Sustainable Land Management Research Articles

Evaluating theimpactof land use and land cover change on soil moisture variability usingGIS and remote sensing technology in southwestern Ethiopia.

Soil moisture dynamics are critical for agriculture, water resources, and climate resilience in Ethiopia, influencing crop yields, water availability, and ecosystem health. Assessing soil moisture variability in relation to land use and cover(LULC) changes is essential for effective ecosystem conservation and climate change adaptation, ensuring sustainable development and resilience to environmental challenges. This study examines the impact of LULC changes on soil moisture variability in Southwestern Ethiopia over 30years (1994-2024). Utilizing Landsat Thematic Mapper (1994), Enhanced Thematic Mapper Plus (2004), and Operational Land Imager/Thermal Infrared Sensor (2024) data, LULC changes were analyzed using supervised classification approach with the Maximum Likelihood Algorithm. The classification process was conducted using ERDAS Imagine 2015 software. In addition, vegetation health and soil moisture dynamics were assessed through the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). Results reveal a significant increase in cultivated land from 3595.2 km2 (42.0%) to 6183.3 km2 (72.2%), with a corresponding decrease in forest cover from 3119.2 km2 (36.4%) to 2030.4 km2 (23.7%).This significant shift indicates intensive agricultural expansion at the expense of forest cover, highlighting increased land conversion pressures. NDVI values dropped from 0.71 in 1994 to 0.52 in 2024, this decline in NDVI values signifies a substantial reduction in vegetation cover and density over the 30-year period. The NDMI results indicated a decrease in peak moisture levels and average soil moisture, emphasizing a growing trend of intensified dryness. This soil moisture decline is attributed to factors such as reduced precipitation, increased evaporation, and changes in LULC. The conversion of forest land to cultivation led to a significant decrease in NDMI values, reflecting a reduction in soil moisture due to the loss of forest cover and increased evapotranspiration from agricultural activities. A strong correlation (R2 = 0.98) between NDVI and NDMI highlights that higher vegetation cover is associated with higher soil moisture. Therefore, the study highlights the profound impact of land use and land cover changes on soil moisture, underscoring the urgent need for sustainable land management practices. These practices are critical to combat environmental degradation, improve soil moisture retention, and bolster ecosystem resilience, ensuring a sustainable and climate-resilient future.

Effects of urban driven land use/land cover change on forest reserves in Zambia: a geospatial approach

ABSTRACT In sub-Saharan Africa, forest resources near urban areas are affected by the demand for land; however, information on the long-term changes is still lacking. This study examined two forest reserves, Mwekera and Luano, near Kitwe and Chingola mining towns in Zambia, respectively. Using Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) imagery, we assessed forest cover changes from 1990 to 2020. A pixel-based classification approach was used, and the results were refined with an object-based approach. The findings revealed a significant decline in forest cover, with Mwekera Forest Reserve losing 62.5% and Luano Forest Reserve losing 57.5% of their forest area over the 30 years. The annual deforestation rate was approximately 2.08% for Mwekera Forest Reserve and 1.92% for Luano Forest Reserve. Overall accuracy across the study period showed an overall accuracy improvement over time: 83.35% (1990) to 91.44% (2020) for Mwekera Forest Reserve and 90.52% (1990) to 90.13% (2020) for Luano Forest Reserve, with Kappa Coefficients improvement from 76.22% to 89.73%. Apart from urban settlements, agricultural expansion driven by adjacent urban communities was one of the major factors. This study provides critical insights into forest conservation and the need for sustainable land management practices near urban centers.

A Three-Dimensional Feature Space Model for Soil Salinity Inversion in Arid Oases: Polarimetric SAR and Multispectral Data Synergy

Effective soil salinity monitoring is crucial for sustainable land management in arid regions. Most current studies face limitations by relying solely on single-source data. This study presents a novel three-dimensional (3D) optical-radar feature space model combining Gaofen-3 polarimetric synthetic aperture radar (SAR) and Sentinel-2 multispectral data for China’s Yutian Oasis. The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). Based on the interactions of these three optimal features within the 3D feature space, we constructed the Optical-Radar Salinity Inversion Model (ORSIM). Subsequent validation using measured soil electrical conductivity (EC) data (May–June 2023) demonstrated strong model performance, with ORSIM achieving R2 = 0.75 and RMSE = 7.57 dS/m. Spatial analysis revealed distinct salinity distribution patterns: (1) Mildly salinized areas clustered in the central oasis region, and (2) severely salinized zones predominated in northern low-lying margins. This spatial heterogeneity strongly correlated with local topography-higher elevation (south) to desert depression (north) gradient. The 3D feature space approach advances soil salinity monitoring by overcoming traditional 2D limitations while providing an accurate, transferable framework for arid ecosystem management. Furthermore, this study significantly expands the application potential of SAR data in soil salinization research.

English

The rise in atmospheric CO2 due to global temperature rise necessitates a targeted strategy to reduce CO2 concentration. The current study aimed to understand how C sequestration occurs in soils from various agro-ecosystems by using MBC and key soil physicochemical properties. Soil samples from various land types in the Pothwar Plateau, Punjab, Pakistan, were collected at two depths —0–15 cm and 15–30 cm. Physicochemical parameters analyzed included electrical conductivity, pH, TOC, nitrogen, phosphorus, organic matter, soil moisture, bulk density, and MBC. Results indicated significant variations in MBC across agro-ecosystems, with forest soils exhibiting the highest values (181.6 µg/g in summer and 176.18 µg/g in winter), followed by shrub land (143.57 µg/g), agricultural land (137.83 µg/g) and grassland (96.07 µg/g). Seasonal differences were observed, with MBC levels higher in summer compared to winter. C sequestration was more pronounced in the upper soil layer (0–15 cm) than in the subsurface (15–30 cm). Forest ecosystems significantly contribute to C sequestration, a crucial aspect of climate change mitigation, and are recommended for large-scale reforestation and sustainable land management practices.

Using SoilGrids250m for Overlooking Spatial and Vertical Distribution of Soil Physico-chemical Properties Over Tropical Climate Asia

Background: Understanding the interaction, spatial and vertical distribution of soil chemical properties over climate type in tropical Asia and various depths of soil is essential for sustainable land management, particularly in regions experiencing dynamic conditions.Aims & Methods: This study investigates the relationships of each parameter such as cation exchange capacity (CEC), soil pH, and soil organic carbon (SOC) tropical Climate Asia. Using stratified random sampling based on Köppen–Geiger climate classifications and a consistent spatial resolution of 0.25° × 0.25°, we analyzed 45 sample points distributed across tropical rainforest, monsoon, and savanna climates. The data were extracted from SoilGrids 250m and reconciled using conservative remapping and bilinear interpolation techniques. Corresponding soil chemical data were obtained from validated regional databases.Result: The results show that a correlation matrix analyzing relationships among key soil physico-chemical properties across multiple depths. Strong positive correlations were found between soil organic carbon (SOC) and total nitrogen (N) (r > 0.8), reflecting their shared origin in organic matter. Bulk density (BD) exhibited moderate to strong negative correlations with SOC and N (r ≈ -0.5 to -0.8), particularly in surface layers, indicating the influence of organic matter on soil structure. Correlations weaken with depth, reflecting reduced nutrient interaction. These patterns highlight the importance of organic matter inputs and minimal soil disturbance in maintaining soil health and guiding sustainable land management strategies.

A Novel Dual-Modal Deep Learning Network for Soil Salinization Mapping in the Keriya Oasis Using GF-3 and Sentinel-2 Imagery

Soil salinization poses a significant threat to agricultural productivity, food security, and ecological sustainability in arid and semi-arid regions. Effectively and timely mapping of different degrees of salinized soils is essential for sustainable land management and ecological restoration. Although deep learning (DL) methods have been widely employed for soil salinization extraction from remote sensing (RS) data, the integration of multi-source RS data with DL methods remains challenging due to issues such as limited data availability, speckle noise, geometric distortions, and suboptimal data fusion strategies. This study focuses on the Keriya Oasis, Xinjiang, China, utilizing RS data, including Sentinel-2 multispectral and GF-3 full-polarimetric SAR (PolSAR) images, to conduct soil salinization classification. We propose a Dual-Modal deep learning network for Soil Salinization named DMSSNet, which aims to improve the mapping accuracy of salinization soils by effectively fusing spectral and polarimetric features. DMSSNet incorporates self-attention mechanisms and a Convolutional Block Attention Module (CBAM) within a hierarchical fusion framework, enabling the model to capture both intra-modal and cross-modal dependencies and to improve spatial feature representation. Polarimetric decomposition features and spectral indices are jointly exploited to characterize diverse land surface conditions. Comprehensive field surveys and expert interpretation were employed to construct a high-quality training and validation dataset. Experimental results indicate that DMSSNet achieves an overall accuracy of 92.94%, a Kappa coefficient of 79.12%, and a macro F1-score of 86.52%, positively outperforming conventional DL models (ResUNet, SegNet, DeepLabv3+). The results confirm the superiority of attention-guided dual-branch fusion networks for distinguishing varying degrees of soil salinization across heterogeneous landscapes and highlight the value of integrating Sentinel-2 optical and GF-3 PolSAR data for complex land surface classification tasks.

Characterization and Analysis of Farming System in Buno Bedele and Ilu Ababor Zones of Oromia Regional State, Ethiopia

Farming system characterization and analysis is a roadmap for dynamic agricultural production constraints and opportunities identification and prioritization. Hence, this activity was initiated to identify and characterize the existing farming system, its constraints and opportunities in Buno Bedele and Ilu Ababor zones. A cross sectional research design with two-stage sampling was employed. Quantitative and qualitative data were collected from primary and secondary sources. A total of 386 household heads were selected for quantitative data whereas qualitative data were collected from focus group discussion and key informants via face to face interviews. Secondary data were collected from relevant published and unpublished documents. In SPSS version 20 software, simple descriptive statistics like mean, standard deviation, percentage and pair-wise ranking were used for data analysis. The result revealed that, there was a diverse crop-livestock mixed farming system where crop farming system was the dominant and characterized as rain fed and irrigation-based farming system. Cereal, horticulture, and coffee-khat-based farming systems were common in rain fed whereas few cereal and horticultural crops under irrigation farming systems were practiced in the study areas. The types of livestock reared in the areas were cattle, poultry, sheep, goats, and equines. Even though, there were numerous development supporting government and non-governmental organizations including research centers, universities, agricultural offices, climate action through landscape management (CALM) program for results project, sustainable land management (SLM) project, more young entrepreneurs in silk honey (MOYESH) project and private sectors that are contributing in crop and livestock improvement, natural resource management and job creation; high price of agricultural inputs, lack of improved seed and breeds, delay of fertilizers supply, low production and productivity, lack of capital, shortage of land, crop and livestock diseases, feed shortage, poor soil fertility, soil erosion and lack of common understanding on lime application were the major agricultural production constraints in the study areas. Therefore, all government and non-government development practitioners in the areas should consider the existing agricultural production systems, constraints and opportunities for fruitful interventions.

Potential Carbon Stocks in the Kasepuhan Karang Customary Area

Customary areas in Indonesia, including Kasepuhan Karang, play a crucial role in forest conservation and climate change mitigation. According to data from the Indigenous Territory Registration Agency (BRWA), the Kasepuhan Karang customary area covers 1.081 hectares, with land cover composition including primary dry forest, settlements, mixed dry agriculture, and rice fields. The methods used in this study include GIS analysis and remote sensing with high-resolution imagery from PlanetScope, as well as field data verification. This study aims to analyze the potential above-ground carbon stocks in the customary area of Kasepuhan Karang, Banten Province. The results show that the primary dry forest land cover has the highest biomass potential of 38.507 Mg and carbon stocks of 18.099 Mg C. The total carbon stocks in the Kasepuhan Karang customary area are 42.986 Mg C, with varying distribution across different land cover classes. Mixed dry agriculture, which dominates this area, also has significant biomass potential and carbon stocks. These findings emphasize the importance of sustainable land management to optimize carbon sequestration potential and support climate change mitigation.

Connecting the dots: Network structure as a functional trait in arbuscular mycorrhizal fungi

Societal Impact StatementSoil health and sustainable land management are critical to addressing global challenges such as food security, climate resilience, and biodiversity loss. Arbuscular mycorrhizal (AM) fungi form underground networks that enhance plant nutrient uptake and improve soil structure, yet their functional diversity remains poorly understood, limiting their application in agriculture and ecosystem restoration. By proposing potential fungal transport strategies, we provide a framework for predicting AM fungal contributions across different environments. This knowledge can inform agricultural practices, conservation strategies, and land‐use policies, ultimately supporting efforts to harness beneficial microbes for resilient and sustainable ecosystems.SummaryArbuscular mycorrhizal (AM) fungi construct extensive mycelial networks in soil, serving as critical mediators of plant–soil interactions and nutrient exchange. However, the ability to harness AM fungal diversity for ecosystem management remains constrained by gaps in functional understanding. Trait‐based frameworks offer a promising approach to overcoming these limitations, yet their development has been hindered by methodological challenges and the complexity of AM fungal symbioses. Here, we propose that mycelial network connectivity, a structural trait reflecting the organization of fungal hyphae for nutrient transport, provides a mechanistic basis for distinguishing AM fungal functional groups. Drawing on network theory, we identify two key trade‐offs that shape AM fungal transport strategies: (1) a trade‐off between transport efficiency and resilience to structural disruption and (2) a positive correlation between network heterogeneity and soil heterogeneity. Based on these relationships, we classify AM fungi into potential functional groups and argue that these connectivity‐based classifications provide a predictive framework for understanding AM fungal ecological strategies across environmental gradients, with implications for sustainable land management. Future research should integrate experimental measurements of fungal carbon allocation, network plasticity, and species‐specific responses to environmental change to refine this framework further. By linking mycelial architecture to functional diversity, this approach enhances our ability to predict AM fungal contributions to ecosystem processes and optimize their use in applied contexts.

Sustainability in Every Sip: The Future of the Global Coffee & Beverage Industry

The global coffee and beverage sector is among the most resource-dependent industries, requiring the sustainable management of water, energy, and land to address environmental and social challenges. This paper explores sustainability strategies employed by major companies such as Starbucks, Nestlé, and Coca-Cola, focusing on economic, social, and environmental practices. The research highlights sustainable sourcing, waste reduction, and corporate social responsibility (CSR) as critical components of these strategies. This study also explores barriers such as high implementation costs and complex supply chains while providing recommendations for overcoming these challenges. By analyzing case studies and literature, this research offers insights into how companies can align with global sustainability goals, such as the United Nations Sustainable Development Goals (SDGs

Conceptual modelling of land cover change effects on groundwater quality in the Amman-Zarqa Basin, Jordan

The Amman-Zarqa Basin, a critical groundwater resource in Jordan, has experienced substantial land cover changes over recent decades due to accelerated urbanisation, industrial development, and agricultural intensification. This study assesses the effects of these changes on groundwater quality by employing an integrated methodological framework that combines Geographic Information Systems (GIS), remote sensing (RS), and conceptual hydrogeochemical modelling. Multitemporal satellite imagery (2002–2022) was analysed to detect land cover transformations using supervised classification techniques and change detection algorithms. Groundwater quality data, collected from monitoring wells across the basin over the same period, were subjected to hydrochemical analysis, including the evaluation of major ions and trace metals. Spatial overlays were used to correlate groundwater quality trends with specific land cover changes, identifying pollution hotspots and vulnerable zones. Hydrogeochemical characterisation was performed using Piper, Durov, Wilcox, and Schoeller diagrams to classify water types and assess its suitability for various uses. Results indicate a marked deterioration in groundwater quality, particularly increased concentrations of total dissolved solids (TDS), chloride, sulphate, and heavy metals such as chromium, lead, and manganese–especially near industrial and agricultural zones. The methodology proved effective in capturing both spatial and temporal dynamics of groundwater degradation. The integration of GIS and RS tools with long-term hydrochemical data provided a robust framework for understanding the interactions between land use change and groundwater quality. These findings emphasise the urgent need for implementing sustainable land and water management strategies to protect the Amman-Zarqa Basin from further environmental stress and ensure long-term water security.

Consolidating the Polish Land Use Cadastral Register with the Austrian and German Systems: An Extension of the Polish Cadastre Model Towards Sustainable Land Management

Research on the semantic approach to different land use classes is considered an important aspect of overcoming challenges related to proper land management. This research has direct implications for sustainable land management. The aim of this study is to introduce a new land use class in the Polish cadastre based on land use registration systems that function in other European countries. To achieve this, the existing land use registration systems in selected European countries were analyzed. The criterion for including land in the new class will be its actual use. The proposed new land use class may be a highly promising solution for the clear identification of areas with a special functional nature. By proposing the introduction of this new class, authors highlights the areas that, under the current land use registration system, are not clearly identified within the broadly understood categories of built-up and urbanized land. The research findings may also serve as a practical guideline for local authorities responsible for land administration and property taxation. Moreover, accurate land use classification is essential for sustainable land management, as it enables better planning and resource allocation. Improved clarity in land categorization supports environmental protection and balanced development, contributing to long-term sustainability goals.

An Exploratory Estimation of the Willingness to Pay for and Perceptions of Nature-Based Therapy for Cardiovascular Diseases

There is increasing evidence of the benefits of natural environments for human health. Interest is growing in nature-based therapy (NBT), organised initiatives that promote human–nature interactions with the aim of achieving positive health outcomes. Although the benefits of spending time in nature are now widely recognised, the public’s perspective of NBTs is still not well understood nor quantified. At the same time, chronic non-communicable diseases such as cardiovascular disease are on the rise, increasing costs and pressure for public health services. Using a sample of 96 respondents in Italy, this exploratory study investigates the economic value and perceptions of an NBT for cardiovascular disease. We employed the contingent valuation method to estimate marginal willingness to pay (WTP) for a nature-based rehabilitation programme compared to a standard indoor clinic-based programme. Logistic regression was used to estimate median WTP and influencing factors. We investigated the preferences of patients for the features and potential benefits of nature-based rehabilitation. We show that patients with cardiovascular disease in Italy have a positive WTP between EUR 14.01 to EUR 42.69 per day (median value EUR 27.26). Our findings indicate that NBTs could offer a promising alternative to standard indoor programmes. We provide recommendations for designing NBTs based on the preferences of our sample, aiming to contribute to sustainable health and land management policies.

Spatio-Temporal Dynamics of LULC in the Kolkata Metropolitan Area (2016–2024): Insights from Landsat and MODIS Geospatial Data

Aims: This study explores the dynamics of Land Use and Land Cover (LULC) and their thermal impacts within the Kolkata Metropolitan Area (KMA) from 2016 to 2024, utilizing data from Landsat 8 and MODIS. LULC maps generated through Support Vector Machine (SVM) classification indicate an increase in barren and transitional surfaces, accompanied by a decline in forested, developed, and aquatic zones. Methodology: Biophysical indices, including NDVI, NDBI, and NDWI, were analyzed to assess vegetation levels, impervious surfaces, and the presence of water. The NDVI remained stable due to greening initiatives in peri-urban areas, while the NDBI exhibited an increase corresponding to the growth of impervious surfaces. Results: Spectral analysis revealed a rise in NDWI values, indicating an intensification of water presence in fewer water bodies. MODIS-derived Land Surface Temperature (LST) maps reveal a regional thermal increase, particularly in areas characterized by high Normalized Difference Built-up Index (NDBI) and low Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values. Correlation analyses indicate strong relationships: a positive correlation between NDBI and LST, and a negative correlation between NDVI/NDWI and LST. This highlights the influence of surface materials and land cover on urban warming. The study validates remote sensing as an effective tool for environmental monitoring and underscores the importance of urban land transitions in exacerbating climatic stress. The findings provide spatial evidence to support sustainable land management practices and enhance urban climate resilience.

Экологические и экономические ограничения формирования землепользования объектов инфраструктуры

The article examines ecological and economic constraints on land use formation for infrastructure facilities, particularly in the planning and construction of roads. The study addresses legal frameworks, landscape and climatic features, and economic impacts influencing infrastructure placement in the regions of the Russian Federation. Special attention is given to the interaction between regulatory requirements and land use planning practices on agricultural lands, as well as within forest and water protection zones. It is established that the absence of integrated analysis at the design stage leads to ecosystem fragmentation, biodiversity loss, and increased construction and maintenance costs. The necessity of preliminary environmental and engineering surveys, assessment of alternative costs, consideration of wildlife corridors, and adaptation of routes to natural and socio-economic conditions is substantiated. The article presents practical examples of negative consequences from Altai and Perm regions and provides a financial assessment of related inefficiencies. An innovative alternative to traditional asphalt – plastic pavement – is proposed, offering enhanced durability, environmental sustainability, and long-term cost-effectiveness. A comparative analysis of pavement types is provided, highlighting operational and financial indicators. The study concludes on the need for a transition to sustainable land management and the implementation of advanced technologies in infrastructure development, ensuring minimized environmental damage and more efficient public spending.

Restoration, Indicators, and Participatory Solutions: Addressing Water Scarcity in Mediterranean Agriculture

Agricultural water resource management is increasingly challenged by climate variability, land degradation, and socio-economic pressures, particularly in the Mediterranean region. This study, conducted in 2023–2024 within the REACT4MED project (PRIMA initiative), addresses sustainable water use through a comparative analysis of organic and conventional farms in the Stornara and Tara area (Puglia, Italy). The research aimed to identify critical indicators for sustainable water management and develop ecosystem restoration strategies that can be replicated across similar Mediterranean agro-ecosystems. An interdisciplinary, participatory approach was adopted, combining technical analyses and stakeholder engagement through three workshops involving 30 participants from diverse sectors. Fieldwork and laboratory assessments included soil sampling and analysis of parameters such as pH, electrical conductivity, soil organic carbon, nutrients, and salinity. Cartographic studies of vegetation, land use, and pedological characterization supplemented the dataset. The key challenges identified were water loss in distribution systems, seawater intrusion, water pumping from unauthorized wells, and inadequate public policies. Soil quality was significantly influenced by salt stress, hence affecting crop productivity, while socio-economic factors affected farm income. Restoration strategies emphasized the need for water-efficient irrigation, less water-intensive crops, and green vegetation in infrastructure channels while incorporating also the native flora. Enhancing plant biodiversity through weed management in drainage channels proved beneficial for pathogen control. Proposed socio-economic measures include increased inclusion of women and youth in agricultural management activities. Integrated technical and participatory approaches are essential for effective water resource governance in Mediterranean agriculture. This study offers scalable, context-specific indicators and solutions for sustainable land and water management in the face of ongoing desertification and climate stress.

Spatial Distribution and Management of Trace Elements in Arid Agricultural Systems: A Geostatistical Assessment of the Jordan Valley

Sustainable land management in arid regions such as the Jordan Valley (JV) is essential as climate pressures and water shortages intensify. The extended use of treated wastewater (TWW) for irrigation, while necessary, brings potential risks related to the accumulation of trace elements and their impact on soil health and food safety. This study examined the spatial distribution, variability, and potential sources of five trace elements (Co, Hg, Mo, Mn, and Ni) in agricultural soils across a 305 km2 area. A total of 127 surface soil samples were collected from fields irrigated with either TWW or freshwater (FW). Trace element concentrations were consistently higher in TWW-irrigated soils, although all values remained below WHO/FAO recommended thresholds for agricultural use. Spatial modeling was conducted using both ordinary kriging (OK) and empirical Bayesian kriging (EBK), with EBK showing greater prediction accuracy based on cross-validation statistics. To explore potential sources, semivariogram modeling, principal component analysis (PCA), and hierarchical clustering were employed. PCA, spatial distribution patterns, correlation analysis, and comparisons between TWW and FW sources suggest that Co, Mn, Mo, and Ni are primarily influenced by anthropogenic inputs, including TWW irrigation, chemical fertilizers, and organic amendments. Co exhibited a stronger association with TWW, whereas Mn, Mo, and Ni were more closely linked to fertilizer application. In contrast, Hg appears to originate predominantly from geogenic sources. These findings provide a foundation for improved irrigation management and fertilizer application strategies, contributing to long-term soil sustainability in water-limited environments like the JV.

Land Subsidence Susceptibility Modelling in Attica, Greece: A Machine Learning Approach Using InSAR and Geospatial Data

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts.

Land Use and Land Cover Change Analysis in Choke Mountain, Ethiopia (2013-2023)

In this study, spatial and temporal trends of Land Use and Land Cover Change (LULCC) in Choke Mountain, Ethiopia, between 2013 and 2023 were explored employing a mixed-methods approach. Satellite imagery (Landsat 8 OLI/TIRS) was analyzed through supervised classification and post-classification change detection techniques, supported by field observations, key informant interviews, and focus group discussions. The results reveal extreme changes: agricultural land raised by 4.5% (1,207.95 ha), predominantly at the expense of grasslands (−11.9%) and shrublands (−34.8%). Forest cover unexpectedly raised by 28.6% (1,156.26 ha) due to Eucalyptus plantations, while natural forest decline persists. Settlement areas raised by 133%, which reflects heavy urbanization. Slope analysis revealed that 67.4% of the area is composed of gentle slopes (<30%), and 32.6% is composed of steep slopes that are susceptible to erosion. The main drivers of these changes are population growth, agricultural expansion, fuelwood requirements, and unsustainable land-use policies. These changes have deep implications for ecosystem services, soil conservation, and climate resilience in this critical Upper Blue Nile Basin region. The study recommends that integrated land-use planning, forest conservation programs, and sustainable agriculture practices are required to balance ecological integrity with livelihood needs. These findings constitute the scientific basis on which policymakers can base policies to facilitate sustainable land management in the highland ecosystems of Ethiopia.

An assessment of forest degradation in the Sonitpur East Forest division, Assam, India using object-based image analysis

ABSTRACT Forest ecosystems in Assam are increasingly threatened by anthropogenic activities, leading to significant forest degradation despite being granted protection by legislation. This study examined the extent of forest degradation in the Sonitpur East Forest division which includes the Behali Wildlife Sanctuary, Biswanath, Gohpur, Naduar and Singlijan Reserved Forests using object-based image analysis over 30 years (1990–2020). These analyses employed multi-resolution segmentation, spectral difference segmentation and a support vector machine classifier using Landsat imagery. The results revealed a significant decline in dense forest cover across the study area, except for Singlijan Reserved Forest. The most pronounced forest loss was observed in Biswanath Reserved Forest, followed by Gohpur and Naduar Reserved Forests and Behali Wildlife Sanctuary. Simultaneously, agricultural land and open forest areas expanded, with agricultural land in Gohpur Reserved Forest increasing by 60.28% at the expense of dense forest and barren land. These results underscore the crucial need for targeted conservation policies and sustainable land management practices to mitigate forest degradation. The application of remote sensing and object-based image analysis methodology offers a reliable framework for continuous forest monitoring supported by data-driven decision-making, facilitating effective conservation planning in northeastern India and other forested regions experiencing significant degradation.