Search Results (10,806)

Driven by the rapid expansion of the new energy industry and the growing demand for photovoltaic (PV) power plant construction, optimizing site selection to ensure operational efficiency and stability has emerged as a critical imperative. Addressing the lack of precise zonation assessment systems for PV development in China’s arid and semi-arid regions, this study introduces a multi-dimensional, five-tier suitability evaluation framework. By integrating Boolean logic, the Bayesian Best–Worst Method (B-BWM), and Equal Interval classification, we developed a multi-resolution integrated assessment framework where spatial boundaries are mainly constrained by 30 m topographic and land-cover data. Results indicate that the candidate PV construction space (the upper two suitability classes, PDSI ≥ 3.40) spans 0.85 million km² concentrated in central-western Inner Mongolia and southeastern Xinjiang, and model validation achieves an Area Under the Curve (AUC) of 0.79. The annual technical potential reaches 19,069 TWh, equivalent to approximately 207% of China’s total electricity consumption in 2023. If fully developed, this potential offers a theoretical annual CO₂ emission reduction ranging from 12.69 to 21.48 billion tons under different conversion efficiency scenarios, with a baseline estimate of 14.65 billion tons. These multi-resolution integrated findings provide a useful spatial reference for preliminary site screening in arid and semi-arid regions, support China’s “Dual Carbon” goals, and offer a practical methodological approach for renewable energy planning on marginal lands. Full article

This study proposes an adaptive Support Vector Machine (SVM) classification method based on an enhanced Starfish Optimization Algorithm (SFOAE-SVM) for hyperspectral image (HSI) classification. HSI classification remains a critical challenge in remote sensing due to the high dimensionality of spectral features, spectral mixing, scarcity of labeled samples, and complex land-cover distributions. The SFOAE algorithm is used for global hyperparameter optimization of SVMs, accounting for the distributional characteristics of the target HSI data. The approach aims to improve search capability and reduce the likelihood of convergence to local optima by combining multi-dimensional topology-oriented expansion with global exploration. Experimental results demonstrate that SFOAE-SVM achieves competitive classification accuracy and stable performance compared with conventional SVM parameter selection strategies and other optimization-based methods across three benchmark hyperspectral remote-sensing datasets. These results indicate that the proposed method offers a promising optimization-assisted SVM framework for hyperspectral remote-sensing image classification. Full article

High-risk landslide extraction from remote sensing imagery is a fundamental task for geological disaster prevention, emergency response, and land-use planning in mountainous regions. Although deep-learning semantic segmentation has substantially advanced landslide detection from optical imagery, existing methods still suffer from three critical limitations: (i) Interferometric Synthetic Aperture Radar (InSAR) deformation data are treated as auxiliary channels and dominated by optical features during fusion; (ii) predicted masks exhibit fragmented boundaries and incomplete delineation due to the absence of deformation continuity constraints reflecting the physical coherence of slope movements; and (iii) heavy Transformer backbones hinder practical deployment over large areas. To address these issues, we propose a Deformation-Integrity-Guided Symmetric Fusion Network (DIGSFNet) for high-risk landslide extraction from InSAR and optical imagery. The framework consists of three components: a Symmetric Deformation-Aware Encoder (SDAE) that treats InSAR and optical modalities as equal information sources through modality-aware adapters and dynamic sparse cross-modal fusion; a Deformation Integrity Prior Decoder (DIPD) that imposes deformation continuity and boundary-gradient consistency as physical priors to enforce mask completeness and boundary accuracy; and a Lightweight Deployable Student Network (LDSN) obtained via cross-modal knowledge distillation and INT8 quantization for efficient inference. Experiments on the Nanning High-Hazard Landslide Segmentation (Nanning-HHLS) dataset and the public HAEFNet benchmark covering the Qinghai–Tibet–Sichuan landslide-prone regions show that the full DIGSFNet achieves state-of-the-art extraction accuracy, reaching 83.57% and 78.92% mIoU on the two datasets and surpassing the strongest competing method by 2.63 and 3.68 percentage points with a Recall of 91.48% on Nanning-HHLS, while its distilled lightweight student retains 79.24% mIoU at 218 frames per second after INT8 quantization, enabling efficient large-area operational deployment. Full article

Soil erosion and the nutrient loss it causes are core issues threatening sustainable land use in arid and semi-arid regions. In this study, our aim was to reveal the spatiotemporal differentiation characteristics of soil erosion and soil nutrients in an ecologically fragile area of eastern Inner Mongolia—Tuquan County and clarify the relationship between them in order to provide a scientific basis for the precise management of water and soil resources and ecological construction in this region. Based on four sets of remote sensing images and ground observation data from 2012, 2016, 2020, and 2024, the Revised Universal Soil Loss Equation (RUSLE) was used to evaluate the dynamics of soil erosion, statistical methods were employed to analyze the spatial distribution and grade characteristics of soil nutrients (organic carbon, SOC; total nitrogen, TN, total phosphorus, TP) and pH values, and correlation analysis was conducted to explore their association with environmental factors (rainfall erosivity, R; soil erodibility, K; slope length, LS; vegetation cover and management factor, C). Our results demonstrate the following: (1) From 2012 to 2024, the intensity of soil erosion in the study area showed an increasing trend, with the average annual soil erosion modulus increasing from 551.4 t/(km²·a) to 859.6 t/(km²·a), and the high-intensity erosion areas were mainly distributed in the northwest. (2) The soil nutrient content was generally at medium to low levels, with the SOC and TN in the study area mainly categorized as “deficient” and “adequate”. The SOC ranged from 5.8 to 33.8 g·kg⁻¹, with an average content of about 23.5 g·kg⁻¹, while the TN content ranged from 0.45 to 4.63 g·kg⁻¹, with an average content of about 1.50 g·kg⁻¹, and was significantly affected by soil type. (3) There was a significant negative correlation between the soil erosion modulus and the SOC and TN content (p < 0.05), which was a key driving factor for nutrient loss. This conclusion suggests that soil erosion in Tuquan County is intensifying: the risk of nutrient loss is severe, and its spatial pattern is jointly restricted by topography, vegetation cover, and soil background characteristics. Therefore, future ecological engineering should focus on high-intensity erosion areas and combine the prevention of soil and water loss with the conservation of soil fertility in order to achieve sustainable land use in the region. Full article

Research Highlights: This study quantifies aboveground biomass for Oreopanax oroyanus and Escallonia resinosa in an Andean relict forest and examines their conservation relevance related to the scale of emissions voluntarily reported by small corporate emitters. Background and Objectives: Andean relict forests face severe fragmentation and degradation. This research evaluates carbon stocks in the Bosque de Zárate Reserved Zone (Peru) and explores how these findings may inform climate mitigation and conservation initiatives by examining their potential alignment with emissions voluntarily reported by Peruvian firms participating in a carbon disclosure system. Materials and Methods: A total of 27 plots were evaluated between 3034 and 3200 m a.s.l., tree height and diameter (DBH ≥ 10 cm) were measured for key species, and biomass was estimated using a pantropical allometric equation. Landsat imagery (1985–2025) was analyzed to assess long-term vegetation conditions, while Dynamic World land cover and Sentinel-1 radar (2018–2025) were used to assess forest cover and canopy structure changes. Voluntarily reported emissions of Peruvian firms participating in the “Carbon Footprint Peru” system (2012–2024) were analyzed to contextualize the forest results in the potential corporate interest in climate mitigation in Peru. Results: Total aboveground carbon stock for the altitudinal belt in the study area was 919.4 Mg C (18.6 Mg C ha⁻¹), equivalent to 3374.2 Mg CO₂, with Escallonia resinosa accounting for approximately 71% of the estimated stock. Multi-decadal satellite observations indicated persistent forest cover within the evaluated belt, while analysis of voluntarily reported corporate emissions identified numerous service-sector firms with annual emissions below 100 Mg CO₂ eq, providing context for the potential scale of future conservation-financing initiatives. Conclusions: Relict forests offer relevant localized carbon storage linked to other ecosystem services. Providing field-based carbon data may support the development of locally relevant community-led initiatives meaningful to climate-financing initiatives. However, the existing carbon stock does not by itself represent a source of carbon credits, and carbon capture-specific studies would need to be implemented to fully assess the mitigation capacity of these ecosystems. Full article

Black-sand mining is an under-studied anthropogenic pressure on arid coastal environments, where sparse vegetation and slow natural recovery limit conventional impact assessment. Despite the recent expansion of heavy-mineral extraction along the Mauritanian coast, no spatio-temporal analysis has quantified its effects on land cover and surface-water dynamics in the N’Diago region. We analysed the N’Diago–LGUWYCHICH coastal sector (south-western Mauritania) using three Landsat scenes (2014, 2020, and 2026) in QGIS (free and open-source Geographic Information System), four spectral indices (NDVI-Normalized Difference Vegetation Index, NDWI-Normalized Difference Water Index, BSI- Bare Soil Index, and CI -Coloration Index), supervised Support Vector Machine classification and a 30 m SRTM Digital Elevation Model over a 97.82 ha area of interest. Bare soil dominated the landscape at every date (92.6–95.7%) and vegetation stayed below 7%, indicating that canopy-based metrics underestimate disturbance in this setting. The clearest change was hydrological: an inland water body shrank from 1.02 ha in 2020 to 0.40 ha in 2026, a 60.6% loss. This individual inland water body, measured directly and independently from the NDWI index, is our primary hydrological observation: the wet feature was smaller in the 2026 image than in the 2020 image and was located near the mapped concession areas, but the available data do not establish the cause of this change. Similar bare-soil and colour index values (BSI ≈ 0.23, CI ≈ 0.79–0.80) were observed in the processed images, while residual seasonal and radiometric differences between the Landsat 8 and DOS-corrected Landsat 9 products cannot be excluded; BSI and CI are treated only as candidate or contextual spectral patterns, so any delineation of the disturbed footprint is provisional and requires confirmation from independent field data. This study illustrates a low-cost exploratory workflow that may support preliminary monitoring in data-scarce arid coastal settings, pending validation with denser time series and field observations. Full article

Low-carbon sustainable development requires a better understanding of how urban land expansion reshapes carbon emission patterns within and across cities. Impervious surface expansion is a visible land use expression of urbanization and a key carrier of energy consumption, industrial activity, and ecological loss. Yet, its cross-city effects on carbon emissions remain insufficiently understood, particularly in highly integrated urban agglomerations. Using a balanced panel of 41 cities in the Yangtze River Delta (YRD), China, from 2008 to 2022, this study combines EDGAR gridded CO₂ emissions, annual land cover data, global and local spatial autocorrelation analysis, and a two-way fixed-effects Spatial Durbin Model (SDM) to examine the local and spillover effects of impervious surface expansion. The results show that impervious surfaces and carbon emissions both evolved from core agglomeration toward peripheral diffusion, while carbon emissions maintained significant positive spatial autocorrelation, with Moran’s I remaining positive and significant throughout the study period. SDM estimates indicate that local impervious surface expansion significantly increases local carbon emissions, whereas the estimated indirect effect on neighboring cities is significantly negative. Effect decomposition confirms a positive direct effect and a negative indirect effect under both inverse-distance and contiguity weight matrices. A rolling-window analysis further shows that the negative spillover effect strengthened over time. These findings demonstrate that urban land hardening should be evaluated not only as a local emission driver but also as a spatially embedded process within regional carbon governance. By linking impervious surface expansion with spatial carbon emission interactions, this study contributes to sustainability research by providing empirical evidence for sustainable urban agglomeration development. Full article

Rainfall-induced landslides result from interactions between terrain predisposition and hydrometeorological forcing. In event-scale susceptibility modelling, uncertainty often arises from non-landslide controls. Locations without recorded failures may differ in rainfall history, storm exposure, or inventory completeness, which can bias models trained on static absence samples. This study develops a hydrometeorological control sampling strategy for rainfall induced landslide susceptibility modelling. The strategy defines each sample by grid cell and rainfall date, and constructs non landslide controls from storm related risk sets. A background predisposition prior is used to screen candidate controls. Regional same date controls, annular hard controls near failed slopes, and cross year rainy season background controls are then integrated to represent complementary hydrometeorological and terrain conditions. Design weights and density ratio calibration are applied to account for control reliability and reduce distribution mismatch between the training sample and the mapping domain. In the sample-level evaluation, the method was evaluated in Pubei County, Guangxi, China, using terrain, geology, land cover, daily and antecedent rainfall, and surface wetness. It outperformed Buffer, LowSlope, and IV Low across five classifiers. Relative to IV Low, mean AUC increased from 0.887 to 0.958, accuracy from 81.8% to 91.6%, and Kappa from 63.6% to 83.3%. Holdout validation of two July 2006 landslide clusters also showed greater concentration in top-ranked areas. Averaged over RF and GBDT, top 10% capture rose from 0.227 to 0.322, while the frequency ratio increased from 2.264 to 3.213. These findings suggest that, under the evaluated conditions, the strategy improves sample discrimination, increases landslide concentration in areas ranked as highly susceptible, and reduces uncertainty in the selection of nonlandslide controls. Full article

Accurate mapping of terraces is important for farmland protection, food security, and the management of terrace abandonment. However, extracting terraces from high-resolution remote sensing imagery remains challenging because of complex backgrounds, fragmented ridge structures, weak boundary contrast, and high similarity between terraces and surrounding land-cover types. This study proposes AMF-Deep, an Attention Multi-scale Fusion DeepLabv3+ model, for binary terrace extraction across representative terrace regions and morphologies. AMF-Deep was evaluated using 0.61 m high-resolution remote-sensing imagery from five representative terrace regions in China, encompassing diverse terrace morphologies and background conditions. In the proposed model, MobileNetV2 is adopted as the backbone to reduce computational cost, a modified multi-scale ASPP module is used to capture terrace morphology at different spatial scales, CBAM enhances low-level boundary and texture features, ECA refines high-level channel responses before ASPP, and NAM recalibrates semantic features after ASPP. A residual decoder is further introduced to support low-level feature transfer and boundary recovery. Experimental results show that AMF-Deep achieved the highest regional performance in Huangling, with mIoU, mPA, Accuracy, Recall, and Precision of 89.47%, 95.17%, 96.70%, 94.99%, and 93.68%, respectively. Stepwise ablation experiments further indicated that the modified ASPP module, the differentiated attention arrangement, and the residual decoder contributed to the final performance. Qualitative seasonal visualization suggests that winter images and dryland terraces in Shexian remain more challenging because of weak texture contrast and high background similarity. These results indicate that AMF-Deep has potential for terrace extraction across representative regions, although further quantitative seasonal evaluation and cross-region validation are still needed. Full article

Assessing ecosystem services, identifying their driving factors, and optimizing their spatial patterns are essential for coordinating ecological conservation and socioeconomic development in mountainous regions. Taking the Anning River Basin in southwestern China as the study area, this study quantitatively assessed ecosystem services from 2010 to 2024, integrated trade-off intensity into the Integrated Ecosystem Services Index, applied a Bayesian network model to identify the driving factors of ecosystem services, and proposed strategies for spatial pattern optimization. The results showed that: (1) the mean values of water conservation and soil conservation in the Anning River Basin were 94.45 mm and 1179.64 t ha⁻¹, respectively, both showing substantial interannual fluctuations. The habitat quality index was 0.83, and the mean carbon storage was 132.68 t ha⁻¹; both habitat quality and carbon storage declined slightly. The mean food production was 0.46 t ha⁻¹, indicating an improvement in the food production function. (2) The trade-off intensity among ecosystem services was 0.33, and the Integrated Ecosystem Services Index was 0.51, remaining generally stable overall. Ecosystem services were mainly influenced by land use, precipitation, population count, and fractional vegetation cover. Their spatial heterogeneity was pronounced, with relatively low values in the Anning River Plain, the Jinsha River dry-hot valley, and the Yanyuan Basin. (3) The identified priority conservation areas for ecosystem services covered 8307 km² and were mainly distributed within ecological conservation redline areas and regulated zones. The general functional areas for ecosystem services covered 149 km² and were concentrated in urban construction areas and along major transportation corridors in the basin. Targeted strategies were further proposed to enhance the synergistic improvement of ecosystem service supply. This study provides theoretical and practical support for ecosystem management in the Anning River Basin and other mountainous watersheds. Full article

Understanding how urbanization modifies stream thermal regimes is essential for assessing freshwater ecosystem responses to climate variability and land-use and land-cover (LULC) change. This study investigated air temperature (AT)–water temperature (WT) relationships at annual, monthly, and diurnal timescales in two adjacent, relatively flat watersheds with contrasting urbanization levels in North Central Texas: the urbanized Doe Branch and less urbanized Little Elm Creek during 2012–2021. A single harmonic analysis was applied to characterize annual and diurnal thermal patterns, including mean temperature, amplitude, and phase, while statistical analyses were used to evaluate seasonal and daily thermal variability and peak timing. At the annual scale, AT and WT metrics were strongly correlated at both sites (r = 0.85–0.94, p < 0.01) indicating that atmospheric conditions were the dominant control of annual stream temperature variability. Annual mean WT increased with AT, suggesting strong air–water thermal coupling and the potential for warmer stream temperatures under future climate warming. However, Doe Branch exhibited higher annual mean WTs, delayed seasonal peak WTs, and reduced annual temperature ranges compared with Little Elm Creek, reflecting the influence of urban watershed characteristics on seasonal thermal responses. At the diurnal scale, daily mean WT remained strongly coupled with daily mean AT, whereas daily temperature range and peak timing showed weaker relationships with AT. The greater variability in peak WT timing at Doe Branch suggests that short-term stream thermal dynamics were influenced by additional watershed characteristics beyond atmospheric forcing alone. Full article

Land-use and land-cover (LULC) change is a major driver of ecosystem degradation and ecosystem service loss in tropical landscapes. This study assessed the impacts of LULC changes between 1985 and 2023 on the potential of different LULC classes to supply ecosystem services in the Upper Rio Pardo Basin (Rio Pardo and Rio São João do Paraíso watersheds), northern Minas Gerais, Brazil. LULC data from the MapBiomas Project were used to quantify transitions among LULC classes within the study area. The potential supply of ecosystem services was assessed using the Burkhard matrix, adapted to local environmental conditions and informed by expert knowledge, while the relative importance of ecosystem services was evaluated through a participatory assessment involving local communities. The expansion of pasturelands and commercial forest plantations was identified as the primary driver of ecosystem service loss in the study region. Native ecosystems showed the highest potential to provide regulating, supporting, provisioning, and cultural ecosystem services, particularly water regulation, soil protection, carbon sequestration, and biodiversity conservation. In contrast, anthropogenic land uses were primarily associated with provisioning services and showed limited capacity to sustain regulating and supporting services, indicating clear trade-offs between production and ecosystem functioning. Local communities identified water-related services as the highest priority, followed by climate regulation, soil fertility, and food provision. These findings demonstrate the value of integrating expert-based and participatory approaches to ecosystem service assessment and provide a basis for territorial planning strategies that prioritize the conservation and restoration of native vegetation, particularly in hydrologically sensitive areas. Full article

Accurate discrimination between rice and artificial grassland remains challenging in regional agricultural monitoring because both herbaceous types share similar spectral signatures during vegetative growth, and existing land-cover products do not treat artificial grassland as a separate class. Using Jurong City, Jiangsu Province, as the study area, we propose a framework that optimizes red-edge vegetation index selection within crop-specific phenological windows to separate rice from grassland. Using Unmanned Aerial Vehicle (UAV) multispectral imagery and Sentinel-2 satellite data, we quantified spectral separability across eight phenological stages using Fisher ratios. We identified two optimal discrimination windows: early tillering (mid-June) and heading–flowering (early September). Within the heading–flowering window, a dual-index classification rule combining Normalized Difference Red-Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI) was transferred from UAV to Sentinel-2 and used to produce a 10 m rice–grassland map for the entire city. Spatial agreement with two publicly available rice datasets reached 75.2% and 79.5% for rice pixels, reflecting differences in spatial resolution, reference year, and class definition rather than classification error. Independent field validation using 200 samples yielded an overall accuracy of 92.50% (F1-score = 0.93), confirming the effectiveness of the VI–window optimization strategy. The framework offers an interpretable, physiology-driven alternative for crop-type mapping that relies solely on widely available multispectral bands. Full article

CORINE Land Cover (CLC) is one of the most used land databases intended for pan-European-scale analysis. Despite periodic updates, the data suffer from generalization, subjectivity, and inconsistent local knowledge, leading to a distorted representation of reality. In this study, we raise awareness of the use of CLC data in local analysis, for which it was never intended. Our study investigates whether incorporating auxiliary spatial data and local geographical knowledge can yield a higher-accuracy CLC product, without departing from the official CLC definitions and standards. We critically remapped polygon by polygon the 1990, 2000, and 2006 CLC layers, for Iași County (NE Romania), using topographic maps (1972–1989), aerial imagery (1978–2008), and satellite data (1980–2006), and compared it to the original CLC, through change detection analysis. The new maps revealed several issues imposed by (a) generalization—cartographical omissions among settlements, due to the application of a 25 ha minimum mapping unit and a 100 m minimum mapping width; (b) confusions between land cover and land-use classes, such as pasture and wetlands, especially under varying climatic conditions, or imposed by landforms, where we suggest the use of complementary data, such as a Digital Elevation Model (DEM); and (c) the inconsistencies of mapping between successive CLC editions. Our results indicate that the CLC should not be used for downscaling analysis. Therefore, the authors advocate integrating multiple temporal remote sensing layers to achieve a more accurate assessment of land cover classes, thereby compensating for the data’s top-down character. Based on these findings, we propose an error classification approach to serve as a reference for risk mitigation in downscaled spatial analysis. We emphasize the need for CLC data users to validate their data against ground truth to mitigate analytical uncertainties. Full article

Over the past seven decades, rapid economic growth has been accompanied by a substantial increase in environmental degradation worldwide. Given the magnitude and transboundary nature of environmental externalities, country-specific policies may be insufficient, and regionally coordinated environmental strategies are likely to be more effective. For such frameworks to succeed, convergence in levels of environmental pressure among member countries is essential. In this study, convergence is examined using the ecological footprint (EFP), a comprehensive indicator of environmental pressure. The analysis covers the six member states of the Gulf Cooperation Council (GCC)—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over 1971–2024 and tests for the stochastic convergence of per-capita footprints relative to the year-t regional average. Conventional ADF tests indicate convergence for Kuwait and Qatar—with Saudi Arabia at the 10% level—and Phillips–Perron results point in the same direction. Quantile ADF and Fourier quantile KSS tests with bootstrap inference show that adjustment is regime-dependent and country-specific: mean reversion is confined to below-average states in Bahrain, to above-average states in Kuwait and Saudi Arabia, and to both extremes in Qatar, while the United Arab Emirates and Oman revert to almost no quantile. The Phillips–Sul log-t procedure does not reject full-panel convergence: all six countries form a single convergence club, the result survives leave-one-country-out and cross-sectional-dependence checks, and subsample estimates date the convergence process mainly to the post-1990 period. Component-level analysis shows that aggregate convergence is carried by the carbon component (84–99% of the GCC footprint), whereas cropland, grazing, forest, and built-up components diverge. The findings support regionally coordinated energy–carbon policy combined with nationally tailored land-use policy. Full article