Search Results (189)

Groundwater plays a key role in maintaining groundwater-dependent ecosystem stability. However, existing ecological thresholds for groundwater depth mainly focus on fractional vegetation coverage (FVC), with insufficient consideration of soil salinity, limiting the accuracy of ecological risk assessments. Focusing on the Songnen Plain, we determined more accurate ecological thresholds and established a groundwater depth-based ecological risk classification standard using groundwater depth, FVC, and soil salinity data from 2001, 2010, 2019, and 2024. A combined Kalman filter and Long Short-Term Memory (LSTM) neural network model was used to predict groundwater depth changes in 2030 based on current conditions and assess ecological risks. Results indicated that both models showed good performance (R² = 0.95 and 0.79, respectively). From 2001 to 2024, groundwater depth increased, with increasing FVC and decreasing saline soil area. Groundwater depth exhibited significant threshold effects on vegetation growth and soil salinization, with 4.0–6.5 m identified as the optimal range for vegetation growth, while soil salinity accumulated when groundwater depth was <5.5 m. The proposed ecological risk classification revealed significant spatial pattern changes of ecological risk from 2001 to 2030, with medium warning dominating in 2030. The study provides valuable insights for groundwater management, ecological risk early warning, and ecological conservation. Full article

Within the framework of the dual carbon strategy, investigating the spatiotemporal characteristics and driving factors of carbon sequestration in metropolitan areas through land use analysis is important for mitigating climate change and promoting regional ecological protection and sustainable development. On the basis of land use time points for five phases from the Jinan metropolitan area (JMA) covering the period from 2000 to 2024, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was coupled with the extreme gradient boosting (XGBoost)–Shapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) models to explore the spatiotemporal variations in carbon storage and its driving factors. In the last 24 years, cropland has been the predominant land use category in the JMA, representing almost 62% of the overall area. Throughout the five periods, the transition from cropland to construction land predominated, resulting in an 11.78% reduction in farmland and a 49.73% expansion in construction land. Between 2000 and 2024, carbon storage in the JMA decreased overall, with a total reduction of 3.70 Tg. The occupation of farmland for construction purposes was the primary cause of the decrease in carbon storage. The spatial pattern of carbon storage was similar to that of land use in the JMA, characterized by a distribution pattern with elevated values in the southeast and reduced values in the northwest. The SHAP analysis results demonstrated that the contributions of driving factors such as elevation, vegetation coverage, human footprint, and population density were generally high, making them the main drivers affecting carbon storage, with a significantly greater contribution of natural factors than human activity factors. The MGWR model results revealed that the digital elevation model and fractional vegetation cover positively influenced carbon storage in the JMA, whereas the population density imposed a negative effect. These results could guide the judicious allocation and utilisation of resources in urban regions, the establishment of ecological conservation areas, and the advancement of regional sustainability. Full article

The Taklimakan Desert Highway shelterbelt is the world’s largest ecological protection system established along a highway in a shifting desert environment and plays a critical role in mitigating wind-blown sand hazards and ensuring transportation safety. However, its long-term stability and protective capacity after more than two decades of operation remain insufficiently understood. In this study, Landsat imagery from 2005 to 2025 was used to monitor the long-term evolution of the shelterbelt along the Middle Section (~180 km) of the Taklimakan Desert Highway. A Random Forest classifier was employed to extract shelterbelt distribution, and classification results were validated using high-resolution Google Earth imagery and unmanned aerial vehicle observations. To quantify shelterbelt condition, a Shelterbelt Stability Index (SSI) was developed by integrating fractional vegetation cover (FVC), connectivity index (CI), percentage of landscape (PLAND), and perimeter-area fractal dimension (FRAC). The shelterbelt experienced initial seedling decline from 2005 to 2011, followed by progressive restoration during 2011–2020 and finally entered a stable saturated stage after 2020. Affected by saline water drip irrigation, wind-sand erosion and pipeline clogging, the overall vegetation condition deteriorated continuously before 2011. After targeted irrigation regulation, optimization of planting patterns and replanting measures were implemented; the degradation trend was reversed, contributing to the sustained improvement of vegetation thereafter. Significant spatial heterogeneity was observed along the highway, with certain sections maintaining high continuity and vegetation coverage, while others exhibited fragmentation, local discontinuities, area shrinkage, and increasing structural complexity. The proposed SSI effectively captured long-term structural dynamics and identified vulnerable sections subject to degradation. This study provides new insights into the life-cycle evolution of desert highway shelterbelts and offers scientific support for the sustainable management of ecological protection systems in arid environments. Full article

As an important infrastructure for the construction of a new power system, pumped-storage hydropower stations may exert certain impacts on the surrounding fractional vegetation cover (FVC) during their construction. Based on Landsat remote sensing imagery and China Land Cover Dataset (CLCD) land use data from 2013 to 2024, this study investigated the dynamic changes in FVC and the spatial extent of engineering disturbance associated with the Zhen’an Pumped-Storage Hydropower Station in Shaanxi Province, China. The analysis integrated the Pixel Dichotomy Model, Theil-Sen trend analysis, Mann–Kendall significance test, coefficient of variation, landscape pattern indices, and correlation analysis. The results showed that: (1) FVC in the study area exhibited distinct stage-dependent evolution characteristics that were highly consistent with the construction timeline of the project. (2) The spatial influence of engineering disturbance on FVC was mainly concentrated within 1250 m, with the 0–250 m zone identified as the core impact area. Landscape fragmentation in this zone was higher than in other distance ranges, and vegetation degradation gradually weakened with increasing distance from the project. (3) Land use change within the study area was primarily characterized by the conversion of forest to cropland and impervious surfaces, resulting in a reduction in high coverage vegetation areas. Landscape patterns exhibited pronounced buffer-gradient characteristics. Within 500 m, the Largest Patch Index (LPI) decreased while the Shannon Diversity Index (SHDI) increased, indicating weakened continuity of dominant landscape patches. Beyond 500 m, LPI generally increased and SHDI decreased, suggesting a trend toward a more stable landscape structure. (4) Both air temperature and precipitation exhibited interannual fluctuations, but neither showed a significant long-term trend. The correlations between climatic factors and FVC were relatively weak, and the multiple regression model demonstrated limited explanatory power for FVC variation. Full article

Clarifying the dynamic changes in vegetation cover and the driving mechanisms under the combined influence of the natural environment and human activities is a crucial foundation for understanding the evolutionary processes of ecosystems in arid and semi-arid regions and for improving the effectiveness of ecological restoration. Taking the Kuye River Basin, a typical resource exploitation zone in the middle reaches of the Yellow River, as the research area, this study retrieved 30 m resolution annual maximum NDVI datasets from 1986 to 2020 to calculate the Fractional Vegetation Cover (FVC). Utilizing methods such as Theil–Sen slope analysis, Mann–Kendall significance test, Hurst exponent, stability analysis, geographical detector, and sensitivity index, this study systematically revealed the spatiotemporal patterns of vegetation change, future evolution trends, and the response mechanisms of FVC dynamics to multiple factors including climate, topography, and land use. The results indicated that from 1986 to 2020, FVC in the study area exhibited an overall increasing trend (0.0105 a⁻¹), with the average FVC rising from 0.21 to 0.61. Regions with very low and low vegetation coverage continued to decrease, while areas with high and very high vegetation coverage showed significant increases, particularly in the very high vegetation coverage category, which experienced the largest growth (CV = 179.32%). The regions with moderate vegetation coverage demonstrated the highest stability (CV = 48.42%). Analysis of the driving mechanisms revealed that precipitation and land use types were the primary factors influencing changes in FVC, with land use demonstrating a more stable explanatory power (CV = 3.63%). Furthermore, the interaction between these two factors significantly enhanced the explanatory power related to vegetation changes. Sensitivity analysis indicated that the increase in forest and grassland effectively mitigated the negative impact of cropland on moderate to high coverage areas; industrial and mining land had a notable impact on very low coverage areas. It can be inferred that the Grain for Green program and the expansion of industrial and mining lands might generate differentiated impacts across diverse vegetation coverage classes. Future projections indicate that 91.19% of the region exhibits potential for FVC improvement in the future. However, a risk of sustained vegetation degradation exists in densely populated areas and regions with concentrated industrial and mining land. The study demonstrates that under the combined influences of climate change and land use adjustments, optimizing land use structures and coordinating ecological restoration with resource development are critical approaches to enhancing the stability of ecosystems in arid and semi-arid regions, as well as promoting sustainable regional ecological development. Full article

The reconstruction of vegetation in opencast mining areas constitutes an intricate process of ecological restoration within human-altered systems. A systematic characterization of the multi-dimensional synergistic successional pathways—encompassing morphology, structure, and function—and the corresponding delineation of key recovery phases holds significant potential to inform and refine land reclamation strategies. This study took the southern dump of the Antaibao Coal Mine within the Pingshuo mining area on the Loess Plateau as the study area. Using the Google Earth Engine (GEE) platform, time series Landsat remote sensing images from 1990 to 2023 were processed to derive three indicators representing vegetation coverage morphology, landscape pattern structure, and ecosystem service function: Vegetation Fractional Coverage (VFC), Mining Landscape Restoration Index (MLRI), and Remote Sensing Ecological Index (RSEI). A Reconstructed vegetation Restoration Comprehensive Index (RRCI) was established through the multi-dimensional collaborative analysis of morphology–structure–function. Based on the long-term evolutionary sequence of RRCI, the S-logistic growth curve model was employed for nonlinear fitting, and critical restoration stages of reconstructed vegetation were quantitatively delineated using preset threshold rules. The results demonstrate that time series RRCI data of the screened sample plots effectively characterize the spatiotemporal restoration dynamics of reconstructed vegetation, with a high model goodness of fit (R² > 0.7). In accordance with the criteria for delineating critical stages of reconstructed vegetation restoration, the average durations of the accelerated development period, consolidation development period, and overall recovery development period of reconstructed vegetation in the study area are 5.09 years, 4.64 years, and 9.73 years, respectively. Significant differences exist in the accelerated development period and overall recovery development period between arbor forest lands and arbor shrub forest lands (p < 0.05), and the time required for vegetation restoration at each stage is longer in arbor forest lands than in arbor shrub forest lands. This study constructs a multi-dimensionally collaborative RRCI and quantifies critical stages of reconstructed vegetation evolution, which is of great significance for promoting the sustainable evolution and dynamic management of reconstructed vegetation in opencast mining areas. Full article

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

Shrub encroachment represents a widespread shift in vegetation structure, yet its influence on the relationship between vegetation greening and ecosystem productivity across global aridity gradients remains poorly understood. Focusing on global grasslands as the study domain, we systematically examined shrub-encroached areas across diverse climatic zones spanning aridity gradients from arid to humid regions. Here, we integrated multi-source remote sensing datasets, including MODIS land cover, leaf area index (LAI), and gross primary productivity (GPP), with a global aridity index to systematically detect shrub encroachment. By combining trend analysis, Pettitt change-point detection, and a moving-window pairwise comparison approach, we characterized the spatiotemporal dynamics of encroachment and quantified its differential effects on vegetation dynamics. Our results showed that the global extent of shrub encroachment expanded continuously from 2002 to 2022, with an abrupt change detected between 2010 and 2014. Relative to comparable non-encroached coverage pixels (NSEC), comparable shrub-encroached coverage pixels (CSEC) exhibited generally increasing trends in LAI, fractional vegetation cover (FVC), and GPP, with enhancement magnitudes showing strong aridity gradient dependence, peaking in semi-arid and dry sub-humid regions, indicating that shrub encroachment is substantially regulated by water limitation. Furthermore, joint trend analysis revealed that the concurrent increase in both LAI and GPP (LAI+GPP+) was the dominant pattern, observed in approximately 70% of encroached areas, although decoupling persisted at high latitudes and in certain humid regions (LAI+GPP−). These findings demonstrate that shrub encroachment is fundamentally regulated by moisture gradients and exhibits pronounced spatial heterogeneity, providing new evidence for understanding carbon cycling dynamics and informing grassland ecosystem management under increasing aridity. Full article

Fractional Vegetation Cover of Crops (CropFVC) is a critical canopy parameter for monitoring crop growth, yet the behavior of widely used global FVC products (GLASS, GEOV1, GEOV2, and GEOV3) over croplands remains insufficiently understood due to fragmented validation references and limited crop-specific assessments. This study compiled a multi-source global CropFVC reference dataset (2000–2024) by integrating five international validation networks, the literature-derived samples, and newly acquired UAV and Jilin-1 satellite-derived CropFVC samples from China in 2024. The references were organized into three complementary validation contexts (V1~V3) to examine product behavior under different temporal coverage, crop purity, and reference conditions, together with spatio-temporal observations at the KONZ site. Results show that (1) across validation contexts, the evaluated products showed consistent behavior patterns, including shared overestimation under dense canopy conditions and reduced differences at low FVC levels; (2) spatio-temporal analysis at the KONZ site confirmed that peak-season deviations reflect shared response behavior rather than site-specific reference uncertainties; (3) historical mixed references (V1~V2) showed similar bias structures, whereas crop-specific validation (V3) preliminary revealed clearer crop-dependent responses, with predictive difficulty following winter wheat > maize > rice > soybean and improved stability after integrating 2024 observations. The integration of recent high-resolution crop observations expands existing global CropFVC references and enables behavior-oriented interpretation of global FVC products beyond simple accuracy ranking, providing an updated validation perspective for future development and application of global CropFVC products in agricultural monitoring. Full article

This study analyzed the spatiotemporal dynamics of the vegetation ecological quality under climate change. Focusing on the vegetation conditions, a vegetation ecological quality index was constructed, expressing as the product of vegetation fraction cover (VFC), net primary productivity (NPP), and geographic coverage area. The results of trend and significance analysis showed that from 2000 to 2023, the VEQI in the Qinling Mountains exhibited a significant improvement, with an average slope of 4.91 gC·a⁻¹ and 96.2% of the area showing high stable improvement. Partial correlation analysis revealed that precipitation had a stronger positive influence on VEQI than temperature, with over 98% of the area showing a positive correlation with precipitation, while temperature was positively correlated in 95.0% of the area but negatively correlated in high-altitude mountain zones. Therefore, four climate-driven patterns were identified: precipitation-driven (31.2%), temperature-driven (2.3%), co-driven (54.2%), and climate-stable (12.3%), suggesting that vegetation ecological quality in most regions is co-driven by both temperature and precipitation. Based on the results of trend and significance analysis and climate-driven patterns, the Qinling Mountains were divided into three ecological risk zones: low-risk (36.1%), middle-risk (56.9%), and high-risk (7.0%), with corresponding differentiated control measures proposed. Full article

Dongting Lake, a vital freshwater lake in China with substantial ecological, economic, and social significance, has fractional vegetation coverage (FVC) as a core indicator of regional ecological balance. To characterize the ecosystem’s health and support targeted protection, this study analyzed FVC’s spatio-temporal evolution and associated spatial factors in the Dongting Lake ecological restoration area using 2005–2020 MODIS imagery, integrating the dimidiate pixel model, slope trend analysis, and geographic detector model (noting the latter quantifies spatial explanatory power but not direct ecological causality). Results revealed distinct FVC heterogeneity: 2011 had the poorest vegetation (mean FVC = 0.60), while 2005, 2010, and 2012 showed higher FVC (mean = 0.65); summer exhibited the most vigorous growth due to favorable hydrothermal conditions. Slope was the dominant single factor with the highest spatial explanatory power for FVC (q = 0.50), its distribution strongly associated with soil moisture and erosion. The slope–soil moisture interaction had the strongest joint spatial explanatory power (q = 0.625), reflecting topographic–hydrological synergistic spatial association, implying slope may indirectly modulate vegetation water availability (inferred from spatial correlation, not causality). The slope–DEM interaction (q = 0.534) confirmed combined topographic explanatory effects. Overall, 70.3% of the region saw significant FVC improvement (notably in spring) from 2005 to 2020, with degradation in February, March, and December. Slope emerged as a key factor consistent with interannual and seasonal FVC variations. These findings provide a reliable scientific basis for targeted wetland restoration, emphasizing enhanced vegetation management in summer, autumn, and the growing season. Limitations include: MODIS’s 250 m resolution leading to mixed-pixel effects in fragmented wetlands, limited validation coverage of extreme habitats and single-year verification, and the Geodetector model’s reliance on spatial stratification and factor independence assumptions (deviating from wetland’s continuous factor variation) that preclude causal inference. Full article

Fractional Vegetation Cover (FVC) dynamics on the Qinghai–Tibetan Plateau (QTP) are critical indicators for assessing ecosystem condition. However, uncertainties persist in the accuracy of existing FVC products over the QTP due to retrieval differences, scale effects, and limited validation data. This study utilized the Google Earth Engine platform to integrate unmanned aerial vehicle (UAV) observations, Sentinel-2, MODIS, climate, and topography datasets, and proposed a comprehensive framework incorporating dual-index screening, machine learning optimization, and multidimensional validation to systematically assess the accuracy of GEOV3, GLASS, and MuSyQ FVC products in the alpine grasslands. The dual-index screening reduced validation uncertainty by improving the spatial representativeness of ground data. To build a high-precision evaluation dataset with limited inter-class coverage, recursive feature elimination and grid search were applied to optimize five ML models, and CatBoost achieved the superior performance (R² = 0.880, RMSE = 0.122), followed by XGBoost, GBM, LightGBM, and RF models. Four validation scenarios were implemented, including direct validation using 250 m UAV plot FVC and multi-scale validation using a 10 m FVC reference aggregated to product grids. Results show that GEOV3 (R² = 0.909–0.925, RMSE = 0.082–0.103) outperformed GLASS (R² = 0.742–0.771, RMSE = 0.138–0.175) and MuSyQ (R² = 0.739–0.746, RMSE = 0.138–0.181), both of which exhibited systematic underestimation. This framework significantly enhances FVC product validation reliability, providing a robust solution for remote sensing product validation in alpine grassland ecosystems. Full article

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal remote sensing data from 1985 to 2015, this study takes the Inner Mongolia Hetao Plain as the research area, constructs a “multifunctionality–dynamic evolution” dual-principle classification system for urban–agricultural–ecological space, and adopts the technical process of “separate interpretation of each single land type using the maximum likelihood algorithm followed by merging with conflict pixel resolution” to improve the classification accuracy to 90.82%. Through a land use transfer matrix, a standard deviation ellipse model, surface temperature (LST) inversion, and vegetation fractional coverage (VFC) analysis, this study systematically reveals the spatiotemporal differentiation patterns of spatial structure evolution and surface parameter responses throughout the project’s life cycle. The results show the following: (1) The spatial structure follows the path of “short-term intense disturbance–long-term stable optimization”, with agricultural space stability increasing by 4.8%, the ecological core area retention rate exceeding 90%, and urban space expanding with a shift from external encroachment to internal filling, realizing “stable grain yield with unchanged cultivated land area and improved ecological quality with controlled green space loss”. (2) The overall VFC shows a trend of “central area stable increase (annual growth rate 0.8%), eastern area fluctuating recovery (cyclic amplitude ±12%), and western area local improvement (key patches increased by 18%)”. (3) The LST-VFC relationship presents spatiotemporal misalignment, with a 0.8–1.2 °C anomalous cooling in the central region during the construction period (despite a 15% VFC decrease), driven by irrigation water thermal inertia, and a disrupted linear correlation after completion due to crop phenology changes and plastic film mulching. (4) Irrigation and drainage projects optimize water resource allocation, constructing a hub regulation model integrated with the Water–Energy–Food (WEF) Nexus, providing a replicable paradigm for ecological effect assessment of major water conservancy projects in arid regions. Full article

Changes in vegetation coverage reflect the status and dynamic processes of ecosystems and serve as a crucial foundation for regional ecological protection. Using Landsat-5 and Sentinel-2 data, this study calculated the vegetation coverage in the Three-River Headwaters (TRH) region from 1990 to 2022 with the pixel dichotomy model, identified land cover changes over the past three decades via a deep neural network, and analyzed the primary influencing factors behind vegetation coverage dynamics. The results indicate that vegetation coverage in TRH has generally increased, as very high vegetation coverage expanded by 10.3%, while very low and low vegetation coverage decreased by 4.2%. Extensive bare land in the western region decreased and transformed into grassland, while the areas of shrubland and forest in the central and eastern TRH areas increased. The areas of grassland, shrubland, and forest increased by 3.7 × 10⁴ km², 2.1 × 10⁴ km², and 4.7 × 10³ km², respectively. Precipitation, elevation, and temperature are the main factors influencing the spatial variation in vegetation coverage. We found that the contributions of the permafrost active layer thickness and precipitation to changes in vegetation coverage are high. Finally, we provide a detailed and timely analysis of recent vegetation distribution and type changes on the Tibetan Plateau, offering a strengthened scientific foundation for monitoring, assessment, and ecological conservation efforts aimed at supporting ecosystem restoration in the region. Full article

Although surface soil moisture (SSM) is particularly important in crop yield prediction, irrigation scheduling optimization, and runoff generation mechanisms, accurate monitoring of time-series SSM is still challenging for agricultural and hydrological research. This study presented an improved approach integrating Sentinel-1 C-band SAR and MODIS optical data (2019–2020) to estimate surface soil moisture. To address vegetation effects, we developed a piecewise function using fractional vegetation coverage (FVC) to correct soil moisture and backscatter extrema and established the normalized difference enhanced vegetation index (NDEVI) to characterize backscatter-vegetation relationships across various land covers. Furthermore, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm identified anomalous surface changes, enabling segmentation of long-term series into invariant periods that satisfy the change detection method assumptions. Validation in the Shandian River Basin demonstrated significant improvement over traditional methods, achieving determination coefficients (R²) of 0.844 and root mean square errors (RMSE) of 0.030 m³/m³. The method effectively captured soil moisture dynamics from precipitation and irrigation events, providing reliable monitoring in heterogeneous landscapes. This integrated approach offers a robust technical framework for multi-source remote sensing of soil moisture in semi-arid areas, enhancing capability for agricultural water resource management. Full article