Search Results (8,532)

The Qinghai Lake Basin, a typical ecologically vulnerable, high-altitude, cold region, requires coordinated ecosystem conservation and socio-economic development to achieve territorial sustainability. Based on the Production–Living–Ecological Space (PLES) framework, this study used land use data from five periods between 2000 and 2020 and integrated the PLUS and InVEST models to examine and simulate the evolution of PLES patterns and carbon stock under four scenarios—natural development, ecological protection, economic development, and sustainable development—in 2035. The results show that the PLES pattern in the Qinghai Lake Basin remained generally stable from 2000 to 2020, with ecological space dominating the landscape, while production and living spaces expanded slowly. Carbon stock increased from 214.73 × 10⁶ Mg to 264.70 × 10⁶ Mg, representing a growth rate of 23.27%. Its spatial distribution is highly consistent with the PLES pattern, with ecological space being the main contributor. By 2035, carbon stock is projected to slightly increase under the natural development scenario; under the ecological protection scenario, the expansion of ecological space leads to an increase in carbon stock; it decreases under the economic development scenario due to the encroachment of ecological space by construction land expansion; and under the sustainable development scenario, which balances economic development and ecological protection, carbon stock increases by 4.87 × 10⁶ Mg, achieving the best overall performance. Therefore, it is essential to properly coordinate the relationships among PLES components to achieve synergistic enhancement of ecosystem services and regional sustainable development. The findings provide methodological references and decision support for sustainable development in the Qinghai–Tibet Plateau and other ecologically vulnerable regions. Full article

Protecting native coastal wetland vegetation and controlling the invasion of Spartina alterniflora (SA) have long been key ecological and management priorities in China. The accurate and rapid mapping of vegetation distribution is critical for effective invasion control and wetland restoration. While phenological information improves remote sensing classification, most studies rely on the Normalized Difference Vegetation Index (NDVI), which has limited capability to distinguish morphologically similar species in coastal wetlands. To better exploit the unique reddening phenology of one such species, Suaeda salsa (SS), this study builds on our previously developed Red Suaeda salsa Index (RSSI) and introduces two novel phenological indicators: the Redness Contribution Coefficient (RCC) and Reddening Rate Index (RCI). Using the coastal wetlands of Jiangsu Province as the study area, we employed multi-temporal Sentinel-2 image composites (spring, summer, autumn) from 2019, 2022, 2024, and 2025 to construct a multi-dimensional feature set and implemented classification using a random forest algorithm. Results showed that the feature scheme integrating SS reddening phenological parameters achieved the highest accuracy, with an overall accuracy of 97.32% and a Kappa coefficient of 0.9625 in 2019, confirming the method’s reliability at the provincial scale. Between 2019 and 2025, SA coverage in Jiangsu decreased by 90.8%, with most cleared areas converting to non-vegetated land, indicating the remarkable effectiveness of recent control projects. This study scales up a locally validated high-precision classification approach to the provincial scale, supporting sustainable coastal wetland management in line with United Nations (UN) SDG 14 (Life Below Water) and SDG 15 (Life on Land). Full article

Residual film recovery is a crucial approach to mitigating agricultural “white pollution” and ensuring sustainable land use. Currently, the development of residual film recovery machines relies primarily on theoretical analysis and field performance tests. The lack of support from computational simulation models often leads to suboptimal mechanical performance, severely restricting the design and optimization of recovery equipment. To address this, this study proposes a method for constructing and experimentally validating a discrete element model of plow-layer residual film using EDEM software. First, field tests were conducted to measure soil compaction and residual film distribution at various depths. The ultimate tensile force of the residual film was also evaluated to provide fundamental data for model development. Using the Hertz–Mindlin with bonding contact model in EDEM, the intrinsic parameters of the residual film were selected and optimized. Combined with a Box–Behnken experimental design, a quadratic regression model relating normal stiffness per unit area, critical normal stress, and bond radius to the ultimate tensile force of the film was constructed. The optimal parameter combination was determined as follows: normal stiffness = 1.11 × 10⁶ N·m⁻³, critical normal stress = 2.45 × 10⁶ Pa, and bond radius = 0.03 mm. Under these parameters, the theoretically predicted ultimate tensile force was 1.18 N, and the simulated value yielded a relative error of only 1.69%, validating the effectiveness of the single-film model. Furthermore, using the field-measured data, a coupled film–soil model was established via the “rainfall” method to conduct simulated penetration tests. Parameter calibration was executed using the multivariate Newton–Raphson iteration method. The optimal bonding parameters for soil particles were identified as follows: normal stiffness per unit area = 9.6 × 10⁵ N/m², shear stiffness per unit area = 9.6 × 10⁵ N/m², critical normal stress = 5.38 × 10⁵ Pa, critical shear stress = 5.38 × 10⁵ Pa, and bond radius = 4.3 mm. The average simulated penetration resistance was 59.61 N, showing a relative error of 5.91% compared to the field-measured value of 56.28 N. These results demonstrate that the developed coupled film–soil DEM can be effectively applied to simulate the lifting and throwing processes of plow-layer residual film recovery machines, thereby providing vital modeling support for the design and optimization of residual film recovery mechanisms. Full article

Research Objective: To explore governance pathways for small-property-right housing from the perspective of land appreciation revenue distribution, thereby promoting high-quality urban renewal. Research Methods: The study employs theoretical analysis, inductive summarization, and logical reasoning. Research Findings: (1) Land appreciation revenue consists of absolute rent, differential rent I, and differential rent II, corresponding respectively to land ownership, land development rights, and land management rights; (2) A framework for the distribution of land appreciation gains that “balances public and private interests and promotes multi-stakeholder sharing” is established, clarifying the revenue boundaries for entities such as the government, village collectives, and housing operators; (3) Two governance pathways are proposed: converting retained collective property rights into affordable rental housing, and categorizing and disposing of properties after government expropriation and conversion to state ownership. These are further refined into five implementation models. Research Conclusions: The rational distribution of land appreciation gains is key to resolving the governance challenges of small-property-right housing and coordinating the objectives of urban renewal with housing security. Full article

Socio-spatial inequality remains a defining feature of climate vulnerability in South Africa, where historically formed patterns of segregation continue to shape uneven access to infrastructure, services, and environmental resources. This study presents a narrative review of how historical spatial planning has structured persistent disparities in exposure, sensitivity, and adaptive capacity across urban and rural landscapes. Evidence from the literature demonstrates that apartheid-era spatial planning established durable inequalities in water and sanitation provision, green infrastructure distribution, and proximity to environmental hazards, which continue to influence contemporary climate risk profiles. These inequalities are further reinforced through socio-economic stratification, particularly in the context of energy transitions, where access to private renewable energy systems is concentrated among wealthier households, while poorer communities remain dependent on unstable public electricity infrastructure. The review also incorporates land and soil systems as critical but often minimized dimensions of vulnerability, showing how soil degradation and unequal access to productive land contribute to livelihood insecurity and reinforce rural and peri-urban marginalization. In addition, emerging responses such as just transition frameworks, grassroots environmental justice movements, and energy democracy initiatives are examined with regard to the structural constraints that limit their effectiveness in addressing entrenched inequalities. Overall, the analysis highlights that climate vulnerability in South Africa is deeply embedded in historical and ongoing socio-spatial and socio-economic inequalities that continue to shape differentiated environmental outcomes. Full article

We assessed the current and possible future predicted distributions of the Mexican narrow-mouthed toad, Amphibia, Microhylidae Hypopachus variolosus (Cope, 1866) across its range to evaluate vulnerability under global change. (2) Methods: We integrated 481 validated occurrence records across the species’ distribution range, including 120 records from Mexico, with bioclimatic and land-cover predictors to build ensemble ecological niche models. We additionally incorporated human footprint metrics to evaluate anthropogenic pressure and projected future habitat suitability under climate and land-use change scenarios. (3) Results: Models showed high performance (TSS > 0.80; AUC > 0.90), identifying temperature and precipitation extremes as main drivers. Suitable habitats extended across both coasts and revealed novel areas in central Mexico. The most suitable habitat occurred under low human pressure, although localized impacts were detected. Deforestation in the Yucatán Peninsula reduced tree cover despite high climatic suitability. Future projections for 2050 under RCP 8.5 indicated marked reductions in modeled high-suitability areas, particularly in central Mexico. (4) Conclusions: These findings indicate high vulnerability to climate and land-use change and support updating distribution limits, incorporating new regions into conservation planning, and reassessing threat status to promote long-term persistence. Full article

The White-naped Crane (Antigone vipio), a first-class national protected bird species in China, exhibits a declining global population. To investigate the spatiotemporal patterns and drivers of wintering habitat suitability, data from 71 valid distribution sites were collected from 2015 to 2025 during the wintering period. Using the MaxEnt model, current and future (2050 and 2070) potential suitable habitat distributions were simulated under three climate scenarios: SSP126 (low emissions), SSP245 (medium emissions), and SSP585 (high emissions). The modeling yielded an average AUC value of 0.984, indicating high predictive accuracy. Key environmental variables influencing the wintering distribution of the White-naped Cranes include elevation, distance to major water, precipitation of the driest month, slope, temperature seasonality, and mean temperature of the wettest quarter. The current high-suitable area for the White-naped Cranes spans 5.64 × 10⁴ km² and is primarily distributed in the middle and lower reaches of the Yangtze River and in coastal wetlands along the North China. Among these, Hunan, Hubei, Jiangxi, and Anhui provinces contain relatively concentrated high-suitable areas for the species. Primarily influenced by elevation, distance to major water, precipitation of the driest month, and land-use classification, the suitable wintering habitat of the White-naped Cranes is projected to undergo significant contraction, shifting predominantly to the middle reaches of the Yangtze River. The most severe contraction is projected under the SSP585 scenario by 2070, with a reduction of 4.11 × 10⁵ km². Contraction areas are primarily concentrated along the Bohai and Yellow Sea coasts and in the middle and lower reaches of the Yangtze River, while minimal expansion occurs in Hubei, Anhui, and Zhejiang. The overall southwestward shift in the species’ distribution centroid may be associated with changes in elevation and distance to major water. Finally, habitat conservation strategies for the White-naped Cranes are proposed, providing a scientific basis for population protection and habitat management under future climate change. Full article

Siziwang Banner in Inner Mongolia is a typical arid and semi-arid grassland region where ecological environmental quality is highly sensitive to climate variability and land use and land cover change (LULCC). Clarifying the long-term coupling relationship between LULCC and ecological environmental quality is essential for regional ecological protection and sustainable land management. Based on the Google Earth Engine (GEE) platform, this study integrated multi-temporal Landsat imagery and CLCD-based land use datasets, including an updated 2024 land use layer, to construct a Remote Sensing Ecological Index (RSEI) using standardized and direction-corrected principal component analysis. land use transition matrix analysis, spatial autocorrelation analysis, ecological contribution rate calculation, and GeoDetector were further applied to reveal the spatiotemporal evolution patterns, ecological effects, and driving mechanisms of LULCC in Siziwang Banner from 2000 to 2024. The results showed that: (1) grassland was consistently the dominant land use type, accounting for more than 90% of the total area. The overall land use pattern was characterized by stable grassland dominance, decreasing farmland and unused land, and slight increases in grassland and construction land; forestland showed a high relative growth rate but remained very small in absolute area. (2) The regional ecological environmental quality remained at a lower-to-medium level, with mean RSEI values ranging from 0.27 to 0.47. RSEI showed a phased pattern of initial improvement, subsequent decline, and partial recovery; the marked decline around 2015 was associated with the combined effects of drought stress and land use degradation rather than a single driving factor. RSEI exhibited significant positive spatial autocorrelation, with Moran’s I values ranging from 0.898 to 0.993. High-value clusters were mainly distributed in the southern region, whereas low-value clusters were concentrated in the central and northern regions. (3) Different land use transitions produced differentiated ecological effects. The conversion of unused land to grassland contributed positively to ecological restoration, while grassland degradation and construction land expansion exerted negative effects. The positive RSEI response of some grassland-to-farmland transitions should be interpreted cautiously in relation to local irrigation and intensive farmland management. (4) GeoDetector results indicated that land use type and DEM were the dominant factors controlling the spatial differentiation of RSEI, with average q values of 0.7188 and 0.6178, respectively. The interaction between DEM and land use type showed the strongest explanatory power, indicating that ecological quality was jointly shaped by land use structure and natural background conditions. This study provides a scientific basis for grassland protection, unused-land restoration, farmland management, and spatially differentiated ecological restoration in Siziwang Banner and similar ecologically fragile arid and semi-arid grassland regions. Full article

Open-set classification for remote sensing requires models that simultaneously achieve high accuracy on known land-cover types and reliably detect novel classes absent from the training distribution—a capability essential for real-world deployment where new classes routinely emerge. Existing multimodal fusion approaches for hyperspectral imagery (HSI) and LiDAR are primarily designed for closed-set scenarios and lack robust uncertainty modeling for unknown detection. We propose a post hoc calibrated multimodal open-set framework with three tightly integrated components. First, an Uncertainty-Aware Gating Fusion (UAGF) module dynamically weights HSI and LiDAR features per sample based on modality reliability and produces a gating uncertainty signal reflecting fusion confidence. Second, an Iterative Feedback Refinement (IFR) module progressively refines fused representations over multiple iterations and captures convergence dynamics, where stable convergence indicates known samples while high feature-change variance identifies potential unknowns. Third, a compact two-signal open-set detector combines gating uncertainty and refinement variance through an EVT (Weibull)-based post hoc calibration mechanism fitted exclusively on known validation samples. The framework follows a strict zero-unknown-supervision protocol: the multimodal backbone is trained using only known-class samples, and the open-set decision threshold is derived solely from the known validation score distribution. This design decouples representation of learning from open-set decision learning, improving robustness and avoiding the objective conflicts that arise in joint training. Comprehensive experiments on three benchmark datasets—Houston2013, Muufl, and Augsburg—demonstrate that the proposed method achieves 92.79%, 84.47%, and 80.99% overall accuracy and 76.48%, 63.91%, and 56.81% unknown accuracy, outperforming the closest multimodal competitor HyLiOSR by up to 32.4 pp in unknown accuracy while maintaining competitive closed-set performance. Full article

Land surface temperature (LST) is crucial for understanding winter landslide evolution. This study combines Unmanned Aerial Vehicle (UAV) photogrammetry and infrared thermography (IRT) to monitor winter landslides in China’s Wumeng Mountain region. Using the Yangjiazhai landslide—induced by underground coal mining—as a case study, we demonstrate significant correlations between IRT-detected LST anomalies and surface cracks: (1) cracks with elevated temperatures are likely connected to subsurface goaf zones; (2) excessively widened cracks show no thermal anomalies due to enhanced air convection. The research reveals that key landslide components have distinct LST signatures, governed by differential soil–rock moisture and crack networks. For accurate high-altitude winter LST acquisition, UAV thermal surveys should be conducted under overcast, fog-free conditions to reduce solar interference. This validates UAV visible–infrared fusion for extracting landslide boundaries, cracks, slumping zones, bedrock patterns, and moisture distribution. The methodology establishes a new pathway for investigating winter landslide deformation and instability, confirming IRT’s operational viability in high-altitude alpine regions. Full article

This study focuses on the quantitative simulation of the spatiotemporal distribution characteristics of permafrost area, providing scientific value for Mongolian Plateau permafrost dynamics. Understanding the permafrost area of the Mongolian Plateau and accurately predicting future changes in permafrost area are crucial for sustainable environmental development. In this study, ERA5-Land surface temperature (LST) combined with the temperature at the top of permafrost (TTOP) model are used to calculate the annual permafrost area from 1980 to 2024. In addition, this study used the long short-term memory (LSTM) model to predict permafrost area on the Mongolian Plateau from 2025 to 2100. In this study, it is concluded that (1) the study area is not uniformly covered with permafrost, and its distribution is mainly limited to the northern part of the Mongolian Plateau, with a permafrost area of 53.20 × 10⁴ km²; (2) the permafrost area is estimated with an accuracy and precision of 0.94 when compared to the baseline value derived from borehole permafrost data; (3) under the CMIP6 three different shared socioeconomic pathway (SSP) 1-2.6, 2-4.5, and 5-8.5 future scenarios, the distribution of permafrost area shows a downward trend. This study provides a theoretical reference for distribution permafrost area in geographical space, which can help achieve the sustainable development of ice and snow resources. Full article

This study adapted and applied a spatially distributed analytical model to estimate the annual representative leached fraction and the annual potential leached mass of atrazine in the Cauquenes catchment in Chile under contrasting Mediterranean hydroclimatic conditions. The model was based on van der Zee and Boesten and Rakonjac et al. and was modified to account for the strong seasonality of precipitation and evapotranspiration by using representative daily hydrological conditions derived from monthly averages. Spatially distributed soil, climate, land-cover, and atrazine application data were integrated at the pixel scale, including locally corrected soil organic carbon, hydraulic properties, precipitation, evapotranspiration, leaf area index, and annual atrazine dose. The model was applied to two contrasting years, 2018 and 2023, and outputs were aggregated at the pixel, land-cover, hotspot, and catchment scales. The results showed a marked hydroclimatic control on potential atrazine leaching. In the drier year, 2018, both the annual representative leached fraction and the annual potential leached mass were generally very low across the catchment, whereas in the wetter year, 2023, moderate-to-high leaching values became much more spatially extensive, and hotspot areas expanded substantially. At the catchment scale, potential leached mass increased from 0.088 kg in 2018 to 179.784 kg in 2023, while the percentage of applied mass potentially leached increased from 5.50 × 10⁻⁵% to 0.112%. Land-cover classes influenced the results both through the spatial allocation of atrazine application and through LAI-dependent partitioning of evapotranspiration. Global sensitivity analysis using the Morris method identified K_OC and DT50 as the dominant controls on annual potential leached mass, and spatial uncertainty propagation was performed. Overall, the proposed framework provides a potential annual screening estimate and may serve as a preliminary screening tool to prioritize areas for targeted monitoring and future model benchmarking in Chile. Full article

Remote-sensing land surface deformation (LSD) is a powerful and effective approach for investigating regional alpine permafrost variations. However, alpine permafrost is often distributed in areas characterized by earthquakes, and the LSD of alpine permafrost is potentially contaminated or diminished by earthquake-related LSD. Therefore, this study aimed to derive the effective LSD in the alpine permafrost of the Source Area Yellow River (SAYR) by removing LSD originating from the M_w 7.4 Maduo earthquake in 2021-05-22 and analyzing the spatiotemporal variations in LSD during 2017–2024. Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) was used to obtain the initial LSD time series from Sentinel-1 images acquired during 2017–2024. The LSD of the M_w 7.4 Maduo earthquake, its aftershocks and the post-seismic relaxation in SAYR was simulated separately by considering its temporal process and removed from the LSD time series in SAYR. The final LSD was validated against in situ Global Navigation Satellite System (GNSS) measurements, and the spatiotemporal variations in LSD in SAYAR were subsequently analyzed. The study found the following: (1) the removal of the earthquake-related LSD was successful both spatially and temporally and the final LSD has mean absolute error (MAE) of 3.22 mm and root mean squared error (RMSE) of 3.92 mm; (2) during 2017–2024, the vertical LSD in SAYR was mostly −8–8 mm/y; (3) soil moisture determined the spatial distribution of the LSD direction in SAYR as a result of local drainage conditions, air temperature, precipitation and snow melt. This study demonstrated the necessity of removing the earthquake-related LSD when investigating the alpine permafrost LSD in tectonically active areas. The strategy adopted in this study serves as a technical reference for future investigations of this kind. The findings in this study provide insight for a thorough understanding of permafrost evolution on the Tibetan Plateau in the context of climate change. Full article

The present study investigates the statistical distribution of radar reflectivity slopes [S-Ze] in the lower troposphere along the west coast of India using a C-band radar during the pre-monsoon and monsoon seasons in 2024. The study period spans a range of meteorological conditions, from a drier atmosphere during pre-monsoon months to a moist atmosphere during the monsoon months, with varying updraughts and downdraughts. To investigate the S-Ze, we calculated the difference in Ze between 4 km and 2 km altitudes in the lower troposphere. The S-Ze could be either positive or negative, where, in a positive [negative] S-Ze, the Ze decreases [increases] towards the surface. The monthly variations in S-Ze from the pre-monsoon to monsoon months are observed in the lower troposphere and are higher in monsoon months compared to pre-monsoon months, which are too near the coast. The land–ocean contrasts of the vertical profiles contributing to +ve and −ve S-Ze are lower compared to north–south gradients and higher in monsoon months. The average S-Ze shows the highest +ve and −ve S-Ze magnitude near the coast among all the months. The highest magnitude in S-Ze is observed in March and April and is associated with the lower and higher numbers of vertical Ze profiles. The increase or decrease in hydrometeor size is less during the monsoon months (June, July, August, and September) compared to pre-monsoon months, where the March–April months have the highest increase or decrease in the hydrometeor’s size in the lower troposphere. The variations in the S-Ze are the combined effect of the atmospheric, thermodynamic (relative humidity (RH) and moisture flux), and dynamic conditions (zonal, meridional, and vertical velocity). Strong updraughts that carry RH to higher altitudes make the lower atmosphere drier and contribute to a +ve S-Ze; Ze tends to decrease in the lower troposphere. However, a weaker updraught or a moderate downdraught with sufficient RH provides sufficient time for hydrometeors to grow and contributes to −ve S-Ze, and Ze tends to increase in the lower troposphere. For example, in March and April, the atmosphere is dry, and we observe the largest decrease in hydrometeors near the coastal boundary. However, we also see significantly higher negative radar reflectivity slopes, and weak downdraughts provide enough time for hydrometeors to grow. In June and July, there are strong updraughts (downdraughts) with high (low) RH, making the atmosphere more conducive to a decreasing tendency in Ze and contributing to a higher fraction of +ve S-Ze. The results presented here would be an extension of the study from the satellite-based observations, revealing the extension of climatology for the inclusion of stratiform precipitation. Full article

Deep excavation dewatering is an energy-intensive groundwater control process in underground engineering, especially under strong recharge and heterogeneous hydrogeological conditions. Conventional dewatering designs often rely on conservative pumping schemes to ensure the required drawdown, which may generate redundant groundwater extraction, unnecessary electricity consumption, additional carbon emissions, and excessive drawdown-induced settlement. To address this problem, this study develops a coupled improved genetic algorithm and MODFLOW optimization model, termed IGA-M, for dewatering well-group operation under engineering safety constraints. The purpose of the proposed model is not to reduce pumping arbitrarily, but to identify and eliminate redundant pumping while satisfying prescribed requirements for target water levels, settlement control, and hydraulic-gradient safety. Through the FloPy interface, the Improved Genetic Algorithm is dynamically linked with MODFLOW to establish a closed-loop simulation-optimization framework. In each optimization iteration, candidate well operation schemes are automatically transferred to MODFLOW, and the simulated hydraulic heads and settlement responses are returned to evaluate the objective function and safety constraints. In this framework, groundwater extraction, electricity consumption, carbon emissions, and land subsidence are treated as physically linked performance indicators of the optimized dewatering scheme. Validation using an idealized case shows that, under the same safety requirements, the IGA-M model reduces redundant hydraulic loading compared with the traditional uniformly distributed pumping method. By removing redundant pumping beyond the safety requirement, the optimized scheme reduced groundwater extraction by 62.7%, which was accompanied by a 44.9% decrease in both carbon emissions and comprehensive costs, as well as a 57.7% reduction in settlement at observation points. In a practical high-permeability deep excavation adjacent to the Yellow River, the model achieved well-group flow regulation under strong recharge conditions. Compared with the traditional scheme, it eliminated approximately 661,000 m³ of redundant groundwater extraction, corresponding to a 17.7% decrease, and consequently saved 26,800 kWh of electricity and reduced CO₂ emissions by nearly 16,000 kg during the dewatering period. These results demonstrate that the proposed IGA-M framework can transform MODFLOW from a post-design verification tool into an active optimization engine for dewatering design. It provides a physically based decision-support method for reducing redundant pumping and improving energy efficiency while maintaining engineering safety. Full article