Search Results (546)

Soil wind erosion (SWE) remains a significant challenge to improving ecological environmental quality and achieving sustainable socioeconomic development in drylands of northern China. An in-depth understanding of the spatio-temporal variations and underlying mechanisms of regional SWE is a prerequisite for the scientific prevention and mitigation of erosion-related hazards. However, in regions with high variability in intra-annual climate, quantitative studies on the spatial heterogeneity and intra-annual variability of drivers of SWE are scarce. This knowledge gap poses challenges for policymakers in developing effective landscape management strategies that are spatially and temporally specific. Here, the dynamics of SWE in the Xilingol typical steppe of China were simulated using the Revised Wind Erosion Equation (RWEQ) at seasonal and annual scales during 2000–2020. Stepwise regression and geographically weighted regression (GWR) were employed to examine the spatial heterogeneity in the relationships between SWE and environmental variables. The results revealed that RWEQ simulations were significantly correlated with the frequency of dust storm events at the seasonal scale (R² = 0.807, p < 0.01). SWE in spring accounted for approximately two-thirds of the annual total, indicating that spring was the critical period for SWE control. High SWE intensity was concentrated in sandy soil regions, with the Otindag Sandy Land and Gahai Elesu Sandy Land being identified as priority areas for desertification prevention and control. Over the study period, SWE exhibited an overall decreasing trend at both seasonal and annual scales, suggesting an enhancement in the ecosystem’s capacity for windbreak and sand stabilization. The stepwise regression results indicated that climatic factors generally had greater explanatory power than topographic and landscape pattern variables. Wind speed showed the strongest association with SWE across different time scales, whereas the relationships of normalized difference vegetation index (NDVI) and precipitation with SWE exhibited clear seasonal dependence. The GWR results further revealed pronounced spatial heterogeneity and seasonal variability in both the direction and magnitude of the associations between SWE and climatic and landscape pattern variables. These findings provide scientific support for identifying priority areas for desertification prevention and for developing spatio-temporally targeted landscape management strategies in dryland sandy regions. Full article

The conventional “resources–energy–environment–economy” growth paradigm has imposed severe environmental pressures on China, including land desertification and smog pollution. In the context of carbon peaking and carbon neutrality, the synergistic advancement of pollution reduction, carbon mitigation, ecological expansion, and economic growth (PCEG) has become a critical development pathway. Drawing on Pareto improvement theory, this study applies a super-efficient slack-based measure (SBM) model to evaluate PCEG synergistic efficiency across 30 Chinese provinces from 2003 to 2020. We further investigate its temporal evolution, regional heterogeneity, and convergence characteristics. The empirical results reveal that (1) PCEG synergistic efficiency follows a U-shaped trajectory; (2) both technological change and efficiency change contribute positively to post-2018 recovery; (3) substantial regional heterogeneity and cross-regional overlap are observed, with intra-regional disparities playing an equally important role in shaping overall inequality as inter-regional differences; and (4) no σ-convergence is observed at the national or regional level; β-convergence is significant in the non-spatial setting but drops sharply once spatial dependence is incorporated, indicating that administrative barriers, market segmentation, and frictions in factor mobility hinder the convergence process. These results inform a policy mix that addresses within-region heterogeneity, sustains the post-2018 momentum of technological progress, and dismantles spatial barriers to factor mobility. Full article

Karst regions are characterized by fragmented topography and significant micro-relief mosaics, leading to prominent spectral aliasing of land features, which can result in insufficient spatial representativeness of remote sensing samples for Land Use and Land Cover (LULC). The accuracy of LULC data directly affects the scientific basis of decision-making for rocky desertification control and ecological conservation. This study selected the Beipanjiang River Basin in Guizhou Province, a typical karst region, as the study area. The study selected the SOS, LOS, OM, and EOS indices from the 2001–2020 MODIS MCD12Q2 phenological dataset, combined with topographic zoning data. This study developed a sample spatial optimization scheme for complex karst terrain by integrating Spearman’s correlation analysis, SKATER spatially constrained clustering, statistical tests, adaptive stratified sampling, and Random Forest classification. The scheme was designed to test a phenology–landform joint stratification strategy for spatial sample allocation. The results indicate that (1) the study area was divided into six phenological pattern subregions, with significant spatial differentiation observed among them; (2) the “phenology–landform joint stratification + dual-weighted sample allocation” method was associated with improved sample representativeness and greater internal homogeneity within sample strata under the current experimental setting; and (3) compared to simple random sampling, the remote sensing phenological pattern-driven spatial optimization scheme improved overall accuracy from 71.33% to 77.55% and increased the Kappa coefficient from 0.43 to 0.62. These results suggest that, under the current study-area, sample-size, and validation settings, the phenology–landform joint stratification and dual-weighted allocation scheme can improve the spatial organization of training samples and classification performance over complex karst terrain, although weakly vegetated or bare classes remain difficult to separate. Full article

Karst critical zones in Southwest China are highly vulnerable to soil–water loss because thin soils, exposed carbonate bedrock, well-developed epikarst, and strong surface–subsurface connectivity promote both surface erosion and subsurface leakage. Although soil erosion, subsurface leakage, karst rocky desertification, and ecological restoration have been widely studied, the coupling between landscape structural patterns and soil–water loss remains insufficiently synthesized. This semi-systematic critical review synthesizes evidence from karst hydrology, soil erosion, karst rocky desertification, landscape structure, and critical zone studies, with a primary focus on Southwest China. The reviewed evidence indicates that geomorphic setting, land use vegetation structure, bare-rock exposure, and epikarst development jointly regulate runoff generation, infiltration, sediment detachment, subsurface leakage, and sediment connectivity. Peak–cluster depressions commonly favor internal sediment storage and vertical leakage, whereas valley and canyon systems tend to enhance surface runoff connectivity and channelized sediment export. However, pathway dominance varies with rainfall intensity, soil moisture, soil thickness, land use, karst rocky desertification degree, and fracture–conduit connectivity. Long-term soil–water loss may further reshape landscape structure through soil thinning, vegetation degradation, bedrock exposure, and karst rocky desertification feedbacks. Current research is limited by insufficient quantification of subsurface soil loss, weak integration between landscape metrics and hydrological models, and scarce long-term monitoring data. Future studies should integrate field monitoring, tracers, remote sensing, landscape metrics, and coupled surface–subsurface models to support geomorphic-setting-specific karst rocky desertification control. Full article

Photovoltaic desert control (PVDC), an innovative model integrating clean energy development and desertification control, faces complex coordination challenges among local governments, local communities, and photovoltaic enterprises. This study constructs a tripartite evolutionary game model to identify the conditions that drive PVDC toward coordinated governance. The model defines a three-dimensional strategy space: government regulatory intensity (Strong vs. Lax), community willingness to cooperate (Active Cooperation vs. Passive Resistance), and enterprise ecological integration (Active Ecological Integration vs. Passive Land Occupation). Replicator dynamic equations are derived to characterize nonlinear interactions, and the stability conditions of eight pure-strategy equilibrium points are identified through Jacobian matrix eigenvalue analysis. Numerical simulations are conducted using a baseline parameter set that satisfies the Evolutionary Stable Strategy conditions for the ideal equilibrium E8, namely Strong Regulation, Active Cooperation, and Active Ecological Integration. The results show that the system can converge to E8 when higher-level rewards cover government regulation, subsidy, and community-support costs; when community cooperation benefits exceed livelihood opportunity costs and compensation incentives from resistance; and when enterprises’ effective ecological integration costs are lower than the combined benefits of subsidies, avoided fines, and long-term returns. Sensitivity analysis further indicates that government subsidies, fines, community support, cooperation income, and enterprise long-term benefits are key drivers of system evolution, while excessive regulation costs, high opportunity costs, and high ecological integration costs may hinder coordination. Qualitative evidence from four PVDC-related cases in Xinjiang provides practical illustrations broadly consistent with the model mechanisms. This study offers a dynamic analytical framework for designing incentive-compatible governance mechanisms in PVDC and similar multi-stakeholder ecological restoration projects. Full article

Karst regions are commonly characterized by highly interwoven bare rock–bare soil–vegetation mosaics, strong coupling between surface and subsurface processes, and pronounced geomorphic fragmentation. Conventional land cover classification systems, which are primarily organized around land use patterns or generic ecological types, are often unable to accurately represent these key surface components and their roles in ecological processes. From the perspective of reconstructing classification semantics, this study proposes a Natural-Attribute-Based Karst Land Cover Classification framework (NALCC). The framework takes bare rock, bare soil, vegetation, water bodies, and impervious surfaces as primary classes, and further develops a hierarchical system consisting of subclasses, attribute labels, hierarchical coding, multi-scale organization, and parameter mapping with ecosystem service models. Compared with conventional land cover classification systems, the innovation of this framework lies not in increasing the number of categories, but in reconstructing the semantic organization of classification units, so that land cover classification can move beyond surface-type description toward the expression of process-sensitive information. The classification objective of NALCC is not to develop a universal land cover classification system, but to establish a process-oriented classification framework for ecosystem service monitoring, rocky desertification diagnosis, and governance zoning in karst regions, which can directly represent key surface components and their ecological-process significance. However, its regional transferability and mapping performance still need to be further validated through case studies in representative areas. Full article

Blue–green spaces serve as the core carriers of urban ecosystems, and their conservation and optimization have emerged as pivotal issues in territorial spatial planning and ecological governance. Taking Guilin, a national innovation demonstration zone for China’s Sustainable Development Agenda, as the study area, a deep learning-based DBDTAF-Net classification model is constructed using 2020 Sentinel-2 remote sensing imagery and AW3D30 Digital Surface Model (DSM) data. The model achieves a mean Intersection-over-Union (mIoU) of 86.05% on the test set and an IoU of 94.67% for rocky desertification areas. Based on the classification results, 21 derived indicators (including landscape patterns of BGSs) and six meteorological and topographic factors, alongside three core ecosystem service indicators—Aboveground Biomass (AGB), Net Primary Productivity (NPP), and soil conservation—are extracted to characterize their spatial patterns. The XGBoost-SHAP framework is employed to quantify the driving effects and threshold responses of BGS patterns on ecosystem services. The results indicate that (1) BGSs in Guilin display a spatial pattern of “green-dominated, blue-supplemented, generally contiguous yet locally fragmented,” and all three ecosystem services exhibit significant spatial clustering. (2) Landscape pattern factors of green spaces constitute the dominant influencing factors, with contribution rates ranging from 22.3% to 28.6%. Specifically, green space_COHESION demonstrates a stable linear positive effect. A green space ratio below 45% suppresses AGB, whereas exceeding 45% shifts to a positive effect and represents an efficient enhancement interval for NPP while exerting a continuously positive influence on soil conservation. A cultivated land proportion below 30% leads to a strongly increasing inhibitory effect on AGB and soil conservation, whereas its inhibition on NPP weakens beyond 20%. A construction land proportion exceeding 10% significantly suppresses NPP, and the inhibitory effect stabilizes above 20%. Green space patch density below 0.8 shows a pronounced negative effect, which diminishes above 0.8. Blue space factors exert relatively weak effects. (3) The ecosystem service supply capacity varies across functional zones in Guilin, with the ecological barrier zone performing the best, the modern agricultural zone performing moderately, and the six central urban districts of the Shanshui Metropolis Area exhibiting the lowest levels. This study provides a technical framework for high-precision extraction of urban BGSs and quantitative analysis of factors influencing ecosystem services, offers decision support for ecological conservation and restoration in Guilin, and furthermore proposes insights for the coordinated development of rational land resource utilization and ecosystem service enhancement in other karst cities. Full article

Ecological vulnerability assessment serves as a prerequisite for ecological governance, yet evaluating large-scale ecological vulnerability remains challenging. To address this challenge, this study integrates geological elements into ecological vulnerability assessment, taking Ruyuan Area in the Northern Guangdong Mountains, China, as a case study. The area faces ecological hazards such as land desertification and soil erosion, indicating severe governance challenges. This study selected 14 ecological vulnerability factors and constructed assessment models based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). A total of 800 ecological vulnerability sampling points were obtained by combining field survey data with remote sensing imagery. The models were trained using binary vulnerability labels. The resulting continuous probability outputs were then classified into five vulnerability levels using the natural breaks method to generate the final ecological vulnerability map. It should be noted that the multi-level vulnerability map represents graded probability-based differentiation rather than supervised multi-class prediction. Model performance was validated using three metrics: Area Under Receiver Operating Characteristic Curve (AUC–ROC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The CNN (AUC = 0.916) model outperformed the DNN model (AUC = 0.895). According to the CNN-based classification results, non-vulnerable, slightly vulnerable, mildly vulnerable, moderately vulnerable, and highly vulnerable areas accounted for 36.19%, 22.85%, 14.24%, 12.31%, and 14.41% of the total area, respectively. High ecological vulnerability zones were concentrated in Daqiao, Luoyang, Dabu, and parts of Rucheng towns, with soil parent material and vegetation coverage identified as the main contributing factors, among which parent material was the most important. This finding underscores the notable impact of geological factors on local ecological vulnerability. Based on these results, nine ecological–geological subareas were delineated, and targeted ecological protection and restoration recommendations were proposed. This study, employing machine learning techniques, constructed an ecological vulnerability assessment model incorporating geological elements, thereby providing scientific support for targeted ecological governance in the study area. Full article

Addressing the environmental challenges posed by the massive stockpiling of phosphogypsum (PG) has become a global concern, highlighting the urgency of developing large-scale, low-cost and resource-efficient utilization approaches for PG. This study was conducted in the rocky desertification areas of southwestern China, where land and water resources are scarce. Two land creation techniques—layered reconstruction (GA) and integrated construction (GB)—were adopted with modified PG to systematically investigate their impacts on soil properties and potato growth, yield and quality. The results showed that both techniques significantly improved soil conditions and enhanced potato yield and quality, with each presenting distinct characteristics in soil improvement. Specifically, the GA technique showed relatively better performance in soil nutrient enrichment, while the GB technique was more conducive to enhancing soil enzyme activity. Compared with the local red soil control, both techniques reduced heavy metal accumulation in potato tubers; however, Pb and Cd contents still exceeded national food safety limits, indicating potential food safety risks. In summary, land creation using modified PG can effectively increase arable land area, improve soil quality in rocky desertification regions, and simultaneously promote potato yield and quality. Nevertheless, as the current results are based on a single-season field trial, they cannot reflect the long-term patterns of heavy metal migration and accumulation. Therefore, for large-scale application, it is necessary to strengthen the monitoring of heavy metal levels in imported soil and long-term regional environmental impacts so as to ensure the quality and safety of agricultural products from reclaimed land. Full article

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments. Full article

Alternanthera philoxeroides is a highly invasive alien species in China that causes waterway blockages, agricultural yield loss, biodiversity decline, and ecosystem degradation. This study assessed the invasion risk and environmental drivers of A. philoxeroides in Guizhou Province, a karst mountainous region in Southwest China. Occurrence records were obtained from field surveys and the Chinese Virtual Herbarium. The genetic algorithm for rule-set production (GARP) model and the jackknife method were employed to identify 13 key environmental indicators for predicting invasion risk. The global invasion risk index (GIRI) was applied to quantify the overall invasion risk. Additionally, the Geodetector model was utilized to analyze the spatially differentiated effects of six environmental factors. The results showed that A. philoxeroides poses a high invasion risk in Guizhou Province, and the invasion risk in the Yangtze River Basin within Guizhou is higher than that in the Pearl River Basin. The environmental factors influencing invasion risk, in order of impact, were slope, elevation, land use, river density, rocky desertification, and soil pH. Moreover, interactions among these factors further amplify the invasion risk. These findings provide valuable insights for developing targeted management strategies for A. philoxeroides in karst mountainous regions and support biodiversity preservation and regional ecological sustainability. Full article

Net primary productivity (NPP) and its response to global climate change are one of the hot topics in global change research. Based on Net primary productivity remote sensing data and meteorological data, this study analyzed the spatiotemporal variation in vegetation NPP in Maowusu sandy land by using Sen trend analysis, Mann–Kendall significance test, coefficient of variation stability analysis, partial correlation and complex correlation analysis, and quantitatively analyzed the response of vegetation NPP to climate factors. The results showed that from 2001 to 2023, the overall vegetation NPP showed a significant upward trend, and the annual average increased from 124.28 g·(m⁻²·a)⁻¹ to 221.41 g·(m⁻²·a)⁻¹. The Theil–Sen median slope of NPP was +3.87 g·(m⁻²·a)⁻¹ with a coefficient of variation (CV) of 0.19, suggesting a robust but spatially variable greening trend. In total, 98.53% of the area showed an upward trend, with a very significant and significant increase area. The overall stability of vegetation NPP was strong, with an average coefficient of variation (CV) of 0.19 and a CV< of 0.30 in 97.96% of the regions, but the local area from southwest to east was highly volatile and there was a risk of secondary desertification. The influence of climate factors on vegetation NPP had significant spatial heterogeneity: precipitation was the key driving factor, and most areas were positively correlated. Potential evapotranspiration was positively correlated in the central and northern regions, and negatively correlated in some southern areas. The overall temperature has a negative effect, and only the local area has a weak promoting effect. Multi-correlation analysis shows that vegetation NPP is the result of the synergy of multiple climatic factors, and the hydrothermal coupling mechanism plays a decisive role in its spatial pattern. This study can provide a scientific basis for the restoration of vegetation ecosystems, environmental protection policy formulation, ecological protection and high-quality development of the Yellow River Basin in Maowusu Sandy Land. Full article

Karst rock desertification is an extreme form of land degradation that poses a serious threat to regional ecological security and sustainable development in Southwest China. Understanding the response patterns of plant communities and soil properties along desertification gradients is critical for developing effective ecological restoration strategies. This study focused on Qingzhen City, Guizhou Province, a representative karst desertification region. Using remote sensing to classify rock desertification intensity, together with systematic vegetation surveys and soil sampling, we investigated variation patterns in ecological parameters along the degradation gradient. The results revealed three key patterns. First, rock desertification was widespread across Qingzhen and exhibited pronounced spatial differentiation. Second, as desertification intensified, vegetation community structure became progressively simplified, transitioning from forests to shrublands. Biomass and vegetation cover declined from 77.25 kg/m² and 83% to 0.62 kg/m² and 15%, respectively. Notably, species diversity exhibited a bell-shaped relationship with desertification intensity, peaking at the potential desertification stage before declining under increasing environmental stress. Third, soil physicochemical properties showed complex nonlinear responses along the desertification gradient. Soil bulk density decreased, and pH increased with increasing desertification intensity, while volumetric water content fluctuated across stages. Soil total carbon and total nitrogen exhibited temporary enrichment during the light-to-moderate desertification stages, likely due to shifts in litter quality. Overall, these findings demonstrate that both plant communities and soil properties respond nonlinearly to rock desertification gradients. Together, the results enhance the understanding of the ecological processes underlying karst rock desertification and support the development of targeted regional restoration strategies. Full article

The Aravalli Mountain System (AMS) is one of the oldest fold orogens in the world, serving as a natural boundary against desertification in north-western India. The AMS has high environmental importance and faces accelerated soil degradation driven by both anthropogenic pressures and climatic shifts. Still, high-resolution measurements of soil erosion processes have not been conducted on the AMS scale. The present study assesses long-term LULC transitions between 2001 and 2021, identifies high-resolution short-term LULC dynamics between 2017 and 2024, and models spatiotemporal soil erosion dynamics using the RUSLE model. The findings indicate that LULC has changed rapidly, with built-up areas increasing by 53 per cent at the expense of rangelands and croplands. These drivers resulted in a 13.8 per cent increase in the mean annual soil loss between 2017 and 2024, from 1.59 to 1.81 t/ha/yr, while forest cover has increased over the timescale, as is evident in this study. The steep slopes, susceptible soils, and mining areas are strongly associated with erosion hotspots. Increased soil erosion in the AMS despite a significant increase in afforestation highlights that local conservation cannot compensate for massive land conversion. The present study provides a scalable, high-resolution framework for assessing soil erosion in vulnerable old mountain systems globally for sustainable land-use planning, mineral governance, and integrated conservation to protect for future generations. Full article

Under multiple stresses such as an arid climate, water scarcity, and desertification, inland river basins in arid regions represent a typically fragile ecosystem worldwide, and their ecological security faces increasingly complex and severe challenges. To address the limitations of traditional assessment methods characterized by single-perspective approaches, difficulties in quantifying indicators, and lack of a systematic framework for arid basins, this study constructed an innovative Health–Service–Risk–Sensitivity (HSRS) framework. Taking the Tarim River Basin (TRB) as a case study, the validity and necessity of this framework were validated through the Remote Sensing Ecological Index (RSEI) and correlation analysis. Furthermore, the XGBoost–SHAP model was further integrated to identify key threshold responses of multidimensional driving factors within the basin. The findings indicate that the ecological security of the TRB progressively improved, with approximately 11.64% of the area showing significant enhancement. The four most influential driving factors were land use, NDVI, human activity intensity, and soil moisture. Notably, the study identified critical environmental thresholds: when DEM ranged from 1500 to 3000 m and slope from 2° to 30°, constraining effects on the Comprehensive Ecological Security Index (CESI) increased. When annual precipitation exceeded 150 mm, NDVI was greater than 0.35, and soil moisture content exceeded 0.14 m³/m³, the constraint effect was further strengthened. Overall, the integration of the HSRS framework and the XGBoost-SHAP model offers a novel and effective approach for ecological security assessment in arid inland basins. Moreover, this approach has substantial practical implications for achieving precise coordination between regional ecological protection and sustainable development. Full article