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Wildfires have intensified in several regions worldwide, and the Brazilian Pantanal has become increasingly vulnerable due to the combined effects of human activities and climate change. This study analyzed the spatiotemporal patterns of burned areas and burn severity in the Brazilian Pantanal over 39 years (1985–2023), integrating burned-area dynamics, land use and land cover information, and hydroclimatic variables. Burned areas were quantified using MapBiomas Fire Project data, including annual burned areas, affected land use and land cover classes, seasonal fire distribution, fire-scar size, and fire recurrence. Burn severity was assessed using the Differenced Normalized Burn Ratio (ΔNBR), and hydroclimatic trends were evaluated using the Mann–Kendall test. The largest burned areas occurred in 1999 (27,260.65 km²) and 2020 (25,602.65 km²), with grassland representing the most affected land use and land cover class throughout the historical series. Fires were concentrated during the late dry season, and recurrent burning was more evident in the southwestern Pantanal and in smaller northern areas. The 2020 fire season showed the greatest extent of high-, moderate–high-, and moderate–low-severity classes. Wildfire occurrence, recurrence, extent, and severity were associated with hydroclimatic variability, especially reduced precipitation and relative humidity and increased air and land surface temperatures. These findings provide a long-term basis for understanding changes in fire regimes in the Brazilian Pantanal and can support fire management, ecological restoration, biodiversity conservation, and climate adaptation strategies. 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

Global climate warming has intensified in recent years, with extreme weather events occurring more frequently and severely impacting ecosystems and social production. According to the “China Climate Change Blue Book (2023),” China’s temperature rise rate exceeds the global average, with increasingly significant impacts on ecosystems. Hyphantria cunea, an invasive forest pest first discovered in China in 1979, has spread widely, causing serious damage to forestry and agriculture and posing a significant threat to China’s ecological security. To address this threat, this study employed seven modeling algorithms (GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt) from the R Biomod2 package to develop an ensemble model. The core research objective of this work is to quantify climate-driven range shifts of H. cunea under ongoing global climate change. Previous nationwide SDM studies on invasive forest pests have consistently demonstrated that climatic variables dominate broad-scale nationwide suitable habitat patterns at the macro-regional level. Supplementary topographic, vegetation cover, and human land-use disturbance layers were incorporated to capture fine-scale habitat filtering effects and long-distance pest dispersal facilitated by human activities, which together fully characterize the suitable regional environments of this pest. By integrating climate, topography, vegetation, and human disturbance data, we predicted the potential geographical distribution of H. cunea in China under four future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The ensemble model achieved excellent performance with TSS and ROC values of 0.901 and 0.984, respectively. Currently, highly suitable areas for H. cunea are concentrated in 12 provinces, including Shandong, Jiangsu, Hebei, Henan, and Anhui, covering 56.33 × 104 km², with Shandong showing the highest proportion (25.48%). The suitable habitat range is projected to expand northeastward, with significant increases under high emission scenarios (SSP5-8.5). Analysis of environmental variables reveals that nighttime light brightness, precipitation in the warmest season, the seasonal temperature variation coefficient, and average temperature in the driest season are key factors influencing H. cunea distribution. Nighttime light brightness shows the highest contribution (27.7%), indicating significant human impact on species spread. Response curves suggest that H. cunea favors warm, humid areas with pronounced seasonal changes. This study demonstrates that climate change will increase H. cunea expansion risk, necessitating strengthened cross-regional monitoring and biological control techniques. These findings provide a scientific foundation for understanding H. cunea spatiotemporal distribution patterns under future climate scenarios and for developing effective prevention and control strategies. Full article

With the growing adoption of electric vehicles, the proper siting of electric vehicle charging stations (EVCSs) has become a critical issue in urban transportation planning. This study addresses the EVCS siting problem in Kuwait through a spatial decision-making approach. A total of 25 spatial criteria covering transportation, land use, environmental conditions, and energy infrastructure were evaluated. Criterion weights were calculated from expert judgments using the Fuzzy SIWEC and SWARA methods. The results showed a high level of consistency between the two weighting methods, with a Spearman rank correlation coefficient of ρ = 0.9090 and a Pearson correlation coefficient of r = 0.8376. The final weights indicated that tourism, culture, and entertainment areas (C2.3, 0.05046), parking areas (C1.3, 0.04910), road accessibility (C1.4, 0.04813), and retail and dining areas (C2.6 to C2.7, 0.04708 to 0.04757) were the most influential factors in EVCS planning. All criteria were spatially represented in a geographic information systems environment, normalized to the [0–1] range according to their benefit and cost directions, and integrated through weighted overlay analysis to produce a continuous EVCS suitability map. Based on this suitability surface, 133 candidate EVCS alternatives were assigned to areas with relatively high suitability values and active urban land-use characteristics. The extracted raster suitability values of these candidate alternatives ranged approximately between 0.640 and 0.860, indicating that the assigned points were concentrated in spatially favorable areas rather than being randomly distributed. The ranking results obtained from TOPSIS and VIKOR showed that the top six alternatives were identical in both methods, and alternative A123 ranked first with a VIKOR value of 0.007548 and a TOPSIS value of 0.884213. Sensitivity analysis showed that changes in criterion weights affected suitability values and transition zones, while the overall spatial pattern of highly suitable areas remained stable. The findings suggest that the proposed GIS-MCDM framework provides a practical preliminary decision-support basis for spatial screening and investment prioritization in EVCS planning, particularly in hot-climate cities. Full article

Anthropogenic land-use change influences soil organic carbon (SOC) dynamics by altering both biotic and abiotic soil factors. The carbon stable isotope ratio of SOC (δ¹³C) indicates the vegetation sources of organic carbon and legacy effects of historical land use, providing important information for carbon dynamics. However, the mechanisms driving SOC dynamics in deep soils (>100 cm) under different land cover and land-use types remain poorly understood. Here, we analyzed the SOC content and δ¹³C in thick soil profiles (a thickness of 160 cm or 200 cm) under different land cover/land-use types in the typical black soil region of the Songnen Plain, Northeast China. The results showed that the average SOC content at 0–30 cm depth in natural forest land (38.87 g kg⁻¹) was higher than that in the forest land converted to cultivated land (31.66 g kg⁻¹), artificial forest land (22.63 g kg⁻¹), and perennial cultivated land (18.16 g kg⁻¹). Similarly, the average SOC content below 100 cm depth was higher in natural forest land (7.99 g kg⁻¹) than in artificial forest land (6.90 g kg⁻¹), the conversion of natural forest to cropland (6.59 g kg⁻¹), and perennial cultivated land (4.39 g kg⁻¹). Notably, significant positive correlations between δ¹³C and SOC were observed in both natural forest land and perennial cultivated land, presenting the synergistic effects on SOC probably influenced by carbon input, microbial communities, and environmental conditions. Further investigation revealed that soil moisture content and pH significantly influenced SOC content, probably by regulating organic matter decomposition rates. The natural forest land with high moisture content and low pH conditions created favorable environments for carbon preservation, whereas long-term cultivated cropland with low moisture content and high pH conditions accelerated carbon mineralization processes. These results indicate that land cover and land-use change not only significantly alter surface SOC content but also drive deep soil carbon cycling dynamics by regulating soil moisture content, pH and δ¹³C values. This study elucidates the intrinsic relationships between SOC content, δ¹³C, pH, and moisture content under land-use change, providing scientific support for land use-aware carbon management strategies in black soil regions. Full article

Farmland abandonment is a widespread land-use transition that may reshape surface soil organic carbon (SOC), yet its effects in the hilly red-soil region of South China remain insufficiently understood. Here, 30 m-resolution CLCD land-cover data, 90 m-resolution SOC data, and environmental variables were integrated with trajectory tracking, paired-sample comparison, temporal gradient analysis, geostatistics, and geographically weighted regression (GWR) to identify abandoned farmland and assess the SOC responses. The results showed that farmland abandonment rates fluctuated between 0.7% and 6.6% during 2000–2020, with abandonment hotspots progressively shifting toward the 2015–2020 period. Approximately 87% of abandoned farmland occurred in low-slope areas (0–15°). At the regional scale, farmland abandonment did not produce a consistent enhancement of surface SOC, with a negligible mean difference between abandoned and control farmland (−0.020 g/kg). This weak regional mean response masked contrasting local changes, with 43.76% of samples showing SOC gains and 46.59% showing SOC losses, indicating that spatial heterogeneity rather than the net regional effect is central to interpreting abandonment-induced SOC responses. Abandonment duration exhibited pronounced temporal gradient effects, with the strongest SOC accumulation occurring during the 5–10-year stage, followed by gradual stabilization. The GWR results indicated that abandonment duration was negatively associated with SOC change rates, whereas the annual mean NDVI showed a positive association, reflecting the combined effects of vegetation recovery, topographic conditions, and human activity intensity. These findings support spatially differentiated carbon accounting and abandoned-farmland management in subtropical hilly agroecosystems. 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

While terrestrial carbon storage is vital for achieving global carbon neutrality, its spatiotemporal evolution in ecologically fragile regions—such as the Pisha sandstone area—is complicated by intense erosion and complex environmental drivers. Widely known as the Pisha sandstone area, often referred to as the “Earth’s ecological cancer” due to its unique geological instability (“hard as rock when dry, soft as mud when wet”), this area is a critical but vulnerable carbon sink in the Yellow River Basin. This study aims to clarify these dynamics and identify their non-linear driving mechanisms by integrating a coupled PLUS–InVEST model with an XGBoost–SHAP framework to simulate land-use cover change and quantify carbon sequestration potential from 1990 to 2040. Our results reveal: (1) a robust path dependence in land use, where grassland remained the dominant landscape matrix (>75%), which partly explains the stable regional carbon-stock structure and the moderate FoM value of the PLUS validation; (2) carbon storage followed a fluctuating but overall increasing trajectory, projected to reach a peak of 3.19 × 10⁵ tC by 2040 under the Ecological Conservation Scenario (ECS), which significantly outperforms the economic-driven and natural growth modes; (3) hot spot analysis showed that statistically notable low-carbon cold spots were concentrated mainly along valley corridors, marginal transition zones, and locally disturbed patches, whereas high-carbon hot spots were spatially limited; and, (4) crucially, XGBoost–SHAP results should be interpreted as model-based associations rather than direct causal proof; the whole-region model and the regional models jointly suggest that topography, water availability, socioeconomic pressure, and erosion-related factors contribute differently across bare, loess-covered, and sand-covered Pisha sandstone units. These findings support differentiated land-use and restoration strategies rather than uniform regional management. The findings suggest that future management in the Pisha sandstone area should transition from general restoration toward targeted and differentiated regulation to improve regional ecosystem services. Full article

Against the backdrop of coordinated development in the Beijing–Tianjin–Hebei region, Hebei Province serves as an ecological safety barrier for the Beijing–Tianjin–Hebei urban agglomeration. Conducting research on land use and land cover change (LUCC) and ecosystem service value (ESV) holds significant theoretical and practical value for elucidating the mechanisms underlying ESV evolution under the combined effects of rapid urbanization and major ecological engineering projects, and for applying these findings to regional land-use planning and ecological conservation and restoration efforts. This research aligns with the United Nations Decade on Ecosystem Restoration (2020–2030). Based on land-use data from 2000, 2010, and 2020, along with 11 categories of natural and socio-economic drivers, this study systematically analyses regional LUCC and calculates ESV using locally adjusted equivalence factors. It examines the spatiotemporal evolution patterns of ESV through the analysis of local spatial autocorrelation indices (LISAs), centroid, and standard deviation ellipses, and employs a GeoDetector to measure ESV drivers. Three scenarios—a natural evolution scenario (NES), economic development scenario (EDS), and ecological protection scenario (EPS)—were established. The patch-generating Land use simulation (PLUS) model was employed to simulate LUCC for 2030 (Kappa = 0.840) and calculate ESV. Results show that from 2000 to 2020, forest land and impervious surfaces in Hebei Province continued to expand, while cropland and grassland decreased. The cumulative ESV increased by 4.85 billion yuan. Slope was the primary driver of spatial variation in ESV, and the interaction between natural and socioeconomic factors demonstrated significantly stronger explanatory power. In 2030, the total ESV under all three scenarios was lower than in 2020. The EPS reached an ESV of 344.72 billion yuan, representing a relatively suitable model that balances development and conservation. Full article

This paper focuses on the analysis of long-term land surface temperature (LST) dynamics and land-use changes in the city of Košice, Slovakia, during the period 1985–2025. The analysis is based on multi-temporal Landsat satellite imagery processed within a geographic information system (GIS) environment. Non-parametric statistical methods, including the Mann–Kendall trend test and the Theil–Sen slope estimator, were applied at the pixel level to identify the direction, magnitude, and statistical significance of long-term trends. Land-use changes were evaluated using CORINE Land Cover data together with the NDVI and NDBI spectral indices. The results revealed a statistically significant increase in land surface temperature across almost the entire urban area, with the mean LST increasing by 5.83 °C between 1985 and 2025. The analysis also confirmed a strong positive correlation between built-up areas and LST values, whereas vegetation cover exhibited a significant cooling effect represented by a strong negative correlation with surface temperature. Spatial analysis identified pronounced warming hotspots concentrated mainly in industrial and newly urbanized areas, while vegetation-stabilized zones showed lower warming intensity or localized cooling trends. The findings highlight the dominant influence of urbanization processes on the city’s thermal regime and emphasize the importance of urban vegetation as a key adaptation element for mitigating the surface urban heat island effect. The study also illustrates the added value of integrating remote sensing data, GIS tools, and pixel-based trend analysis in the assessment of long-term changes in the urban thermal environment of medium-sized Central European cities. The results provide a spatial basis for climate adaptation planning and future assessments of urban thermal comfort and environmental quality. 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

Spatial differentiation of urban natural basement conditions leads to significant differences in urbanization development patterns and land evolution patterns in different regions. Taking Fuzhou, a typical coastal basin city located in the Minjiang River Estuary, as the study area, this paper analyzes the spatiotemporal evolution characteristics of land use/cover change (LUCC) and quantifies its driving mechanism from 1990 to 2020, by using the land use transition matrix (LUTM), the center-of-gravity model (CGM), the standard deviation ellipse (SDE), and the optimal parameters-based geographical detector (OPGD). The results show that (1) the land use structure has undergone drastic restructuring, the built-up land has increased significantly, the grassland has decreased significantly, and the cropland and forest land have shown phased evolution characteristics: a light increase from 1990 to 2000 and a continuous decline from 2000 to 2020. Water exhibited a fluctuating pattern: shrinking from 1990 to 2000, expanding from 2000 to 2010, and shrinking again from 2010 to 2020. (2) Constrained by the terrain of the Minjiang Estuary Basin, the gravity centers of cropland and grassland shifted northwestward, forest land moved southeastward, water shifted northeastward, and built-up land expanded northward. (3) Driving factors exhibited stagewise differences: socioeconomic factors played a dominant role from 1990 to 2000, with population density ($q$ = 0.4029) and nighttime light ($q$ = 0.3639) being significantly higher than other factors. From 2000 to 2010, the terrain constraint effect continued to intensify, with GDP ($q$ = 0.4470), nighttime light ($q$ = 0.3658) and DEM ($q$ = 0.3638) as the dominant factors. From 2010 to 2020, urban land pattern evolution was jointly driven by multiple factors. This study clarifies the land use evolution mechanism of coastal basin cities during urbanization, providing a scientific reference for the sustainable development of similar coastal basin cities. Full article

The research analysed the space-use and habitat-preference characteristics of red deer (Cervus elaphus) in the Sopron Mountains, Hungary, utilising high-resolution Global Positioning System (GPS) telemetry data and two distinct land-cover databases. Hourly location data from 10 individuals were processed using the minimum convex polygon (MCP) and kernel home range (KHR) methods. Additionally, a relative stability index (RSI) was developed to describe seasonal shifts in area use. Significant sexual dimorphism was identified in the extent of annual home ranges: the mean space use of stags (3381 ha) significantly exceeded that of hinds (1391 ha). Geomatical analyses highlighted the seasonality of space use: the smallest extent was recorded in June, and shifts in home ranges within a single year were significant, while the winter period exhibited the least seasonal variation. Regarding habitat selection, significant seasonality was observed in hinds, reflecting temporal changes in resource availability, whereas this pattern was not observed in stags. The study concluded that the applied methods are appropriate for gathering baseline information; however, integrating high-precision databases is essential for accurate modelling of deer–forest interactions. 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