Search Results (13,471)

Optimizing the spatial pattern of water conservation services (WCSs) is essential for enhancing regional water retention and promoting sustainable water resource management. The Saihanba region, a critical ecological barrier in northern China, has experienced severe degradation due to historical over-logging, leading to weakened WCS functions. This study used remote sensing techniques to interpret land use/land cover change (LULC) and combined it with meteorological and basic ecological data to assess changes in WCS capacity in the Saihanba region, China, under multiple 2035 scenarios using CA-Markov and Bayesian network models. The Bayesian belief network identified priority areas for spatial optimization. Results showed the following: (1) The spatial distribution patterns of WCSs showed a strong dependence on land-use types, with both forest and grassland areas demonstrating superior water conservation capacity compared to other land cover categories; (2) although total WCS capacity varied across scenarios, spatial distribution remained consistent—high-value zones were mainly in the south and central-east, while lower values occurred in the west; and (3) WCS areas were categorized into key optimization, ecological protection, and general management zones. Notably, the Sandaohekou Forest Farm and the western Qiancengban Forest Farm emerged as critical areas requiring urgent optimization. These findings offer practical guidance for spatial planning, ecological protection, and water resource governance, supporting long-term WCS sustainability in the region. The study also contributes to cleaner production strategies by aligning ecosystem service management with sustainable development goals. Full article

There is increasing interest in the role of parks as potential cool refuges in the age of climate change. Such potential refuges result from the Park Cool Island (PCI) effect, reflecting the temperature differential between the park and surrounding urban areas. However, this study of different park typologies in Perth, Australia, illustrates that while surface temperatures are 10–15 °C lower in parks during summer afternoons (much less than at other times), air temperatures are generally no different from the adjacent streetscape for the smaller parks. Only the largest park in the study had 1–2 °C lower morning and mid-afternoon air temperature differentials. The study illustrates that while the PCI is a real phenomenon, the magnitude in terms of air temperature is small, and it is of less relevance to the conditions felt by humans in average summer daytime conditions than the direct effects of solar radiation. Many studies have assessed the PCI effect, an indicator that has shown a wide range across different studies and measurement techniques. However, this novel paper utilises satellite remote-sensed land surface temperatures, on-ground measurements of surface temperatures, air temperatures, and humidity, as well as modelling using the microclimatic simulation software ENVI-met version 5.0. A reliance on land surface temperature, which in isolation has a marginal correlation with human experience of thermal comfort, has led some researchers to overstate the PCI effect and its influence on adjoining urban areas. The research reported in this paper illustrates that it is the shade provided by the canopy in parks, rather than parks themselves, that provides meaningful thermal comfort benefits. Accordingly, adaptation to increasing temperatures requires the creation of a continuous canopy, ideally over parks, streetscapes, and private lots in an interconnected network. Full article

The Chinese sturgeon (Acipenser sinensis), a critically endangered anadromous fish species, spends over 90% of its life cycle in marine habitats, yet research on its marine ecology and habitat requirements is limited due to sparse data. To address this, we integrated satellite remote sensing with ecological modeling to assess spatiotemporal dynamics in marine habitat suitability across China’s continental shelf (2003–2020). Nine key habitat factors were derived from multi-source remote sensing data and inverted transparency algorithms. Species occurrence data were coupled with the Maximum Entropy (MaxEnt) model to evaluate habitat preferences and seasonal shifts. Results revealed distinct environmental preferences: shallow depths (≤20 m), sea surface and bottom temperature (10–30 °C and 10–25 °C), salinity (10–35‰), transparency (0.40–3.00 m), eastward and northward seawater velocity (−0.20–0.15 m/s and −0.20–0.20 m/s), moderate productivity (1000–3000 mg/m²), and zooplankton carbon (0.20–6.00 g/m²). Habitat factor importance varied seasonally—salinity, depth, and net primary productivity dominated in spring; bottom temperature and productivity in summer/autumn; salinity and transparency in winter. Spatially, high-suitability areas peaked in autumn (70% total suitable habitat), concentrating near the Yangtze Estuary, northern Jiangsu coast, and Zhoushan Archipelago. This study emphasizes the need to prioritize these areas for protection and inform proliferation and release schemes for Chinese sturgeon. It also demonstrates the efficacy of remote sensing for mapping essential habitats of migratory megafauna in complex coastal ecosystems and provides actionable insights for targeted conservation strategies. Full article

Forests play a vital role in the global carbon cycle; however, the carbon sink capacity of African forests is increasingly threatened by wildfires, rising temperatures, and ecological degradation. This study analyzes the spatiotemporal dynamics of forest carbon fluxes across Africa from 2001 to 2023, based on multi-source remote sensing and climate datasets. The results show that wildfires have significantly disrupted Africa’s carbon balance over the past two decades. From 2001 to 2023, fire activity was most intense in the woodland–savanna transition zones of Central and Southern Africa. In countries such as the Democratic Republic of the Congo, Angola, Mozambique, and Zambia, each recorded burned areas exceeding 500,000 km², along with high recurrence rates (e.g., up to 0.7584 fires per year in South Sudan). These fire-affected regions often exhibited high ecological sensitivity and carbon density, which led to pronounced disturbances in carbon fluxes. Nevertheless, the Democratic Republic of the Congo maintained an average annual net carbon sink of 74.2 MtC, indicating a high potential for ecological recovery. In contrast, Liberia and Eswatini exhibited net carbon emissions in fire-affected areas, suggesting weaker ecosystem resilience. These findings underscore the urgent need to incorporate wildfire disturbances into forest carbon management and climate mitigation strategies. In addition, climate variables such as temperature and soil moisture also influence carbon fluxes, although their effects display substantial spatial heterogeneity. On average, a 1 °C increase in temperature leads to an additional 0.347 (±1.243) Mt CO₂ in emissions, while a 1% increase in soil moisture enhances CO₂ removal by 1.417 (±8.789) Mt. However, compared to wildfires, the impacts of these climate drivers are slower and more spatially variable. Full article

The intensification of urban heat in high-density cities has raised growing concerns for public health, infrastructural resilience, and environmental sustainability. As large-scale, multi-functional open spaces, sports stadiums play an underexplored role in shaping urban thermal patterns. This study investigates the spatial and temporal thermal characteristics of eight representative stadiums in central Shanghai and the Pudong New Area from 2018 to 2023. A dual-framework approach is proposed: the Stadium-based Urban Island Regulation (SUIR) model conceptualizes stadiums as active cooling agents across micro to macro spatial scales, while the Multi-source Thermal Cognition System (MTCS) integrates multi-sensor satellite data—Landsat, MODIS, Sentinel-1/2—with anthropogenic and ecological indicators to diagnose surface temperature dynamics. Remote sensing fusion and machine learning analyses reveal clear intra-stadium thermal heterogeneity: track zones consistently recorded the highest land surface temperatures (up to 37.5 °C), while grass fields exhibited strong cooling effects (as low as 29.8 °C). Buffer analysis shows that cooling effects were most pronounced within 300–500 m, varying with local morphology. A spatial diffusion model further demonstrates that stadiums with large, vegetated buffers or proximity to water bodies exert a broader regional cooling influence. Correlation and Random Forest regression analyses identify the building volume (r = 0.81), NDVI (r = −0.53), nighttime light intensity, and traffic density as key thermal drivers. These findings offer new insight into the role of stadiums in urban heat mitigation and provide practical implications for scale-sensitive, climate-adaptive urban planning strategies. Full article

Burrowing mammals function as ecosystem engineers by creating spatial heterogeneity in the soil structure and vegetation composition, thereby providing microhabitats for a wide range of organisms. These keystone species play a crucial role in maintaining local ecosystem functions and delivering ecosystem services. However, in Mongolia, where overgrazing has accelerated due to the expansion of a market-based economy, scientific knowledge remains limited regarding the impacts of human activities on such species. In this study, we focused on the Siberian marmot (Marmota sibirica), an ecosystem engineer inhabiting typical Mongolian steppe ecosystems. We assessed the relationship between the spatial distribution of marmot burrows and vegetation conditions both inside and outside Hustai National Park. Burrow locations were recorded in the field, and the Normalized Difference Vegetation Index (NDVI) was calculated, using Planet Lab, Dove-2 satellite imagery (3 m spatial resolution). Through a combination of remote sensing analyses and vegetation surveys, we examined how the presence or absence of anthropogenic disturbance (i.e., livestock grazing) affects the ecological functions of marmots. Our results showed that the distance between active burrows was significantly shorter inside the park (t = −2.68, p = 0.0087), indicating a higher population density. Furthermore, a statistical approach, using beta regression, revealed a significant interaction between the burrow type (active, non-active, off-colony area) and region (inside vs. outside the park) on the NDVI (e.g., outside × non-active: z = −5.229, p < 0.001). Notably, in areas with high grazing pressure outside the park, the variance in the NDVI varied significantly as a function of burrow presence or absence (e.g., July 2023, active vs. off-colony area: F = 133.46, p < 0.001). Combined with vegetation structure data from field surveys, our findings suggest that marmot burrowing activity may contribute to the enhancement of vegetation quality and spatial heterogeneity. These results indicate that the Siberian marmot remains an important component in supporting the diversity and stability of steppe ecosystems, even under intensive grazing pressure. The conservation of this species may thus provide a promising strategy for utilizing native ecosystem engineers in sustainable land-use management. Full article

Compound dry and hot events or extremes (CDHEs) have emerged as major climatic threats to agricultural production and food security in the middle reaches of the Yangtze River Basin (MRYRB), a critical grain-producing region in China. However, agricultural risks associated with CDHEs, incorporating both natural and socio-economic factors, remain poorly understood in this area. Using a Hazard-Exposure-Vulnerability (HEV) framework integrated with a weighting quantification method and supported by remote sensing technology and integrated geographic data, we systematically assessed the spatiotemporal dynamics of agricultural CDHE risks and corresponding crop responses in the MRYRB from 2000 to 2019. Results indicated an increasing trend in agricultural risks across the region, particularly in the Poyang Lake Plain (by 21.9%) and Jianghan Plain (by 9.9%), whereas a decreasing trend was observed in the Dongting Lake Plain (by 15.2%). Spatial autocorrelation analysis further demonstrated a significant negative relationship between gross primary production (GPP) and high agricultural risks of CDHEs, with a spatial concordance rate of 52.6%. These findings underscore the importance of incorporating CDHE risk assessments into agricultural management. To mitigate future risks, we suggest targeted adaptation strategies, including strengthening water resource management and developing multi-source irrigation systems in the Poyang Lake Plain, Dongting Lake, and the Jianghan Plain, improving hydraulic infrastructure and water source conservation capacity in northern and southwestern Hunan Province, and prioritizing regional risk-based adaptive planning to reduce agricultural losses. Our findings rectify the longstanding assumption that hydrological abundance inherently confers robust resistance to compound drought and heatwave stresses in lacustrine plains. Full article

The Euphrates–Tigris Basin is experiencing significant environmental transformations due to climate change, Land Use and Land Cover Change (LULCC), and anthropogenic pressures. This study employs Earth Map, an open-access remote sensing platform, to comprehensively assess climate trends, vegetation dynamics, water resource variability, and land degradation across the basin. Key findings reveal a geographic shift toward aridity, with declining precipitation in high-altitude headwater regions and rising temperatures exacerbating water scarcity. While cropland expansion and localized improvements in land productivity were observed, large areas—particularly in hyperarid and steppe zones—show early signs of degradation, increasing the risk of dust source expansion. LULCC analysis highlights substantial wetland loss, irreversible urban growth, and agricultural encroachment into fragile ecosystems, with Iraq experiencing the most pronounced transformations. Climate projections under the SSP245 and SSP585 scenarios indicate intensified warming and aridity, threatening hydrological stability. This study underscores the urgent need for integrated water management, Land Degradation Neutrality (LDN), and climate-resilient policies to safeguard the basin’s ecological and socioeconomic resilience. Earth Map is a vital tool for monitoring environmental changes, offering rapid insights for policymakers and stakeholders in this data-scarce region. Future research should include higher-resolution datasets and localized socioeconomic data to improve adaptive strategies. Full article

The rapid and accurate assessment of regional soil salinity is crucial for effective salinization management. This study proposes an enhanced remote sensing inversion method by integrating both driving and response environmental variables to address lag effects and incomplete factor consideration in existing models. The Yellow River Delta, a coastal saline–alkaline region, was selected as the study area, where soil salinity-sensitive spectral parameters were derived from Sentinel-2 MSI imagery. Six environmental variables, including precipitation, distance from the sea, and soil moisture, were analyzed. Four scenarios were constructed: (1) using only spectral parameters; (2) spectral parameters with driving variables; (3) spectral parameters with response variables; and (4) combining both types. Four modeling methods were employed to assess inversion accuracy. The results show that incorporating either driving or response variables improved accuracy, with validation R² increasing by up to 0.149 and RMSE decreasing by up to 0.097 when both were used. The suitable model, integrating soil moisture, distance from the sea, and chlorophyll content, achieved a calibration R² of 0.813 and validation R² of 0.722. These findings demonstrate that combining both driving and response variables enhances model performance and provides valuable insights for soil salinization management. Full article

The increasing demand for critical minerals is forcing the mineral exploration industry to search for deposits beneath deeper cover and over larger areas. MobileMT, an airborne passive, broadband, total-field AFMAG-class system, couples three-component measurements of airborne magnetic field variations with a remote electric-field base station to image electrical resistivity from the surface to depths of >1–2 km. We present a workflow that integrates MobileMT data with the parallelized, adaptive finite-element 2.5D open-source inversion code MARE2DEM, accompanied by automated mesh generation procedures, to create a rapid and scalable workflow for deep ore exploration. Using this software on two field trials, we demonstrate that (i) high-frequency (>2 kHz) data are essential for recovering not only shallow geology but also, when combined with low frequencies, for refining deep structures and targets and that (ii) base station effects modify the shape of the apparent conductivity curve but have negligible impact on the inverted sections. The proposed workflow is a reliable and effective approach for identifying mineralization-related features and refining geologic models based on data from extensive airborne geophysical surveys. Full article

With rapid urbanization, the development of Urban Underground Space (UUS) has become essential to addressing various urban challenges. However, the accelerated expansion of UUS has also introduced problems such as duplicated infrastructure, functional deficiencies, and underutilized spaces. Fundamentally, these issues result from imbalances between the supply and demand for UUS, a phenomenon particularly pronounced in the central areas of major cities. Therefore, employing scientific methods to accurately identify and quantify these gaps is crucial. Leveraging recent advances in remote sensing and point-of-interest (POI) data, this study constructs a multi-source data-driven framework for assessing UUS supply–demand relationships, applied using a grid-based analysis to the central urban area of Nanjing. The results indicate that both the highest supply capacity and demand intensity occur in Xinjiekou Street in Nanjing’s Old City. Most high and medium–high supply and demand zones are concentrated in the Old City. Areas with prominent supply–demand conflicts are identified and classified into five types using the Jenks natural breaks method, further categorized into three groups based on their spatial characteristics, with tailored development strategies proposed accordingly. The proposed evaluation framework provides a robust scientific approach for analyzing UUS supply–demand relationships, offering significant theoretical and practical value for refined urban governance in large cities with extensive data availability. Full article

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration. Full article

Urban greening distribution critically impacts residents’ quality of life and environmental sustainability. While the Green View Index (GVI), derived from street view imagery, is widely adopted for urban green space assessment, its limitation lies in the inability to capture non-street-area vegetation. Remote sensing imagery, conversely, provides full-coverage urban vegetation data. This study focuses on Beijing’s Third Ring Road area, employing DeepLabv3+ to calculate a street-view-based GVI as a predictor. Correlations between the GVI and Sentinel-2 spectral bands, along with two vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), were analyzed under varying buffer radius. Regression and classification models were subsequently developed for GVI prediction. The optimal classifier was then applied to estimate green perception levels in non-street zones. The results demonstrated that (1) at a 25 m buffer radius, the near-infrared band, NDVI, and FVC exhibited the highest correlations with the GVI, reaching 0.553, 0.75, and 0.752, respectively. (2) Among the five machine learning regression models evaluated, the random forest algorithm demonstrated superior performance in GVI estimation, achieving a coefficient of determination (R²) of 0.787, with a root mean square error (RMSE) of 0.063 and a mean absolute error (MAE) of 0.045. (3) When evaluating categorical perception levels of urban greenery, the Extremely Randomized Trees classifier (Extra Trees) demonstrated superior performance in green vision perception level estimation, achieving an accuracy (ACC) score of 0.652. (4) The green perception level in non-road areas within Beijing’s Third Ring Road is 56.8%, which is considered relatively poor. Moreover, the green perception level within the Second Ring Road is even lower than that in the area between the Second and Third Ring roads. This study is expected to provide valuable insights and references for the adjustment and optimization of green perception distribution in Beijing, thereby supporting more informed urban planning and the development of sustainable, human-centered green spaces across the city. Full article

Accurate delineation of urban built-up areas is critical for urban monitoring and planning. We evaluated the performance and consistency of three widely used methods—thresholding, multi-temporal image fusion, and support vector machine (SVM)—across three major nighttime light (NTL) datasets (DMSP/OLS, SNPP/VIIRS, and Luojia-1). We developed a unified methodological framework and applied it to Wuhan, China, encompassing data preprocessing, feature construction, classification, and cross-dataset validation. The results show that SNPP/VIIRS combined with thresholding or SVM achieved highest accuracy (kappa coefficient = 0.70 and 0.61, respectively) and spatial consistency (intersection over union, IoU = 0.76), attributable to its high radiometric sensitivity and temporal stability. DMSP/OLS exhibited robust performance with SVM (kappa = 0.73), likely benefiting from its long historical coverage, while Luojia-1 was constrained by limited temporal availability, hindering its suitability for temporal fusion methods. This study highlights the critical influence of sensor characteristics and method–dataset compatibility on extraction outcomes. While traditional methods provide interpretability and computational efficiency, the findings suggest a need for integrating deep learning models and hybrid strategies in future work. These advancements could further improve accuracy, robustness, and transferability across diverse urban contexts. Full article

Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task, but its effectiveness is limited by persistent cloud cover in tropical regions. A Synthetic Aperture Radar (SAR) offers a cloud-independent alternative for burned area mapping. This study investigates the performance of a Stacking Ensemble Neural Network (SENN) model using polarimetric features derived from both C-band (Sentinel 1) and L-band (Advanced Land Observing Satellite—Phased Array L-band Synthetic Aperture Radar (ALOS-2/PALSAR-2)) data. The analysis covers three representative sites in Indonesia: peatland areas in (1) Rokan Hilir, (2) Merauke, and non-peatland areas in (3) Bima and Dompu. Validation is conducted using high-resolution PlanetScope imagery(Planet Labs PBC—San Francisco, California, United States). The results show that the SENN model consistently outperforms conventional artificial neural network (ANN) approaches across most evaluation metrics. L-band SAR data yields a superior performance to the C-band, particularly in peatland areas, with overall accuracy reaching 93–96% and precision between 92 and 100%. The method achieves 76% accuracy and 89% recall in non-peatland regions. Performance is lower in dry, hilly savanna landscapes. These findings demonstrate the effectiveness of the SENN, especially with L-band SAR, in improving burned area detection across diverse land types, supporting more reliable fire monitoring efforts in Indonesia. Full article