Search Results (427)

Hydrological modeling is critical for assessing water availability and guiding sustainable resource management, particularly in monsoon-dependent, data-scarce basins such as the Chalakudy River Basin (CRB) in Kerala, India. This study integrated the Soil Conservation Service Curve Number (SCS-CN) method within the Google Earth Engine (GEE) platform, making novel use of multi-source, open access datasets (CHIRPS precipitation, MODIS land cover and evapotranspiration, and OpenLand soil data) to estimate spatially distributed long-term runoff (2001–2023). Model calibration against observed runoff showed strong performance (NSE = 0.86, KGE = 0.81, R² = 0.83, RMSE = 29.37 mm and ME = 13.48 mm), validating the approach. Over 75% of annual runoff occurs during the southwest monsoon (June–September), with July alone contributing 220.7 mm. Seasonal assessments highlighted monsoonal excesses and dry-season deficits, while water balance correlated strongly with rainfall (r = 0.93) and runoff (r = 0.94) but negatively with evapotranspiration (r = –0.87). Time-series analysis indicated a slight rise in rainfall, a decline in evapotranspiration, and a marginal improvement in water balance, implying gradual enhancement of regional water availability. Spatial analysis revealed a west–east gradient in precipitation, evapotranspiration, and water balance, producing surpluses in lowlands and deficits in highlands. These findings underscore the potential of cloud-based hydrological modeling to capture spatiotemporal dynamics of hydrological variables and support climate-resilient water management in monsoon-driven and data-scarce river basins. Full article

Severe water scarcity has become a critical constraint to global agricultural development. Enhancing both the timeliness and accuracy of crop evapotranspiration (ET_c) retrieval is essential for optimizing irrigation scheduling. Addressing the limitations of conventional ground-based point-source measurements in rapidly acquiring two-dimensional ET_c information at the field scale, this study employed unmanned aerial vehicle (UAV) remote sensing equipped with multispectral and thermal infrared sensors to obtain high spatiotemporal resolution imagery of a representative citrus orchard (Citrus reticulata Blanco cv. ‘Yichangmiju’) in western Hubei at different phenological stages. In conjunction with meteorological data (air temperature, daily net radiation, etc.), ET_c was retrieved using two established approaches: the Seguin-Itier (S-I) model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The thermal-infrared-driven S-I model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The findings indicate that: (1) both the S-I model and the single crop coefficient method achieved satisfactory ET_c estimation accuracy, with the latter performing slightly better (accuracy of 80% and 85%, respectively); (2) the proposed multi-source fusion model consistently demonstrated high accuracy and stability across all phenological stages (R² = 0.9104, 0.9851, and 0.9313 for the fruit-setting, fruit-enlargement, and coloration–sugar-accumulation stages, respectively; all significant at p < 0.01), significantly enhancing the precision and timeliness of ET_c retrieval; and (3) the model was successfully applied to ET_c retrieval during the main growth stages in the Cangwubang citrus-producing area of Yichang, providing practical support for irrigation scheduling and water resource management at the regional scale. This multi-source fusion approach offers effective technical support for precision irrigation control in agriculture and holds broad application prospects. Full article

The Yellow River Basin (YRB), a pivotal ecoregion in China, has long been plagued by a range of ecological problems, including water loss, soil erosion, and ecological degradation. Despite previous reports on the ecological environment of YRB, systematic studies on the multi-factor driving mechanism and the coupling between the ecological and hydrological systems remain scarce. In this study, with multi-source remote-sensing imagery and measured hydrological data, the random forest (RF) model and the geographical detector (GD) technique were employed to quantify the dynamic spatiotemporal changes in the ecological environment of YRB in 2000–2024 and identify the driving factors. The variables analyzed in this study included gross primary productivity (GPP), fractional vegetation cover (FVC), land use and cover change (LUCC), meteorological statistics, as well as runoff and sediment data measured at hydrological stations in YRB. The main findings are as follows: first, the GPP and FVC increased significantly by 37.9% and 18.0%, respectively, in YRB in 2000–2024; second, LUCC was the strongest driver of spatiotemporal changes in the ecological environment of YRB; third, precipitation and runoff contributed positively to vegetation growth, whereas the sediment played a contrary role, and the response of ecological variables to the hydrological processes exhibited a time lag of 1–2 years. This study is expected to provide scientific insights into ecological conservation and water resources management in YRB, and offer a decision-making basis for the design of sustainability policies and eco-restoration initiatives. Full article

In recent years, under the dual pressures of climate change and human activities, wetlands in Liaoning Province, China, are increasingly threatened, raising concerns about regional ecological security. To better understand these changes, we developed a vulnerability assessment framework integrating a 30 m wetland dataset (2000–2020) with multi-source environmental and socio-economic data. Using the XGBoost–SHAP model, we analyzed wetland spatiotemporal evolution, driving mechanisms, and ecological vulnerability. Results show the following: (1) ecosystem service functions exhibited significant spatiotemporal differentiation; carbon storage has generally increased, water conservation capacity has significantly improved in the northern region, while wind erosion control and soil retention functions have declined due to urban expansion and agricultural development; (2) driving factors had evolved dynamically, shifting from population density in the early period to increasing influences of precipitation, vegetation index, GDP, and wetland area in later years; (3) ecologically vulnerable areas demonstrated a pattern of fragmented patches coexisting with zonal distribution, forming a three-level spatial gradient of ecological vulnerability—high in the north, moderate in the central region, and low in the southeast. These findings demonstrate the cascading effects of natural and human drivers on wetland ecosystems, and provide a sound scientific basis for targeted conservation, ecological restoration, and adaptive management in Liaoning Province. Full article

Non-grain conversion threatens food security in karst mountainous regions where fragmented terrain and shallow soils create unique agricultural challenges. This study examines Yunnan Province (28% karst coverage) in the Yunnan-Guizhou Plateau, where cultivated land faces distinct pressures from limited soil depth (average < 30 cm in karst areas) and poor water retention capacity. Using multi-source data (2001–2021) and an integrated Dynamic Spatial-Temporal Clustering Model (DSTCM), we quantify non-grain conversion through a clearly defined Non-Grain Conversion Index (NGCI = 0.35 × CPI + 0.25 × LUI + 0.20 × RSI + 0.20 × PSI). Results reveal the NGCI declined from 45.91 to 21.05, indicating a 54% intensification in conversion (lower values = higher conversion intensity). Spatial analysis shows significant clustering (Moran’s I = 0.57, p < 0.001), with karst areas experiencing 23% higher conversion rates than non-karst regions. Key drivers include soil fertility limitations (t = 2.35, p = 0.027), crop type transitions (t = 3.12, p = 0.047), and economic pressures (t = 2.88, p = 0.012). Model predictions (accuracy: 92.51% ± 2.3%) forecast continued intensification with NGCI reaching 9.31 by 2035 under current policies. Spatial distribution mapping reveals concentrated conversion hotspots in southeastern karst regions, with 73% of high-intensity conversion occurring in areas with >30% karst coverage. This research provides critical insights for managing cultivated land in karst landscapes facing unique geological constraints. Full article

Urban clusters face escalating atmospheric challenges, with elevated PM_2.5 concentrations representing a critical environmental constraint. This study investigates spatiotemporal evolution mechanisms of PM_2.5 within China’s Taiyuan-Yuci-Xinzhou (TYX) urban cluster. Daily PM_2.5 concentrations (July–September 2000–2020) characterized spatiotemporal distributions. The Urban Forest Effects (UFORE) model integrated multisource data—remote sensing, leaf area index (LAI), wind speed, and precipitation—within a Geographically and Temporally Weighted Regression (GTWR) framework, quantifying PM_2.5 dry deposition across land cover types and identifying drivers. Key findings reveal the following: (1) PM_2.5 concentrations followed an initial-rise-then-decline trajectory, with pollution hotspots concentrated in Taiyuan City and neighboring industrial corridors; (2) Construction lands sprawl markedly elevated PM_2.5 levels, whereas green areas and agricultural lands expansion promoted deposition; water areas exhibited no significant effect; (3) Wind speeds and precipitation positively modulated PM_2.5 in green areas and agricultural lands, contrasting with NDVI’s negative influence; elevation showed null correlation, while agricultural lands deposition correlated positively with PM_2.5; (4) GDP displayed an inverted U-curve association with PM_2.5; positive correlations emerged with AVSI, TIOV, NOP, and EC indices, but negative linkage with TOC. This clarifies land cover impacts on urban atmospheric particulates, providing empirical foundations for pollution control. Full article

In this paper, we propose a quantitative evaluation method for navigation safety in coastal waters based on unstructured grids. Initially, a comprehensive analysis was conducted on various factors affecting navigation safety, including natural conditions, traffic conditions, and marine hydro-meteorological conditions, to construct a multi-source fused spatiotemporal dataset. Subsequently, channel boundary extraction was performed using Constrained Delaunay Triangle–Alpha-Shapes, and the precise extraction of ship navigation areas was performed based on Constrained Delaunay Triangle–Voronoi diagrams. Additionally, temporal feature grids were constructed based on the spatiotemporal characteristics of marine hydro-meteorological data. Finally, unstructured grids for evaluating navigation safety were established through spatial overlay analysis. Based on this foundation, a quantitative analysis and evaluation model for comprehensive navigation safety assessment was developed using the fuzzy evaluation method. By calculating the fuzzy relation matrix and weight vectors, quantitative assessments were conducted for each grid cell, yielding safety risk levels from both spatial and temporal dimensions. An analysis was performed using maritime data within the geographic boundaries of lon.119.17–120.41° E and lat.34.40–35.47° N. The results indicated that the unstructured grid division and channel boundary extraction in the demonstrated sea area were closely related to parameters such as the ship traffic flow patterns and the spatiotemporal characteristics of the marine environmental factors. A quantitative evaluation and analysis of the 186 unstructured grid cells revealed that the high risk levels primarily corresponded to restricted navigation areas, the higher-risk grid cells were mainly anchorages, and the low to lower risk levels were primarily associated with channels and navigable areas for ships. Full article

Soil organic carbon (SOC) constitutes the largest terrestrial carbon pool and plays a crucial role in climate regulation, soil fertility, and ecosystem functioning. Understanding its spatiotemporal dynamics is particularly important in semi-arid regions, where fragile environments and extensive ecological restoration may alter carbon cycling. The Loess Plateau, the world’s largest loess accumulation area with a history of severe erosion and large-scale vegetation restoration, provides a natural laboratory for examining how environmental gradients influence SOC storage over time. This study used a random forest model with multi-source environmental data to quantify soil organic carbon density (SOCD) dynamics in the 0–100 cm soil layer of the Loess Plateau from 2005 to 2020. SOCD showed strong spatial heterogeneity, decreasing from the humid southeast to the arid northwest. Over the 15-year period, total SOC storage increased from 4.84 to 5.23 Pg C (a 7.9% rise), while the annual sequestration rate declined from 0.046 to 0.020 kg·m⁻²·yr⁻¹, indicating that the regional carbon sink may be approaching saturation after two decades of restoration. Among soil types, Cambisols were the largest carbon pool, accounting for over 44% of total SOC storage. Vegetation productivity emerged as the dominant driver of SOC variability, with clay content as a secondary factor. These results indicate that although ecological restoration has substantially enhanced SOC storage, its marginal benefits are diminishing. Understanding the spatial and temporal patterns of SOC and their environmental drivers provides essential insights for evaluating long-term carbon sequestration potential and informing future land management strategies. Broader generalization requires multi-regional comparisons, long-term monitoring, and deeper soil investigations to capture ecosystem-scale carbon dynamics fully. Full article

With the growing emphasis on running in urban health initiatives, understanding the spatiotemporal dynamics of running behavior has become essential for smart city development. This study harnesses multi-source big data—including running trajectories, points of interest (POIs), and remote sensing data—to systematically analyze factors influencing running space selection. Through stepwise regression analysis, we identify 16 significant variables encompassing accessibility, diversity, and comfort dimensions. The Geographical and Temporally Weighted Regression (GTWR) model is then employed to uncover distinct spatiotemporal heterogeneity patterns, demonstrating how these factors variably influence running activities across different urban zones and time periods. The methodology and findings contribute to geospatial analysis in urban health studies while providing practical guidance for creating more inclusive, runner-friendly urban environments. Full article

Carbon emissions in the building materialization stage are highly significant and concentrated. Quantification at this stage is essential for assessing carbon reduction potential, guiding energy-saving strategies, and supporting China’s “dual carbon” goals in the construction sector. Distinct from conventional environmental and energy economics analytical approaches, the building carbon emissions in the materialization stage (BCEMS) in 30 provinces of China from 2010 to 2021 were calculated using multi-source data, and the characteristics of their spatio-temporal evolution were analyzed. The key influencing factors were identified using a geographic detector, and their spatial heterogeneity was analyzed with the Geographically and Temporally Weighted Regression (GTWR) model from a geographical analysis perspective. The results indicated the following: (1) From 2010 to 2021, BCEMS exhibited a trend of an “initial increase followed by a decrease and subsequent fluctuation”, with an average annual growth rate of 4.28%. Building materials were the largest contributor to BCEMS, particularly cement and steel. Spatially, the emissions displayed a pattern of “higher in the east, lower in the west”. High–high-agglomeration areas remained stable over time, primarily in Zhejiang and Fujian provinces, while low–low-agglomeration areas were concentrated in Xinjiang. (2) Single-factor detection revealed that fixed assets, population density, and the liabilities of construction enterprises were the dominant factors driving the emissions’ spatial evolution. Two-factor interaction detection identified the economic society and the construction industry as the key influencing domains. (3) The economic development level and the total population showed a positive correlation with BCEMS, with the effect intensity increasing from west to east. The urbanization level and fixed assets also generally showed a positive correlation with BCEMS; however, their effect intensity initially increased positively from west to east and then turned into a negative enhancement. The findings provide references for implementing regionally differentiated carbon reduction measures and promoting green and low-carbon urban transformation in China’s construction industry. Full article

Karst landscapes, characterized by ecological constraints such as thin soil layers, severe rock desertification, and fragile habitats, require a clear understanding of the mechanisms regulating carbon storage and the impacts of ecological restoration measures. However, current research lacks detailed insights into the specific effects of ecological restoration measures. This study integrates multi-source remote sensing data and adjusts InVEST model parameters to systematically reveal the spatiotemporal evolution of carbon storage and its driving mechanisms in typical karst plateau regions of southwest China under ecological restoration measures. The results indicate: (1) From 2000 to 2020, the carbon stock in the study area increased by 6.09% overall. However, from 2020 to 2025, due to the rapid conversion of forest land into building land and grassland, the carbon stock decreased sharply by 7.69%. (2) Severe rock desertification constrains carbon stock, and afforestation provides significantly higher long-term carbon sink benefits. (3) The spatial heterogeneity of carbon storage is primarily influenced by the combined effects of natural factors (rock desertification, elevation, NDVI) and human factors (POP). Based on the research findings, it is recommended that measures to promote close forests be prioritized in karst regions to protect and restore forest ecosystems. At the same time, local habitat improvement and the establishment of ecological compensation mechanisms should be implemented, and the expansion of building land should be strictly controlled to enhance the stability of ecosystems and their carbon sink functions. These research findings provide a solid scientific basis for enhancing and precisely regulating the carbon sink capacity of fragile karst ecosystems, and are of great significance for formulating scientifically sound and reasonable ecological protection policies. Full article

The Gadouras Reservoir, Rhodes Island’s primary water source, experiences recurrent whiting events—milky turbidity from calcium carbonate precipitation—that challenge treatment operations, with impacts compounded by a major 2023 wildfire in this fire-prone Mediterranean setting. To elucidate these dynamics, a pragmatic, multi-source monitoring framework integrates archived Sentinel-2 and Landsat imagery with treatment-plant records (2017–mid-2025). Unitless spectral indices (e.g., AreaBGR) for whiting detection and chlorophyll-a proxies are combined with laboratory measurements of turbidity, pH, total organic carbon, manganese, and hydrological metrics, analyzed via spatiotemporal Hovmöller diagrams, Pearson correlations, and interrupted time-series models. Two seasonal whiting regimes are identified: a biogenic summer mode (southern origin; elevated chlorophyll-a; water temperature > 15 °C; pH > 8.5) and a non-biogenic winter mode (northern inflows). Following the wildfire, the system exhibits characteristics that could be related to possible hypolimnetic anoxia, prolonged whiting, a ~50% rise in organic carbon, and a manganese excursion to ~0.4 mg L⁻¹ at the deeper intake. Crucially, the post-fire period shows a decoupling of AreaBGR from turbidity (r ≈ 0.233 versus ≈ 0.859 pre-fire)—a key diagnostic finding that confirms a fundamental shift in the composition and optical properties of suspended particulates. The manganese spike is best explained by the confluence of a wildfire-induced biogeochemical predisposition (anoxia and Mn mobilization) and a consequential operational decision (relocation to a deeper, Mn-rich intake). This framework establishes diagnostic baselines and thresholds for managing fire-impacted reservoirs, supports the use of remote sensing in data-scarce systems, and informs adaptive operations under increasing climate pressures. Full article

Globally, wetlands have suffered severe degradation due to natural environmental changes and human activities. The wetlands on the Qinghai–Tibet Plateau (QTP) play a unique and critical ecological role, making it essential to understand their spatiotemporal dynamics and driving forces for effective conservation. Based on multi-source remote sensing data and Partial Least Squares Structural Equation Modeling (PLS-SEM), this study comprehensively quantified the spatiotemporal changes in wetlands and their key driving factors on the QTP from 1990 to 2020. The results show a net increase in total wetland area (including both natural and artificial wetlands) of approximately 538.72 km² per year over the 30-year period. Spatially, wetland expansion was most pronounced in the central–western and northern parts of the plateau, primarily driven by the conversion of grasslands, barren lands, and snow/ice cover, while localized degradation persisted in eastern regions. The PLS-SEM demonstrated an excellent fit (R² = 0.962) and identified human activities—such as ecological restoration policies and infrastructure development—as the dominant direct driver of wetland expansion (path coefficient = 0.918). Climate change, improved vegetation cover, and cryospheric loss also contributed positively to wetland gains (path coefficients = 0.056, 0.044, and 0.138, respectively). This study provides a transferable framework for understanding complex wetland dynamics and their drivers in alpine regions under global environmental change, which is crucial for designing more effective wetland conservation strategies. Full article

This study examines the intricate relationship between water resource dynamics and drought indices in the Yinchuan Plain, China, by integrating multi-source remote sensing data with advanced deep learning techniques. Using data from 2002 to 2022, we applied Long Short-Term Memory (LSTM) networks to model the spatiotemporal dynamics of water resources and their relationships with the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Palmer Drought Severity Index (PDSI). Our findings reveal a strong correlation between total water resources and the SPEI (r = 0.81, p < 0.001), underscoring the pivotal role of evapotranspiration in this region’s water balance. The LSTM model outperformed traditional statistical methods, achieving a Root Mean Square Error of 0.142 for water resource predictions and 0.118 for drought index forecasts. Spatial analysis indicated stronger correlations in the northern Yinchuan Plain, likely influenced by its proximity to the Yellow River and regional water management practices. Wavelet coherence analysis identified significant coherence at the 6–12-month scale, highlighting the importance of seasonal to inter-annual strategies for water resource management. These results provide a robust foundation for developing effective water management policies and drought mitigation strategies in arid and semi-arid regions. The methodologies presented are broadly applicable to similar water-scarce regions, contributing to global efforts in sustainable water resource management under changing climatic conditions. Full article

This study investigated the spatiotemporal dynamics and driving mechanisms of urban expansion in the Chongqing Metropolitan Area by integrating multi-source big data and employing a suite of quantitative analytical methods. Drawing upon high-resolution remote sensing imagery, land-use datasets, socioeconomic statistics, and transportation network data spanning 2019 to 2023, the research revealed pronounced spatial and temporal heterogeneity in urban growth. Specifically, expansion manifested through a core-periphery spatial structure and temporal imbalances. The findings underscore a growing economic interconnectedness between core urban districts and peripheral cities such as Guang’an and Luzhou, giving rise to a multilayered and increasingly networked spatial-economic system. Moreover, urban expansion is shown to be tightly coupled with industrial distribution, transportation optimization, and regional integration strategies. In particular, the implementation of the Chengdu-Chongqing Twin-City Economic Circle has significantly facilitated cross-regional factor mobility and spatial restructuring, thereby accelerating coordinated development across the metropolitan area. Looking forward, urban expansion in the Chongqing Metropolitan Region is expected to continue leveraging transportation infrastructure and strategic industrial placement to advance regional economic integration. Full article