Search Results (3,607)

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions. Full article

River and lake ice are sensitive indicators of climate change and important components of hydrological and ecological systems in cold regions. In this study, we develop a simple and transferable “surface water + land surface temperature (LST)” framework on Google Earth Engine to map potential winter ice area across China from 1990 to 2020. The framework enables consistent, large-scale, long-term monitoring without relying on complex remote sensing models or region-specific thresholds. Our results show that, despite a pronounced northwestward shift in the freezing-zone boundary, more than 400 km in the Northeast Plain and about 13 km per year along the eastern coast, the total ice-covered area increased by approximately 1.1% per year. At the same time, the average ice season became slightly shorter. This indicates asynchronous spatial and temporal responses of potential winter ice to warming. We identify a persistent “Northwest–Northeast dual-core” spatial pattern with strong positive spatial autocorrelation, characterized by increasing ice cover in Tibet, Qinghai, Xinjiang, Inner Mongolia, and Northeast China, and decreasing ice cover mainly in Beijing and Yunnan, where intense urbanization and low-latitude warming dominate. Random Forest modeling further shows that water area fraction, nighttime lights, built-up area, altitude, and water–heat indices are the main controls on potential winter ice. These findings highlight the combined influence of hydrological and thermal conditions and urbanization in reshaping potential winter ice patterns under climate change. Full article

Land use/cover change (LUCC) is a dominant driver of ecosystem service dynamics in arid inland basins. Focusing on the Yarkant River Basin (YRB), Xinjiang, we coupled the PLUS land-use simulation with the InVEST Habitat Quality Model to project 2040 land-use patterns under four policy scenarios—Natural Development (ND), Arable Protection (AP), Ecological Protection (EP), and Economic Development (ED)—and to quantify their impact on habitat quality. Model validation against the 2020 map indicated strong agreement (Kappa = 0.792; FOM = 0.342), supporting scenario inference. From 1990 to 2023, arable land expanded by 58.17% and construction land by 121.64%, while forest land declined by 37.45%; these shifts corresponded to a basin-wide decline and increasing spatial heterogeneity of habitat quality. Scenario comparisons showed the EP pathway performed best, with 32.11% of the basin classified as very high-quality habitat and only 8.36% as very low-quality. In contrast, under ED, the combined share of very low + low quality reached 11.17%, alongside greater fragmentation. Spatially, high-quality habitat concentrates in forest and grassland zones of the middle–upper basin, whereas low-quality areas cluster along the oasis–desert transition and urban peripheries. Expansion of arable and construction land emerges as the primary driver of degradation. These results underscore the need to prioritize ecological-protection strategies especially improving habitat quality in oasis regions and strengthening landscape connectivity to support spatial planning and ecological security in dryland inland river basins. Full article

Environmental Impact Assessments (EIAs) are crucial for mitigating flood risks in vulnerable ecosystems, yet their effective application remains inconsistent. This study synthesises global literature to systematically map EIA methodologies, evaluate the extent of hydrological integration, and analyse the evolution of practices against policy frameworks for flood-prone areas. A scoping review of 144 peer-reviewed articles, conference papers, and one book chapter (2005–2025) was conducted using PRISMA protocols, complemented by bibliometric analysis. Quantitative findings reveal a significant gap where 72% of studies lacked specialised hydrological impact assessments (HIAs), with only 28% incorporating them. Post-2016, advanced tools like GIS, remote sensing, and hydrological modelling were used in less than 32% of studies, revealing reliance on outdated checklist methods. In South Africa, despite wetlands covering 7.7% of its territory, merely 12% of studies applied flood modelling. Furthermore, 40% of EIAs conducted after 2016 excluded climate adaptation strategies, undermining resilience. The literature is geographically skewed, with developed nations dominating publications at a 3:1 ratio over African contributions. The study’s novelty is its systematic global mapping of global EIA practices for flood-prone areas and its proposal for mandatory HIAs, predictive modelling, and strengthened policy enforcement. Practically, these reforms can transform EIAs from reactive compliance tools into proactive instruments for disaster risk reduction and climate resilience, directly supporting Sustainable Development Goals 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land). This is essential for guiding future policy and improving EIA efficacy in the face of rapid urbanisation and climate change. Full article

In the context of the “Dual Carbon” goals, accurately predicting the spatiotemporal evolution of urban Net Primary Productivity (NPP) is crucial for resilient urban planning. While recent studies have coupled land use models with ecosystem models to project NPP dynamics, they often face challenges in acquiring high-resolution future vegetation parameters and typically overlook the stability of NPP under changing climates. To address these gaps, this study focuses on Nanjing and develops a long-term, multi-scenario analysis framework based on the Dynamic Land Cover–Climate Model (DLCC–NPP). This framework innovatively integrates the PLUS model with a Random Forest (RF) algorithm. By establishing a direct statistical mapping between macro-climate/micro-land cover and NPP, the RF model functions as a statistical downscaling tool. This approach bypasses the uncertainty accumulation associated with simulating future vegetation indices, enabling precise spatiotemporal NPP prediction at a 30 m resolution. Using this approach, we systematically analyzed the NPP dynamics from 2004 to 2044 under three SSP scenarios. The results revealed that Nanjing’s NPP exhibited a fluctuating upward trend, with urban forests contributing the highest productivity (mean NPP ~266.15 gC/m²). Crucially, the volatility analysis highlighted divergent response characteristics: forests demonstrated the highest stability and “buffering effect,” whereas grasslands and croplands showed high volatility and sensitivity to climate fluctuations. Spatially, a distinct “stable high-NPP core, decreasing periphery” pattern was identified, driven by the interaction of urban expansion and ecological conservation policies. In conclusion, the DLCC–NPP framework effectively overcomes the data scarcity bottleneck in future simulations and characterizes the spatiotemporal heterogeneity of vegetation carbon fixation in urban ecosystems, providing scientific support for optimizing green space patterns and enhancing urban ecological resilience in high-density cities. Full article

High-resolution remote sensing images are gradually playing an important role in land cover mapping, urban planning, and environmental monitoring tasks. However, current segmentation approaches frequently encounter challenges such as loss of detail and blurred boundaries when processing high-resolution remote sensing imagery, owing to their complex backgrounds and dense semantic content. In response to the aforementioned limitations, this study introduces HMA-UNet, a novel segmentation network built upon the UNet framework and enhanced through a hybrid attention strategy. The architecture’s innovation centers on a composite attention block, where a lightweight split fusion attention (LSFA) mechanism and a lightweight channel-spatial attention (LCSA) mechanism are synergistically integrated within a residual learning structure to replace the stacked convolutional structure in UNet, which can improve the utilization of important shallow features and eliminate redundant information interference. Comprehensive experiments on the WHDLD dataset and the DeepGlobe road extraction dataset show that our proposed method achieves effective segmentation in remote sensing images by fully utilizing shallow features and eliminating redundant information interference. The quantitative evaluation results demonstrate the performance of the proposed method across two benchmark datasets. On the WHDLD dataset, the model attains a mean accuracy, IoU, precision, and recall of 72.40%, 60.71%, 75.46%, and 72.41%, respectively. Correspondingly, on the DeepGlobe road extraction dataset, it achieves a mean accuracy of 57.87%, an mIoU of 49.82%, a mean precision of 78.18%, and a mean recall of 57.87%. Full article

Lavender is a perennial Mediterranean plant that has been cultivated throughout history for medicinal, aromatic, and cosmetic purposes. Due to its high economic and commercial value, it has become an important agricultural product worldwide. The low production cost, adaptability to environmental conditions, and demand for its versatile use in the global market make it a significant potential source of income for developing Mediterranean countries. This study aims to identify commercially suitable cultivation sites for Lavandula angustifolia Mill. using remote sensing (RS) and geographic information systems (GIS) technologies to support rural development. Within this scope, suitable cultivation habitat parameters for the species in open fields and natural conditions were determined; these parameters were weighted according to their importance using multi-criteria decision analysis (MCDA), and thematic maps were created for each parameter. The created maps were combined using weighted overlay analysis, and a final map was generated according to the suitability class. The results indicate that within the study area, 75,679.45 ha is mostly suitable, 388,832.71 ha is moderately suitable, 24,068.43 ha is marginally suitable, and 229,327.20 ha is not suitable. As a result, it has been observed that Lavandula angustifolia Mill., which is currently cultivated on approximately 4045 ha of land and contributes 429 tons of product to the regional economy, covers only a relatively small portion of the suitable cultivation sites identified in the study and is not utilized to its full potential. It is understood that the expansion of lavender cultivation in determined suitable sites has significant potential to substantially develop the region and its rural population in terms of both yield and production volume, and to involve women and youth entrepreneurs in agricultural employment. Full article

This study proposes a lightweight framework for transferring pretrained land cover classification architectures without additional training. The system utilizes French IGN imagery and Korean UAV and aerial imagery. It employs FLAIR U-Net models with ResNet34 and MiTB5 backbones, along with the AI-HUB U-Net. The implementation consists of four sequential stages. First, we perform class mapping between heterogeneous schemes and unify coordinate systems. Second, a quadratic polynomial regression equation is constructed. This formula uses multispectral band statistics as hyperparameters and class-wise IoU as the dependent variable. Third, optimal parameters are identified using the stationary point condition of Response Surface Methodology (RSM). Fourth, the final land cover map is generated by fusing class-wise optimal results at the pixel level. Experimental results show that optimization is typically completed within 60 inferences. This procedure achieves IoU improvements of up to 67.86 percentage points compared to the baseline. For automated application, these optimized values from a source domain are successfully transferred to target areas. This includes transfers between high-altitude mountainous and low-lying coastal territories via proportional mapping. This capability demonstrates cross-regional and cross-platform generalization between ResNet34 and MiTB5. Statistical validation confirmed that the performance surface followed a systematic quadratic response. Adjusted R² values ranged from 0.706 to 0.999, with all p-values below 0.001. Consequently, the performance function is universally applicable across diverse geographic zones, spectral distributions, spatial resolutions, sensors, neural networks, and land cover classes. This approach achieves more than a 4000-fold reduction in computational resources compared to full model training, using only 32 to 150 tiles. Furthermore, the proposed technique demonstrates 10–74× superior resource efficiency (resource consumption per unit error reduction) over prior transfer learning schemes. Finally, this study presents a practical solution for inference and performance optimization of land cover semantic segmentation on standard commodity CPUs, while maintaining equivalent or superior IoU. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. 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

The Balaton region is Hungary’s most important recreational area, known for Central Europe’s largest freshwater lake and its traditional vineyard and horticultural landscapes. Since 1990, vineyard and orchard abandonment and intensified shoreline urbanization have increasingly threatened both landscape character and ecological balance. This study analyses land-use changes in the Balaton hinterland and evaluates the effectiveness of regional land-use regulation between 1990 and 2018, with a focus on the 2000 Balaton Law (BKÜRT), which sought to preserve traditional land uses by permitting construction only where at least 80% of vineyard parcels remained cultivated. Spatial–temporal analysis was based on CORINE Land Cover (CLC) data from 1990 to 2018, supplemented by change layers from the Copernicus Land Monitoring Service. The CORINE Land Cover classification is a three-level hierarchical system (5 Level-1 groups, 15 Level-2 classes, and 44 Level-3 classes) developed by the EEA to provide standardized, satellite-based land cover information across Europe. Land cover was aggregated into major categories (using Level-1 and Level-2 classes) relevant to the Hungarian landscape. To address CLC limitations related to representing vineyards as relatively homogeneous units despite substantial differences in the density and scale of built structures, detailed case studies were conducted in three C1 vineyard zones—Alsóörs, Paloznak, and Szentantalfa—using historical aerial photographs, Google Earth imagery, and the Hungarian Ecosystem Map (NÖSZTÉP). Despite the restrictive regulatory framework, the CLC database showed that the share of vineyards in the vineyard regulation zone (C-1, C-2) decreased between 1990 and 2018 from 45.4% to 35.8% (the share of gardens and fruit plantations had changed from 9.7% to 15.5%). In the whole Balaton region, there was an approximately 18% decline in vineyard areas. Considering the M-2 horticultural zone, the garden coverage increased from 18.9% in 1990 (17.7% in 2000) to 30.5% (share of vineyards changed from 54.3% (54.6% in 2000) to 38.8%). At the regional level, gardens and fruit plantations had a smaller decrease (3.2%). Although overall trends were more favorable than at the national level, regulatory measures proved insufficient to prevent the conversion of vineyards and orchards in sensitive areas, particularly on slopes overlooking the lake, in proximity to tourist hubs, and in areas exposed to strong development pressure. By 2018, the C1 zone had expanded spatially but became less targeted, as the proportion of vineyards within it decreased. Boundary refinements failed to substantially improve regulatory precision or effectiveness. The case studies reveal a gradient of regulatory strictness reflecting differing landscape protection priorities and stages of vineyard transformation, with Alsóörs responding to long-standing, partly irreversible changes while attempting to slow further landscape alteration. To counter ongoing negative trends, more targeted and enforceable regulations are required, including a clearer separation of cultivated and recreational land uses, a maximum building size of 80 m² for recreational properties, and a reassessment of vineyard zone boundaries to better reflect active cultivation and protect sensitive landscapes. 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

Flooding is a significant natural hazard in arid regions, particularly in Saudi Arabia, where intense rainfall events pose serious risks to both infrastructure and public safety. Bahrah City, situated between Jeddah and Makkah, has experienced recurrent flooding owing to its topography, rapid urbanization, and inadequate drainage systems. This study aims to develop a comprehensive flood hazard mapping approach for Bahrah City by integrating remote sensing data, Geographic Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA). Key input factors included the Digital Elevation Model (DEM), slope, distance from streams, and land use/land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to assign relative weights to these factors, which were then combined with fuzzy membership values through fuzzy overlay analysis to generate a flood susceptibility map categorized into five levels. According to the AHP analysis, the high-susceptibility zone covers 2.2 km², indicating areas highly vulnerable to flooding, whereas the moderate-susceptibility zone spans 26.1 km², representing areas prone to occasional flooding, but with lower severity. The low-susceptibility zone, covering the largest area (44.7 km ²), corresponds to regions with a lower likelihood of significant flooding. Additionally, hydraulic simulations using the rain-on-grid (RoG) method in HEC-RAS were conducted to validate the hazard assessment by identifying inundation depths. Both the AHP analysis and the RoG flood hazard maps consistently identify the western part of Bahrah City as the high-susceptibility zone, reinforcing the reliability and complementarity of both models. These findings provide critical insights for urban planners and policymakers to improve flood hazard mitigation and strengthen resilience to future flood events. 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

The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images from sources (MERIS, AVHRR, SPOT, PROBA, and Sentinel-3). In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions under the Common Agricultural Policy (CAP) 2023–2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protecting biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes. Our study highlights that the CAP for mountain farms should be targeted, long-term, and compensatory, so as to compensate for the naturally unfavorable farming conditions and support their multifunctional role. The most important assumptions of CAP for mountain farms are a fair system of compensatory payments (LFA/ANCs), support for local and high-quality production, income diversification, and investments adapted to mountain conditions. Full article