Search Results (1,880)

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

The study and understanding of spatial and temporal changes in the landscape is essential for assessing environmental trends and predicting future developments in the area. Changes in land cover and vegetation dynamics are key indicators of the ecological stability of an area. This study analyzes long-term changes in land cover and vegetation dynamics in Jelšava and neighboring municipalities. The selected area has long been classified as one of the areas with poor air quality in Slovakia. The analysis is based on data from the CORINE Land Cover program for the period 1990–2018 and Landsat data from 1990 to 2025. The condition and vitality of vegetation were assessed using the Normalized Difference Vegetation Index (NDVI), while temporal trends were assessed using non-parametric Mann–Kendall and Sen’s slope tests. The results show a decrease in the area of class 31—Forests between 2012 and 2018, accompanied by an increase in the area of class 324—Transitional woodland–shrub. Analysis of the NDVI confirmed a slightly positive trend in vegetation cover development, with statistically significant growth (p < 0.05) recorded on approximately 43% of the territory. The combination of remote sensing data and spatial analysis in a GIS environment has proven to be an effective approach to monitoring ecological dynamics and provides valuable insights for regional environmental management and sustainable land use planning. Full article

Land-use and land-cover change (LULCC) is a crucial indicator of environmental transformation and has significant implications for biodiversity, ecosystem services, and climate change. This study investigates land-cover changes between 2017 and 2023 in three distinct mountain regions: the Bucegi Mountains, the Allgäu High Alps, and Mount Olympus. Using remote-sensing data from Sentinel 2 and Geographic Information System (GIS) tools, we analyzed temporal shifts in land-cover types across these regions. The analysis highlights the varying rates and patterns of land-cover transformation in response to environmental and anthropogenic factors. Additionally, the MOLUSCE model was employed to predict future land-cover changes for the year 2029. The findings emphasize the dynamic nature of land-cover in these mountainous areas and offer insights into the potential environmental implications of predicted changes. The Bucegi and the Olympus regions experienced minor land-use changes, while the Allgäu High Alps have the most dynamic changes. The study contributes to a deeper understanding of land-cover dynamics and the applicability of remote sensing and GIS-based predictive models in ecological monitoring. Full article

The Dal Lake ecosystem is a vital freshwater body situated in the heart of Srinagar, Kashmir, India. It is not only a natural asset but also a cornerstone of environmental health, economic vitality, cultural heritage, and urban sustainability. In the last few decades, the condition of the lake ecosystem and water quality has deteriorated significantly owing to the intensification of the eutrophication process. Effective integrated management of the lake is crucial for the long-term sustainable development of the region and the communities that rely on it for their livelihoods. The main reasons for eutrophication are the substantial quantity of anthropogenic pollution, especially nutrients, discharged from the catchment area of the lake and the overexploitation of the lake space and its biological resources. The research presented in this paper aimed to diagnose the state of the lake by analysing trends in eutrophication development and its long-term changes related to the catchment area and lake ecosystem relationships. The research period was 25 years, from 1997 to 2023. Land use and land cover data and water quality monitoring data, which are the basis for trophic state assessment, allowed us to analyze the long-term dynamics of eutrophication in the reservoir. For these purposes, GIS-generated thematic maps were created by using QGIS software version 3.44.1, and an appropriate methodology for quantifying eutrophication was chosen and adapted to the specifics of Dal Lake. The obtained results provide a foundation for a eutrophication management strategy that considers the specificity of the Dal Lake ecosystem and the impact of the catchment area. The outcomes highlighted the varied trophic conditions in different lake basins and the dominance of eutrophic conditions during the study period. The research highlights the complexity of the problem and underscores the need for a comprehensive lake management system. Full article

Since the mid-20th century, the landscapes of Mediterranean mountain regions have undergone a significant transformation, linked to the socioeconomic changes caused by the opening up of these regions to the market economy. This prompted a rural exodus, the abandoning of farmland and the reduction in livestock, so activating various reforestation processes. In parallel, the “green revolution” promoted the modernization of agrifood systems, so contributing to the decline of traditional ways of farming in mountain areas. The farms on which traditional polyculture and agroforestry are still carried out today are important agrobiodiversity reserves. In this research, we monitor the dynamics of land use and cover and the changes in the structure of the agrifood landscapes on the southern slopes of Sierra Nevada (Spain) by comparing maps from 1956, 1984, 2007 and 2020. The results reveal a sharp decline in cultivated land, from 39.19% to 21.54%, and an expansion of natural covers, especially Mediterranean forest, driven by the abandonment of farmland and reforestation policies. Today, the landscape is composed of a more fragmented, less cohesive mosaic of agroecosystems. These changes indicate a reduction in agrobiodiversity at a landscape level, in line with the tendency observed at farm level in the study area. Full article

This paper investigates land-use as the cornerstone of spatial planning in rapidly urbanising contexts, focusing on the critical gaps at the mesoscale between centralised vision and local implementation. By exploring Java’s complex desakota landscapes, this study employs an innovative GIS-based land-use cluster analysis using multidimensional parameters—including slope, population density, agricultural land, forest cover, and surface water—to categorise land-use patterns. The resulting mesoscale clusters reveal cohesive functional territories that transcend traditional political boundaries, articulating distinctive ‘mixtures’ of urbanity within Java’s rural-urban continuum. This approach not only captures socio-environmental dynamics across administrative silos but also establishes a new strategic framework for regional planning challenges. By advancing boundary-making beyond mere political convention to reflect on-the-ground ecological and functional coherence, this framework responds to the urgent global challenge of reconciling accelerating suburban and regional development pressures with the preservation of local communities, agricultural systems, and natural landscapes. Full article

Oil palm (Elaeis guineensis), a rapidly expanding crop in northeastern Pará, first emerged in the 1970s as a crucial response to the global oil crisis. However, its swift expansion has subsequently generated significant socio-environmental conflicts, profoundly altering local socioecological dynamics. Therefore, we aimed to investigate land-use and land-cover changes within the headwater sub-basin of the Caeté River, focusing specifically on the municipality of Bonito, Pará. To achieve this, we employed remote sensing and geospatial analysis to accurately delineate the study area and perform supervised classifications. Specifically, we used the Random Forest algorithm to map five distinct periods: 1985, 1995, 2004, 2015, and 2023. In addition, we calculate an Anthropogenic Transformation Index (ATI) in order to observe the human influence in the landscape. Our classification models exhibited high accuracy, with overall accuracy values ranging from 0.63 to 0.87 and Kappa coefficients between 0.53 and 0.76, demonstrating consistent discrimination among LULC classes. The results revealed a marked transformation of the landscape, with oil palm monocultures progressively expanding at the expense of dense forest and human-modified vegetation. For instance, the ATI increased from 3.14 in 1985 to 5.56 in 2004, followed by a slight decline to 4.90 in 2023, suggesting a potential stabilisation—but not a reversal—of anthropogenic pressures. Nonetheless, the negative socioecological impacts of the oil palm monocultures in this Amazonian landscape remain severe, encompassing issues such as water pollution and ongoing socio-environmental conflicts. In conclusion, this research highlights the importance of understanding these dynamics to support sustainable management of the Caeté River basin. Furthermore, we underscore the urgent need for further research to rigorously evaluate effective mitigation strategies and foster genuinely sustainable development within the region. Full article

This study examined land-use/land-cover (LULC) change in the Manas River Basin from 2000 to 2020 due to rapid socioeconomic development. It aims to provide a scientific basis for protecting the ecological security of the river basin and achieving sustainable development of the land. The LULC data of 2000, 2010, and 2020 were utilized to establish the LULC transition matrix and calculate the LULC dynamics to analyze the dynamic evolution of LULC in the basin from 2000 to 2020. The PLUS model was constructed to explore the driving mechanism of the conversion between various land types in the basin. The key findings include the following. (1) From 2000 to 2010, grassland experienced the most significant reduction (3222.08 km²), whereas farmland expanded the most (3126.77 km²). (2) The most rapid expansion occurred in farmland (6.24%) and built-up areas (2.25%) in the 2000–2010 and 2010–2020 periods, respectively. Conversely, forest land showed the most rapid decrease, with −6.07% from 2000 to 2010, and −0.86% from 2010 to 2020. (3) The degree of influence of each driving factor on different LULC types (contribution degree) obtained by constructing the PLUS model shows that, during the twenty years, population was the predominant factor affecting farmland changes and built-up areas, with contribution degrees of 0.17 and 0.26, respectively. Temperature was the primary influencer of forest-land changes, with a contribution degree of 0.17, and elevation significantly impacted grassland changes, with a contribution degree of 0.21. This study provides crucial insights into the interaction between LULC dynamics and environmental and demographic factors in the Manas River Basin. Full article

Megacity regions mark a transformative phase of urbanisation, in which interconnected cities undergo land-use and land-cover change (LUCC) that extends beyond administrative boundaries. However, the drivers of cross-boundary LUCC remain insufficiently examined, particularly before the top-down regional integration. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) provides a clear empirical case, having experienced cross-boundary LUCC prior to its formal designation as a megacity region in 2018. This study builds a Landsat-derived LUCC and driver dataset for the GBA. Global and local spatial autocorrelation (Moran’s I and LISA) are used to characterise spatial structure and clustering, and geographically weighted regression identifies the socio-economic and environmental determinants of built-up expansion over 1980–2018, spanning the pre-reform decade and the post-1990 land-transfer era. Findings reveal that: (1) LUCC in the GBA already exhibited a cross-border, spatially networked expansion pattern before formal regional integration policies at the national level, with built-up area growth extending beyond core cities into decentralised urban nodes. Two prominent cross-border cores and one cross-administrative core emerged, suggesting that regional integration was co-led by market forces and local governments before an institutional framework was established. (2) Although the GBA showed a clear trend towards integrated development, urban expansion was highly uneven. Such spatial disparities were mainly driven by varying socioeconomic and natural factors, including gross domestic product, population growth, real estate investment, water resource proximity, and infrastructure development. These findings enhance understanding of megacity-region dynamics and offer insights from the GBA for cross-border urbanisation and sustainable spatial governance. Full article

Dynamic task planning for heterogeneous platforms across land, sea, air, and space is essential for achieving integrated situational awareness, yet current systems suffer from limited spatiotemporal coverage and inefficient resource scheduling. To address these challenges, we propose a novel mission planning method that integrates spatiotemporal segmentation with Deep Reinforcement Learning (DRL). The approach establishes a multidimensional spatiotemporal decomposition model to break down complex observation scenarios into manageable subtasks, while incorporating a unified accessibility–visibility computation framework that accounts for Earth curvature, platform dynamics, and sensor constraints. Using a Spatio-Temporal Adaptive Scheduling Network (STAS-Net) algorithm optimized with a multi-objective reward function covering mission completion rate, temporal coordination, and residual detection capacity, the method enables intelligent coordination of heterogeneous platforms. Experimental results across small-, medium-, and large-scale scenarios demonstrate that the proposed framework consistently achieves high target coverage (up to 98.4% in small-scale and 89.7% in large-scale tasks), with a reduction in coverage loss that is only about half of that exhibited by greedy and genetic algorithms as task scale expands. Moreover, STAS-Net maintains low planning time (as low as 9.5 s in small-scale and only 18.3 s in large-scale scenarios) and high resource utilization (reaching 86.8% under large-scale settings), substantially outperforming both baseline methods in scalability and scheduling efficiency. The framework not only establishes a solid theoretical foundation but also provides a practical and feasible solution for enhancing the overall performance of multi-platform cooperative observation systems. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

The growing global demand for food and energy requires land-use strategies that support agricultural production and renewable energy generation. Agrivoltaic (APV) systems allow farmland to be used for both agriculture and solar power generation. The aim of this study is to critically synthesize the interactions between the key dimensions of APV implementation—technical, agronomic, legal, and economic—in order to create a multidimensional framework for designing an APV optimization model. The analysis covers APV system topologies, appropriate types of photovoltaic modules, installation geometry, shading conditions, and micro-environmental impacts. The paper categorizes quantitative indicators and critical thresholds that define trade-offs between energy production and crop yields, including a discussion of shade-tolerant crops (such as lettuce, clover, grapevines, and hops) that are most compatible with APV. Quantitative aspects were integrated in detail through a review of mathematical approaches used to predict yields (including exponential-linear, logistic, Gompertz, and GENECROP models). These models are key to quantitatively assessing the impact of photovoltaic modules on the light balance, thus enabling the simultaneous estimation of energy efficiency and yields. Technical solutions that enhance synthesis, such as dynamic tracking systems, which can increase energy production by up to 25–30% while optimizing light availability for crops, are also discussed. Additionally, the study examines regional legal frameworks and the economic factors influencing APV deployment, highlighting key challenges such as land use classification, grid connection limitations, investment costs and the absence of harmonised APV policies in many countries. It has been shown that APV systems can increase water retention, mitigate wind erosion, strengthen crop resilience to extreme weather conditions, and reduce the levelized cost of electricity (LCOE) compared to small rooftop PV systems. A key contribution of the work is the creation of a coherent analytical design framework that integrates technical, agronomic, legal and economic requirements as the most important input parameters for the APV system optimization model. This indicates that wider implementation of APV requires clear regulatory definitions, standardized design criteria, and dedicated support mechanisms. Full article

Understanding the driving forces of land use and land cover change (LUCC) is crucial for revealing the coupled dynamics of human–land systems and supporting optimized spatial planning and resource allocation. To overcome the limitations of conventional Geodetector applications in mountainous regions with complex terrain, this study proposes a terrain–population dual-factor adaptive grid designed for use with the Geodetector model. This adaptive grid refines cells in steep and densely populated areas while merging cells in flatter and sparsely populated regions, thus capturing both natural and socioeconomic heterogeneity. Coupled with the Geodetector model, this framework improves the accuracy and computational efficiency of identifying LUCC drivers. Using the Chongqing Metropolitan Area (CMA) as a case study, LUCC dynamics and their driving mechanisms were systematically examined based on five annual land cover datasets (from 2000 to 2020 at five-year intervals.). The results show the following: (1) From 2000 to 2020, cropland, forest land, and built-up land were the dominant land use types. During this period, cropland and forest land declined, whereas built-up land expanded continuously, with the most pronounced changes occurring between 2000 and 2010. (2) The dominant drivers of LUCC shifted over time: socioeconomic factors such as population density and GDP were primary drivers from 2000 to 2010, while both natural and socioeconomic factors exerted strong influence between 2010 and 2020. (3) The proposed terrain–population dual-factor irregular grid performed better than traditional regular grids in detecting socioeconomic drivers while retaining comparable explanatory power for natural factors. Compared with traditional regular grids, with an average q-value improvement of 18.7% and a 55.52% reduction in sampling points, resulting in substantially improved computational efficiency. Overall, the proposed method enhances the applicability of Geodetector in complex mountainous cities and provides practical implications for urban land use regulation and refined spatial management. Full article

Rainfall characteristics proven to trigger general erosive events (EE) and rill erosion events (RE) under reference experimental conditions of soil type, slope, and land use—previously established at a test site in central Italy—are applied as likely thresholds to characterize their spatiotemporal variability across Umbria using 24 years of semi-hourly data from 53 stations. Marked spatial patterns emerge, with mean EE frequencies per station ranging from 1.14 to 2.36 per month, while mean RE frequencies per station vary between 0.04 and 0.45 per season. No significant temporal trends are observed over the study period. Monthly and seasonal comparisons between EE and RE frequencies often deviate from the corresponding USLE R-factor dynamics, highlighting limitations of relying solely on this parameter. These findings are contextualized within common soil conservation practices—such as cover crops—to identify critical periods during which maintaining soil cover. For example, winter—when cover crops are typically present in Central Italian agroecosystems—is among the seasons with the highest EE frequency (4.45 yr⁻¹), second only to autumn (6.47 yr⁻¹). However, when focusing on REs, winter shows the lowest mean frequency (0.08 yr⁻¹). In contrast, the mean RE frequency increases in summer (0.24 yr⁻¹) and reaches its maximum in autumn (0.26 yr⁻¹), when bare soil or poorly developed cover crops are common. Overall, results provide actionable insights for aligning protective measures with high-impact erosive event probabilities. Full article