Search Results (2,672)

The Qinling Mountains ecosystem serves as a vital ecological barrier and geographic demarcation line in China. Monitoring long-term land cover changes in the Qinling Mountains is essential for ecosystem pattern evaluation, environmental protection, and sustainable development. Focusing on the Qinling Mountains in Shaanxi Province, this study aimed to quantify the land cover changes from 1986 to 2020 using remote sensing and GIS technologies. An optimized Support Vector Machine (SVM) classification method was developed using Landsat satellite images and historical field samples. The method was employed to conduct land cover classification across eight discrete time periods: 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. The average overall accuracy (OA) of the classification results for the eight time periods was 96.42%, with a Kappa coefficient (K) of 0.9230, thus confirming the reliability of the mapping results. We subsequently developed a spatiotemporal Geo-information Tupu that facilitated a detailed analysis of land cover changes in the study area across different periods. The results show the following: (1) Forest was the dominant land cover type, followed by cropland. From 1986 to 2020, the forest, impervious surface, and water body areas showed overall increasing trends, although fluctuations were observed over time, and the increase was estimated at 6677.30 km², 557.57 km², and 135.71 km², respectively. In contrast, the areas of cropland, grassland, and bare soil showed a fluctuating decreasing trend, with a decrease in areal coverage of 2790.57 km², 1528.76 km², and 3042.66 km², respectively. During the study period, the forest area experienced the greatest increase but maintained the lowest dynamic degree. In contrast, bare soil showed the largest decrease and the highest dynamic degree. (2) A total of 30.74% of the area underwent dynamic changes during the study period, with the most active transformation occurring after 2010; these changes were mainly manifested in the outflow of cropland (4997.27 km²), the transfer of forest (8557.43 km²), and the expansion of impervious surfaces (771.33 km²). In conclusion, the overall ecological environment is improving. The results demonstrate a land cover reconstruction process that enables the management department to rationally utilize natural resources in the Qinling Mountains. Full article

Alexandrium spp., globally recognized as harmful algal bloom (HAB) species, pose severe threats to marine ecosystems, fisheries, and public health. Based on 469 occurrence records and 24 marine environmental variables, this study employed the Biomod2 ensemble modeling framework to predict the potential distribution of Alexandrium spp. under current and future climate scenarios, and to assess the role of key environmental factors and the spatiotemporal dynamics of habitat centroid shifts. The results revealed that (1) the ensemble model outperformed single models (AUC = 0.998, TSS = 0.977, Kappa = 0.978), providing higher robustness and reliability in prediction; (2) salinity range (bio18, 19.1%) and mean salinity (bio16, 5.8%) were the dominant factors, while minimum temperature (bio23) also showed strong constraints, indicating that salinity determines “whether persistence is possible,” while temperature influences “whether blooms occur”; (3) under present conditions, high-suitability habitats are concentrated in Bohai Bay, the Yangtze River estuary to the Fujian coast, and parts of Guangdong; (4) climate change is predicted to drive a southward shift of suitable habitats, with the most pronounced expansion under the high-emission scenario (RCP8.5), leading to the emergence of new high-risk areas in the South China coast and adjacent South China Sea; (5) centroid analysis further indicated a pronounced southward migration under RCP8.5 by 2100, highlighting a regional reconfiguration of ecological risks. Collectively, salinity and temperature are identified as the core drivers shaping the ecological niche of Alexandrium spp., and future warming is likely to exacerbate HAB risks in southern China. This study delineates key prevention regions and proposes a shift from reactive to proactive management strategies, providing scientific support for HAB monitoring and marine ecological security in China’s coastal waters. Full article

Lakes, as key freshwater reserves and ecosystem cores, supply human water, regulate climate, sustain biodiversity, and are vital for global ecological balance and human sustainability. Lake Chaohu, as a crucial ecological barrier in the middle and lower reaches of the Yangtze River, faces significant environmental challenges to regional sustainable development due to water quality deterioration and consequent eutrophication issues. To address the limitations of conventional monitoring techniques, including insufficient spatiotemporal coverage and high operational costs in lake water quality assessment, this study proposes an enhanced Informer model optimized by the Whale Optimization Algorithm (WOA) for predictive analysis of concentration trends of key water quality parameters—dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and total nitrogen (TN)—across multiple time horizons (4 h, 12 h, 24 h, 48 h, and 72 h). The results demonstrate that the WOA-optimized Informer model (WOA-Informer) significantly improves long-term water quality prediction performance. Comparative evaluation shows that the WOA-Informer model achieves average reductions of 9.45%, 8.76%, 7.79%, 8.54%, and 11.80% in RMSE metrics for 4 h, 12 h, 24 h, 48 h, and 72 h prediction windows, respectively, along with average improvements of 3.80%, 5.99%, 11.23%, 17.37%, and 23.26% in R² values. The performance advantages become increasingly pronounced with extended prediction durations, conclusively validating the model’s superior capability in mitigating error accumulation effects and enhancing long-term prediction stability. Spatial visualization through Kriging interpolation confirms strong consistency between predicted and measured values for all parameters (DO, CODMn, TP, and TN) across all time horizons, both in concentration levels and spatial distribution patterns, thereby verifying the accuracy and reliability of the WOA-Informer model. This study successfully enhances water quality prediction precision through model optimization, providing robust technical support for water environment management and decision-making processes. Full article

To overcome the limited accuracy of existing street vitality assessments under dense occlusion and their lack of dynamic, multi-source data fusion, this study proposes an integrated dynamic model that couples an enhanced YOLOv11 with heterogeneous spatiotemporal datasets. The network introduces a two-backbone architecture for stronger multi-scale fusion, Spatial Pyramid Depth Convolution (SPDConv) for richer urban scene features, and Dynamic Sparse Sampling (DySample) for robust occlusion handling. Validated in Yibin, the model achieves 90.4% precision, 67.3% recall, and 77.2% mAP@50 gains of 6.5%, 5.3%, and 5.1% over the baseline. By fusing Baidu heatmaps, street-view imagery, road networks, and POI data, a spatial coupling framework quantifies the interplay between commercial facilities and street vitality, enabling dynamic assessment of urban dynamics based on multi-source data fusion, offering insights for targeted retail regulation and adaptive traffic management. By enabling continuous monitoring of urban space use, the model enhances the allocation of public resources and cuts energy waste from idle traffic, thereby advancing urban sustainability via improved commercial planning and responsive traffic control. The work provides a methodological foundation for shifting urban resource allocation from static planning to dynamic, responsive systems. Full article

The intensification of agricultural land use (A-LUI) is a central driver of global environmental change, affecting soil health, water quality, biodiversity, and greenhouse gas balances. Monitoring A-LUI remains challenging because it is shaped by multiple management practices, ecological processes, and spatio-temporal dynamics. This review provides a comprehensive synthesis of existing definitions and standards of A-LUI at national and international levels (FAO, OECD, World Bank, EUROSTAT) and evaluates in situ methods alongside the rapidly expanding potential of remote sensing (RS). We introduce a novel RS-based taxonomy of A-LUI indicators, structured into five complementary categories: trait, genesis, structural, taxonomic, and functional indicators. Numerous examples illustrate how traits and management practices can be translated into RS proxies and linked to intensity signals, while highlighting key challenges such as sensor limitations, cultivar variability, and confounding environmental factors. We further propose an integrative framework that connects management practices, plant and soil traits, RS observables, validation needs, and policy relevance. Emerging technologies—such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration—are discussed as promising pathways to advance the monitoring of A-LUI across scales. By compiling and structuring RS-derived indicators, this review establishes a conceptual and methodological foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, thereby supporting both scientific progress and evidence-based agricultural policy. Full article

The comprehensive, accurate, and rapid acquisition of large-scale surface deformation using Interferometric Synthetic Aperture Radar (InSAR) technology provides crucial information support for regional eco-geological safety assessments and the rational development and utilization of groundwater resources. The Linfen-Yuncheng Basin in Shanxi Province is one of China’s historically most frequented regions for geological hazards in plain areas, such as land subsidence and ground fissures. This study employed the coherent point targets based Small Baseline Subset (SBAS) time-series InSAR technique to interpret a dataset of 224 scenes of 5 m resolution RADARSAT-2 satellite SAR images acquired from January 2017 to May 2024. This enabled the acquisition of high-resolution spatiotemporal characteristics of surface deformation in the Linfen-Yuncheng Basin during the monitoring period. The results show that the area with a deformation rate exceeding 5 mm/a in the study area accounts for 12.3% of the total area, among which the subsidence area accounts for 11.1% and the uplift area accounts for 1.2%, indicating that the overall surface is relatively stable. There are four relatively significant local subsidence areas in the study area. The total area with a rate exceeding 30 mm/a is 41.12 km², and the maximum cumulative subsidence is close to 810 mm. By combining high-resolution satellite images and field survey data, it is found that the causes of the four subsidence areas are all the extraction of groundwater for production, living, and agricultural irrigation. This conclusion is further confirmed by comparing the InSAR monitoring results with the groundwater level data of monitoring wells. In addition, on-site investigations reveal that there is a mutually promoting and spatially symbiotic relationship between land subsidence and ground fissures in the study area. The non-uniform subsidence areas monitored by InSAR show significant ground fissure activity characteristics. The InSAR monitoring results can be used to guide the identification and analysis of ground fissure disasters. This study also finds that due to the implementation of surface water supply projects, the demand for groundwater in the study area has been continuously decreasing. The problem of ground water over-extraction has been gradually alleviated, which in turn promotes the continuous recovery of the groundwater level and reduces the development intensity of land subsidence and ground fissures. Full article

This paper presents a proposed LSTM-based vertical-gradient prediction combined with three-dimensional kriging that enables reconstruction of greenhouse 3D temperature fields under sparse-sensor deployments while capturing temporal dynamics and spatial correlations. In northern China, winter solar greenhouses rely on standardized structures and passive climate-control strategies, which often lead to non-uniform thermal conditions that complicate precise regulation. To address this challenge, 24 sensors were deployed, and their time-series data were used to train a long short-term memory (LSTM) model for vertical temperature-gradient prediction. The predicted values at multiple heights were fused with in situ observations, and three-dimensional ordinary kriging (3D-OK) was applied to reconstruct the spatiotemporal temperature field. Compared with conventional 2D monitoring and computationally intensive CFD, the proposed approach balances accuracy, efficiency, and deployability. LSTM–Kriging validation showed Trend + Residual Kriging had the lowest RMSE (0.45558 °C) and bias (−0.03148 °C) (p < 0.01), outperforming Trend-only RMSE (3.59 °C) and Kriging-only RMSE (0.48 °C); the 3D model effectively distinguished sunny and rainy dynamics. This cost-effective framework balances accuracy, efficiency, and deployability, overcoming limitations of 2D monitoring and CFD. It provides critical support for adaptive greenhouse climate regulation and digital-twin development, directly advancing precision management and yield stability in CEA. Full article

At present, the research on the microclimate of urban parks mainly focuses on the univariate or multivariate research contents of park design elements, and there are few analyses that can combine the park with the surrounding regional environment to jointly explore the cooling mechanism of park design elements. This study takes the People’s Park in Urumqi, a typical oasis city in an arid area, as the research object. Combined with different land use natures (park area/residential area), it analyzes the spatiotemporal variation law of temperature through mobile meteorological monitoring in different periods of summer and autumn and optimizes the buffer zone to further compare the performance of the multiple linear regression model and three machine learning models. The selection of the optimal model for collaborative analysis and comparison revealed the dominant variables and their threshold effects affecting the temperature of the park area and the residential area. The results show that: (1) In multi-scenario comparisons, a larger buffer has a better fitting effect. (2) The random forest model is the best model for temperature prediction in the study area. (3) The dominant factors of temperature in different seasons show significant differences, and only a few periods have cross-seasonal persistence. In the park area, the green coverage rate and road network density play a leading and influential role, while in the residential area, the influence of water cover ratio is more obvious. Furthermore, the influence direction of residential area indicators on temperature shows opposite trends in the morning and afternoon periods. (4) There are obvious limited-threshold effects on the influence of dominant factors on temperature in different regions. It is suggested that in the urban spatial layout, while considering the differences for different utilization Spaces, collaborative planning should be carried out. These findings offer new insights into temperature drivers and provide practical references for urban planners. Full article

In the Inner Mongolia Plateau Lake Zone (IMP), situated in China’s semi-arid region, its lake water storage change plays a critical role in wetland ecosystem conservation and regional water security through its lake water storage dynamics. To investigate long-term lake water storage (LWS) changes, this study proposes a novel lake monitoring framework that reconstructs historical lake level time series and estimates water level variations in lakes without altimetry data. Using multi-source satellite data, we quantified LWS variations (2000–2021) across 109 lakes (≥5 km²) on the IMP and examined their spatiotemporal patterns. Our results reveal a net decline of 1.21 Gt in total LWS over the past two decades, averaging 0.06 Gt/yr. A distinct shift occurred around 2012: LWS decreased by 10.82 Gt from 2000 to 2012 but increased by 9.61 Gt from 2013 to 2021. Spatially, significant LWS reductions were concentrated in the central and eastern IMP, resulting from intensive water diversion and groundwater exploitation. In contrast, increases were observed mainly in the western and southern regions, driven by enhanced precipitation and reduced aridity. The findings improve understanding of lake dynamics in semi-arid China over the last two decades and offer technical guidance for sustainable water resource management. Full article

As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics. Full article

African swine fever (ASF) has reshaped wild boar (Sus scrofa) populations and management across Europe since its reintroduction in 2007. ASF reached Slovakia in August 2019, when wild boar population size and harvest were at six-decade maximums. We analyzed data from six years (2019–2024) of national surveillance and control to quantify spatio-temporal ASF patterns in free-ranging wild boar. Using monthly virological (PCR) and serological (antibody) data from active (hunted) and passive (found dead) surveillance, we (1) estimated temporal variation in the effective reproduction number (R_t); (2) modeled spatio-temporal prevalence in Slovakia and its eastern, central, and western regions; (3) linked these dynamics to management indicators such as wild boar density, harvest, and mortality; and (4) proposed measures to increase surveillance and control effectiveness. Passive surveillance showed greater diagnostic sensitivity than active surveillance for case detection (PCR: 46.5% vs. 0.48%; antibodies: 7.62% vs. 0.75%). R_t peaked at 3.83 in March 2021, then declined but periodically exceeded 1.0 through late 2024. Virological prevalence showed strong late-winter/early-spring seasonality and a persistent east-to-west gradient: peaks occurred first in the east (March 2021, March 2023), with the center surpassing the east in October 2023 and a subsequent rise in the west. Seroprevalence lagged and shifted westward later, peaking in March 2023 and increasing in western Slovakia from mid-2024. Wild boar density decreased by 36.3% from 2019 to 2024 and harvest-based density by 42.8%, returning to post-classical swine fever levels (2009–2013). We recommend prioritizing targeted carcass searches and rapid removal, maintaining low wild boar densities through sustained harvest of adult females, modernizing population monitoring methods, enhancing hunters’ compliance, and strengthening cross-border coordination to improve surveillance and control, thereby slowing ASF spread across Europe. Full article

The Yellow River Delta (YRD) is a vital agricultural and ecologically fragile zone in China. Understanding the spatial pattern and evolutionary characteristics of Rural Settlements Area (RSA) in this region is crucial for both ecological protection and sustainable development. This study focuses on Dongying, a key YRD city, and compares four advanced deep learning models—U-Net, DeepLabv3+, TransUNet, and TransDeepLab—using fused Sentinel-1 radar and Landsat optical imagery to identify the optimal method for RSA mapping. Results show that TransUNet, integrating polarization and optical features, achieves the highest accuracy, with Precision, Recall, F1 score, and mIoU of 89.27%, 80.70%, 84.77%, and 85.39%, respectively. Accordingly, TransUNet was applied for the spatiotemporal extraction of RSA in 2002, 2008, 2015, 2019, and 2023. The results indicate that medium-sized settlements dominate, showing a “dense in the west/south, sparse in the east/north” pattern with clustered distribution. Settlement patches are generally regular but grow more complex over time while maintaining strong connectivity. In summary, the proposed method offers technical support for RSA identification in the YRD, and the extracted multi-temporal settlement data can serve as a valuable reference for optimizing settlement layout in the region. Full article

Land surface temperature (LST) is a critical parameter in agricultural drought monitoring, crop growth analysis, and climate change research. However, the challenge of acquiring high-resolution LST data with both fine spatial and temporal scales remains a significant obstacle in remote sensing applications. Despite the high temporal resolution afforded by daily MODIS LST observations, the coarse (1 km) spatial scale of these data restricts their applicability for studies demanding finer spatial resolution. To address this challenge, a novel deep learning-based approach is proposed for LST downscaling: the spatial resolution downscaling attention network (SRDA-Net). The model is designed to upscale the resolution of MODIS LST from 1000 m to 250 m, overcoming the shortcomings of traditional interpolation techniques in reconstructing spatial details, as well as reducing the reliance on linear models and multi-source high-temporal LST data typical of conventional fusion approaches. SRDA-Net captures the feature interaction between MODIS LST and auxiliary data through global resolution attention to address spatial heterogeneity. It further enhances the feature representation ability under heterogeneous surface conditions by optimizing multi-source features to handle heterogeneous data. Additionally, it strengthens the model of spatial dependency relationships through a multi-level feature refinement module. Moreover, this study constructs a composite loss function system that integrates physical mechanisms and data characteristics, ensuring the improvement of reconstruction details while maintaining numerical accuracy and model interpret-ability through a triple collaborative constraint mechanism. Experimental results show that the proposed model performs excellently in the simulation experiment (from 2000 m to 1000 m), with an MAE of 0.928 K and an R² of 0.95. In farmland areas, the model performs particularly well (MAE = 0.615 K, R² = 0.96, RMSE = 0.823 K), effectively supporting irrigation scheduling and crop health monitoring. It also maintains good vegetation heterogeneity expression ability in grassland areas, making it suitable for drought monitoring tasks. In the target downscaling experiment (from 1000 m to 500 m and 250 m), the model achieved an RMSE of 1.804 K, an MAE of 1.587 K, and an R² of 0.915, confirming its stable generalization ability across multiple scales. This study supports agricultural drought warning and precise irrigation and provides data support for interdisciplinary applications such as climate change research and ecological monitoring, while offering a new approach to generating high spatio-temporal resolution LST. Full article

High-resolution meteorological satellite cloud imagery plays a crucial role in diagnosing and forecasting severe convective weather phenomena characterized by suddenness and locality, such as tropical cyclones. However, constrained by imaging principles and various internal/external interferences during satellite data acquisition, current satellite imagery often fails to meet the spatiotemporal resolution requirements for fine-scale monitoring of these weather systems. Particularly for real-time tracking of tropical cyclone genesis-evolution dynamics and capturing detailed cloud structure variations within cyclone cores, existing spatial resolutions remain insufficient. Therefore, developing super-resolution techniques for meteorological satellite cloud imagery through software-based approaches holds significant application potential. This paper proposes a Multi-scale Residual Deformable Attention Model (MRDAM) based on Generative Adversarial Networks (GANs), specifically designed for satellite cloud image super-resolution tasks considering their morphological diversity and non-rigid deformation characteristics. The generator architecture incorporates two key components: a Multi-scale Feature Progressive Fusion Module (MFPFM), which enhances texture detail preservation and spectral consistency in reconstructed images, and a Deformable Attention Additive Fusion Module (DAAFM), which captures irregular cloud pattern features through adaptive spatial-attention mechanisms. Comparative experiments against multiple GAN-based super-resolution baselines demonstrate that MRDAM achieves superior performance in both objective evaluation metrics (PSNR/SSIM) and subjective visual quality, proving its superior performance for satellite cloud image super-resolution tasks. Full article

In recent years, coal-combustion-related air pollution has declined markedly, whereas tropospheric ozone (O₃) pollution has emerged as a growing environmental concern. Long-term exposure to O₃ can severely impact human health and ecosystems, constraining socioeconomic development. The Fenwei Plain has complex topographical conditions and a relatively simple industrial structure, and at present, O₃ is one of the main pollutants affecting air quality in this region. Therefore, studying the distribution of O₃ pollution in the Fenwei Plain can provide a reference for developing plans to control O₃ pollution in the area, which is important for safeguarding local public health and economic development. Currently, the number of pollutant monitoring stations in China is limited, spatially discontinuous, and significantly affected by environmental factors, making it difficult to obtain high-precision, large-scale observational data. Satellite-based remote sensing provides broad spatial coverage and is free from topographic constraints, thereby serving as an effective complement to ground-based monitoring networks. This provides important technical support for studying the distribution characteristics of O₃ pollution and its associated health risks. This study focuses on the Fenwei Plain, utilizing machine learning models to estimate continuous O₃ concentrations from 2015 to 2022 and analyze the spatiotemporal distribution of O₃. Based on this, an assessment and analysis of the health risks associated with near-surface O₃ exposure in the study area will be conducted, incorporating the population exposed in the Fenwei Plain and individuals with chronic obstructive pulmonary disease (COPD). Full article