Search Results (1,180)

Desertification is a serious environmental challenge in regions with desert landscapes, such as the Khorezm Oasis in the Republic of Uzbekistan. Low precipitation rates and shortages of irrigation water have driven dynamic changes in desert-related land use and land cover (LULC) classes, threatening environmental and food security. This study aims to assess desertification safety levels in the Khorezm oasis using geospatial technologies to better understand spatiotemporal dynamics and to support sustainable agricultural management. A multi-criteria decision-making (MCDM) approach was used for desertification assessment. Annual mean values of key indicators—land surface temperature, vegetation index, groundwater (GW) depth, wind speed, soil erodibility (K-factor), precipitation, normalized enhanced sand index, maximum air temperature, and LULC classes—were analyzed for the period 2000–2023. The results indicate that the normalized enhanced sand index and LULC classes exert the strongest influence on desertification processes. Areas classified as high to very high desertification hazard are predominantly concentrated in the Republic of Karakalpakstan, covering a total area of 2345.65 km². Ongoing water shortages in the Amu Darya River basin pose a significant risk of further expansion of desertified areas. The findings provide valuable insights for regional land management and desertification mitigation planning. Full article

Land Use and Land Cover (LULC) change is widely acknowledged as a pivotal driver of environmental change, exerting an escalating influence on surface hydrological processes. The accelerating pace of LULC alterations in response to burgeoning human populations underscores the pressing need for a comprehensive evaluation of their ramifications on surface runoff dynamics. This study investigates the impacts of LULC changes on flood behavior in a Mediterranean watershed in Crete, Greece (Geropotamos watershed). LULC data spanning the years 1990, 2006, and 2018 were procured from the European CORINE Land Cover database at a refined spatial resolution. The HEC-HMS hydrological model is employed to simulate peak discharge and associated hydrograph characteristics under varying recurrence intervals. Subsequently, selected river segments within the studied catchments undergo hydrodynamic flood modelling using the HEC-RAS hydraulic model. Flood depth maps are generated to illustrate the evolution of inundated areas relative to LULC change. The overarching objective of this research is to furnish a comprehensive understanding of how spatiotemporal variations in land use and land cover in-fluence flood characteristics, thereby facilitating informed decision making for sustainable planning. Full article

Global warming is driving large-scale shifts in the climatically suitable habitats of many species. However, climate-only species distribution assessments may overestimate the spatial availability of future suitable habitats when dynamic land-use change is not considered. To assess potential spatial overlaps between climate-driven habitat suitability shifts and human land-use patterns, this study focuses on Quercus L. as a widely distributed keystone forest taxon in China. The genus-level assessment was designed to identify broad-scale habitat–land-use conflict patterns under multiple climate pathways and territorial spatial planning scenarios, rather than to predict species-specific distribution responses. We developed a soft-coupled framework integrating the Maximum Entropy (MaxEnt) model and the Patch-generating Land-Use Simulation (PLUS) model, and applied the Habitat–Land-Use Conflict Index (HLCI) as a categorical spatial overlay framework to classify potential overlaps between projected suitable habitats and future land-use categories across 16 exploratory scenario combinations integrating Shared Socioeconomic Pathway (SSP)-based climate projections and land-use/land-cover (LULC) scenarios for the 2040s at the grid scale. The results indicate that: (1) climate warming may reshape Quercus habitat suitability, characterized by northward/westward expansion and southward contraction in some low-latitude regions; (2) future land-use patterns may reduce the spatial availability of projected suitable habitats by increasing their overlap with built-up land and cultivated land. Under high-emission scenarios, potential newly suitable habitats overlapped with built-up land by up to 5.90 × 10⁴ km² and with cultivated land by up to 36.42 × 10⁴ km²; and (3) the Ecological Protection scenario showed lower overlap with non-ecological land-use categories and a larger area of potentially realizable habitat expansion. This study provides a scenario-based spatial assessment of where future Quercus habitat suitability may overlap with human land-use patterns, offering broad-scale support for adaptive forest conservation and territorial spatial planning. Full article

Land use and land cover (LULC) changes are major drivers of environmental transformation in sensitive regions, such as the Marajó Archipelago in the Brazilian Amazon. This study assessed accumulated anthropization of LULC in the Immediate Geographic Region of Soure–Salvaterra, Eastern Amazon, between the reference years 1985 and 2023, using MapBiomas data and spatial statistical techniques. Bivariate Local Moran’s I (LISA) was applied to evaluate intertemporal spatial associations between areas classified as natural in 1985 and anthropized in 2023. In this approach, High–Low indicates natural areas associated with low anthropization in 2023, whereas High–High indicates areas where natural cover in 1985 was spatially associated with higher anthropization in 2023. The results indicated a strong predominance of High–Low, with values above 94% in all municipalities and up to 99.86% in Santa Cruz do Arari. In contrast, High–High had localized concentrations in Salvaterra (3.53%), Cachoeira do Arari (1.28%), and Soure (1.16%), especially in coastal zones and inland sectors. Low–Low, associated with lower anthropogenic pressure or possible signs of natural regeneration, was extremely low (≤0.0004%). These findings indicate that LISA is useful for identifying local LULC patterns and supporting environmental assessment and territorial planning in tropical regions. Full article

As metropolitan areas (MAs) become increasingly complex, reconciling land development with ecological protection has become a major challenge in spatial governance. Although ecological security patterns (ESPs) are widely used to assess ecological networks, they are often treated as diagnostic outputs after simulation rather than directly incorporated into land use/land cover (LULC) simulation processes. In addition, conventional ecosystem health assessments commonly assign uniform values to broad LULC classes, thereby overlooking variations among patches within the same class. This study proposes a spatially explicit framework that integrates forest-centered ESPs into LULC simulation as scenario-specific conversion constraints. It also applies a modified Pressure–Vitality–Organization (P–V–O) model that incorporates explicit socioeconomic pressures instead of relying on uniform, class-based resilience values and assesses ecosystem health separately for each LULC type. The framework was applied to the Tokyo, Chubu, and Kinki MAs in Japan. From 2000 to 2020, forest-corridor configurations evolved differently among the three MAs. Declines in forest connectivity were more pronounced in Tokyo and Chubu, whereas Kinki remained comparatively stable. Patch-scale ecosystem health showed marked spatial heterogeneity within cultivated land, grassland, and shrubland, and its temporal trends varied among MAs and LULC types. Simulations for 2050 under the Urban Priority, Business-as-Usual, and Ecological Priority scenarios showed that increasing levels of ecological protection imposed progressively broader constraints on land conversion, resulting in region-specific patterns of urban expansion, cultivated land change, and forest retention. The proposed framework shows how ESPs and patch-level ecosystem health information can be operationalized as spatial planning constraints, providing a practical basis for comparing development and conservation priorities and supporting differentiated LULC planning across MAs. Full article

Urban heat stress and deteriorating air quality are environmental risks in semi-arid cities, positioning urban forests as vital nature-based solutions for climate adaptation. Despite growing recognition of urban greening imperatives, South Africa’s (SA) Northern Cape Province remains characterized by sparse vegetation Land Use/Land Cover (LULC) and built environment expansion. The study’s research problem focuses on how vegetation LULC dynamics influence urban forests’ potential in mitigating heat stress and atmospheric pollution in arid urban systems. The study adopts a multi-scale analytical approach, conducting the LULC and NDVI analysis through a multi-temporal Landsat satellite imagery analysis quantifying LULC change from 2004 to 2024. Grounded in the Integrated Spatial Justice-Ecosystem Services (ISJES) Framework, the analysis reveals significant decline in dense vegetation LULC from 9021.77 km² (2.4%) to 1262.10 km² (0.3%), while barren land expanded from 73,417.01 km² (19.7%) to 222,866.82 km² (59.8%) intensifying urban thermal exposure. Built-up areas expanded from 91.06 km² to 357.072 km², further constraining ecological buffers across the province’s urban nodes and undermining urban climate resilience. The Global Moran’s I statistic for the NDVI change surface (I = 0.7843, Z = 443.87, p < 0.0001) confirms spatial clustering of degradation hotspots of NDVI decline affecting 66.5% of the study area. Furthermore, Geographically Weighted Regression (GWR) results confirm that vegetation loss is being driven by the combined and spatially differentiated effects of mining proximity, urban expansion, livestock pressure, declining rainfall, and rising temperatures. In terms of thermal regulation findings, the Getis-Ord Gi hot spot analysis identifies significant NDVI decline covering 23.5% of the study area at the 99% confidence level, expanding to 33.5% and 39.5% at the 95% and 90% confidence levels, respectively; hence, there is a need for urban forest corridors, climate-sensitive spatial planning frameworks, and targeted greening interventions in heat-vulnerable arid geographies. This study provides the first comprehensive, multi-decadal quantification of vegetation loss across SA’s largest province. Full article

Flooding in the Kathmandu Valley has intensified in recent years due to rapid urbanization, unregulated land-use change, and insufficient drainage infrastructure. Existing flood hazard assessments are often based on low-resolution datasets and lack proper field validation. This study presents an integrated flood modeling framework that combines Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs), field-based flood measurements, and hydrological simulations to assess urban flood hazards in the Bagmati-Nakkhu confluence, Nepal. High-resolution UAV-derived DEM and field survey data, including flood marks and high-water levels, were used as the foundation for the analysis. Hydrological modeling was conducted using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) to estimate the peak discharges of the Nakkhu River (2000–2024), which were then used to derive design flows for return periods of 5 to 150 years using the Gumbel distribution. These flows were used as boundary condition inputs for the Hydrologic Engineering Center—River Analysis System (HEC-RAS) to simulate flood depth and inundation extent under different scenarios. Flood extents for the 27 September 2024 event were derived from Sentinel-2 imagery and validated against surveyed flood marks. Additionally, land use/land cover (LULC) mapping based on UAV data was used to support flood impact analysis. The results show that flood depths ranged from approximately 0.5 m to 2.8 m, with inundation areas increasing by 35–50% under extreme rainfall. Model validation demonstrated strong agreement with simulated results, with deviations generally within ±0.3–0.5 m. Scenario analysis further indicates that urban expansion significantly increases runoff and flood extent, particularly in low-lying areas near the river confluence. Socio-economic exposure analysis for the 27 September 2024 event indicates that approximately 2569 residents (56.4% of the study zone population) and 4.011 km (77.42%) of the local road network were exposed to inundation. Overall, the results demonstrate that integrating high-resolution UAV data, field observations, and hydrological modeling greatly improves the accuracy and reliability of flood hazard assessments in data-scarce urban environments. Full article

Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of Pakistan’s first national efforts to address the gap between flood risk assessment and prioritization through a unified geospatial assessment. This study assesses flood susceptibility across Pakistan for 2002, 2012, and 2022 using a GIS-based AHP approach by integrating climatic, environmental, topographic, hydrological, soil, LULC, and anthropogenic indicators. The study results were further analyzed through district-level assessments, risk change analysis, persistence mapping, LULC exposure assessments, and the Comprehensive Flood Risk Priority Index (FRPI). The results show that high and very high flood susceptibility zones are primarily concentrated along the Indus River corridor, lower floodplains, and coastal Sindh, accounting for more than 7% of the total land area of Pakistan. Persistent flood hotspots are identified in Rann of Kutch (66.6%), Jacobabad (65.0%), and Jafarabad (61.1%), indicating strong temporal stability of flood-prone conditions. LULC exposure analysis reveals that cropland is the dominant exposed class, with the highest district-level exposure observed in Badin (17.1%) and Larkana (10.1%). The FRPI further identifies priority flood-risk zones where susceptibility, persistence, risk change, and exposure converge, with the highest FRPI values observed in Jacobabad (0.742), Rann of Kutch (0.738), and Badin (0.711). Model validation demonstrates strong predictive performance, with susceptibility ROC-AUC values ranging from 0.85 to 0.87 and FRPI AUC reaching 0.85. The proposed framework provides a robust decision-support tool for targeted flood-risk management and climate-resilient land-use planning in Pakistan. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

This study assesses the spatio-temporal distribution and transition dynamics of land use and land cover (LULC) in Egypt using satellite-based MODIS observations (SAT-MODIS) and WRF static datasets (WRF-MODIS) from 2001 to 2020. Dominant LULC types, barren areas (BAs), cropland (CR), urban and built-up land (UBL), water bodies (WBs), grassland (GR), and open shrubland (OS), exhibited notable changes associated with agricultural expansion, urbanization, and land reclamation due to human-induced activities. BAs remained dominant, covering more than 94% of Egypt throughout the study period. Comparative analysis between the three WRF-MODIS options (WRF-Opt1, WRF-Opt2, and WRF-Opt3) and SAT-MODIS revealed LULC classification discrepancies, which may be due to differences in algorithms, temporal representation, and spatial resolution. WRF-Opt3 showed the highest spatial consistency with SAT-MODIS, particularly before and around 2010. The findings highlight limitations of static WRF land cover datasets and emphasize the need for higher-resolution and dynamically updated LULC datasets to improve regional climate and land–atmosphere modeling applications over Egypt. Full article

Land-use/cover change threatens the ecological integrity of the Miombo region of south-central Africa. In Angola, Miombo ecosystems are of high ecological and socio-economic importance, providing rural populations with woody and non-timber forest products. Fire plays an important role in regional agricultural and silvicultural land-use systems. This study contextualised Copernicus land-cover classes at the regional level to analyse LULC transition pathways and their association with fire occurrence in Central Angola. LULC change was assessed using a post-classification comparison approach combined with pixel-based trajectory analysis. Fire activity was analysed using MODIS-derived ignition points, burned-area data, and a hexagonal-grid aggregation approach. At the same time, spatial clustering was assessed using hot spot analysis based on the Getis-Ord Gi* statistic. Differences in mean fire size among LULC transition classes were tested using the Kruskal–Wallis test followed by Dunn’s post hoc test. The results indicate a gradual reduction in forest cover and conversion to Cultivated Land, associated with the expansion of agricultural frontiers and urban areas. Fire activity was highest in areas affected by LULC conversion, with seasonal patterns varying notably among classes. Mean fire size differed by more than two orders of magnitude among transition types. Overall, fire activity was strongly associated with areas undergoing land-cover transition, highlighting the need to integrate fire management into sustainable land-use policies for long-term Miombo conservation. Full article

Landslide susceptibility is often treated as a static assessment of present-day conditions, overlooking the temporal evolution of geomorphological and anthropogenic drivers. This limitation is particularly relevant in rapidly urbanising areas, where land use change continuously alters environmental conditions influencing slope stability. This study examines the temporal evolution of landslide susceptibility in the Grigorescu neighbourhood of Cluj-Napoca, Romania, using environmental datasets representing conditions in 1971, 2009, and 2025, along with a projected land use scenario for 2047. The proposed framework integrates multi-temporal landslide inventories and conditioning factors with Maximum Entropy (MaxEnt) modelling and CA–Markov–MLP land use simulation (MOLUSCE). Results indicate a progressive shift towards higher susceptibility classes over time, accompanied by urban expansion onto increasingly steep terrain. However, slope gradient remained the dominant conditioning factor throughout all analysed periods, while land use change influenced the temporal evolution and spatial redistribution of susceptibility through progressive urban expansion into terrain already predisposed to instability. The 2047 scenario suggests that continued urban expansion may increase the exposure of built-up areas to zones of elevated susceptibility. Model performance was robust (AUC > 0.8; Kappa > 0.9). Beyond site-specific findings, the framework provides a transferable methodology for integrating urban growth dynamics into landslide susceptibility assessment, supporting sustainable spatial planning and risk-informed urban development in rapidly urbanising hilly environments. Full article

Recent land use and land cover (LULC) transformation in Vietnam raises the question of whether recent changes reflect a uniform national trend or differentiated regional patterns. This study assesses provincial LULC dynamics across 34 provinces using nationally consistent remote-sensing data for 2000, 2020, and 2023. We combine annualized intensity analysis, transition matrices, Shannon entropy, dominant transition analysis, and spatial autocorrelation to compare the magnitude, structure, and spatial organization of LULC change before and after 2020. The results show that annualized land-change rates were substantially higher during 2020–2023 than during 2000–2023, with all provinces showing increased rates of transformation. However, this more recent intensification has not been spatially uniform. Higher increases have been concentrated in southern and delta provinces, while several northern and upland provinces showed lower acceleration. Structural responses also varied across provinces: only four of 34 provinces (11.8%) were classified as both accelerated and structurally concentrated, whereas diversified regimes accounted for about two-thirds of the provinces. Population density was moderately associated with post-2020 magnitude of change but only weakly related to structural configuration, indicating that the magnitude and composition of LULC change represent distinct dimensions. By separating change intensity from structural configuration, this study provides a reproducible framework for identifying differentiated provincial land-change regimes. The results show that recent land cover transformation in Vietnam is not a single national process, but a mosaic of spatially and structurally distinct change patterns. Full article

Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95, $\kappa$ = 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management. Full article

Small Mediterranean islands relying on the sun–sea–sand (3S) tourism model face growing climate risks that threaten their tourism-dependent economies. This study evaluates climate suitability for 3S tourism on the Island of Vis by integrating the Climate Index for Tourism (CIT) with land- use and land-cover (LU/LC) spatial analysis. The integration is operationalized by overlaying CIT-derived seasonal suitability windows with LU/LC-based spatial vulnerability maps, enabling identification of micro-zones where natural buffers (forest cover and elevation) can offset thermal discomfort during peak heat stress periods. Observed data reveals declining ideal 3S conditions from July to October, with the island already exceeding 50 days per year of Physiologically Equivalent Temperature (PET) above 35.1 °C, increasing by 0.7 days per year. Regional climate models tend to exhibit a cold bias over small Adriatic islands, largely related to their limited spatial horizontal resolution (12.5 km grid spacing). However, they robustly reproduce the direction of recent and projected warming trends. Future projections indicate that the annual number of strong heat stress days with PET above 35.1 °C increase from approximately one per year in the reference period to six under RCP4.5 and nine under RCP8.5, with both scenarios reducing ideal peak-summer conditions while extending favorable periods into transitional seasons. Spatial analysis shows that coastal zones have higher sealed surfaces and less forest cover, reducing natural shade and cooling capacity, while the island interior offers higher elevations, forest buffers, hiking trails, and a UNESCO Global Geopark. Drawing on social–ecological resilience theory, we conceptualize the island’s tourism system as an adaptive unit whose long-term viability depends on spatially diversified resource use and temporally extended seasonality. The integrated analytical framework identifies not only when conditions deteriorate but where alternative tourism resources exist, enabling more targeted adaptation planning and supporting diversification toward outdoor tourism forms. The novelty of this study lies in the systematic spatial integration of bioclimatic suitability assessments (CIT and PET) with LU/LC analysis at the micro-island scale. Such an approach moves beyond temporally focused climate–tourism indices to produce actionable, location-specific adaptation strategies. Full article