.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnmanaged Areas with Forest Potential (18.11%): This category accounts for 347.01km\u003csup\u003e2\u003c/sup\u003e. Located primarily in mid-altitude zones, these areas serve as a vital ecological buffer between the coastal lowlands and steep mountain ridges. While naturally suited for forestry, they currently lack active management.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eManaged Areas with Agricultural Use (17.02%): Representing the region\u0026rsquo;s economic core, this class covers 325.06 km\u003csup\u003e2\u003c/sup\u003e. It is concentrated along the coastal plains and southern borders, characterized by intensive greenhouse farming and citrus orchards.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eManaged Areas with Forest Use (0.47%): The smallest stable category, totaling only 8.97 km\u003csup\u003e2\u003c/sup\u003e. These managed forests appear in small, concentrated patches within the transitional hills of Safita and Dreikish.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the unstable areas cover 9.78% (187.337 km\u003csup\u003e2\u003c/sup\u003e) of the region mainly due to sheet and rill erosion. The Sheet Erosion (9.36%) is the primary driver of degradation in Tartus, affecting 179.26 km\u003csup\u003e2\u003c/sup\u003e. It is most critical along the eastern and northeastern mountainous fringes, such as Qadmous. The distribution follows steep topographical gradients, indicating that deforestation and improper tillage on slopes are likely contributing to this widespread removal of thin topsoil layers. The Rill Erosion (0.42%) a more localized but advanced stage of water erosion, covering 8.11 km\u003csup\u003e2\u003c/sup\u003e. It appears in isolated pockets, particularly near Banyas, where runoff has carved visible channels into the landscape. For the areas classified as \"Not Relevant,\" which include built-up urban environments and water bodies, account for 9.66% (184.66 km\u003csup\u003e2\u003c/sup\u003e) of the total area.\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\u003eAreas of Degradation and Stability Patterns in Tartus\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAreas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLand Category Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(hec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStable Areas\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\u003e34701.44\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\u003e18.11\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\u003e85730.60\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\u003e44.76\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\u003e897.35\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\u003e0.47\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\u003e32505.96\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\u003e17.02\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 Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e153835.35\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\u003e80.36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUnstable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL11\u003c/p\u003e \u003cp\u003eL21\u003c/p\u003e \u003cp\u003eL22\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\u003e17925.51\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\u003e9.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD11\u003c/p\u003e \u003cp\u003eD21\u003c/p\u003e \u003cp\u003eD22\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\u003e810.75\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\u003e0.42\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 Areas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e18736.26\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\u003e9.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNot Relevant (Urban, Water)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18465.50\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\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTotal Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191037.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1910.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.63\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\u003eThe analysis of land degradation risk in the Tartus Governorate reveals a landscape under varying degrees of environmental pressure. While a large portion of the study area is classified as stable, the high-priority zones characterized by active rill and sheet erosion reflect the inherent vulnerability of the region\u0026rsquo;s topography. These findings are supported by quantitative assessments in the Tartus District using the USLE model, which estimated that soil loss can reach as high as 150 t/ha/yr in hilly terrains where vegetation is sparse or absent (AlAbed et al. 2018). These severe erosion rates are consistent with other regional studies, such as the RUSLE analysis in the Basel al-Assad Basin, which identified very high erosion risk across 16% of the area (Barakat et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the CORINE-based assessment of the Al-Abrash River Basin, where 4.81% of the land faces high erosion risk (Barakat et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Distribution of Conservation Priority Classes\u003c/h2\u003e \u003cp\u003eThe conservation priority map of the Tartus study area reveals a clear dominance of stable land units (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which together account for 80.34% of the total area (1534.79 km\u0026sup2;), as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among these, stable medium-priority areas represent by far the largest class, covering 1461.53 km\u0026sup2;, equivalent to 76.50% of the study area, and are widely distributed across all districts within the study area. This extensive coverage indicates that most of the landscape exhibits moderate conservation value, requiring balanced management strategies that combine sustainable land use with preventive conservation measures. In contrast, stable high-priority areas occupy a relatively limited extent (58.06 km\u0026sup2;; 3.04%), reflecting localized zones of elevated ecological or environmental importance that may demand stricter protection, primarily concentrated in the high-altitude northern regions of Al-Qadmus and the eastern hilly terrains of the Tartus center district. While, the stable low-priority areas are marginal, covering only 15.20 km\u0026sup2; (0.80%), suggesting that areas with minimal conservation concern are scarce within the region, and predominantly found along the western boundary between the Dreikish and Tartus districts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnstable areas constitute 9.94% of the total study area (189.95 km\u0026sup2;), highlighting zones potentially affected by land degradation processes or environmental instability. Within this category, unstable high-priority areas account for 108.77 km\u0026sup2; (5.69%), representing the most critical zones where immediate conservation or rehabilitation interventions may be required, occupy the eastern hilly parts of Safita and northern Banyas. Unstable medium-priority areas cover 80.20 km\u0026sup2; (4.20%), indicating areas at moderate risk that warrant monitoring and targeted management, mainly spread in hilly areas in Banyas, Qadmos, and Sheick Bader. Unstable low-priority areas are negligible, comprising only 0.97 km\u0026sup2; (0.05%), which suggests that most unstable zones are associated with moderate to high conservation concern.\u003c/p\u003e \u003cp\u003eOverall, the spatial distribution of conservation priority classes underscores the predominance of stable landscapes in Tartus, while also emphasizing the presence of critical unstable high-priority zones that require focused management efforts to mitigate environmental risks and support sustainable land-use planning.\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\u003eSpatial extent of conservation priority zones in Tartus\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAreas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConservation Priority\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(hec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable Low Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1520.64\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\u003e0.80\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\u003e146153\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\u003e76.50\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\u003e5806.08\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\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Stable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153479.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1534.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUnstable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstable Low Priority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.28\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\u003e0.05\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\u003e8020.48\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\u003e4.20\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\u003e10877.44\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\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Unstable Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18995.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNot Relevant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18580.48\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\u003e9.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191055.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1910.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe spatial distribution of conservation priority classes highlights the dominance of stable medium-priority units, which require sustainable management to prevent future degradation. However, specific geographic \"hotspots\" require immediate attention. Previous research pinpointed areas surrounding towns such as Sheikh Bader, Hamam Wasel, and Safasif as being particularly threatened by water erosion due to the combination of steep slopes and high rainfall intensity (AlAbed et al., 2018). Furthermore, in the mountainous regions of Al-Qadmus, the degradation of stable high-priority zones is often accelerated by anthropogenic pressures. A study of the Acha'ra Acharquieh Reserved documented the deterioration of natural forests due to overgrazing and unmanaged cutting (Dayoub and Abbas \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Consequently, the stable high-priority classes identified in this study (3.04%) demand integrated forest management to prevent these critical ecosystems from transitioning into active degradation categories. The identification of mountainous eastern districts as high-priority zones for intervention is consistent with localized findings in the Al-Abrash basin, where the steepest slopes were found to be the most susceptible to severe water erosion (Jouhra \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Land use and land cover change mapping and detection\u003c/h2\u003e \u003cp\u003eThe analysis of Land Use/Land Cover (LULC) dynamics in the Tartus Governorate between 1985 and 2022 reveals a period of significant environmental restructuring (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the 1985 baseline, the landscape was heavily dominated by \"Agricultural Area,\" which accounted for 1232.89 km\u003csup\u003e2\u003c/sup\u003e, representing 64.54% of the total study area (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). By 2022, while agriculture remained the primary land use at 1199.36 km\u003csup\u003e2\u003c/sup\u003e (62.78%), the emergence and fluctuation of forest and urban classes indicate a highly dynamic anthropogenic environment.\u003c/p\u003e \u003cp\u003eA notable feature of this transformation is the shifting classification of tree cover. While the \"Tree Cover - Open Mixed Leaf Forest\" class was not recorded in 1985, it emerged in subsequent years, peaking around 2012 before settling at 22.15 km\u003csup\u003e2\u003c/sup\u003e (1.16%) in 2022. Conversely, original forest types present in 1985, such as \"Closed Broadleaf Deciduous Forest\" and \"Sparse Vegetation,\" faced near-total depletion by the end of the study period, reflecting a loss of primary natural cover. Urban Areas experienced the most aggressive expansion, growing from 15.84 km\u003csup\u003e2\u003c/sup\u003e (0.83%) in 1985 to 59.18 km\u003csup\u003e2\u003c/sup\u003e (3.10%) in 2022. This represents a massive net increase of 273.61%, signaling rapid infrastructure development and population pressure across the governorate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOther changes observed include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBare Land: Decreased by 30.37% (from 93.68 km\u003csup\u003e2\u003c/sup\u003e to 65.23 km\u003csup\u003e2\u003c/sup\u003e), suggesting conversion into built-up areas or more intensive cultivation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eShrubland: Declined by 14.85%, falling to 19.84 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMixed Crop \u0026amp; Natural Vegetation: Remained the most stable category, maintaining a consistent presence of approximately 535.77 km\u003csup\u003e2\u003c/sup\u003e (28.05%) by 2022.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCollectively, these shifts highlight a transition toward a human-dominated landscape. The reduction in natural vegetation density and the expansion of urban surfaces suggest an increased vulnerability to land degradation and soil erosion, particularly on the region's sloping terrains.\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\u003eLanduse/Landcover Change Analysis in Tartus (1985\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(Km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e\u003cb\u003e1985\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Closed Broadleaf Deciduous Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Closed Needleleaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123288.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1232.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSrubland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2329.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSparse Vegetation (Tree, Shrub, Herbaceous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed Crop \u0026amp; Natural Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53233.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e532.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9368.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaterbodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e695.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1583.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Closed Broadleaf Deciduous Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Closed Needleleaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Open Mixed Leaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1729.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSrubland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9379.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSparse Vegetation (Tree, Shrub, Herbaceous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed Crop \u0026amp; Natural Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80216.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e802.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87713.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e877.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8940.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaterbodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e597.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1863.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003e2012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Closed Broadleaf Deciduous Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e260.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Open Mixed Leaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1746.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed Crop \u0026amp; Natural Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79929.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e799.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSrubland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9500.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87998.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e879.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6509.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaterbodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e605.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4486.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Closed Needleleaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree Cover - Open Mixed Leaf Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2214.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSrubland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1984.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed Crop \u0026amp; Natural Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53576.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e535.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgricultural Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119936.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1199.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6523.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWaterbodies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e780.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5917.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.10\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\u003eThe cumulative changes in land cover across the Tartus Governorate are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which highlights the net gains and losses over the 37-year study period. The data reveals a landscape in transition, where human-driven expansion is the primary catalyst for change. The most striking transformation is found in Urban Areas, which underwent a massive expansion of 273.61%, reflecting the region's rapid demographic and infrastructural growth. While Agricultural Area remained the dominant land use, it experienced a subtle net contraction of 2.72% (-33.53 km\u003csup\u003e2\u003c/sup\u003e), likely due to the conversion of peri-urban farmland into built-up environments. In terms of natural cover, the region witnessed a replacement of original 1985 forest types\u0026mdash;specifically the \"Closed Broadleaf\" and \"Sparse Vegetation\" classes\u0026mdash;with the emergence of \"Open Mixed Leaf Forest.\" However, the overall decline in Bare Land (-30.37%) and Shrubland (-14.85%) further underscores the intensifying pressure of anthropogenic activities on the remaining natural spaces.\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\u003eLanduse/Landcover Net Change Analysis in Tartus between 1985 and 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanduse/Landcover LULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (Km\u003csup\u003e2\u003c/sup\u003e) 1985\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (Km\u003csup\u003e2\u003c/sup\u003e) 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet Change (Km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNet Percentag Change (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e1232.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1199.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-33.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.72%\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\u003e93.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-28.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.37%\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\u003e23.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.85%\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\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-67.30%\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\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;12.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e532.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e535.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.64%\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\u003e15.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;43.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;273.61%\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\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLost Class (1985)\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLost Class (1985)\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGain Class (2022)\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\u003eThese observed fluctuations in agricultural land are corroborated by recent statistical analyses of land use in the governorate. Saqer et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) noted an instability in arable land area between 2001 and 2022, finding a negligible annual growth rate of only 0.07%, which fails to meet the growing food demands of the local population. Furthermore, their study highlighted a statistically significant decrease in non-arable land at an annual rate of 0.17%, reflecting ongoing land reclamation efforts to bring marginal lands into agricultural production (Saqer et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The observed transition in forest classes specifically the near-total depletion of Closed Broadleaf Deciduous Forest is clarified by high-resolution disturbance data. According to Global Forest Watch (2024), the Tartus Governorate lost approximately 1.3 kha of tree cover between 2001 and 2024, representing a 13% reduction of the forest extent present in 2000. A significant catalyst for this restructuring has been forest fires, which accounted for 550 ha of the total loss during this period. The impact of fire was most devastating in 2021, when 230 ha were lost to fires alone, constituting 60% of all tree cover loss for that year. Geographically, these losses are not uniform; the Baniyas district emerged as a primary hotspot, accounting for 560 ha of tree cover loss nearly double the governorate average. These disturbances explain why primary forest types present in the 1985 baseline have struggled to persist, leading to the modified forest structures identified in this study (GFW 2026). The analysis of Land Use/Land Cover (LULC) dynamics reveals a period of profound environmental restructuring within the Tartus Governorate. In the 1985 baseline, the landscape was heavily dominated by 'Agricultural Area,' representing 64.54% of the total study area. Over the study period, original primary forest types, such as 'Closed Broadleaf Deciduous Forest,' faced near-total depletion. The emergence of 'Tree Cover - Open Mixed Leaf Forest' in later years reflects a regional trend in the coastal range where forest structural parameters have shifted following historical disturbance events (Karmoka \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These structural changes are closely linked to the high susceptibility of the region's forests to fire. Najjar (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) developed forest fire risk maps for the Tartus region, identifying very high-risk areas primarily in the Center and North of the governorate, while high-risk zones were concentrated in the Southeast. These recurring fire hazards act as a primary driver for the degradation of dense natural forests and their subsequent transition into more open or modified forest classifications (Najjar \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Karmoka \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Vegetation Dynamics and NDVI Trend Analysis\u003c/h2\u003e \u003cp\u003eThe temporal analysis of the Normalized Difference Vegetation Index (NDVI) for the Tartus Governorate between 1985 and 2022 indicates a progressive increase in both peak and average photosynthetic activity (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 Mean NDVI rose from 0.313 in 1985 to 0.459 by 2022, representing a notable enhancement in overall greenness across the study area. Similarly, the Maximum NDVI values reached 0.857 in 2022, up from 0.692 in 1985, signaling higher peak biomass productivity in specific locations.\u003c/p\u003e \u003cp\u003eWhile the LULC data shows that Agricultural Area has remained the dominant land cover (staying above 62% throughout the study period), the significant upward trend in NDVI reflects a radical intensification of land use within those areas. The shift from traditional farming to managed, high-biomass systems, such as irrigated citrus orchards, olive groves, and intensive greenhouse farming has contributed to higher and more consistent vegetation indices compared to the 1985 baseline.\u003c/p\u003e \u003cp\u003eFurthermore, the emergence of the \"Tree Cover - Open Mixed Leaf Forest\" class and the relative stability of the \"Mixed Crop \u0026amp; Natural Vegetation\" category (which covered 28.05% by 2022) have provided additional layers of chlorophyll activity. Despite slight fluctuations between 2000 and 2012 likely due to localized climatic variations, the overall trajectory suggests that while primary forest types (e.g., Closed Broadleaf) have been lost, the \"greenness\" of the governorate has been anthropogenically enhanced through agricultural modernization and a shift toward modified vegetation structures.\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\u003eComprehensive NDVI Trend Analysis for Tartus Area (1985\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \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\u003eMax NDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean NDVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrend Summary\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\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaseline vegetation period\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\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInitial increase in productivity\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\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinor fluctuation/stabilization\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\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeak productivity driven by agriculture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe increased Mean NDVI (from 0.313 to 0.459) is not only a result of agricultural intensification but also reflects significant tree cover gains in specific managed or regenerating areas. While Tartus faced tree cover losses, Global Forest Watch records a simultaneous gain of 1.7 kha of tree cover between 2000 and 2020. Remarkably, this represents 30% of all tree cover gains in Syria during this period. When accounting for both losses and gains, the region experienced a net tree cover increase of 970 ha (4.0%). This net gain, coupled with the emergence of the \"Tree Cover - Open Mixed Leaf Forest\" class in our LULC analysis, provides a structural explanation for the peak NDVI productivity recorded in 2022. This suggests that while primary natural forests have been degraded by fire and other drivers, the \"greenness\" of the governorate is being maintained and even enhanced by a combination of agricultural modernization and the growth of secondary or managed tree cover (GFW 2026).\u003c/p\u003e \u003cp\u003eThe rising Mean NDVI (from 0.313 to 0.459) reflects an \"anthropogenic greening\" driven by agricultural intensification and forest regeneration. However, this upward trend is often interrupted by localized disturbances that impact biomass productivity. The fluctuations observed in the NDVI trend, particularly the minor stabilization between 2000 and 2012 can be attributed to the impact of forest fires on vegetation health. According to fire risk assessments conducted in the region, fire events significantly alter the spectral signatures of vegetation by reducing chlorophyll activity and biomass in high-risk zones (Najjar \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These disturbances are further supported by Global Forest Watch data, which noted that in 2021 alone, fire was responsible for 60% of all tree cover loss in Tartus. Despite these periodic losses, the overall net gain in tree cover (1.7 kha between 2000\u0026ndash;2020) and agricultural modernization continue to drive the long-term increase in the region\u0026rsquo;s vegetation index.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Recommendations for Land Degradation Control","content":"\u003cp\u003eTo address identified land degradation risks and promote sustainable land management in Tartus, the following strategic actions are recommended based on the PAP/RAC priority mapping and observed LULC dynamics:\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.1. Curative Interventions for High-Priority Unstable Areas\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTargeted Erosion Control: Immediate technical interventions are required in the 108.77 km\u0026sup2; identified as high-priority unstable zones, particularly in the eastern mountainous fringes of Safita and northern Baniyas. Measures should include the construction of check dams and mechanical runoff barriers to stabilize rill and sheet erosion.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAgro-Ecological Restoration: In areas where primary forest cover has been depleted, reforestation should utilize indigenous species such as \u003cem\u003eQuercus\u003c/em\u003e and \u003cem\u003ePinus\u003c/em\u003e. Promoting agro-forestry\u0026mdash;integrating fruit trees with protective vegetation\u0026mdash;can help restore soil organic matter while maintaining agricultural productivity.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2. Preventive Measures for Stable Agricultural Zones\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAdoption of Conservation Agriculture: Given that agriculture occupies 62.78% of the governorate, farmers should be encouraged to adopt \"zero-tillage\" or \"contour plowing\" to minimize topsoil disturbance, especially on the mid-altitude plateaus of Sheick Bader and Dreikish.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSustainable Intensification Management: As rising NDVI values indicate higher biomass productivity through intensification, it is critical to implement balanced fertilization and irrigation to prevent soil salinization and long-term depletion.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGreenhouse Runoff Management: To mitigate the impact of rapid urban and greenhouse expansion (+\u0026thinsp;273.6%), localized drainage systems should be implemented around greenhouse clusters to prevent concentrated runoff from causing erosion on adjacent lower-lying lands.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3. Institutional and Policy Frameworks\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGeomatics-Based Planning: The 857.31 km\u0026sup2; of \"Unmanaged Areas with Agricultural Potential\" should be developed under a strict land-use plan that prohibits the clearing of remaining natural forest patches for new agricultural plots.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eContinuous Monitoring: A permanent GIS-based monitoring unit should be established to track NDVI and LULC changes biennially to identify new degradation \"hotspots\" before they reach an irreversible stage\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study successfully integrated Remote Sensing and GIS techniques to assess land degradation risk in the Tartus Governorate, Syria, using the UNEP PAP/RAC methodology. The results reveal a landscape in transition; while \u003cb\u003e80.36%\u003c/b\u003e of the area remains classified as stable, significant portions are under increasing environmental pressure. The analysis of LULC dynamics between 1985 and 2022 highlights a fundamental restructuring of the region. While Agricultural Area remained the dominant land use at 62.78%, the period was marked by a massive 273.61% expansion of Urban Areas and a significant shift in forest composition. Specifically, original 1985 forest types\u0026mdash;Closed Broadleaf and Sparse Vegetation\u0026mdash;faced near-total depletion, partially replaced by the emergence of the Open Mixed Leaf Forest class by 2022. Despite the loss of primary natural cover, the Mean NDVI rose from 0.313 to 0.459 over the study period. This \"anthropogenic greening\" does not signify natural recovery, but rather the radical intensification of the agricultural sector, characterized by high-biomass irrigated orchards and greenhouse farming. However, this productivity remains vulnerable, as 9.78% of the region is undergoing active degradation through sheet and rill erosion, particularly on steep topographical gradients.\u003c/p\u003e \u003cp\u003eThe conservation priority mapping identifies 108.77 km\u0026sup2; as high-priority unstable areas requiring Simmediate curative intervention. Ultimately, this research provides a vital geomatics framework for decision-makers to balance necessary agricultural productivity with essential soil conservation, ensuring the long-term environmental sustainability of Syria\u0026rsquo;s coastal ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Author 4 has recieved research support from The British Academy/Cara/Leverhulme Researchers at Risk Research Support Grant. The University of Manchester\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;The other authors declare that no funds, grants or other support were recived during the prepartion of this manuscript\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: \u0026rdquo;The authors have no relevent financial or non-financial to disclose\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e: Data fully available upon request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e: All 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\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\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 Mohammad AlAbed and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdelRahman M (2023) An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications. Rend Lincei \u0026ndash; Scienze Fis e Naturali 34:767\u0026ndash;808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12210-023-01155-3\u003c/span\u003e\u003cspan address=\"10.1007/s12210-023-01155-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlAbed Mohammad J, Salhab, Hani Ebrahim and Safa Dweri (2018) Quantitative Estimation of Annual Soil Loss by Integration of Remote Sensing, GIS, and Universal Soil Loss Equation (Case Study: Tartus District, Syria). Ann Arid Zone 57(34):129\u0026ndash;134\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarakat M, Mahfoud I, Jouhra A (2017) Assessment of Soil Erosion Risk within Basel al-Assad Basin Area Using GIS and RUSLE. Tishreen Univ J Res Sci Stud - Biol Sci Ser 39(4):165\u0026ndash;184\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarakat MA, Al-Abed M, Hasan E, Jouhra A (2020) Preparation of Water Erosion Risk Map for AL-Abrash River Basin Soil in Tartus Using CORINE Model and GIS. Tishreen Univ J Res Sci Stud - Biol Sci Ser 42(5):139\u0026ndash;160\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorrelli P, Robinson DA, Fleischer LR et al (2017) An assessment of the global impact of 21st century land use change on soil erosion. \u003cem\u003eNature Communcations.\u003c/em\u003e 8, 2013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41467-017-02142-7\u003c/span\u003e\u003cspan address=\"10.1038/s41467-017-02142-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCentral Bureau of Statistics (CBS). Statistical abstract (2018) Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cbssyr.sy/index-EN.htm\u003c/span\u003e\u003cspan address=\"https://cbssyr.sy/index-EN.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDayoub F, Abbas H (2009) Ecological and Phytosociological Study of Acha'ra Acharquieh Reserved. Tishreen Univ J Res Sci Stud - Biol Sci Ser 31(2):123\u0026ndash;145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeneral Organization of Remote Sensing (GORS) Ministry of Agriculture \u0026amp; Agrarian Reform, Ministry of Local Administration \u0026amp; Environment. (2021) \u003cem\u003eInventory of coastal area land degradation using remote sensing and GIS techniques\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Forest Watch (GFW) (2024) Interactive Forest Dashboard: Syria, Tartus. World Resources Institute. Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.globalforestwatch.org\u003c/span\u003e\u003cspan address=\"https://www.globalforestwatch.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed: January 18, 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Ruiz E, Nadal-Romero Noem\u0026iacute;, Lana-Renault, Santiago, Beguer\u0026iacute;a (2013) Erosion in Mediterranean landscapes: Changes and future challenges. Geomorphology 198:20\u0026ndash;36\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJouhra AS (2021) Estimating of Soil Erosion Quantity and Sediments in the AL-Abrash river basin using Remote Sensing, GIS and Mathematical models. Ph.D. Thesis. Faculty of Agriculture, Tishreen University, Syria\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarmoka RF (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\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosmas C, Kairis O, Karavitis C et al (2014) Evaluation and selection of indicators for land degradation and desertification Monitoring: Methodological approach. Environ Manage 54:951\u0026ndash;970. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00267-013-0109-6\u003c/span\u003e\u003cspan address=\"10.1007/s00267-013-0109-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLhoussaine EM, Meryem M, Moncef B, Mustapha M, Noureddine A, Abdessalam BH, Yousra R, Brahim D (2024) A GIS-based modified PAP/RAC model and Caesium-137 approach for water erosion assessment in the Raouz catchment, Morocco. Environ Res 251(1):118460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2024.118460\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2024.118460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahmoud Daoud F, Al-Ghamaz Y, Ahmed, Balull M (2019) Classification Guide for Tartus Governorate Soils. Ministry of Agriculture and Agrarian Reform. Syria\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMesrar H, Sadiki A, Navas A, Faleh A, Quijano L, Chaaouan J (2015) Mod\u0026eacute;lisation de l'\u0026eacute;rosion hydrique et des facteurs causaux: cas de l'Oued Sahla, rif central, \u003cem\u003eMaroc. Zeitschrift f\u0026uuml;r Geomorphologie\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNajjar D (2019) \u003cem\u003eMapping forest fire risk in Tartus region using remote sensing and GIS technologies\u003c/em\u003e. Master\u0026rsquo;s Dissertation. Faculty of Agriculture, Aleppo University, Syria\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagos Panos P, Borrelli J, Poesen C, Ballabio E, Lugato K, Meusburger L, Montanarella, Alewell C (2015) The new assessment of soil loss by water erosion in Europe. Environ Sci Policy 54:438\u0026ndash;447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envsci.2015.08.012\u003c/span\u003e\u003cspan address=\"10.1016/j.envsci.2015.08.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePriority Actions Program Regional Activity Centre (PAP/RAC) (1997) Guidelines for mapping and measurement of rainfall-induced erosion processes in the Mediterranean coastal areas. Split, Croatia\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadiki A, Mesrar H, Faleh A (2012) Mod\u0026eacute;lisation et cartographie des risques de l'\u0026eacute;rosion hydrique: cas du bassin versant de l\u0026rsquo;Oued Larbaa, Maroc. Papeles de Geograf\u0026iacute;a, 55\u0026ndash;56,179\u0026ndash;188\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaqer I, Ahmed A, Arhaya A (2024) Land use analysis in Tartus governorate. Tishreen Univ J Res Sci Stud - Biol Sci Ser, 46(4)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTahouri J, Sadiki A, Karrat L, Johnson VC, Chan NW, Fei Z, Kung HT (2022) Using a modified PAP/RAC model and GIS for mapping water erosion and causal risk factors: case study of the Asfalou watershed, Morocco. Int Soil Water Conserv Res 10:254\u0026ndash;272\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Environment Programme (UNEP)/Mediterranean Action Plan (MAP)/ Priority Actions Programme (2000) \u003cem\u003eGuidelines for erosion and desertification control management with particular reference to Mediterranean coastal areas\u003c/em\u003e. Avalable at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iczmplatform.org/storage/documents/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf\u003c/span\u003e\u003cspan address=\"https://iczmplatform.org/storage/documents/Vn1Imo6Q5bY3ozfyUJy0B2tnXdwHII5K8PlKZdlo.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Environment Programme (UNEP)/ Mediterranean Action Plan (MAP)/ Priority Actions Programme (PAP) (2004) \u003cem\u003eImproving coastal land degradation monitoring in Lebanon and Syria: Country report Syria\u003c/em\u003e. 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\u003eUnited States Geological Survey (USGS). Landsat Collection 2 Level-2 Science Products (2021) Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.usgs.gov/landsat-missions\u003c/span\u003e\u003cspan address=\"https://www.usgs.gov/landsat-missions\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVrieling Anton (2006) Satellite remote sensing for water erosion assessment: A review. \u003cem\u003eCATENA\u003c/em\u003e. Volume 65, Issue 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":true,"highlight":"","institution":"Fluminense Federal University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil erosion, land degradation, landuse change detection, remote sensing, GIS, NDVI, Tartus","lastPublishedDoi":"10.21203/rs.3.rs-8870510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8870510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand degradation remains a critical environmental challenge in the Mediterranean basin, exacerbated by rapid land-use transitions, climate variability, and anthropogenic pressure. This study assesses the land degradation risk and environmental dynamics in the Tartus Governorate, Syria, over a comprehensive 37-year period (1985\u0026ndash;2022). Utilizing the United Nations Environment Programme (UNEP) Priority Actions Programme/Regional Activity Centre (PAP/RAC) methodology, we integrated multi-temporal Landsat imagery (5 TM, 8 OLI, and 9 OLI) with Geographic Information Systems (GIS) to map Land Use/Land Cover (LULC) changes and vegetation health via the Normalized Difference Vegetation Index (NDVI). Our spatial analysis incorporated 14 diverse socio-economic and biophysical variables to determine the land degradation risk according to the PAP/RAC consolidated methodology.\u003c/p\u003e \u003cp\u003eResults indicate a profound environmental restructuring of the governorate. While Agricultural Area remained the dominant land cover (64.54% in 1985 to 62.78% in 2022), Urban Areas experienced a massive expansion of 273.61%, largely at the expense of primary Closed Broadleaf Deciduous forests, which were almost entirely depleted. Concurrently, a significant \"anthropogenic greening\" trend was observed, with the Mean NDVI rising from 0.313 to 0.459, primarily driven by the intensification of irrigated agricultural practices and greenhouse farming. Despite this apparent greening, the PAP/RAC model identifies that 80.34% of the landscape is stable, yet approximately 108.77 km\u0026sup2; (5.71% of the territory) is classified as a high-priority unstable zone. These hotspots are predominantly characterized by severe sheet and rill erosion in the mountainous eastern and northern districts. These findings emphasize that vegetation density alone does not equate to land stability. The study concludes that immediate curative interventions and integrated coastal zone management are essential to mitigate irreversible soil loss in these high-priority unstable zones, providing a scalable model for Mediterranean environmental recovery.\u003c/p\u003e","manuscriptTitle":"Evaluating Multi-Decadal Land Degradation and Anthropogenic Greening in Mediterranean Coastal Ecosystems: A Geospatial-Based Assessment of Tartus, Syria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 04:19:21","doi":"10.21203/rs.3.rs-8870510/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"796a1f42-5a4b-45b0-a6c7-9b06b19ca9cb","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62873146,"name":"Agronomy"},{"id":62873147,"name":"Agroecology"},{"id":62873148,"name":"Forestry"}],"tags":[],"updatedAt":"2026-02-16T04:19:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 04:19:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8870510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8870510","identity":"rs-8870510","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}