Search Results (832)

Digital Elevation Models (DEMs) are essential tools for terrain analysis. However, their selection often focuses on spatial resolution or overall vertical accuracy, overlooking aspects such as geometric consistency or fitness for specific applications. This misalignment between DEM’s capabilities and study requirements can lead to inaccurate interpretations, undermining the reliability of the results. To address this, we assessed the accuracy of seven freely available global DEMs (SRTM, ASTER, ALOS, Copernicus DEM, MERIT DEM, NASADEM, and FABDEM) against a 1 m LiDAR reference in a coastal region of El Salvador. Our workflow combined traditional metrics (RMSE, MAE, ME) with spatial error visualizations (histograms and heatmaps) and introduced the Spatial Error Robustness Index (SERI) with RMSE/SD ratio to jointly quantify error magnitude and variability. Elevation and slope were selected for DEM–LiDAR comparisons. Results show that all DEMs systematically overestimate elevation, with slope errors amplified by scale discrepancies. FABDEM achieved the highest elevation accuracy across coastal and tectonic landscapes, ALOS the lowest slope RMSE overall and Copernicus performed best in coastal zones; DEM performance depends on terrain and parameters. The SERI and RMSE/SD ratio, combined with spatial visualizations, revealed systematic error patterns and improved geomorphic coherence interpretation. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference ($\Delta$GLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean $\Delta$GLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher $\Delta$GLI, while low severity areas showed limited or negative values. $\Delta$GLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery. Full article

Protected areas (PAs) are increasingly challenged by the need to reconcile biodiversity conservation with sustainable human use, particularly in landscapes containing underutilized or resting area (RA). This study evaluated the potential of resting forest and agricultural lands to enhance biodiversity and support sustainable land use within protected areas of Cesme, Türkiye. A Geographic Information System (GIS)-based multi-criteria evaluation approach was employed, integrating land cover data, soil group maps, topographic parameters, and protected area classifications to generate Plant Suitability Maps (PSMs). Eight thematic layers were developed, incorporating soil depth, slope, erosion risk, and land capability classes to identify suitable plant species and land-use options. The results indicate that the strategic use of resting agricultural lands could contribute up to 35.5% to ecological enhancement, while resting forest lands could contribute an additional 18%. The proposed plant assemblages include medicinal and aromatic species, erosion-control plants, and economically valuable perennial species that support ecosystem services such as pollination, beekeeping, and agro-tourism. Overall, the findings demonstrate that integrating RA management into conservation planning can simultaneously strengthen biodiversity, improve ecosystem services, and generate socio-economic benefits for local communities. The proposed GIS-based framework offered a transferable and scalable methodology for sustainable land management in Mediterranean landscapes and other protected regions worldwide. Also, in this research, the aim was to determine plant species using GIS-based suitability analyses of multi-spatial datato guide vegetation decisions in multi-criteria PA. Full article

Mediterranean-type forest ecosystems are becoming increasingly vulnerable to intensifying drought, threatening the resilience of even highly adapted ecosystems such as the Northern Jarrah Forest in south-western Australia. This study quantifies multi-decadal dynamics of canopy water stress using a 36-year multispectral satellite archive (1988–2024) and the newly developed Infrared Canopy Dryness Index (ICDI). We combined this spatiotemporal dataset with a MaxEnt-based risk assessment framework to identify the biophysical drivers of drought-induced canopy loss and to delineate high-risk zones under accelerating climate-forcing changes. Our results demonstrate a systematic spatial expansion of canopy dryness, paralleling a deteriorating regional climatic water balance. Hotspot analysis revealed a transition from localized, peripheral stress to widespread, chronic drought conditions across the landscape. The modelling achieved high diagnostic accuracy (AUC = 0.952), significantly outperforming conventional assessment methods. Regolith depth was identified as the primary determinant of drought-induced canopy collapse, followed by ICDI, NDVI, and slope. Crucially, high-biomass stands exhibited disproportionately higher risk of collapse, revealing a density-dependent vulnerability that suggests productive forests are approaching critical hydraulic thresholds. Conversely, lower-stature forests to the east of the study area demonstrated greater stability, likely due to reduced evapotranspirative demand. These findings provide robust spatial evidence for transitioning from reactive monitoring to proactive forest management. We conclude that targeted interventions, such as ecological thinning and prescribed burning in identified high-risk zones, are imperative to protect the forest and preserve the structural integrity of Mediterranean ecosystems in a drying climate. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Wetland restoration plays a crucial role in enhancing hydrologic resilience amidst the challenges posed by climate change and evolving land uses. The historical reduction in beaver populations due to the fur trade and alterations to riparian zones have compromised the ecological stability of many landscapes. Presently beaver populations are increasing as there are now protections in place for them. In response, Beaver Dam Analogs (BDAs) have emerged as an effective restoration strategy, particularly in regions where natural beaver activity is limited due to inadequate habitat conditions. BDAs are a human-made structure that mimics the function and form of natural beaver dams. This paper focuses on a restoration project within the Fish Creek area between the year 2019 and 2021, which is a part of the Weber River watershed in northern Utah, where BDAs were installed to rehabilitate a degraded wetland and rectify an incised channel network. Over the initial two years following the installation (2019–2021), significant ecological transformations were observed. Notably, there was an increase in the areal coverage of sediments that sizes ranged from 1 to 256 mm within the stream channel, alongside a corresponding decrease in coarser substrates. These changes facilitated a reduced channel slope, indicating substantial sediment deposition above the installed BDAs. Concurrently, there was an expansion in riparian vegetation along an approximate stretch of 40 m, primarily grasses, reflecting an adjustment in habitat conditions favorable to riparian recovery. The preliminary outcomes from this study contribute to a broader understanding of the dynamics involved in BDA-driven restoration efforts in semiarid regions like the western United States, highlighting the potential shifts in riparian habitats prompted by such interventions. Full article

Landslide mapping and susceptibility assessment are essential for hazard identification, infrastructure protection, and risk management. The middle and lower reaches of the Nujiang River have high relief, rapid geomorphic change, and fragile landscape conditions, which increase landslide susceptibility and hinder timely detection. To improve the spatiotemporal characterization of landslide activity, we developed a multi-source Earth observation framework for annual landslide mapping and susceptibility assessment. First, interannual embedding-change intensity maps were generated to guide the visual interpretation of landslide-related surface disturbances. Second, annual landslide and non-landslide samples were collected through field validation and visual interpretation. Third, annual 10 m landslide maps for 2017–2025 were generated using random forest on Google Earth Engine. Finally, 24 multidimensional environmental factors were incorporated into landslide susceptibility modeling. Landslides were concentrated mainly along the Nujiang River corridor and adjacent high-relief canyon slopes, with marked interannual variability but relatively stable hotspot regions. SHAP analysis further identified BSI_mean as the most important predictor, with a mean absolute SHAP value of 0.116, followed by NDVI_mean and terrain-related variables, indicating that bare-surface exposure, vegetation condition, and terrain dissection were strongly associated with mapped landslide occurrence. This study provides annual landslide inventories and susceptibility information for hazard mitigation and infrastructure planning. Full article

Ensuring accessibility in alpine ski areas remains a critical challenge for social sustainability and inclusive tourism because physical, seasonal and organisational constraints interact across the visitor experience. This paper reframes accessibility as a dynamic and relational landscape attribute and proposes a flow-chain framework for assessing accessibility as a sequence of interdependent phases, from pre-trip information to arrival, lift access, slope use, rest and return. Developed within the Ski-Ability project in the ArgeAlp working community, the study draws on exploratory field observations, stakeholder engagement and co-design activities conducted across seven Alpine pilot resorts. The pilot resorts are not treated as a statistically representative sample, but as field cases used to understand current operational conditions in a context where academic literature, technical standards and regulatory guidance specific to accessible ski areas remain limited. The framework is operationalised through a qualitative toolkit based on Basic, Comfort and Plus levels, priority categories and non-compensatory decision rules. The results provide methodological validation and practical guidance rather than quantitative benchmarking. They show that accessibility in alpine ski areas depends on the continuity of routes, services, information and assistance, and on coordination among multiple actors. The paper contributes to social sustainability research by linking Universal Design, accessible tourism and mountain governance within a transferable, process-oriented assessment model. Full article

Large-scale landscape transformation in mountainous regions during the Late Iron Age remains insufficiently integrated into broader debates on European urbanism. In southwestern Transylvania, extensive slope terracing came to define the spatial core of the Dacian political centre. This study examines the scale, organization, and social implications of this engineered landscape using high-resolution LiDAR data and spatial modelling. Over 4000 anthropogenic terraces were identified, and their spatial patterning was analysed through Kernel Density Estimation (300 m and 800 m radii) in order to evaluate intensity gradients and territorial articulation. The results indicate compact nuclei of high terrace concentration embedded within a broader, yet continuous, system structured along ridge corridors and circulation routes. The spatial correlation between terrace density and elevated architectural features suggests differentiated building practices and hierarchical organization within a territorially extensive settlement pattern. Rather than representing isolated fortified sites, the Dacian mountain core emerges as an integrated and infrastructurally connected landscape. These findings support the interpretation of the area as a form of Late Iron Age low-density urbanism, in which habitation, mobility, and social differentiation were materially embedded in large-scale topographic modification. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

Land degradation and declining groundwater availability threaten the sustainability of rural livelihoods across semi-arid regions. This study evaluates the hydrological performance of contour-based trenches as a low-cost and replicable nature-based solution (Nbs) for soil restoration, runoff regulation, and potential distributed managed aquifer recharge (MAR) in Guerrero, Mexico. The structures were installed on 12% slopes and designed using a simplified water balance criterion based on trench storage capacity, runoff coefficient, and representative rainfall events. Each trench was constructed along contour lines with overflow notches and connecting micro-trenches to improve hydraulic continuity, reduce erosion, and enhance infiltration opportunities under degraded field conditions. After one year of field monitoring, the trenches reached an average filling efficiency of approximately 90% per effective rainfall event, with estimated infiltration rates ranging from 0.0069 to 0.011 L·s⁻¹. Soil moisture in the upper soil layer showed a relative increase of approximately 10–18% compared to adjacent untreated areas, while visible reductions in runoff velocity, sediment transport, and surface erosion were observed across the treated plot. Based on trench storage capacity, observed infiltration behavior, and assumed deep percolation fractions, the potential induced recharge was estimated between 216 and 360 m³·yr⁻¹ (43–72 mm·yr⁻¹). These values represent indicative plot-scale estimates rather than direct measurements of aquifer recharge, since no tracer studies or piezometric validation were performed. The results demonstrate that contour-based trenches contribute not only to infiltration enhancement and runoff control, but also to short-term soil restoration and improved water availability in rainfed agricultural systems. Their low-cost implementation, combined with community-based maintenance and adaptation to local environmental conditions, makes them a viable complementary strategy for strengthening decentralized water management, soil resilience, and climate adaptation in semi-arid rural landscapes. However, long-term effectiveness remains dependent on maintenance continuity, institutional support, and local governance conditions. Further multi-year monitoring and direct hydrogeological validation are recommended to improve the design and replicability of decentralized MAR systems. Full article

Across the Saharan region of North Africa, oasis territories constitute the dominant form of human settlement. In Algeria, the Sahara is undergoing rapid urban and agricultural expansion, resulting in significant spatial and demographic transformations and increased environmental pressures on oasis systems. Despite these critical dynamics, existing studies have addressed oasis sustainability only superficially, lacking quantitative, territory-scale indicators that integrate both spatial and demographic dimensions. As a result, preserving oasis territories has become a critical challenge for national economic and industrial development. Spatial planning and demographic balance are key drivers for oasis landscape sustainability. This study focuses on the Tolga oasis territory, one of the largest in North Africa, to investigate the spatial and demographic relationships among the built environment, urban perimeters, population dynamics, and palm grove areas. The methodology combines: (1) historical cartographic analysis using georeferenced maps from 1900 to 2020 processed in QGIS (RMSE < 5 m); (2) GIS-based digitization of built-up areas (BuA) and palm grove areas (PGA) across four reference periods (1900, 1940, 1980, 2020); (3) polynomial regression modeling for urban perimeter vs. inter-oasis distance; and (4) least squares method for the population–palm tree correlation. Using spatial and statistical analyses, the results indicate that the built-up area should remain below a threshold ratio of 0.05 relative to the cultivated area to maintain the oasis landscape. Strong polynomial correlations (0.5876 ≤ R² ≤ 0.974) confirm the structural link between urban perimeter growth and inter-oasis distance, outperforming linear regression (mean ΔR² = +0.226). In addition, a strong correlation is identified between population size and palm tree abundance, as expressed by the relationship PT = 1.6376 Po + 755,050, where P denotes population size (F-statistic = 178.4; p < 0.01; N = 24; 95% CI of slope = ±0.24). Adopting a territorial-scale approach, this study proposes novel quantitative indicators, including ratio and formula-based models that can be integrated into Saharan territorial planning strategies to support sustainable oasis development. Full article

This study examines the landscape characteristics of high-suitability habitats for the Black-tailed Godwit (Limosa limosa) during the non-breeding season in inland and coastal wetlands of the middle and lower Yangtze River regions, and seeks to elucidate the distribution patterns and their drivers. Using the MaxEnt model and landscape analysis, the following conclusions were obtained: (1) High-suitability habitats for the Black-tailed Godwit cover approximately 128,800 km² and are primarily distributed across the middle and lower Yangtze River regions. (2) The dominant environmental variables were identified as elevation, distance to water source, slope, distance to paddy field, land use classification, and minimum temperature of the coldest month. (3) Landscape fragmentation, habitat connectivity, human disturbance, and climate change were found to be associated with the shift in the Black-tailed Godwit’s distribution from coastal to inland areas. (4) The distribution of the Black-tailed Godwit in the Nanji Wetland showed significant moderate positive correlation with shallow-water area (r = 0.38, p < 0.05) and significant moderate negative correlation with deep-water area (r = −0.48, p < 0.01). (5) At large spatial scales (coastal and inland wetlands), habitat connectivity and fragmentation were found to exert a greater influence, whereas at smaller spatial scales (Nanji Wetland) land use areas (wetlands and shallow-water areas) and food resources were found to exert greater influence on the Black-tailed Godwit’s distribution. This study synthesizes findings from multiple sources and aims to provide a reference for the conservation of the Black-tailed Godwit. Full article