Search Results (4,163)

Rapid industrialization and urbanization have driven China’s economic growth but also worsened air pollution, posing serious challenges to sustainable development and public health. Balancing economic growth and environmental protection has become essential for achieving the UN Sustainable Development Goal of “Climate Action.” The National Ecological Civilization Construction Demonstration Zone policy, a major institutional innovation for green transition, aims to integrate ecological protection, industrial upgrading, and spatial governance to achieve both economic and environmental goals. Using county-level panel data from 2010 to 2022, this study applies a difference-in-differences (DID) approach, treating the phased establishment of NECCDZs as an exogenous policy shock. It further explores the mediating effects of green innovation capability and land use efficiency. The results show that the NECCDZ policy significantly reduces PM2.5 concentrations in pilot regions, and the findings remain robust under multiple tests. Improvements in green innovation and land use efficiency are identified as key transmission mechanisms, while policy effects vary across city hierarchies, industrial base types, and regions. Overall, the NECCDZ policy demonstrates the effectiveness of institutionalized ecological governance and offers policy insights for developing countries seeking coordinated progress in economic growth and environmental sustainability. Full article

In response to the widespread decline in ecosystem service value (ESV) caused by rapid industrialization and urbanization-driven land-use transitions in Coastal China—characterized by shrinking farmland and expanding built-up land and crystallized in the “core-city sprawl and surrounding-farmland encroachment” pattern—this study integrated land-use and socioeconomic data from 1980 to 2020. Employing the equivalent-factor method and Geodetector model, we quantified the spatiotemporal evolution of ESV and its driving mechanisms across the entire coastal region. The results show that (i) the total ESV experienced a fluctuating increase. (ii) Spatially, the ESV exhibited a “high in the south, low in the north, and higher inland than along the immediate coast” pattern, with mountain–hill belts and estuarine wetlands in the south forming high-value clusters, whereas the Bohai Rim in the north emerged as a low-value zone. (iii) Socioeconomic factors increasingly dominated the driving forces, while NDVI became the most influential natural factor; the interactions between the drivers consistently produced bi-factor enhancement effects. These findings provide a scientific basis for implementing the “Two-Mountains Theory” and optimizing coastal territorial spatial planning. Full article

To enhance ecosystem services (ESs) benefits and promote ecological–economic–sociologic sustainability in highly urbanized regions such as the Beijing–Tianjin–Hebei (BTH) region, it is essential to assess the dynamic changes in ESs within these regions from a functional zoning perspective and to explore the interactions between ESs. This research delved into how ESs change over space and time, using land-use projections for 2035 based on Natural Development (ND), Ecological Protection (EP), Economic Construction (EC) scenarios. This study also took a close look at the interplay of these ESs across BTH and its five distinct functional zones: the Bashang Plateau Ecological Protection Zone (BS), the Northwestern Ecological Conservation Zone (ST), the Central Core Functional Zone (HX), the Southern Functional Expansion Zone (TZ), and the Eastern Coastal Development Zone (BH). We utilize the Multiple Ecosystem Service Landscape Index (MESLI) to assess the capacity to supply multiple ESs. Key results include the following: (1) Projected land-use changes for 2035 scenarios consistently show cropland and grassland declining, while forest and urbanland expand, though the magnitude of change varies by scenario. (2) Habitat quality, carbon storage, and soil conservation displayed a “high northwest–low southeast” gradient, opposite to water yield. The average MESLI value declined in all scenarios relative to 2020, with the highest value under the EP scenario. (3) Synergies prevailed between habitat quality, carbon storage, and soil conservation, while trade-offs occurred with water yield. These relationships varied spatially—for instance, habitat quality and soil conservation were weakly synergistic in the BS but showed weak trade-offs in the HX. These insights can inform management strategies in other rapidly urbanizing regions. Full article

The lower Mahanadi basin in eastern India is experiencing significant land and soil transformations that directly influence agricultural sustainability and ecosystem resilience. In this study, we used geospatial techniques to analyze the spatial-temporal variability of soil quality and land cover between 2011 and 2020 in the lower Mahanadi basin. The results revealed that the cropland decreased from 39,493.2 to 37,495.9 km², while forest cover increased from 12,401.2 to 13,822.2 km², enhancing soil organic carbon (>290 g/kg) and improving fertility. Grassland recovered from 4826.3 to 5432.1 km², wastelands declined from 133.3 to 93.2 km², and water bodies expanded from 184.3 to 191.4 km², reflecting positive land–soil interactions. Soil quality was evaluated using the Simple Additive Soil Quality Index (SQI), with core indicators bulk density, organic carbon, and nitrogen, selected to represent physical, chemical, and biological components of soil. These indicators were chosen as they represent the essential physical, chemical, and biological components influencing soil functionality and fertility. The SQI revealed spatial variability in texture, organic carbon, nitrogen, and bulk density at different depths. SQI values indicated high soil quality (SQI > 0.65) in northern and northwestern zones, supported by neutral to slightly alkaline pH (6.2–7.4), nitrogen exceeding 5.29 g/kg, and higher organic carbon stocks (>48.8 t/ha). In contrast, central and southwestern regions recorded low SQI (0.15–0.35) due to compaction (bulk density up to 1.79 g/cm³) and fertility loss. Clay-rich soils (>490 g/kg) enhanced nutrient retention, whereas sandy soils (>320 g/kg) in the south increased leaching risks. Integration of LULC with soil quality confirms forest expansion as a driver of resilience, while agricultural intensification contributed to localized degradation. These findings emphasize the need for depth-specific soil management and integrated land-use planning to ensure food security and ecological sustainability. Full article

Satellite-derived bathymetry (SDB) using Ice, Cloud, and Land Elevation satellite-2 (ICESat-2) LiDAR data and remote sensing images faces challenges in the difficulty of uniform coverage of the inversion area by the bathymetric control points due to the linear sampling pattern of ICESat-2. This study proposes a novel virtual control point optimization framework integrating inverse distance weighting (IDW) and spectral confidence analysis (SCA). The methodology first generates baseline bathymetry through semi-empirical band ratio modeling (control group), then extracts virtual control points via SCA. An optimization scheme based on spectral confidence levels is applied to the control group, where high-confidence pixels utilized a residual correction-based strategy, while low-confidence pixels employed IDW interpolation based on a virtual control point. Finally, the preceding optimization scheme uses weighting-based fusion with the control group to generate the final bathymetry map, which is also called the optimized group. Accuracy assessments over the three research areas revealed a significant increase in accuracy from the control group to the optimized group. When compared with in situ data, the determination coefficient (R²), RMSE, MRE, and MAE in the optimized group are better than 0.83, 1.48 m, 12.36%, and 1.22 m, respectively, and all these indicators are better than those in the control group. The key innovation lies in overcoming ICESat-2’s spatial sampling limitation through spectral confidence stratification, which uses SCA to generate virtual control points and IDW to adjust low-confidence pixel values. It is also suggested that when applying ICESat-2 satellite data in active–passive-fused SDB, the distribution of training data in the research zone should be adequately considered. Full article

This study collected 820 topsoil samples from cultivated lands across Ningxia, covering the Yellow River irrigation area, the central arid zone, and the southern mountainous region. The ordinary kriging were spatially interpolated to analyze As, Hg, Cd, Cr, and Pb heavy-metal pollution spatial patterns. Pollution was evaluated using the Nemerow and geoaccumulation (I_(geo)) indices, and sources quantified via Pearson correlations, PCA (Principal Component Analysis), and PMF (Positive Matrix Factorization). The results indicated that Hg and Cd posed the highest ecological risks. The overall mean concentrations (mg.kg⁻¹) of Hg, Cd, As, Pb, and Cr were 0.04, 0.27, 9.91,23.81, and 57.34, respectively. Compared with the background values, they were 1.90, 2.41, 0.83, 1.14, 2.74 times higher, respectively. Geospatially, regions with higher pollution probabilities for Cd, Cr, Pb, Hg, and As were concentrated in the northern and central parts of Ningxia, whereas the southern region exhibited lower pollution probabilities. pH significantly influenced the accumulation and spatial distribution of heavy metals in soil. Source apportionment identified three primary contributors: transportation and natural parent materials (As, Pb, Cr), industrial activities (Hg), and agricultural practices (Cd). Hg and Cd were identified as the key risk elements requiring prioritized management. These results enhance understanding of the pollution levers of heavy metals in Ningxia cultivated soils, and also provide foundation for developing more scientific and precise soil risk control policies, offering significant practical value for environmental risk management. Full article

The marginal arid region encompassing the western Nafud in the east to Wadi Tabuk in the west has only been subject to limited archaeological survey. This paper reports on data from a systematic remote sensing survey of the region as part of the Endangered Archaeology in the Middle East and North Africa project, using the results to produce preliminary models of settlement, occupation, and land-use, and contextualising within the broader archaeological landscapes of northern Arabia. It also provides datasets that can be used to outline broad trends in modern disturbances and threats to these sites, in part demonstrating the effectiveness of this approach for producing a cost-effective baseline dataset for the management of heritage sites at a landscape level. While confirming that long-term settlement and agriculture were largely confined to the Wadi Tabuk region from the later prehistoric period onwards, including the identification of a significant new fortified settlement south of Tabuk, it also demonstrates evidence of a broader complex landscape of pastoralism, funerary monuments, and other monumental structures across much of the survey area. Most notably, this area may mark a border zone when geographically distinct distributions of Neolithic-adjacent kites and mustatil meet with minimal overlap. Full article

Urbanization markedly influences the microclimatic conditions in semi-arid regions by elevating land surface temperatures (LST) and contributing to ecological degradation. This study examined the spatial and temporal evolution of LST and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024), employing high-resolution satellite imagery and in situ sensor data. Urban expansion was quantified using thermal bands from Landsat imagery, the Normalized Difference Built-up Index (NDBI), and the Built-up Index (BU), whereas thermal comfort was evaluated through the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV) using air temperature and humidity data collected via spatial sensor and the Sniffer Bike mobile sensor network. These urban transformations have intensified the UHI effect, resulting in a 29.34 °C increase in mean LST to 41.82 °C in 2024 across built-up areas. Statistical modeling revealed strong linear relationships between LST and urban indices, with R² values ranging from 0.93 to 0.96, and correlation coefficients around 0.98 (all p-values < 0.001), indicating a reliable model fit. Furthermore, the analysis of thermal comfort trends underscores urbanization’s impact on human well-being. In 1994, 34.2% of the population experienced slight warmth and 65.8% experienced hot conditions. By 2024, conditions had shifted dramatically, with 76.7% experiencing hot conditions and 16.2% exposed to very hot conditions, leaving only 7.1% in the slight warmth category. These findings highlight the urgent need for adaptive urban planning strategies. The implementation of urban greening initiatives, the use of reflective materials, and the integration of data-driven planning approaches are essential to mitigate thermal stress and enhance urban resilience. Leveraging climate modeling and spatial analytics can support the identification of high-risk zones and inform targeted interventions to effectively address the escalating UHI phenomenon. Full article

Cultural ecosystem services (CES) represent fundamental expressions of human-environment interactions. A comprehensive assessment of CES supply and demand offers a robust scientific foundation for optimizing the transformation of ecosystem service values to improve human well-being. This study integrates multi-source datasets and employs Maximum Entropy (MaxEnt) modeling with the ArcGIS platform to analyze the spatial distribution of CES supply and demand in Hunan Province, a typical mountain tourism regions in China. Furthermore, geographical detector methods were used to identify and quantify the driving factors influencing these spatial patterns. The findings reveal that: (1) Both CES supply and demand demonstrate pronounced spatial heterogeneity. High-demand areas are predominantly concentrated around prominent scenic locations, forming a “multi-core, clustered” pattern, whereas high-supply areas are primarily located in urban centers, water systems, and mountainous regions, exhibiting a gradient decline along transportation corridors and river networks. (2) According to the CES supply-demand pattern, Hunan Province can be classified into demand, coordination, and enhancement zones. Coordination zones dominate (45–70%), followed by demand zones (20–30%), while enhancement zones account for the smallest proportion (5–20%). (3) Urbanization intensity and land use emerged as the primary drivers of CES supply-demand alignment, followed by vegetation cover, distance to water bodies, and population density. (4) The explanatory power of two-factor interactions across all eight CES categories surpasses that of any individual factor, highlighting the critical role of synergistic multi-factorial influences in shaping the spatial pattern of CES. This study provides a systematic analysis of the categories and driving factors underlying the spatial alignment between CES supply and demand in Hunan Province. The findings offer a scientific foundation for the preservation of ecological and cultural values and the optimization of spatial patterns in mountain tourist areas, while also serving as a valuable reference for the large-scale quantitative assessment of cultural ecosystem services. Full article

In Mediterranean semi-arid regions, rainfed olive groves are increasingly being abandoned due to drought, low profitability, and rural depopulation. The long-term impact of abandonment on soil conditions is debated, as it may promote vegetation recovery or lead to degradation. In contrast, some farmers are adopting low-disturbance management practices that allow spontaneous vegetation to establish. These contrasting scenarios offer valuable opportunities for comparison. This study aims to develop a framework to assess the impact of different management regimes on soil health and to investigate (1) the impact of spontaneous vegetation cover (SVC) and tillage regimes on soil organic carbon (SOC), and (2) the long-term ecological dynamics of abandoned groves, through a combination of field surveys, remote sensing, and object detection. SOC was assessed using both ground-based and remote sensing-derived indicators. Vegetation cover was quantified via a grid point intercept method. Field data were integrated with a land-use monitoring framework that includes abandonment assessment through historical orthophotos and a deep learning model (YOLOv12) to detect active and abandoned olive groves. Results show that abandoned zones are richer in SOC than active ones. In particular, the active groves with SVC exhibit a mean SOC of 1%, which is higher than that of tilled groves, where SOC is 0.45%, with no apparent moisture loss. Abandoned groves can be reliably identified from aerial imagery, achieving a recall of 0.833 for abandoned patches. Our results demonstrate the potential of YOLOv12 as an innovative and accessible tool for detecting zones undergoing ecological regeneration or degradation. The study underscores the ecological and agronomic potential of spontaneous vegetation in olive agroecosystems. Full article

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

In Korea, rural regions increasingly function as peri-urban zones integrated into urban systems. To assess vulnerabilities in these transitional areas characterized by mixed land use and uneven access to infrastructure, this study developed a three-tiered peri-urban livability vulnerability framework by integrating the analytic hierarchy process and the entropy weight method. The results indicated that medical facilities, childcare and education centers, and village communities consistently emerged as key indicators, linking peri-urban livability directly to the stability of settlement environments and the quality of life of residents. Contrastingly, expert evaluations and data-driven outcomes related to road networks and agricultural infrastructure displayed substantial discrepancies, revealing gaps between perceived importance and actual provision levels. Such differences highlight the risk of underestimating infrastructure-related vulnerabilities when subjective assessments are employed exclusively. By synthesizing subjective and objective weights, this study advances urban and environmental analysis and supports evidence-based decision-making for policy prioritization. The findings demonstrate that peri-urban vulnerability is shaped less by productive capacity than by social infrastructure and community stability. This conclusion offers crucial insights for enhancing livability and guiding urban planning strategies. Full article

Understanding traffic congestion in urban areas is crucial for ensuring mobility, especially in metropolitan cities of developing countries. This study presents new spatial and temporal data to analyze congestion in Casablanca. Spatial data, collected using QGIS, covers 22 ZIP code areas and includes built environment factors such as land use, road types, and public transport stations. Temporal data consists of 440 randomly generated trajectories per commune, with real-time travel data collected hourly over one week using the Waze Route Calculator. A Python script was used to compute the Travel Time Index (TTI) for each zone. To classify zones based on congestion patterns, we applied fuzzy c-means clustering, allowing for nuanced grouping and interpretation of overlapping characteristics. This dataset supports traffic modeling, simulation, and congestion analysis in developing urban contexts. Full article

This study investigates landslide occurrence in Taiwan, a region highly susceptible to landslides due to steep mountains and frequent typhoons (TYPs). The primary objective is to understand how both geomorphological factors and TYP characteristics contribute to landslide occurrence, which is essential for improving hazard prediction and risk management. The research analyzed landslide events that occurred during the TYP seasons of 2019 and 2023. The methodology involved using satellite-derived landslide inventories from SPOT imagery for events larger than 0.1 hectares, tropical cyclone track and intensity data from IBTrACS v4 (classified by Saffir–Simpson Hurricane Scale), and detailed topographic variables (elevation, slope, aspect, Stream Power Index) extracted from a 30 m Shuttle Radar Topography Mission Digital Elevation Model (SRTM-DEM). Land use and land cover classifications were based on Landsat imagery. To establish a timeline, landslides were matched with TYPs within a ±3-day window, and proximity was analyzed using buffer zones ranging from 50 to 500 km around storm centers. Key findings revealed that landslide susceptibility results from a complex interplay of meteorological, topographic, and land cover factors. The critical controls identified include elevations above 2000 m, slope angles between 30 and 45 degrees, southeast- and south-facing aspects, and low Stream Power Index values typical of headwater and upper slope locations. Landslides were most frequent during Category 3 TYPs and were concentrated 300 to 350 km from storm centers, where optimal rainfall conditions for slope failures exist. Interestingly, despite the stronger storms in 2023, the number of landslides was higher in 2019. This emphasizes the importance of interannual variability and terrain preparedness. These findings support sustainable disaster risk reduction and climate-resilient development, aligning with Sustainable Development Goals 11 (Sustainable Cities and Communities) and 13 (Climate Action). Furthermore, they provide a foundation for improving hazard assessment and risk mitigation in Taiwan and similar mountainous, TYP-prone regions. Full article

The spatiotemporal dynamics of soil nutrients in the crop row zone are critical determinants of crop yield, necessitating precision fertilization for optimal plant growth. However, previous studies have predominantly focused on plant-available nutrient status at the scale of entire cropping systems, yet a granular understanding of their distribution patterns across precise temporal and spatial dimensions remains limited. Therefore, this study investigated maize–legume intercropping systems to quantify the dynamics of soil alkaline-hydrolyzable nitrogen (AN), available phosphorus (AP), and available potassium (AK) across distinct growth stages, soil depths, and row positions. The experiment comprised five treatments: maize–soybean intercropping, maize–peanut intercropping, and monocultures of maize, soybean, and peanut. Throughout the two-year study, maize–soybean intercropping significantly enhanced the plant height of both maize and soybean relative to their respective monocultures (p < 0.05). In contrast, within the maize–peanut system, intercropping significantly promoted peanut plant height but suppressed stem diameter in both species (p < 0.05); these effects were consistent across both study years. Both systems exhibited a “benefit-sacrifice” pattern, where dry matter was preferentially allocated to maize, thereby increasing total system productivity despite suppressing legume growth. Furthermore, during the mid-to-late growth stages, intercropped maize showed an enhanced capacity for nitrogen uptake from deeper soil layers. In contrast, the alkaline-hydrolyzable nitrogen content in intercropped soybean and peanut remained lower than in their respective monocultures throughout the growth period, with reductions ranging from 8.49% to 34.79%. Intercropping significantly increased the soil available phosphorus content in the root zones of maize, soybean, and peanut compared to their respective monocultures. The available phosphorus content in the 0–20 cm soil layer was consistently higher than in monoculture systems, with a maximum increase of 41.70%. Moreover, intercropping effectively mitigated soil potassium depletion, resulting in a smaller decline in available potassium. This effect was most pronounced in the maize–peanut intercropping pattern within the 20–40 cm soil layer. The distribution of soil available nutrients (N, P, K) was also influenced by drip tape placement. The levels of these nutrients for soybean and peanut were higher at 50 cm from the drip tape than at 30 cm, while for maize, levels were higher at 80 cm than at 40 cm. Intercropping increased the thousand-kernel weight of maize and soybean but decreased that of peanut. Overall, the strategic row configuration optimized the yield performance of both intercropping systems, resulting in land equivalent ratios greater than 1, which indicates distinct yield advantages for both intercropping patterns. Full article