Search Results (123)

The watershed boundaries in arid and semi-arid regions are critical zones where ecological vulnerability and socio-economic development are in severe conflict. The upper and middle reaches of the Yellow River basin are a typical example of this dilemma. Intensive land use and human developmental interventions in this region have severely disrupted the integrity and balance of the ecosystem. While spatially designated, networked conservation areas can effectively promote the integrity and balance of regional ecosystems, these areas may fail to capture dynamic changes in vulnerability. This study develops a “functional diagnosis-structural diagnosis-integrated optimization” framework. It integrates various scenarios to diagnose vulnerability under uncertainty and identifies bottlenecks in ecological networks. For functional diagnosis, the coupling of the sensitivity–resilience–pressure (SRP) model and the Ordered Weighted Averaging (OWA) algorithm accurately locates vulnerable areas within the regional ecosystem. In terms of structural diagnosis, the Morphological Spatial Pattern Analysis (MSPA), Minimum Cumulative Resistance model (MCR), and Circuit Theory are integrated to identify structural bottlenecks. The main findings of this study are as follows: (1) Functional Diagnosis: The coupling of SRP and OWA reveals the non-linear vulnerability responses to policy preferences and identifies areas that consistently exhibit functional vulnerability across different scenarios. (2) Structural Diagnosis: The circuit theory combined with MSPA and MCR analysis identifies 72 ecological pinch points. These bottlenecks represent the weakest structural nodes crucial for maintaining regional ecological robustness. (3) Coupled Delineation and Differentiated Restoration Strategies: High vulnerability areas identified by SRP and consistently vulnerable areas identified by OWA are combined to delineate four distinct ecological restoration units: Alpine Fragile Matrix Unit, Loess Hilly Soil Conservation Unit, Anthropogenic Pressure Pinch Point Unit, Key Structural Stepping Stone Unit. Differentiated ecological restoration strategies are proposed based on the varying sensitivity, resilience, and pressure characteristics of these units. The “functional-structural” coupled ecological vulnerability evaluation framework can precisely identify vulnerable areas. The delineated restoration units and their corresponding restoration strategies provide reference and supplementation for the protected areas system, offering transferable tools for enhancing regional ecological efficiency. Full article

Soil erosion is a critical issue on the Loess Plateau due to weak soil and intense summer rainfall. Plant roots provide essential soil stabilization. A split-plot field experiment was conducted in Liulin County, Shanxi Province, to evaluate the effects of biodegradable mulch and organic amendments on maize root development and soil stabilization. The main plots included no mulch (N) and biodegradable mulch (M). The subplots comprised five treatments: control (CK, no amendment), peat (PT), biochar (BC), fermented pig manure (PM), and corn stover (CS). Correlation and principal component analyses were used to elucidate the underlying mechanisms. The results showed that organic amendments were the primary factor influencing the root and soil properties. Peat and biochar significantly raised the root surface area density (RSAD, p < 0.05) and root–soil composite cohesion (with increases of 122.56% and 109.06% for NPT and NBC compared to NCK, respectively). Biodegradable mulch, and its interaction with the organic amendments, had no statistically significant effect on either the root–soil composite cohesion or root system parameters. The strong positive correlations of cohesion with the root length density (RLD, r = 0.80) and root volume density (RVD, r = 0.81) highlight that root occupancy is the key mechanism for enhanced shear resistance. Therefore, biochar is recommended for its effectiveness in enhancing soil retention and its potential co-benefits for carbon sequestration. This study provides a technical reference for sustainable agriculture on the Loess Plateau, while also acknowledging the need for further research on long-term carbon dynamics. Full article

Strip intercropping of maize and soybean is a key practice for improving land productivity and ensuring food and oil security in the hilly regions of the Loess Plateau. However, complex interspecific interactions generate highly heterogeneous canopy structures, making it difficult for traditional linear models to capture yield variability within mixed pixels. Based on a single-season (2025) field experiment, this study developed a UAV multispectral imagery-based yield estimation framework integrating multiple machine-learning algorithms. Shapley additive explanations (SHAP) and partial dependence plots (PDP) were used to interpret the spectral–yield relationships under different spatial configurations. The predictive performance of linear regression and eight nonlinear algorithms was compared using 20 spectral features. Ensemble learning outperformed linear approaches in all intercropping scenarios. In the maize–soybean 3:2 pattern, the GBDT model delivered the highest accuracy (R² = 0.849; NRMSE = 9.28%), whereas in the 4:2 pattern with stronger shading stress on soybean, the random forest model showed the greatest robustness (R² = 0.724). Interpretation results indicated that yield in monoculture systems was mainly driven by physiological traits characterized by visible-band indices, while yield in intercropping systems was dominated by structural and stress-response traits represented by near-infrared and soil-adjusted vegetation indices. The generated centimeter-scale yield maps revealed clear strip-like spatial variability driven by interspecific competition. Overall, explainable machine learning combined with UAV multispectral data shows promise for within-season yield estimation in intercropping systems and can support spatially differentiated precision management under the sampled conditions. Full article

The complex topography and harsh natural environment of the Loess Plateau in Longzhong have been suffering from an undefined living circle structure, which has hindered rural planning and development. A rural community living circle is a spatial unit centered on meeting the needs of villagers, within which various service facilities are rationally allocated within a specific spatial scope. To refine its spatial patterns, the concept of living circles was introduced to address travel challenges. The extent of these living circles is affected by the accessibility of public service facilities and barriers to travel. Using land use data, DEM, population density, and road networks, this study employed the MCR model, gravity model, and ArcGIS spatial analysis to examine the patterns of rural community living circles. The focus was on analyzing the living circle structure of rural communities on the Loess Plateau in Longzhong, considering both natural and artificial environmental constraints. The results show: (1) Rural community living circles present multi-scale spatial features. The basic living circle covers a 15 min slow-travel area. The central living circle corresponds to village-level needs, accessible within 35 min by both slow and motorized travel. The town living circle covers a 10 km radius, reachable within 60 min by a mix of transport modes. The county living circle, dominated by motorized travel, represents the top tier of public service configuration. (2) Quantitatively, the delineation identified 2753 basic, 444 central, 19 township, and 1 county-level living circles in the Anding District of Dingxi City. The Northern, Eastern, and Southwest Zones suffer from fragmented mountainous landscapes, limiting mobility and accessibility. The Central Zone, however, benefits from a combination of mountainous terrain and river valley plains, offering superior service accessibility. (3) The analysis results based on the MCR model and gravity model aligned more closely with reality, reflecting the scale patterns of rural community living circles. The results of this study can provide theoretical guidance for rural planning, construction, and management in the hilly and gully areas of the Loess Plateau. Full article

Soil erosion in the hilly and gully region of the middle reaches of the Yellow River is severe, threatening regional ecological security and the water–sediment balance of the Yellow River. The area features fragmented topography and significant spatial heterogeneity in soil thickness, forming a unique binary “soil–rock” structural system. The soil in the study area is characterized by silt-based loess, and the underlying bedrock is an interbedded Jurassic-Cretaceous sandstone and sandy shale. It has strong weathering, well-developed fissures, and good permeability, rather than dense impermeable rock layers. However, the spatiotemporal differentiation mechanism of soil moisture in this system remains unclear. This study focuses on the typical hilly and gully region—the Geqiugou watershed. Through field investigations, soil thickness sampling, multi-scale soil moisture monitoring, and analysis of meteorological data, it systematically examines the cascade relationships among microtopography, soil–rock combinations, soil moisture, and meteorological drivers. The results show that: (1) Based on the field survey of 323 sampling points in the study area, it was found that soil samples with a thickness of less than 50 cm accounted for 85%, which constituted the main structure of soil thickness in the region. Macrotopographic units control the spatial differentiation of soil thickness, forming a complete thickness gradient from erosional units (e.g., Gully and Furrow) to depositional units (e.g., Gently sloped terrace). Based on this, five typical soil–rock combination types with soil thicknesses of 10 cm, 30 cm, 50 cm, 70 cm, and 90 cm were identified. (2) Soil–rock combination structures regulate the vertical distribution and seasonal dynamics of soil moisture. In thin-layer combinations, soil moisture is primarily retained within the shallow soil profile with higher dynamics, whereas in thick-layer combinations, under conditions of substantial rainfall, moisture can percolate deeply and become notably stored within the fractured bedrock, sometimes exceeding the moisture content in the overlying soil. (3) The response of soil moisture to precipitation is hierarchical: light rain events only affect the surface layer, whereas heavy rainfall can infiltrate to depths below 70 cm. Under intense rainfall, the soil–rock interface acts as a rapid infiltration pathway. (4) The influence of meteorological drivers on soil moisture exhibits vertical differentiation and is significantly modulated by soil–rock combination types. This study reveals the critical role of microtopography-controlled soil–rock combination structures in the spatiotemporal differentiation of soil moisture, providing a scientific basis for the precise implementation of soil and water conservation measures and ecological restoration in the region. Full article

The simulation accuracy of crop models is highly dependent on the proper calibration of key parameters. To enhance the applicability of the Next-Generation agricultural production systems sIMulator (APSIM NG) in dryland wheat production within the Loess Hilly Region, this study proposes a crop model parameter calibration framework that deeply integrates Morris and DREAM-zs methodologies. Morris was employed to conduct a global sensitivity analysis on parameters related to the APSIM NG dryland wheat grain and leaf growth sub-models. The DREAM-zs algorithm was then utilized for multi-objective collaborative optimization of key parameters. Results indicate that Morris excels at capturing nonlinear and coupled relationships among model parameters. Optimized key parameters include maximum grain size (0.055 g), radiation use efficiency (1.540 g·MJ⁻¹), and extinction coefficient (0.443). Post-optimization, the root mean square error (RMSE) and mean absolute error (MAE) for wheat yield decreased by 24.1% and 23.2%, respectively, while those for LAI decreased by 16.9% and 19.2%. This framework conserves computational resources and accelerates convergence when handling nonlinear internal model parameters and complex coupling relationships, providing technical support for the localized application of APSIM NG in the Loess Hilly Region of Northwest China. Full article

Ground instability represents a major environmental hazard in the Loess Hilly region of Northwest China, threatening infrastructure and human safety. This study establishes an integrated information-theoretic framework for evaluating regional instability risk by coupling the information value model with analytic hierarchy process (AHP) weighting and subsequent hazard–exposure synthesis. Seven conditioning factors—geomorphic type, slope, aspect, lithology, distance to faults, river system, and NDVI—were analyzed to derive susceptibility, while rainfall, peak ground acceleration, and human engineering activity were incorporated as triggering elements of hazard. Exposure was quantified from population density and infrastructure exposure, and overall risk was defined as the product of hazard and exposure after normalization and calibration. Results indicate that hilly landforms, slopes of 10–20°, and NDVI values between 0.3 and 0.6 are the dominant controls on instability occurrence. Extreme-risk zones are concentrated in central Guyuan and northwest Shizuishan (0.16% of the study area), with high-risk zones covering 21.87%, moderate-risk zones covering 41.65%, and low-risk zones covering 6.32%. Model validation yields an AUC of 0.833 and a consistent increase in observed disaster-point density from low to extreme classes, confirming strong predictive reliability. These results demonstrate that the proposed calibrated framework provides a practical and transferable tool for ground-instability risk assessment and land-use planning in loess terrains. Full article

Understanding the effectiveness of engineering measures in mitigating surface erosion is crucial for sustainable land management. However, studies explicitly quantifying the combined effects of large-scale engineering measures and environmental factors remain limited. In this study, multisource remote sensing data were integrated with interpretable machine learning to quantify and analyze the regional influence of erosion control measures. We constructed a comprehensive indicator system encompassing spectral, textural, and topographic variables derived from high-resolution satellite imagery and DEM data. To address model transparency and enhance the interpretability of the results, we employed an interpretable machine learning framework capable of both accurate prediction and explicit attribution of feature importance. The results indicate that the implementation of engineering measures substantially reduces erosion intensity across the study area. Spatial heterogeneity in erosion mitigation effectiveness was closely associated with the distribution patterns of engineering measures and site-specific environmental conditions. Basins with a high proportion of check dams showed average elevation gains of up to 2.5 m compared with those without check dams, and terraces contributed to elevation increases of ~1.9 m in typical loess hilly regions. The interpretable machine learning model achieved R² = 0.62 at Basin 1 (average area ~100 km²) and R² = 0.73 at Basin 2 (~600 km²), demonstrating reliable predictive capability. The findings not only validate the role of engineering interventions in erosion mitigation but also provide a transparent analytical framework that connects remote sensing analytics with process-based geomorphological understanding. Full article

As a critical ecological barrier and key dryland agricultural zone in China, the Loess Plateau is faced with acute tensions between food security risks arising from cropland abandonment (CA) and the imperatives of ecological conservation. Yet, existing research has failed to adequately capture the long-term, high-spatiotemporal-resolution dynamics of abandonment in this region or to quantitatively couple its driving mechanisms with implications for food security. To address these gaps, this study establishes a high-precision identification system for CA tailored to the Plateau’s complex topographic conditions, distinguishing among interannual abandonment, multiyear abandonment, conversion to forest/grassland, and reclamation. Leveraging long-term data from 2000 to 2023 and integrating the Mann–Kendall test with the random forest algorithm, we examine the spatiotemporal trajectories, driving forces, and food security consequences of CA. Guided by a “type differentiation–grade classification–temporal tracking” framework, the analysis reveals a marked transition in dominant drivers from “socioeconomic factors” to “topographic–climatic factors.” It further identifies an “increasing loss–slowing growth” effect of abandonment on grain production, alongside a “pressure alleviation” trend in per capita carrying capacity. The results showed that: (1) Between 2000 and 2023, the area of CA on the Loess Plateau expanded from 2.72 million ha to 6.96 million ha, with high-grade abandonment (≥8 years) accounting for 58.9% of the total and being spatially concentrated in the hilly–gully regions of northern Shaanxi and eastern Gansu; (2) The Grain for Green Project (GFGP) peaked at approximately 340,000 hectares in 2018, followed by a slight decline, but has generally remained at around 300,000 hectares since then; (3) The reclamation rate of CA remained between 5% and 12% during 2003–2015, with minimal overall fluctuations, but after 2016, it gradually increased and peaked at 23.4% in 2022; (4) In terms of driving forces, population density (14.99%) was the primary determinant in 2005, whereas by 2020, slope (15.43%) and mean annual precipitation (15.63%) emerged as core factors; and (5) Grain yield losses attributable to abandonment increased from less than 100 t to nearly 450 t, though the growth rate slowed after 2016, accompanied by gradual alleviation of pressure on per capita carrying capacity. Overall, the study offers robust empirical evidence to inform cropland protection, food security strategies, and sustainable agricultural development policies on the Loess Plateau. Full article

Dew is an important water source for vegetation growth in arid regions and plays a significant role in maintaining ecosystem balance. The characteristics of dew formation vary under different topographic conditions. In response to the challenges posed by climate change to the sustainability of water resources and ecosystems, this study explored the impact of topography on dew formation, and leaf wetness sensors (LWSs) were employed to conduct field observations from April 2023 to April 2025 in typical hilly and gully regions of China’s Loess Plateau. We analyzed the characteristics, influencing factors, and ecological significance of near-surface water vapor condensation. The main conclusions are as follows: (1) During the observation period, dew primarily occurred between 19:00 and 07:00 the next day, peaking between 05:30 and 07:00 in the early morning. The monthly average dew amounts for the hilly region and gully region were 2.15 mm and 3.38 mm, respectively, and the monthly maximum dew amounts were 8.57 mm and 11.88 mm, respectively, both peaking in autumn, with the gully region exhibiting higher dew amounts. (2) Dew formation at a 0.2 m height was favored when relative humidity at 0.2 m exceeded 70%, the air temperature–dew point difference was less than 8 °C, the wind direction was between 150 and 210° and 240 and 270° for the hilly region and gully region, respectively, and the standardized wind speed at a 10 m height was less than 0.5 m/s and 1.5 m/s for the hilly region and gully region, respectively. (3) Moderate rainfall facilitates dew condensation. The monthly average dew-to-precipitation (dew and rain) ratio reached its maximum in November for both the Loess hilly region and gully region, at 12.88% and 18.91%, respectively. (4) The gully region experienced larger dew events more frequently than the hilly region, resulting in a higher overall dew amount in the gully region during the observation period. The dew formation characteristics observed in this study can provide a scientific basis for assessing the future supply potential of non-precipitation water sources in the Loess Plateau under climate change and their supporting role in the ecological environment. Full article

Gravity erosion is one of the main physical processes of soil erosion and sediment sources in catchments, and its spatiotemporal patterns and driving mechanisms are seriously understudied, mainly due to the the great difficulties in monitoring and quantifying. This study obtained gravity erosion amounts by runoff scouring experiments on the field slope of the hilly–gully region of the Chinese Loess Plateau. The terrain point cloud before and after gravity erosion was obtained based on the TLS, SfM and the fusion of single-scan TLS and SfM, and then the gravity erosion was estimated by four terrain change detection algorithms (DoD, C2C, C2M and M3C2). Results showed that the M3C2 algorithm plus fused data had the highest quantization accuracy among all the algorithms and data sources, with a relative error of 14.71%. The fused data combined with M3C2 algorithm performed much better than other algorithms and data sources for the different gravity erosion magnitudes (mean relative error < 17.00%). The DoD algorithm plus TLS data were preferable for collapse areas, while the M3C2 algorithm plus TLS was suitable for the alcove area. This study provides a useful reference for the monitor and quantitative research of gravity erosion in complex topographic areas. Full article

This study takes the Jinfoping Small Watershed in the Loess Hilly Region as the research area. Through field investigation and laboratory analysis, combined with methods such as spatial autocorrelation analysis, the ordinary least squares method (OLS), and the geographically weighted regression model (GWR), it deeply explores the spatial distribution characteristics and driving factors of Formicidae in the study area. The research results are as follows: (1) Spatial autocorrelation analysis indicates that the distribution of Formicidae is significantly regulated by spatial dependence and has significant spatial autocorrelation (global Moran’s I = 0.332; p < 0.01). (2) The spatial visualization analysis of the GWR model reveals that soil physical and chemical properties and topographic factors have local influences on the spatial distribution of Formicidae. Available phosphorus (AP) and slope (SLP) were significantly positively correlated with the number of ants. Hydrogen peroxidase (HP) and topographic relief (TR) were significantly negatively correlated with the number of ants. This study reveals the spatial distribution pattern of Formicidae in the Loess Hilly Region and its complex relationship with environmental factors, and clarifies the importance of considering spatial heterogeneity when analyzing ecosystem processes. The research results provide a scientific basis for the protection and management of soil ecosystems, and also offer new methods and ideas for future related research. Full article

To clarify the response mechanisms of soil nematodes as bioindicators of ecosystem health to precipitation variations in loess hilly forests, this study investigated soil nematodes in pure Populus hopeiensis forests across different precipitation gradients in Wuqi County. Through soil physicochemical analysis and high-throughput sequencing of soil nematodes, we analyzed the characteristics of soil nematode communities and their responses to precipitation variation. The results demonstrated the following: (1) Dominant genera and trophic groups of soil nematodes were significantly influenced by precipitation, with Acrobeloides prevailing across all gradients while Paratylenchus reached maximum abundance (26.8%) in moderate precipitation zones. (2) Bacterivorous nematodes prevailed in both low- and high-precipitation zones, while herbivorous nematodes constituted the highest proportion in moderate precipitation zones. The abundances of herbivorous and fungivorous nematodes exhibited an initial increase followed by a decrease with rising precipitation, whereas predatory–omnivorous nematodes displayed the opposite trend. (3) The Chao1 and Shannon indices of soil nematodes initially increased and then decreased with increasing precipitation, reaching a peak in the Jinfoping site. Moreover, there were significant differences in nematode community structure among different precipitation gradients. (4) Redundancy analysis and PLS-PM modeling identified soil water content (SWC), total nitrogen (TN), and capillary water holding capacity (CWHC) as key drivers of nematode communities. Precipitation indirectly regulated nematode functionality by modifying soil physicochemical properties and microbial activity. (5) Ecological function analysis revealed bacterial-dominated organic matter decomposition (Nematode Channel Ratio, NCR > 0.75) in the Changcheng and Baibao sites, contrasting with fungal channel predominance (NCR < 0.75) in Jinfoping. This research elucidates the mechanism whereby precipitation drives nematode community divergence through regulating soil physicochemical properties and microbial activity. The findings provide scientific basis for soil biodiversity conservation and ecological restoration benefit assessment in regional ecological restoration projects, and soil health management and sustainable land use in agricultural ecosystems. Full article

Microtopography plays a crucial role in regulating soil moisture in arid and semi-arid regions, thereby significantly influencing vegetation growth and distribution. The Loess Plateau, characterized by a deeply incised and fragmented landscape, necessitates an in-depth understanding of the microtopograph–soil moisture–vegetation relationship to guide effective vegetation restoration. This study, based on field investigations and laboratory analyses in the hilly-gully region of the Loess Plateau, employed one-way ANOVA, Duncan’s multiple range test, and structural equation modeling to examine the effects of microtopography on vegetation community characteristics. The results revealed that microtopography significantly affects vegetation diversity and stability. Vegetation diversity and stability were higher on shady slopes than on sunny slopes, with diversity indices increasing by approximately 38% in certain regions. Additionally, downslope positions exhibited greater vegetation diversity than upslopes, with richness indices increasing by approximately 33% and the M. Godron index decreasing by 8.49, indicating enhanced stability. However, the effects of gullies varied significantly across different regions. Soil moisture content was higher on shaded slopes than on sunny slopes and greater at downslope positions than at upslopes, reaching up to 12.89% in gullies. Slope position exerted a direct and significant positive effect on soil moisture, which, in turn, indirectly influenced vegetation diversity and stability. This study reveals the dominant regulatory role of slope position in soil moisture, vegetation diversity, and stability, providing new perspectives and evidence for developing vegetation restoration strategies on the Loess Plateau and promoting the sustainable growth of regional vegetation. Full article

This study aims to develop a forest landscape stability assessment framework that integrates structure, function, and resilience to assess forest landscape stability under different landform types on the Loess Plateau, and to propose differentiated optimization strategies. Remote sensing images and ground survey data were combined to compare the effectiveness of different machine learning models in aboveground biomass (AGB) inversion. Meanwhile, forest fragmentation and landscape multifunctionality were assessed, and a Landscape Stability Index (LSI) was proposed to quantify regional forest landscape stability. The main findings are as follows: (1) between 2000 and 2022, the degree of forest fragmentation and multifunctionality in the hilly gully region improved significantly, and the Simpson’s Diversity Index (SDI) value showed an increasing trend; the plateau gully region showed a decreasing trend in the SDI value. The degree of forest fragmentation in the hilly gully region was higher and showed significant changes, while the plateau gully region was more stable, with the “Interior” and “Dominant” types dominating. (2) The eXtreme Gradient Boosting model outperformed other models in AGB estimation, with R² = 0.81 and RMSE = 24.67 ton ha⁻¹. (3) The LSI of the hilly gully region generally increased, especially in Yanchang, showing a significant increase in ecological stability; the LSI of the plateau gully region generally decreased, especially in Baishui, showing a trend of weakening stability. Based on the assessment results, optimization strategies for different stabilities were proposed, including the hierarchical management of fragmentation, multi-objective management to improve the SDI, and adaptive management for AGB. The forest landscape stability assessment framework proposed in this study can effectively assess the stability of forest landscapes, reveal the differences in ecological restoration in different regions, and provide new perspectives and strategies for forest landscape management and optimization in the Loess Plateau. Full article