Search Results (956)

The rapid expansion of seaweed aquaculture has profound impacts on coastal ecosystems, yet the lack of long-term, high-precision spatiotemporal monitoring methods has constrained systematic understanding of aquaculture dynamics and their environmental effects. This study integrated Landsat (1984–2025) and Sentinel-2 (2015–2025) imagery with an attention-enhanced U-Net deep learning model to achieve 41 years of continuous monitoring of seaweed aquaculture in the Dongtou Archipelago, Zhejiang Province, China. The model achieved high extraction accuracy for both Landsat and Sentinel-2 aquaculture areas (F1 scores of 0.972 and 0.979, respectively). On this basis, the cultivation zones were further classified into Porphyra sp. and Sargassum fusiforme cultivation areas by incorporating local aquaculture planning and field survey data. Results showed that the aquaculture area underwent three developmental stages: slow initiation (1984–2000, <3 km²), rapid expansion (2001–2015, 3–8 km²), and high-level fluctuation (post-2015, typically 8–20 km²), reaching a peak of ~30 km² during 2018–2019. Long-term retrieval of water quality parameters revealed that the decline in total suspended matter (from ~80 to 60 mg/L) and chlorophyll (from ~3 to 2 μg/L) within aquaculture zones was significantly greater than that in non-aquaculture areas, providing direct observational evidence for local water quality improvement by appropriately scaled aquaculture. Meanwhile, sea surface temperature showed a sustained increasing trend, with extremely high-temperature days (≥25 °C) exhibiting strong interannual variability, posing potential thermal stress risks to cold-preferring seaweed species. The NDVI (Normalized Difference Vegetation Index) and FAI (Floating Algae Index) indices effectively captured aquaculture phenology (seeding, growth, maturation, harvest), with their interannual peaks exhibiting an inverted U-shaped correlation with corresponding yields (R = 0.82 and 0.79, respectively, based on quadratic regression fitting), preliminarily demonstrating the potential of remote sensing in indicating density-dependent effects. This study systematically demonstrates the comprehensive capability of multi-source satellite remote sensing in long-term dynamic monitoring, environmental effect assessment, and yield relationship analysis of seaweed aquaculture, providing key technical support and scientific basis for aquaculture carrying capacity management and ecological risk prevention in island waters. Full article

Timely and accurate mapping of tree species is essential for forest resource inventory, biodiversity conservation, and sustainable ecosystem management, particularly in dryland environments where structural heterogeneity, spectral similarity, and data scarcity complicate classification. This study develops a machine learning-based framework implemented in Google Earth Engine to map dominant tree species in the Elnour Natural Forest Reserve (ENFR), Blue Nile, Sudan, using multi-temporal and multi-sensor remote sensing data. Multi-temporal Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 MSI imagery were integrated with vegetation index (NDVI), topographic variables derived from a digital elevation model (DEM), and field observations. The performance of Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and an unweighted ensemble approach was evaluated across four reference years (2008, 2013, 2018, and 2021). Results show that RF and SVM consistently achieved high classification performance, with overall accuracy (OA) ranging from 85.0% to 92.0% and Kappa coefficients (κ) from 0.81 to 0.89, while maintaining stable and ecologically realistic species-area estimates. CART showed greater sensitivity to class imbalance and overestimated minor species (OA = 72.0–80.0%, κ = 0.65–0.74), whereas the ensemble approach amplified misclassification of rare classes (OA = 78.0–84.0%, κ = 0.70–0.78). The integration of Sentinel-2 data improved species discrimination due to enhanced spatial and spectral resolution, particularly in the red-edge region; however, algorithm selection remained the dominant factor controlling performance. Feature importance analysis identified near-infrared (NIR), shortwave infrared (SWIR), and NDVI variables as the most influential predictors. Multi-temporal analysis revealed declining class separability, reflected by decreasing MCC values, and a shift in species composition, including a decline in Acacia seyal (Delile) and an increase in Sterculia setigera Delile. These patterns indicate increasing ecological complexity driven primarily by anthropogenic pressures, with climatic variability acting as an additional stressor. Full article

China’s rapidly aging population poses a significant challenge to the equitable allocation of primary healthcare resources. Conventional accessibility assessments often rely solely on economic indicators, overlooking the ecological constraints that shape human settlement and service provision. To address this problem, this study proposes a socio-ecological framework integrating remote sensing data with spatial accessibility modeling. This study employs the Vegetation Nighttime Condition Index (VNCI)—a fusion of VIIRS nighttime lights and MODIS NDVI—as a proxy for human activity intensity under ecological constraints. The spatial accessibility of primary healthcare for the elderly (aged 65+) is evaluated across 31 provinces in mainland China using the Enhanced Two-Step Floating Catchment Area (2SFCA) method. Furthermore, a coupling coordination model and the Relative Development Index (RDI) are applied to examine the relative alignment between healthcare accessibility and the socio-ecological development context represented by VNCI. Empirical results reveal a distinct East–West gradient. Eastern coastal regions exhibit high accessibility; however, the coupling analysis identifies that healthcare accessibility lags behind high socio-ecological development intensity (low RDI). Conversely, western and rural regions generally suffer from a “low-level trap,” characterized by both low accessibility and weak socio-ecological coordination. The findings demonstrate that satellite-derived indices like VNCI effectively capture fine-scale human-environment interactions, offering a basis for spatially differentiated healthcare planning. Full article

Although the coupling coordination relationship (CCR) between ecological environment and economic development has received extensive scholarly attention, investigations into the underlying mechanisms of this coupling coordination remain insufficient. Taking the Chishui River Basin (CRB) in Southwest China as the study area, this study integrates remote sensing data and county-level statistical datasets. Firstly, the quality of the ecological environment and economic development level of the CRB are systematically evaluated. Secondly, an improved coupling coordination degree model (ICCDM) is adopted to quantify the CCR between the ecological environment and economic development, as well as its spatiotemporal evolution characteristics. Finally, an obstacle degree model and panel Tobit model are employed to explore the influencing factors of the CCR from both intrinsic and extrinsic perspectives. The results show that during the study period, both the ecological environment index (EEI) and the economic development index (EDI) in the CRB exhibited upward trends, with pronounced inter-county disparities. The CCR between ecological environment and economic development was continuously optimized, and the coupling coordination degree (CCD) displayed a distinct spatial gradient pattern of downstream regions > midstream regions > upstream regions. Obstacle degree analysis identifies significant heterogeneity in the obstacle factors for CCR improvement across the basin: Renhuai and Zunyi are dominated by ecological environment constraints, while 11 counties including Chishui and Xishui are mainly restricted by economic development constraints. Industrial structure, ecological endowment, industrialization level and government capacity are vital positive driving factors for the CCR in the CRB, whereas Terrain conditions act as a key negative restraining factor. This study indicates that the overall coupling coordination level between ecological environment and economic development in the CRB is still relatively low and requires further enhancement. Therefore, region-specific differentiated regulation strategies are urgently needed to achieve high-level coordinated development between the ecological environment and economy in the CRB. Full article

This study focuses on the Dabie Mountain Comprehensive Station in Anhui Province, constructing a multi-scale analytical framework and integrating remote sensing and socio-economic data to systematically assess the spatiotemporal evolution of ecosystem service bundles (ESBs) and landscape ecological risks using SOFM, XGBoost, and SHAP models. The research categorizes ecosystem service functions into four types: water conservation core areas, carbon storage–habitat optimization areas, carbon storage–water production composite areas, and multifunctional synergy areas. From 2013 to 2023, the proportion of multifunctional synergy areas increased from 39.85% to 42.86%, while carbon storage-habitat optimization areas and water conservation core areas decreased by 28,035.47 hm² and 2118.8 hm², respectively, indicating significant spatial restructuring of regional ecosystem service functions. The landscape ecological risk exhibits a pattern of “medium risk dominance with high-low polarization,” where high-risk areas overlap with urban expansion zones, and low-risk areas are concentrated in ecological conservation zones. Quantitative analysis reveals that climatic factors (e.g., annual precipitation) dominate the risk patterns in water conservation core areas and ecological conservation zones, topographic factors (e.g., elevation) influence regional spatial differentiation, and socio-economic factors (e.g., nighttime light index) significantly affect agricultural production core areas. The findings elucidate the evolutionary patterns of ecosystem service functions and the mechanisms of risk formation in the Dabie Mountain region, providing a scientific basis and technical support for regional land use optimization, ecosystem function enhancement, and ecological security assurance. Full article

Urban green spaces (UGSs) provide critical ecosystem services (ESs) in rapidly urbanising cities but are increasingly threatened by land-use change, population growth, and socio-economic pressures. This study assessed spatial and temporal changes in UGS in Zomba City, Malawi, from 1998 to 2021 using geospatial and remote sensing methods. Landsat imagery from 1998, 2007, 2013, and 2021 was analysed through post-classification change detection to map land-use/land-cover (LULC) transitions, while the relationship between ward-level population density and vegetation condition was evaluated using the Normalised Difference Vegetation Index (NDVI). Results show a decline in total UGS cover from 60% in 1998 to 51% in 2021, primarily due to the expansion of built-up areas. Tree cover increased from 11% to 18%, with NDVI values rising from 0.700 to 0.947; these changes may reflect both natural vegetation growth and targeted restoration, indicating localised improvements in vegetation condition. An inverse relationship was observed between population density and NDVI, though some high-density wards exhibited NDVI gains associated with restoration initiatives. These findings underscore the role of both institutional and community efforts in sustaining urban vegetation and highlight the potential of ecological restoration to mitigate UGS loss and support ESs. Policymakers and planners should prioritise the protection, restoration, and equitable distribution of UGS, particularly in dense and underserved areas, as strategic urban greening enhances city resilience and human well-being. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin. Full article

Desertification is one of the main threats to high Andean ecosystems, particularly in arid and semi-arid regions subject to increasing climatic and anthropogenic pressures. This study evaluated the spatial-temporal dynamics of desertification in the province of Candarave (Tacna, Peru) by integrating the Remote Sensing-based Desertification Index (RSDI), constructed from a principal component analysis incorporating four biophysical indicators: vegetation greenness, surface moisture, soil grain size, and fraction of solar radiation reflected (albedo), derived from Landsat 5 and 8 satellite images processed in Google Earth Engine. Temporal trends were analyzed using the Mann–Kendall test, while system stability was evaluated using the coefficient of variation, allowing different degrees of stability and environmental degradation to be characterized during the period 2010–2025. The results show that moderate and severe desertification classes predominate in higher altitude areas, covering approximately 92% of the study area, and are characterized by insignificant to weakly significant negative trends associated with high to relatively high temporal volatility. In contrast, stable areas with no significant changes represent 5.3% of the territory, while restoration processes occupy a small proportion, close to 2.7%. The high variability observed in the high Andean sectors is mainly linked to the interaction between reduced water availability, climate variability, and extreme events, as well as anthropogenic pressures, particularly overgrazing and aquifer exploitation. This multitemporal analysis allows us to anticipate the evolution of desertification and highlights the need to strengthen conservation planning in order to reduce the degradation of strategic high Andean ecosystems in the Tacna region. Full article

Accurate and continuous monitoring of flood dynamics is fundamental to understanding wetland hydrological processes and their ecological implications, yet it remains challenging due to the inherent trade-off between spatial and temporal resolution in remote sensing observations. This study advances flood monitoring methodology by developing and validating a spatiotemporal fusion framework specifically designed for multi-source Synthetic Aperture Radar (SAR) data—an approach that has remained underdeveloped despite its critical importance for all-weather wetland observation. We propose the Fusion SAR Operational Monitoring (FSOM) framework, which integrates three established components—the Flexible Spatiotemporal Data Fusion (FSDAF) model, the Sentinel-1 Dual-Polarized Water Index (SDWI), and automated thresholding classification—into a coherent processing chain that generates consistent high-resolution flood extent time series from multi-sensor SAR data (Sentinel-1 and GF-3). The FSOM was applied to the Chen Lake Wetland from 2020 to 2024, producing a monthly flood map dataset at 5 m spatial resolution. Quantitative validation demonstrated the superiority of the FSOM-derived products. Compared to water classifications using original Sentinel-1 data, the FSOM results achieved a significantly higher overall accuracy (exceeding 90%) and Kappa coefficient (>0.90) than the Sentinel-1 results, which had overall accuracy (exceeding 86%) and Kappa coefficient (>0.75). Critically, the producer’s accuracy for water bodies consistently surpassed 91%, indicating a substantial reduction in omission errors and markedly improved detection of small water bodies. These results confirm the effectiveness of the proposed FSOM framework in mitigating the spatiotemporal resolution trade-off, thereby providing a reliable high-fidelity data foundation to support precise wetland conservation and flood disaster emergency response. The framework thus offers a practical tool for scientists and water resource managers seeking to enhance monitoring capabilities in the world’s most dynamic and ecologically significant wetland ecosystems. Full article

In the face of the dual challenges of global climate change and rapid urbanization, optimizing the ecosystem services of urban green spaces has become a key strategy for building resilient and sustainable cities. This is particularly crucial in ecologically fragile arid and semi-arid regions. To accurately assess the thermal regulation function of urban green spaces, this study selected 20 parks in Xi’an, China. Combining remote sensing and Geographic Information System (GIS) technology, we adopted four established cooling indicators—Park Cooling Area (PCA), Park Cooling Efficiency (PCE), Park Cooling Intensity (PCI), and Park Cooling Gradient (PCG)—to systematically evaluate the thermal regulation functions of urban parks and their landscape-driving mechanisms. The results indicated that the average cooling amplitude of the parks was 2.53 °C, with an effective influence distance reaching 323.9 m, exhibiting a significant spatial gradient decay. We found a non-linear trade-off between green space scale and efficiency: while large parks provided a wider absolute cooling range, small and medium-sized parks demonstrated higher efficiency per unit area. Furthermore, a blue-green synergistic configuration significantly enhanced the mitigation of the urban heat island effect. The study confirmed that Park Area (PA), Park Perimeter (PP), and the Normalized Difference Vegetation Index (NDVI) significantly promoted cooling effects, whereas landscape fragmentation inhibited ecological benefits. This study elucidates the comprehensive regulation mechanism of urban parks on the urban microclimate, providing planning guidance for implementing Nature-based Solutions (NbS) and achieving climate-adaptive development in arid and semi-arid cities within the context of urban renewal. Full article

Under global climate change, frequent droughts threaten ecosystem functions, but how drought characteristics affect ecosystem resilience remains unclear. Focusing on the Dongjiang River Basin, China, we identified drought events at an 8-day scale from 2000–2024 using multi-source remote sensing and reanalysis data. The water use efficiency-based resilience index (R_de) was calculated, and a random forest model quantified the contributions of 21 potential driving factors. The model explained 68% of R_de variance (R² = 0.68, RMSE = 0.12). Downward shortwave radiation was the primary factor, followed by antecedent water use efficiency and soil moisture anomaly, with drought intensity and air temperature ranking fourth and fifth. All dominant factors exhibited nonlinear threshold effects: R_de decreased significantly after radiation exceeded ~110 W·m⁻²·(8d)⁻¹; R_de declined when standardized soil moisture anomaly fell below −2.0; and R_de increased sharply when drought intensity exceeded 12%. Drought intensity far outweighed duration and severity, establishing it as the key drought attribute. This study reveals the dominant drivers and their thresholds governing ecosystem resilience in the Dongjiang River Basin, providing quantifiable indicators for ecological drought early warning. Full article

Accurate mapping of grass species in biodiverse ecosystems, such as wetlands, is critical for ecological protection. Rapid advancements in remote sensing have established satellite data as a critical tool for wetland grass species mapping; however, its relatively coarse spatial resolution and susceptibility to cloud contamination limit the distinction of co-occurring species at fine scales. While Unmanned Aerial Vehicle (UAV) remote sensing offers high resolution and operational flexibility, relying on single-source features is often insufficient for fine-scale wetland species mapping due to the spectral similarity of co-occurring species. On the other hand, the fusion of multi-source remote sensing features (i.e., spectral, textural, and geometric features) likely provides a promising solution for achieving accurate, fine-scale grass species mapping in biodiverse ecosystems. In this study, we developed a wetland grass species mapping framework integrating spectral, textural, and geometric features derived from UAV RGB and multispectral imagery. Using a dataset of 95,880 image objects representing 24 wetland grass species classes collected in two years in Dajiu Lake National Wetland Park of China, we evaluated three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—across various feature combinations. We found that while spectral features (i.e., red edge, normalized green–red difference index [NGRDI], and normalized difference vegetation index [NDVI]) (related to leaf pigment concentrations and cellular structures) exhibited the highest importance in wetland grass species mapping, textural (i.e., contrast) and geometric features (i.e., aspect ratio) significantly enhanced classification performance as complementary information, yielding improvements of up to 10.5% in overall accuracy (OA) and 0.103 in Macro-F1 scores. Specifically, the fusion of spectral, textural, and geometric features achieved optimal performance with an OA of 81.9% and a Macro-F1 of 0.807. Furthermore, the XGBoost model outperformed SVM and RF, improving OA by 9.4% and 2.8%, and Macro-F1 by 0.08 and 0.035, respectively. By identifying the optimal feature combination and machine learning algorithm, this study establishes an accurate method for wetland grass species mapping, offering new opportunities for ecological assessment and precision conservation in biodiverse landscapes. Full article

Active Channel Width (ACW) provides a robust indicator for tracking river corridor dynamics, yet automated extraction from multisensory imagery remains limited by spatial and temporal variability in spectral conditions. We developed and validated a workflow in Google Earth Engine (GEE) to delineate the active channel using multispectral indices derived from annual composite Landsat and Sentinel-2 imagery. The indices include the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI). The 34 km study segment of the Lircay River (Chile) served as a demonstration site undergoing substantial geomorphic change over a 20-year period (2003–2023) that spanned a decade-long mega drought (2010–2023) and two major floods (2006, 2023). Multispectral index thresholds were calibrated using manually digitized active channel polygons for a reference year and validated for five different years within the study period to assess their spatial transferability across reaches and temporal stability under varying hydrologic regimes. Sentinel-2 annual composites with the MNDWI-EVI pairing achieved the highest overall accuracy in estimating ACW (mean Kling-Gupta Efficiency = 0.72; Percent Bias = 12.69 across study reaches). Threshold values were tested at the cross-sectional and reach scales. Using cross-section-specific thresholds enhanced the accuracy of ACW estimation, indicating that threshold performance is strongly conditioned by the local characteristics present in the immediate surroundings of each cross section. These results suggest that spectral threshold selection is sensitive to small scale factors that vary across the river corridor, underscoring the need to explicitly consider local geomorphic and ecological conditions when defining thresholds. This reproducible, open-source workflow links automated channel delineation with cross-section-based morphology and explicitly quantifies uncertainty from spatiotemporal spectral variability. It enables high-resolution, repeatable measurements of river corridor change and underscores the need to consider evolving spectral and vegetation conditions when interpreting remotely sensed geomorphic indicators. Full article

Accurate urban green space (UGS) mapping is essential for assessing urban ecosystem health and supporting sustainable development planning. However, deep learning-based UGS segmentation from Red–Green–Blue (RGB) remote sensing imagery faces two major challenges. First, the absence of near-infrared (NIR) information in RGB imagery hinders the ability to discriminate spectrally similar classes, such as vegetation and non-vegetation. Second, conventional convolutions with fixed receptive fields struggle to model the complex and irregular boundaries characteristic of UGS. To address these challenges, this study combined the Normalized Green–Red Difference Index with the Deformable Convolutional Network Lab (NGRDI-DCNLab) model, a semantic segmentation model tailored specifically for RGB-only imagery. Based on the DeepLabV3+ framework, the model introduced three core improvements: (1) The Normalized Green–Red Difference Index (NGRDI) was incorporated to compensate for the absence of NIR information, enhancing the spectral separability of vegetation pixels. (2) Standard convolutions in the decoder were replaced with deformable convolutions, enabling the network to more effectively adapt to irregular boundaries of UGS. (3) An NGRDI-weighted loss function was designed to assign higher weights to challenging samples and uncertain boundary regions, guiding the model toward more accurate edge delineation. Comprehensive evaluations on two public high-resolution datasets—the Wuhan Dense Labeling Dataset (WHDLD) and the Beijing subset of the Urban Green Space-1m dataset (UGS-1m_Beijing)—demonstrated that the NGRDI-DCNLab model outperformed existing popular deep learning models (like Unet++, etc.). Specifically, the deformable convolution effectively enhances the feature modeling capability for irregular boundaries; incorporating the NGRDI vegetation index as a fourth channel strengthens spectral feature representation and improves the distinction between vegetation and non-vegetation; and adding the dynamic NGRDI-weighted loss enables targeted learning for challenging samples. Through the synergistic effect of these three modules, the model achieves mean Intersection over Union (MIoU) scores of 84.77% and 77.66%, as well as F1-scores of 91.75% and 87.27%, on the WHDLD and UGS-1m_Beijing datasets, respectively. Furthermore, the model exhibited certain generalization capability on the unmanned aerial vehicle (UAV) dataset, the Urban Drone Dataset 6 (UDD6), attaining an MIoU of 87.43%. Our results confirm that high-precision UGS extraction is achievable using only RGB remote sensing imagery, providing a cost-effective and practical technical solution for refined urban governance and ecological monitoring. Full article