Search Results (2,692)

Understanding the interactions between vegetation dynamics and surface water availability is essential for assessing the resilience of tropical floodplain ecosystems under increasing hydroclimatic variability. This study analyzes the spatio-temporal dynamics of vegetation cover, surface water, and land use in Zambrano, a floodplain-dominated sector of the lower Magdalena River basin (Colombian Caribbean), over the period 1990–2025. Multi-temporal Landsat imagery was used to derive the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), enabling the evaluation of seasonal and interannual ecohydrological variability under contrasting dry and rainy conditions. In addition, land-use classification was performed using a CORINE Land Cover methodology adapted for Colombia (CLC-C) to characterize the spatial organization of the landscape and its influence on vegetation–water interactions. Results show that vegetation dynamics are strongly controlled by hydroclimatic seasonality. Dense vegetation consistently expands during rainy periods, while dry seasons promote the expansion of open and sparse vegetation, reflecting seasonal vegetation stress rather than long-term degradation. NDWI patterns indicate that surface water and soil moisture are highly seasonal and spatially constrained, with open water largely confined to the Magdalena River channel and localized floodplain depressions. Extreme hydroclimatic events associated with the El Niño–Southern Oscillation (ENSO) produce abrupt but temporary changes in vegetation structure and surface moisture distribution. A strong inverse correlation between NDVI and NDWI reflects the contrasting spectral responses of vegetation and water surfaces resulting from the shared near-infrared (NIR) band in both indices. This spectral relationship is consistent with the observed seasonal variations in vegetation greenness and surface moisture across the floodplain. Land-use analysis reveals the progressive consolidation of the landscape, where the agropastoral matrix expanded from ~18,000 ha in 1990 to over 22,000 ha by 2025, driving a systematic reduction in natural and semi-natural forest structures. Forest conservation areas serve as critical ecological buffers, exhibiting lower seasonal variability in vegetation greenness. Overall, the results indicate that the Zambrano floodplain functions as a structurally stable yet highly responsive ecohydrological system, where vegetation dynamics and surface water availability are predominantly governed by interannual hydroclimatic pulses rather than long-term directional degradation. These findings demonstrate that while the structural matrix of the floodplain exhibits strong baseline resilience, its ecological functioning remains critically coupled with, and vulnerable to, the extreme phase shifts in ENSO cycles. Full article

Hainan Tropical Rainforest National Park is a prime example of a “continental island” tropical rainforest and holds significant value for biodiversity conservation. However, human activities have led to frequent changes in land use and increased habitat fragmentation within the park; a precise analysis of the underlying mechanisms is necessary for ecological restoration. Consequently, drawing upon land-use data from 2000 to 2020, this study coupled multi-dimensional fragmentation metrics (CFI, AFI, and SFI) with the XGBoost-SHAP framework to systematically unravel the spatiotemporal dynamics and underlying driving mechanisms of landscape fragmentation in Hainan Tropical Rainforest National Park. Our findings revealed that the spatial configuration of fragmentation predominantly propagated along river networks and transport corridors, accompanied by a fluctuating ‘decline–rise–decline’ temporal trajectory. Notably, the XGBoost-SHAP attribution highlighted a distinct temporal shift in the dominant drivers: fragmentation was primarily mitigated (negatively driven) by NDVI between 2000 and 2010 but was subsequently exacerbated (positively driven) by GDP growth from 2010 to 2020. The findings of this study provide data support and a scientific basis for ecosystem restoration and land use planning in Hainan Tropical Rainforest National Park. Full article

Understanding how desert riparian vegetation responds to managed flow releases is essential for ecological restoration in arid inland river basins. This study examines vegetation dynamics and hydrological responses in the desert reach of the Hotan River, a seasonal river crossing the Taklimakan Desert. To avoid temporal inconsistency, two data windows were explicitly separated: Landsat-derived vegetation information was used to describe long-term vegetation changes from 1985 to 2020, while environmental flow release, river-section water consumption, and groundwater-depth analyses were limited to the period with available hydrological observations, 2006–2020. NDVI and vegetation-cover classes were derived from cloud-screened and atmospherically corrected Landsat imagery, and the response of vegetation indicators to cumulative environmental flow release and groundwater depth was evaluated using transparent regression models with diagnostic statistics. Results indicate that vegetation cover improved overall during the study period, although the response was spatially heterogeneous. Vegetation conditions were generally better near the upper and terminal parts of the desert reach, whereas a relatively vulnerable zone occurred approximately 15–115 km downstream of the river confluence. During 2006–2020, NDVI and grassland area generally increased with cumulative environmental flow release, whereas annual grassland-area change showed large interannual fluctuations and was not significantly explained by cumulative release alone. The revised analysis clarifies that the study contributes a reach-scale synthesis linking long-term vegetation mapping with monitored environmental flow releases and groundwater response in the Hotan River desert reach, rather than a full 40-year ecohydrological attribution. These findings provide a basis for improving environmental flow scheduling and monitoring design in arid desert rivers. Full article

Flood susceptibility maps are essential tools for identifying high-risk areas. However, traditional approaches often face limitations in spatial resolution and adaptability under changing climatic conditions, particularly in data-scarce regions. This study addresses these limitations through a data-driven geospatial approach that integrates high-resolution Earth Observation and Geographic Information Systems (GIS) data to improve flood susceptibility assessment in a small river basin in Romania. Ten flood conditioning factors were analyzed, including Elevation, Slope, Topographic Wetness Index (TWI), Topographic Position Index (TPI), Profile Curvature, Aspect, Soil Texture, Distance to the River, Normalized Difference Vegetation Index (NDVI), and Soil Moisture. Historical flood extent data extracted from PlanetScope imagery were used for model training and validation. Two statistical methods, Frequency Ratio (FR) and Weight of Evidence (WoE), were applied to map flood susceptibility at a 12.5 m resolution. Results indicate that both models captured the spatial variability of flood-prone areas, but WoE achieved higher predictive performance (AUC = 0.945) than FR (AUC = 0.876), while FR tended to underestimate flood-prone zones. Half of the basin falls within low to very low susceptibility classes, whereas high and very high susceptibility together occupy about 25–29% of the basin and concentrate along river corridors in the central and southern sectors, overlapping with built-up areas. Consequently, about 38% (WoE) and 30% (FR) of the total built-up area fall within high and very high susceptibility classes. The results demonstrate that integrating high-resolution open-source Earth Observation data with statistical modeling provides a reliable, transferable framework for flood susceptibility assessment and land-use planning in data-scarce environments. Full article

Accurate and transferable land cover mapping from satellite imagery is a prerequisite for national-scale agrienvironmental monitoring and climate change impact assessment. The joint use of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery offers complementary structural and spectral information, yet systematic evaluation of fusion strategies and their geographic transferability remains limited. We trained and tested five U-Net fusion architectures S1-only, S2-only, early (input-level), feature-level (middle), and decision-level (late) alongside a SegFormer-b2 transformer baseline over two German cities (Munich and Berlin) using the Multi-Sensor Land Cover Classification (MSLCC) dataset (single-date 2017 Sentinel-1B/Sentinel-2A acquisitions) at 10 m resolution. Three cross-city transfer protocols (Munich → Berlin, Berlin → Munich, and combined training) quantify model transferability across contrasting urban–rural gradients. Early fusion achieved the highest in-city macro-averaged F1 score among U-Net variants (0.8278), a small but statistically significant improvement over the optical-only baseline (0.8236; patch-level paired bootstrap, $p=0.006$); feature-level (middle, 0.8164) and decision-level (late, 0.8196) fusion were, by contrast, significantly worse than the optical-only baseline ($p<0.001$ and $p=0.030$, respectively), and the SAR-only model (0.6864) trailed substantially. The built-up class was the primary beneficiary of SAR inclusion under early fusion. SegFormer-b2 (0.8214) was numerically close to, but statistically significantly below, the best convolutional configuration ($p=0.005$), and exhibited strong cross-city transfer (0.8632–0.8608 macro-F1), consistent with the geographic invariance conferred by its ImageNet-pretrained encoder. Combined training across both cities improved over the single-direction transfer average by 0.012 macro-F1 points for U-Net and 0.006 points for SegFormer, offering a practical route to national-scale deployment without requiring explicit domain adaptation. Spectral index augmentation (NDVI, NDWI, ExG) and SE channel attention did not significantly improve over plain early fusion when derived from percentile-normalized inputs, with the best variant statistically indistinguishable from the baseline at macro-F1 = 0.8268 ($p=0.365$); the result is attributable to a specific preprocessing dependency: NDVI, NDWI, and ExG are only physically meaningful when computed from calibrated reflectance, whereas here they were derived after scene-level 2nd–98th percentile stretching, which strips the absolute radiometric referencing the indices rely on; practitioners combining spectral indices with percentile-normalized (rather than physically calibrated, e.g., Level-2A surface-reflectance) inputs should expect a similar null result. Full article

Arid inland river basins are highly sensitive to land-use change, yet the spatially heterogeneous predictors of ecosystem carbon storage remain insufficiently understood. In this study, a locally calibrated InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model was used to estimate carbon storage in the Hei River Basin for 2010, 2015, 2020, and 2025. A CA–Markov (Cellular Automata-Markov chain Model) model was then used to project the 2030 land-use pattern and associated carbon storage under the assumption that the current policy framework remains unchanged. Random Forest models interpreted with GeoShapley were applied to quantify the nonlinear and spatially varying contributions of seven environmental and anthropogenic predictors. Total modeled carbon storage increased slightly from 5.29181 × 10⁸ t in 2010 to 5.33214 × 10⁸ t in 2020, before declining marginally to 5.32310 × 10⁸ t in 2025. The decline was mainly associated with reduced modeled grassland carbon storage. Carbon storage was generally higher in the southern and eastern parts of the basin, particularly in the Qilian Mountains, and lower in the northern and western desert regions. The whole-basin simulation projected a 4.34% increase by 2030, mainly associated with projected expansion of forestland and grassland. Predictor importance varied spatially: elevation ranked first in the upstream region, NDVI in the midstream oasis, and population density in the downstream desert. NDVI was the most important basin-wide predictor, although its importance partly reflected spatial covariance with land-cover classes. These findings demonstrate the value of integrating carbon-storage modeling, land-use simulation, and spatially explicit model interpretation for differentiated ecosystem management in arid inland basins. Full article

Understanding vegetation phenology is critical for assessing the impacts of climate change, particularly in regions vulnerable to environmental fluctuations. This study investigates the temporal trends and spatial variability of the start of photosynthetic activity (SOP) in natural vegetation across Central Asia over the past four decades (1982–2022) using satellite-derived Normalized Difference Vegetation Index (NDVI) data. The research emphasizes the critical role of vegetation phenology in understanding responses to climate change. To extract spring phenological data, various smoothing techniques were applied, including filter-based methods, asymmetrical Gaussian fitting, and three nonlinear and piecewise linear methods, ensuring accurate representation from continuous time series data. NDVI time series were smoothed using a phenological extraction package. The results indicate an average advance in SOP of 1.26 days per decade, with forest ecosystems exhibiting the greatest shift at 3.05 days per decade. A spring temperature threshold near 0 °C was identified as a reliable predictor for dormancy break. Ensemble Empirical Mode Decomposition (EEMD) was utilized to differentiate between cumulative and instantaneous trends, revealing dynamic phenological responses. A notable shift around 2005 was observed, with approximately 76.28% of pixels showing a change in SOP trend, while 23.60% displayed a stable, monotonic trend. Vegetation at elevations between 1500 and 3000 m experienced a significant SOP advance of 2.27 days per decade, whereas vegetation above 3000 m showed no significant change over the study period. Spatially, SOP trends exhibited a latitudinal gradient, with delays observed in the southern regions and advancements north of 45° N. These findings underscore the importance of developing region-specific phenological models to inform environmental management and climate adaptation strategies, particularly in arid and semi-arid ecosystems. Full article

The Three-River Source Region (TRSR) is an environmentally fragile area in China and on the Qinghai–Tibet Plateau that is extremely vulnerable to climate change. The time lag between climate change and vegetation responses in high-altitude regions is a critical component of ecosystem–climate coupling that has not yet been fully quantified. The purpose of this project is to look into the influence of extreme climatic change in the TRSR on vegetation development, as well as to give data support for vegetation restoration and ecological security on the Qinghai–Tibet Plateau. This study used normalized difference vegetation index (NDVI) data from the TRSR from 2000 to 2024 to investigate the pixel-level lag effects of 12 ETCCDI (Expert Team on Climate Change Detection and Indices) extreme climatic indices. Pixel-level maximum Pearson correlation analysis was used to determine the ideal lag period and correlation direction within an 0–6-month lag window, and the lag ratios for seven vegetation kinds were quantified in stratified order. The results show that precipitation, and not temperature, is the primary climatic limiting factor for changes in NDVI in the TRSR. Furthermore, there is a clear distinction between the temperature- and the precipitation-driven lag responses: temperature extreme indices have shorter average response times and highly polarized correlation directions (positive correlation proportions range from 1.0% to 95.9%), whereas precipitation extreme indices have longer average lags and are predominantly positively correlated. Vegetation types have a significant impact on lag sensitivity: grassland and desert respond faster, reflecting shallower root depths and limited soil moisture buffering capacity, whereas meadows and shrubland have the longest lags, consistent with the water-holding capacity promoted by deeper root systems and higher soil organic matter. These findings contribute to our understanding of time-structured vegetation–climate coupling and provide a solid scientific foundation for proactive vegetation management in the TRSR to meet future extreme climate events. Full article

Climate variability represents a major threat to agricultural systems, particularly in arid and semi-arid regions such as the Beni-Amir irrigated perimeter, located in the Tadla plain in central Morocco. In this perimeter, olive trees are exposed to multiple environmental and management-related factors that are associated with variations in phenology and vegetation vigor, such as successive drought episodes. This study represents a spatio-temporal assessment of drought impact on olive using satellite- derived vegetation indices from Sentinel-2 imagery and precipitation satellite data from CHIRPS over the period 2015–2024. The Standardized Precipitation Index (SPI-12) was used to identify wet and dry phases over this period. The results indicate an alternation of dry and wet periods between 2015 and 2021, followed by a predominance of dry conditions from September 2021. Over the same period, the time series of the Normalized Difference Vegetation Index (NDVI) and the other vegetation indices reveals marked interannual variability and a progressive degradation of olive tree phenological cycles. A land cover map derived from a supervised support vector machine (SVM) under three classification scenarios achieved high overall accuracies exceeding 94%. Post-classification change detection highlights a substantial reduction in mapped olive-growing areas between 2016 and 2024, with an estimated 72% loss of the initial area. The findings reported in this study indicate that the succession of drought episodes may have contributed to olive grove degradation, including disruptions in phenological cycles and a decline in maximum NDVI values. Even the most resilient olive groves appeared affected following the severe drought period after 2021. The study underscores the usefulness of satellite-derived vegetation indices and drought indicators for the effective monitoring of drought-related stress and supporting improved management practices under climate change. Full article

Clarifying the dynamic changes in vegetation cover and the driving mechanisms under the combined influence of the natural environment and human activities is a crucial foundation for understanding the evolutionary processes of ecosystems in arid and semi-arid regions and for improving the effectiveness of ecological restoration. Taking the Kuye River Basin, a typical resource exploitation zone in the middle reaches of the Yellow River, as the research area, this study retrieved 30 m resolution annual maximum NDVI datasets from 1986 to 2020 to calculate the Fractional Vegetation Cover (FVC). Utilizing methods such as Theil–Sen slope analysis, Mann–Kendall significance test, Hurst exponent, stability analysis, geographical detector, and sensitivity index, this study systematically revealed the spatiotemporal patterns of vegetation change, future evolution trends, and the response mechanisms of FVC dynamics to multiple factors including climate, topography, and land use. The results indicated that from 1986 to 2020, FVC in the study area exhibited an overall increasing trend (0.0105 a⁻¹), with the average FVC rising from 0.21 to 0.61. Regions with very low and low vegetation coverage continued to decrease, while areas with high and very high vegetation coverage showed significant increases, particularly in the very high vegetation coverage category, which experienced the largest growth (CV = 179.32%). The regions with moderate vegetation coverage demonstrated the highest stability (CV = 48.42%). Analysis of the driving mechanisms revealed that precipitation and land use types were the primary factors influencing changes in FVC, with land use demonstrating a more stable explanatory power (CV = 3.63%). Furthermore, the interaction between these two factors significantly enhanced the explanatory power related to vegetation changes. Sensitivity analysis indicated that the increase in forest and grassland effectively mitigated the negative impact of cropland on moderate to high coverage areas; industrial and mining land had a notable impact on very low coverage areas. It can be inferred that the Grain for Green program and the expansion of industrial and mining lands might generate differentiated impacts across diverse vegetation coverage classes. Future projections indicate that 91.19% of the region exhibits potential for FVC improvement in the future. However, a risk of sustained vegetation degradation exists in densely populated areas and regions with concentrated industrial and mining land. The study demonstrates that under the combined influences of climate change and land use adjustments, optimizing land use structures and coordinating ecological restoration with resource development are critical approaches to enhancing the stability of ecosystems in arid and semi-arid regions, as well as promoting sustainable regional ecological development. Full article

Highway construction in alpine environments of the Qinghai-Xizang Plateau (QXP) raises concerns about vegetation degradation and ecosystem disruption. However, the long-term ecological dynamics along newer expressway corridors on the QXP remain insufficiently understood. This study investigated vegetation dynamics and land use/land cover (LULC) changes along the Gonghe-Yushu Expressway (G0613, 634 km) corridor from 2000 to 2025, spanning the pre-construction, construction (2011–2017), and post-construction operational periods. Using 25 years of Landsat 5/7/8/9 imagery, we analyzed NDVI trends with Sen’s slope and Mann-Kendall tests, classified LULC into four classes (Grassland, Bare land, Water body, Built-up) using Random Forest, and examined NDVI—climate relationships using TerraClimate data. The results revealed a widespread and significant greening trend, with 77.3% of pixels showing significant NDVI increases (+0.0026/year) and no significant decreasing trends. The greening was established before highway construction and continued uniformly through all periods, with no detectable distance-dependent gradient across buffer zones (0–1, 1–2, 2–5, 5–10 km). LULC transitions were limited, with Bare land and Grassland as the dominant classes in the buffer corridor. NDVI was strongly correlated with temperature (ρ = 0.748, p < 0.001), which increased by +0.068 °C/year over the study period, while precipitation showed no significant trend. These findings indicate that vegetation greening co-varied with regional warming, while no persistent distance-dependent highway-associated NDVI signal was detected at the analyzed scale. Localized roadside impacts may remain unresolved at 30 m resolution, and the long-term implications of continued warming warrant continued monitoring. Full article

Mediterranean and semi-arid mountain forests are increasingly affected by recurrent drought, wildfire, overgrazing, and anthropogenic pressure, with direct implications for forest sustainability. This study assesses the spatio-temporal dynamics of vegetation cover in the Ouled Yagoub Forest, Khenchela Province, northeastern Algeria, from 1994 to 2025, using GIS and remote sensing. Multi-temporal satellite images, including Landsat data for historical periods and Sentinel-2 data for recent years, were processed to calculate NDVI, classify NDVI-derived vegetation-cover classes, and detect vegetation changes before and after the 2021 wildfire. Vegetation-cover classes were quantified in hectares and percentages, and NDVI change maps were produced for the periods 1994–2000, 2000–2010, 2010–2020, 2020–2021, 2021–2022, 2021–2025, and 1994–2025. Results showed that dense vegetation increased from 14.15% in 1994 to 20.71% in 2020, indicating improved pre-fire vegetation conditions. After the 2021 wildfire, dense vegetation decreased to 17.44% in 2021 and 13.44% in 2022, while very low vegetation increased sharply to 29.79% in 2022. The 2021–2022 period showed the strongest negative vegetation response, with 32.65% of the mapped area classified as vegetation decrease. By 2025, partial recovery was observed, with vegetation increase covering 20.14% of the mapped area between 2021 and 2025. However, low vegetation remained dominant, indicating incomplete and spatially heterogeneous recovery. These findings highlight the usefulness of NDVI-based multi-temporal analysis for monitoring forest degradation, post-fire recovery, and priority areas for restoration planning in semi-arid Mediterranean mountain forests, while also supporting sustainability-oriented forest management in other fire-prone regions with comparable ecological constraints. Full article

The spatial relationships between lightning-induced fire ignition and topography, vegetation, climate, and weather were analyzed in the region of East Macedonia and Thrace, northeastern Greece. The study was based on reported lightning-induced ignitions during the 2009 fire period. Lightning data for the same period was provided by the ZEUS lightning detection network operated by the National Observatory of Athens, while fire statistics were obtained from the official records of the Greek Fire Service. A total of 198 lightning strike events (66 fire ignitions and 132 non-fire events) were used for model development. Statistical models based on Logistic Regression (LR) and random forests (RF) were developed to estimate the probability of lightning-induced fire using topography, climate, weather, and vegetation indices as predictor variables. According to the analysis results, the probability of an area being affected by lightning-induced fire is primarily determined by the Normalized Difference Vegetation Index (NDVI) and the accumulated precipitation in 24 h equal to or less than 2.5 mm expressed by Dry Thunderstorm (DT) day occurrence in this dataset. The logistic regression model achieved an area under the ROC curve of 0.94 and an overall classification accuracy of 91.9%, while the random forest model produced an Out-Of-Bag (OOB) error rate of 3.0%. Although the models have not been subjected to independent validation and include a single year’s data, the results demonstrate high internal classification performance and provide valuable insights into the primary drivers of fire ignition following lightning strikes in the study region. The outcomes of the present study will be useful in assessing spatially explicit fire risk, the planning and coordination of efforts to identify high-fire-risk areas, and designing long-term fire management and climate change adaptation strategies. Full article

Forest cover change alters land–atmosphere exchanges of energy, water, and carbon, thereby influencing local and regional climate. This study assessed climatic patterns associated with afforestation in Khyber Pakhtunkhwa, Pakistan, from 2003 to 2023 using remote sensing data and Google Earth Engine. Land surface temperature (LST) was treated as the primary response variable, while evapotranspiration (ET) was analyzed as a secondary response variable. Air temperature; precipitation; vegetation indices, including the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI); and elevation were used as supporting variables to interpret the broader climatic and biophysical responses of afforestation. MODIS land-cover, LST, ET, and vegetation-index products, together with climate research unit (CRU) climate data and ALOS-PALSAR DEM, were used to evaluate spatiotemporal trends and variable relationships. The results showed that mean LST increased by 0.520 ± 0.070 °C across KP during 2003–2023; however, areas classified as forest gain showed a localized cooling pattern of 0.490 ± 0.050 °C during the 2013–2023 forest-cover transition assessment window. Afforested areas also exhibited increased ET, whereas forest-loss areas showed reduced ET and higher LST. Specifically, ET increased by 0.013 ± 0.002 mm/8-day in afforested areas, whereas forest-loss areas showed a decline of 0.005 ± 0.001 mm/8-day. CRU-derived regional air temperature showed an increasing tendency of 0.310 ± 0.050 °C, whereas precipitation showed only a weak and statistically non-significant regional tendency; therefore, precipitation was used only as background climatic context. The NDVI and the EVI were negatively correlated with daytime LST, and elevation showed a strong negative relationship with LST. Overall, the findings indicate that forest-cover gain was associated with localized surface cooling patterns and improved vegetation–climate regulation indicators in the study area. Full article

This study evaluates the spatial variability of hydric stress in the State of Sinaloa, northwestern Mexico, through the integrated analysis of hydroclimatic variables, multispectral remote sensing indicators, and environmental factors. Historical hydroclimatic conditions were analyzed using meteorological records from 1961 to 2020, whereas Landsat 8 imagery acquired on 7 July 2025, was used to evaluate the spatial expression of hydric stress. Reference evapotranspiration (ETo) was estimated using the FAO-56 Penman–Monteith methodology, and hydrological deficit conditions were determined from the relationship between precipitation (P) and ETo. Spectral indicators including land surface temperature ($\overline{T}$_a), the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and the NDWI/MNDWI relationship were used to evaluate vegetation response, surface moisture conditions, and thermal anomalies associated with hydric stress. The results revealed persistent conditions where ETo systematically exceeded P, with hydrological deficit values ranging from approximately −1600 mm·year⁻¹ to localized positive values near 50 mm·year⁻¹. The most severe deficits were concentrated within the northwestern and north-central agricultural valleys of Sinaloa. Statistical validation revealed significant negative relationships between hydrological deficit and all evaluated spectral indicators. The strongest association was observed for MNDWI (R² = 0.387), followed by NDWI/MNDWI (R² = 0.277), NDWI (R² = 0.220), and NDVI (R² = 0.134), confirming the sensitivity of vegetation and moisture-related indicators to long-term hydrological stress conditions. Spatial analyses revealed a strong correspondence among low NDVI, negative NDWI and MNDWI responses, elevated $\overline{T}$_a, and regions characterized by high atmospheric evaporative demand. Additional spatial validation integrating land-use and vegetation-cover changes (1993–2011), regional geology, topography, and the distribution of highly productive agricultural valleys demonstrated that the most severe hydrological deficits coincided with areas affected by vegetation-cover loss, agricultural expansion, and intensive land use. Although these datasets correspond to different observation periods, they collectively reflect the cumulative environmental effects associated with persistent hydrological stress across the region. The combined effects of hydrological imbalance, forest-cover reduction, and agricultural intensification have progressively reduced ecosystem resilience and increased environmental vulnerability throughout one of the most productive agricultural regions of northwestern Mexico. These findings provide a scientific basis for water-resource management, territorial planning, ecosystem restoration, and climate-adaptation strategies under increasing water-scarcity conditions. Full article