INTRODUCTION
Forest ecosystems play a vital role in regulating our climate and supporting both human and animal life According to the UN FAO, approximately 44.5% of Vietnam—around 13,797,000 hectares—is covered by forests, with only 0.6% (80,000 hectares) designated as primary, highly biodiverse, and carbon-rich forests Historically, forest cover in Vietnam was at least 43% in 1943, but it has significantly declined over time, with primary forest areas shrinking to 187,000 hectares in 2000 and further down to 85,000 hectares in 2005, reflecting an annual deforestation rate of 10.91% Studies by Rosyadi, Ringrose, and Boakey identify key factors contributing to forest cover loss, including the use of inappropriate agricultural technologies around forest areas and the centralized management policies leading to resource misuse, both of which accelerate deforestation.
Conventional logging operations with unplanned selective logging contribute to deforestation, but illegal logging and trade remain the primary drivers (Atmopawiro, 2004) Forest cover change is a major factor in global environmental change, impacting water, land, and air quality, ecosystem functions, and climate through greenhouse gas emissions and surface albedo effects Monitoring these changes is essential for updating forest cover maps and natural resource management In Vietnam, four forest inventory surveys conducted between 1990 and 2010 used Landsat and SPOT imagery, and currently, the program utilizes SPOT-5, SPOT-6, and VNREDSAT imagery to assess forest dynamics and support sustainable forestry practices.
Historically, forest resource assessment relied on manual survey and mapping methods, which were time-consuming, costly, and often inaccurate, leading to infrequent updates due to changing forest cover In recent years, the adoption of geographic information systems (GIS) and remote sensing technologies has revolutionized land and natural resource management, offering more efficient and reliable alternatives (Bao et al., 2013) Advances in satellite image quality and accessibility now enable large-scale, accurate analysis of forest cover changes, significantly improving monitoring capabilities.
Forests in Thanh Hoa have long been regarded as a national treasure, providing valuable resources such as timber, grazing land, wildlife habitat, and water resources that benefit local communities However, farming activities and illegal logging pose significant threats to the quality and quantity of these forests Forest cover change mapping and change detection analysis are essential methods to monitor these changes over time and space, enabling quantification of forest cover dynamics This study focused on the spatial-temporal changes in forest cover in Yen Nhan – Thuong Xuan from 2000 to 2015, demonstrating the effectiveness of multi-temporal satellite data in assessing deforestation, forest degradation, and urbanization trends The findings offer critical insights for land management policies aimed at conserving forest resources and addressing environmental challenges.
GOALS AND OBJECTIVES
This goal of project is to classify forest and detect the changes from 2000 to 2015 in Yen Nhan commune, Thuong Xuan district, Thanh Hoa province
To segment SPOT 5 imagery based on object-based classification
To classify and map out the different land use / land cover and their spatial distribution in Yen Nhan in 2015
To identity, quantify and map out the forest cover changes in Yen Nhan from 2000 to
STUDY AREA AND DATA
Study Area
Yen Nhan is a mountainous commune in Thanh Hoa Province, characterized by diverse land covers and land uses Covering a total area of approximately 19,094.63 hectares, the commune has a population of around 4,850 residents, resulting in a population density of roughly 0.25 persons per hectare Yen Nhan experiences four distinct seasons—spring, summer, autumn, and winter—with an average annual temperature of 23-24°C and annual rainfall ranging between 1,600 and 2,000 mm.
Figure 1: Location of the Yen Nhan Commune in Thanh Hoa Province.
Data Sources
This study utilizes multi-spectral SPOT5 remote sensing data, stored in GeoTIFF format and projected in UTM-48N (WGS-84 datum) The SPOT5 imagery configuration details are summarized in Table 1 Additionally, provincial forest maps for the years 2000, 2005, 2010, and 2015 were provided by the Institute for Forest Ecology and Environment of Vietnam Forestry University, along with an administrative map of the study area.
Table 1 Technical parameters and properties of the sensors used in this study
480–710 (pan) 500–590 (green) 610–680 (red) 790–890 (NIR) 1580–1750 (mid IR)
METHODOLOGY
Image segmentation (Step1)
Segmentation was conducted using eCognition v8.9 image analysis software, a crucial step in object-based image analysis (Baatz et al., 2004) Image segmentation involves partitioning an image into distinct regions based on specific parameters, focusing on the homogeneity or heterogeneity within those regions (Myint et al., 2008) This process enables accurate delineation of features, facilitating improved analysis of spatial data.
The segmentation algorithm applied in this study is the so-called “multi- resolution segmentation” The algorithm was applied to all four SPOT bands (green,
The segmentation algorithm utilizes multispectral bands—red, NIR, and SWIR—with equal weight assigned to each Its outcome is influenced by a scale factor, which indirectly relates to the average size of the targeted objects, and a heterogeneity criterion that guides the merging process based on spectral layers This heterogeneity measure evaluates two mutually exclusive properties: color, reflecting spectral homogeneity, and shape, which considers the semantic characteristics of objects Shape is further divided into properties such as scale, color, and form, providing a comprehensive approach to object detection and segmentation (Baatz et al., 2000).
The segmentation process relies on key parameters—scale, shape, and compactness—that influence the identification of image objects The scale parameter is affected by pixel heterogeneity, while the colour parameter balances object colour homogeneity with shape homogeneity The form parameter maintains a balance between border smoothness and segment compactness Optimizing these parameters, with a recommended set of scale=70, shape=0.5, and compactness=0.8, results in effective segmentation, as demonstrated in Figure 3 In the SPOT 5 image analysis, 11,135 objects were identified, ranging from 0.11 hectares to 112.11 hectares, indicating highly detailed segmentation that enhances the accuracy of automated classification and reduces errors.
Figure 3: Objects with multi-resolution segmentation
(Scale parameter = 70, shape = 0.5, and compactness = 0.8)
Classification and accuracy (Step2)
The classification scheme was developed based on existing provincial map legends and field surveys, resulting in the identification of 10 distinct land cover types, including various evergreen forests, bamboo, mixed wood and bamboo forests, plantation forests, barren land, shrubs, grasslands, and water bodies An advanced object-based classifier was utilized to accurately assign each land parcel to its respective class, combining the object-based classification framework with interactive visual interpretation, expert insights, training datasets, and existing regional maps This integrated approach ensures precise land cover classification aligned with regional characteristics and enhances the reliability of the results.
Table 2: The object-based classification framework
SPOT 5 image Sample Plot Image Describes
Accuracy assessment is a crucial step in image classification, focusing on either positional or thematic accuracy Positional accuracy evaluates the correctness of the spatial location of features in satellite images relative to ground truth, while thematic accuracy measures how accurately land cover classes are represented compared to real-world conditions In this study, 80 reference points were surveyed in the field to validate the classification, with 50 randomly selected sample points used for analysis.
Figure 4: The distribution of sample points in the study area
An accuracy assessment of the land cover classification was conducted by computing the error matrix alongside descriptive statistics The evaluation utilized the Kappa coefficient, a discrete multivariate technique widely used in accuracy assessments to measure the agreement between predicted and actual land cover classes, ensuring the reliability of the classification results.
[9] The Kappa coefficient represents the proportion of agreement obtained after removing the proportion of agreement that could be expected to occur by chance
The Kappa coefficient, which typically ranges from 0 (indicating no error reduction) to 1 (indicating complete error reduction), is used to assess agreement levels Kappa values are classified into three categories: values above 0.80 (80%) signify strong agreement, those between 0.40 and 0.80 (40% to 80%) indicate moderate agreement, and values below 0.40 (40%) reflect poor agreement According to Jensen and Cowen [11], the Kappa coefficient is calculated using a specific equation.
The matrix consists of N sites, with r representing the number of rows Each element located at row i and column j provides specific data points, essential for analysis The total for each row, denoted as , captures the sum across that row, while the total for each column, , aggregates values down the column Understanding these components is crucial for interpreting the matrix's structure and deriving meaningful insights.
Change Detection (Step 3)
This study classified land cover into 10 categories across different time periods, analyzing changes in forest cover by calculating the area for each class A comprehensive forest cover change map spanning 15 years (2000-2015) was created to illustrate regional trends Focusing on the Yen Nhan commune, the study examined forest cover dynamics over three short-term intervals: 2000-2005, 2005-2010, and 2010-2015 Land cover changes were detected through overlay analysis and post-classification comparison of maps using ArcGIS 10.1, with change pathways quantified via cross-tabulation matrices To enhance interpretability, the change dynamics were visualized in maps grouped by specific transition types, providing clear insights into land cover transformations over time.
RESULTSDISCUSSION
Object – Based Classification
Figure 5 illustrates the classification outcomes of SPOT 5 imagery processed with eCoginition software (2015), encompassing ten land cover classes: rich evergreen, medium evergreen, poor evergreen, rehabilitation evergreen, bamboo, mixed wood and bamboo, plantation forest, bare land, shrub and grass, and water bodies This classification provides detailed insights into the distribution of various vegetation types and land uses within the study area, supporting effective environmental analysis and land management The results demonstrate the accuracy and effectiveness of using SPOT 5 imagery for detailed land cover classification, helping to inform sustainable resource planning and conservation efforts.
Figure 5: Object-based classification result (2015)
The structural features of different forest states vary in complexity, with natural forests exhibiting more intricate structures than plantations Rich forests tend to be more complex than medium, poor, or rehabilitated forests Additionally, bare land and water bodies are the most easily identifiable landscape features.
Classification
Yen Nhan features a diverse range of forest types unique to the region, including rich evergreen, medium evergreen, poor evergreen, rehabilitation evergreen, bamboo, mixed wood and bamboo, and plantation forests (Figure 7) These forest classifications were quantified based on pixel counts to determine their coverage in hectares (ha) and expressed as percentages (%) (Figure 6), highlighting the area's rich forest biodiversity and landscape composition.
The analysis reveals that the rich evergreen forest covers approximately 11.33 hectares, accounting for just 0.1% of the total land area, primarily situated in the northern region Additionally, medium-density evergreen forests are predominantly found in the northern part, with only a few scattered patches elsewhere, highlighting the concentration of these forest types in the northern landscape.
The southwest area covers approximately 1,460.33 hectares, accounting for 7.7% of the total land Poor evergreen forest spans 3,759.15 hectares (24.1%), mainly scattered in the central and western parts of the map The largest land cover is rehabilitated evergreen forest, covering 6,386.6 hectares (33.7%) and distributed across the southern, western, eastern, and small northern patches Plantation forests occupy 670.92 hectares (3.5%) in the central region, while bamboo forests cover 142.09 hectares (0.8%) with small scattered patches in the southern area Additionally, mixed wood and bamboo forests total 3,647.46 hectares, concentrated in the northern part and scattered in the western and southern regions Shrub and grasslands cover 1,861.9 hectares (9.8%), mainly found in the western part Water bodies are primarily located in the southern region and small patches in the central area, covering 115.84 hectares (0.6%) Bare land accounts for 80.57 hectares (0.4%) near water bodies Overall, forested areas total 16,077.9 hectares, representing 89.05% of the total landscape, mostly situated in the northern and western regions Non-forested areas cover 1,977.7 hectares (10.95%) and are scattered around rivers such as Khao, Chu, Dat, and Dan.
Figure 6: Land areas (ha) in 2015: Forest classification classes (a), their percentages (b) and their proportion forest and non-forest (c)
Figure 7: Land use/land cover in Yen Nhan 2015 5.2.2 Accuracy
The accuracy statistics are summarized in Table 3
Table 3: Summarized accuracy of objects in 2015 Map
RG TB RN PH HG RT TN DT DK MN Total
Rich Evergreen Forests (RG) and Mixed Wood-Bamboo Forests (HG) are vital for maintaining biodiversity and ecological balance, making them key priorities for conservation efforts Medium and Poor Evergreen Forests (TB and RN) require targeted rehabilitation strategies to restore their health and productivity Bamboo Forests (TN), especially in plantation settings (RT), contribute significantly to sustainable resource management and local livelihoods Rehabilitation Evergreen Forests (PH) are critical for restoring degraded landscapes, emphasizing the importance of reforestation initiatives Shrub and Grass areas (DT) play a crucial role in soil conservation and habitat diversity, while Bare Land (DK) and Water Bodies (MN) are essential components of the ecosystem that support various flora and fauna Effective forest management must incorporate these diverse land cover types to promote ecological sustainability and resilience.
The matrix indicates discrepancies in the sample data, as the difference spectrum between certain objects within the interference range is unclear This highlights the trend of deviations in image segmentation results from the actual reality.
The study observed 38 agreements, representing 76% of the total observations, significantly exceeding the 5 agreements expected by chance (10%) The calculated Kappa coefficient of 0.733 indicates a strong and reliable level of agreement, with a 95% confidence interval ranging from 0.603 to 0.864 Overall, these results demonstrate a good level of inter-rater reliability, highlighting the consistency of the observed agreement.
Classification errors in the study were primarily caused by spectral overlap among different land cover types, such as rich forest, medium forest, poor forest, rehabilitation forest, mixed forests, plantations, shrubland, grassland, and bare land The use of SPOT 5 imagery, with its relatively low resolution and cloud cover in some areas, negatively impacted the accuracy of the results Additionally, classifying spatial and temporal forest cover change patterns using an object-based classification approach requires specialized expertise To mitigate these issues, careful selection of training sites and the application of majority filters after supervised classification were employed, enhancing the reliability of the classification outcomes.
Land Cover Change
The land cover and land use classification maps of Yen Nhan Commune across four time periods reveal significant changes in forest types and extent Rich evergreen forest was predominantly located in the northern part of the commune from 2000 to 2010, but by 2015, its area had significantly decreased to only 11.33 hectares (0.1%), with medium evergreen forest dominating the landscape, especially in the northern and western parts of the commune Scattered poor evergreen forests were present in the eastern, northwest, and southwest regions, shifting to the central area by 2015 Rehabilitation evergreen forests initially covered small patches from the western and southwestern regions, expanding to eastern and northern parts after 2005 Plantation forests were nearly absent until 2005 but began developing in the central and southeastern areas by 2010, with further expansion by 2015 Additionally, mixed wood and bamboo forests were scattered throughout the commune during 2000-2010, becoming concentrated mainly in the northern areas by 2015.
19 bamboo forest and shrub and grass could be find in many regions in commune, however, in
Between 2000 and 2005, bare land was limited to small areas in the western, central, and southeastern parts of the commune By 2015, sparse patches were still present in the southern and central regions Notably, from 2010 to 2015, there was a rapid increase in land conversion, with large portions of bare land transforming into residential areas, especially in the central and southern regions Throughout this period, the southern part of the commune was consistently characterized by water bodies.
Table 4: Statistics of forest and non-forest area in Yen Nhan (2000-2015)
Looking at table 4, the largest forest area cover is in 2015, representing 89.5 % and the smallest area is 11988.9 ha (representing 63.6 %) in 2000
Figure 8: Land cover/land use classification maps of Yen Nhan commune in year 2000, 2005, 2010 and 2015
Figure 9 illustrates the land cover and land use changes in Yen Nhan commune from 2000 to 2015 It highlights the distribution of various land cover and land use types as percentages across different years, revealing significant shifts over the 15-year period Additionally, the figure depicts the evolution of these land properties, providing insights into how land utilization has transformed over time, which is essential for understanding regional development and planning strategies.
Shrub and Grass Water body Bare Land Bamboo Forest Plantation Forest Mixed Forest Rehabiliation Evergreen Forest Poor Evergreen Forest Medium Evergreen Forest Rich Evergreen Forest (a)
Rich Evergreen Forest Medium Evergreen Forest Poor Evergreen Forest Rehabiliation Evergreen Forest
Water body Shrub and Grass (b)
22 the major land cover/land use types in hectare, (c) the magnitude of the land cover/land use changes in ha/year for each time interval
The study’s figures illustrate the changes in land cover and land use classes over time, highlighting significant trends Shrubland, grassland, bare land, bamboo forest, and rich evergreen forest gradually decreased, with bare land and rich evergreen forest becoming the smallest land cover types by the end of the study period From 2000 to 2015, notable shifts occurred, with rich evergreen forest, poor evergreen forest, bamboo, rehabilitation evergreen forest, bare land, and shrub/grass experiencing the most substantial changes Initially, shrub, grass, and poor evergreen forest decreased, while mixed and rehabilitation forests increased; between 2005-2010, shrub, grass, and poor evergreen forest slowly recovered, but bamboo and rehabilitation forest grew gradually By the end of the period, shrub, grass, and bamboo forest declined sharply, whereas bare land and poor evergreen forest increased significantly Rich evergreen forest and bare land showed little change in the first two intervals but decreased rapidly from 2010 to 2015 Meanwhile, medium evergreen forest remained stable from 2000 to 2010, then increased alongside plantation forests afterward Water bodies and mixed forests maintained a consistent percentage throughout the 15-year span, indicating overall stability in these classes.
Figure 9c displays the annual change magnitudes in hectares, standardized by the duration of each land cover/use interval for the nine primary categories The plantation areas consistently exhibit an increasing trend over time, indicating ongoing expansion In contrast, other land cover classes such as rich forest, poor forest, rehabilitation forest, bamboo forest, shrub and grass, and bare land experienced fluctuations across different periods, reflecting variable dynamics in land use change.
23 magnitudes of change than the limited changes for medium forest, mixed forest and the negligible variation of water bodies
Figure 10 depicts the spatial distribution of land changes across different time intervals Between 2000 and 2005, significant land alterations appeared in large patches scattered throughout the commune From 2005 to 2010, these changes became more fragmented and dispersed By the end of the study period (2010-2015), land modifications reoccurred on a larger scale, indicating a shift toward more extensive changes over time.
Figure 10: Spatial distribution of land cover/land use changes in Yen Nhan commune from 2000-2015
From 2000 to 2015, land cover and land use changes in Yen Nhan commune revealed significant transformations across various classes The area of rich evergreen forest dramatically decreased from 700 ha in 2000 to just 11.3 ha in 2015, with only 5.9 ha remaining from the initial period and substantial conversions to medium evergreen and mixed forests Conversely, new areas of rich forest emerged from poor evergreen forest (2.4 ha) and shrub and grass (1.9 ha) Overall, the sectors experiencing the most notable decline in area included rich forest (694.1 ha), poor forest (1,158.8 ha), bamboo forest (3,029.8 ha), and shrub and grass (6,076.3 ha), representing reductions of 99%, over 97%, and other significant decreases in relative terms Meanwhile, plantation forests expanded by 670.5 ha, mainly derived from shrub and grass (298.6 ha), mixed forest (151.1 ha), and bamboo forest (139.5 ha) Additionally, water bodies experienced notable loss during this period.
56 percent (representing 50 ha) mostly to bare land (48.1 ha)
Table 5: Natural of land cover/land use changes in Yen Nhan commune from 2000 to 2015 in ha(figure are rounds up to entire numbers)
RG TB RN HG PH TN DT DK MN Total Expansion
Rich Evergreen Forests (RG) are vital ecosystems known for their dense, diverse vegetation, supporting rich biodiversity Medium Evergreen Forests (TB) provide important habitats and are essential for maintaining ecological balance, while Poor Evergreen Forests (RN) have reduced density and biodiversity Mixed Wood-Bamboo Forests (HG) combine timber and bamboo resources, contributing to sustainable forestry practices Rehabilitation Evergreen Forests (PH) are areas recovering from deforestation, aiming to restore native vegetation Bamboo Forests (TN) are significant for their fast-growing bamboo species, offering ecological and economic benefits Plantation Forests (RT) are managed forests established for timber production, helping meet industrial demands Shrub and Grasslands (DT) serve as important grazing lands and support various wildlife species Bare Land (DK) indicates areas with little to no vegetation, often resulting from deforestation or land degradation Water Bodies (MN) are crucial for maintaining watershed health and supporting aquatic ecosystems, playing a key role in environmental sustainability.
RECOMENDATIONS
Between 2000 and 2015, Yen Nhan commune experienced a dramatic decline of over 97% in rich evergreen forests, bamboo forests, shrubs, and grasses, primarily due to timber and non-timber forest extraction The expansion of rehabilitation efforts in evergreen and poor evergreen forests, along with the growth of plantation forests, contributed to the loss of bare land, which increased during this period due to population growth, new roads, and socio-economic development Government policies, including 327, 661, and 147 programs promoting plantation forestry, have driven land use and land cover changes, reflecting local socio-economic dynamics These changes aim to balance rural development, ecological stability, and the removal of grain crop quotas while promoting livestock farming.
CONCLUSIONS AND PERSPECTIVES
Conclusions
The object-based change detection method demonstrated high efficiency in accurately identifying forest land cover changes in both deciduous and coniferous stands An overall kappa coefficient of 0.73 indicates a moderate agreement (ranging from 0.40 to 0.80), supporting the method's reliability This study highlights that Yen Nhan commune exhibits a diverse land cover and land use pattern, marking the first detailed analysis of its kind for this area The use of an object-oriented classification approach proved superior to traditional pixel-based methods, as it reduces misclassification issues associated with spectrally heterogeneous land covers.
Between 2000 and 2015, Yen Nhan commune's land cover and land use primarily consisted of rich evergreen, medium evergreen, poor evergreen, rehabilitation evergreen, bamboo, mixed wood and bamboo, plantation forest, bare land, shrub and grass, and water bodies During this period, plantation forests experienced significant growth, while rich evergreen forests and bare land markedly declined Most land cover types showed notable fluctuations over the years, with rich evergreen, poor evergreen, rehabilitation evergreen, bamboo, and shrub and grass exhibiting more substantial changes compared to other categories The increase in plantation forests was primarily due to conversions from poor evergreen forests, mixed forests, rehabilitation evergreen forests, bamboo forests, shrubs, grasses, and barren land.
The plantation areas class demonstrates a consistent upward trend over time, highlighting ongoing expansion In contrast, other land cover and land use types such as rich forest, poor forest, rehabilitation forest, bamboo forest, shrub and grass, and bare land experienced significant fluctuations across different periods Notably, these categories showed larger magnitudes of change compared to medium forest, mixed forest, and the relatively stable water bodies, which exhibited negligible variation throughout the study period.
Perspective
Land cover and land use maps, along with change detection analysis, are essential tools for understanding how past policies, socio-economic trends, and environmental factors influence land use dynamics Analyzing these drivers helps identify the key factors responsible for land use changes, enabling accurate modeling of future land use patterns This knowledge is crucial for guiding effective land use planning policies at the district or provincial level, fostering sustainable development.
Future research should focus on identifying the drivers and impacts of land cover and land use change in the study area, emphasizing the need to understand the factors influencing individuals’ decisions to alter land use and their spatial patterns Investigating the causes of rapid land use dynamics is essential, along with assessing their environmental impacts, effects on local livelihoods and access to natural resources, and implications for community vulnerability to natural hazards and environmental changes.
APPENDICES
Period 2000-2005 RG TB RN HG PH TN DT DK MN Total Expansion
Rich Evergreen Forest (RG) represents the most lush and biodiverse forest type, crucial for maintaining ecological balance Medium Evergreen Forest (TB) serves as an important transition zone, supporting diverse flora and fauna Poor Evergreen Forest (RN) indicates areas with reduced vegetation cover, often impacted by degradation Mixed Wood-Bamboo Forest (HG) combines timber species with bamboo, contributing to forest productivity and habitat diversity Rehabilitation Evergreen Forest (PH) focuses on restoring degraded forest areas to enhance biodiversity and ecological functions Bamboo Forest (TN) offers a sustainable resource for local communities and plays a vital role in forest regeneration Plantation Forest (RT) is established for commercial timber production and conservation efforts Shrub and Grassland (DT) provide essential habitats for wildlife and act as buffer zones Bare Land (DK) signifies areas lacking vegetation cover, often resulting from deforestation or land degradation Water Bodies (MN) are critical for supporting aquatic ecosystems and maintaining hydrological cycles within forest landscapes Incorporating these forest types into conservation strategies enhances biodiversity, ecosystem services, and sustainable land management.
Appendix 2: Natural of land cover/land use changes in Yen Nhan commune from 2005 to 2010 in ha (figure are rounds up to entire numbers)
Period 2005-2010 RG TB RN HG PH TN DT DK MN Total Expansion
Rich Evergreen Forest (RG) represents the most lush and biodiverse forest type, playing a crucial role in maintaining ecological balance and supporting wildlife habitats Medium Evergreen Forest (TB) offers a transitional ecosystem that sustains a variety of plant and animal species, contributing to regional ecological stability Poor Evergreen Forest (RN) indicates areas with diminished forest cover, often resulting from deforestation or degradation, which can threaten local biodiversity Mixed Wood-Bamboo Forest (HG) combines diverse tree species and bamboo, supporting a wide array of flora and fauna while promoting ecological resilience Rehabilitation Evergreen Forest (PH) highlights efforts to restore degraded forests to enhance biodiversity and ecological functions Bamboo Forest (TN) provides important habitat and resource for various species and is vital for sustainable forest management Plantation Forest (RT) involves afforestation initiatives aimed at reforesting degraded landscapes, supporting timber production and environmental conservation Shrub and Grassland (DT) areas are critical for serving as grazing grounds and maintaining soil health Bare Land (DK) signifies regions with minimal vegetation cover, often prone to erosion, requiring targeted restoration efforts Water Bodies (MN) are essential for maintaining hydrological cycles, supporting aquatic ecosystems, and providing resources for surrounding forests, highlighting the importance of integrated landscape management.