The Loess Plateau is one of the most severely affected regions by soil erosion in the world, with a fragile ecological environment. Vegetation plays a key role in the region’s ecological restoration and protection. This study employs the Geographical Detector (Geodetector) model to quantitatively assess the impact of natural and human factors, such as temperature, precipitation, soil type, and land use, on vegetation growth. It aims to reveal the characteristics and driving mechanisms of vegetation cover changes on the Loess Plateau over the past 26 years. The results indicate that from 1995 to 2020, the vegetation coverage on the Loess Plateau shows an increasing trend, with a fitted slope of 0.01021 and an R² of 0.96466. The Geodetector indicates that the factors with the greatest impact on vegetation cover in the Loess Plateau are temperature, precipitation, soil type, and land use. The highest average vegetation coverage is achieved when the temperature is between −4.8 and 2 °C or 12 and 16 °C, precipitation is between 630.64 and 935.51 mm, the soil type is leaching soil, and the land use type is forest. And the interaction between all factors has a greater effect on the vegetation cover than any single factor alone. This study reveals the factors influencing vegetation growth on the Loess Plateau, as well as their types and ranges, providing a scientific basis and guidance for improving vegetation coverage in this region.

Habitat suitability assessment for forest and grassland ecosystems is a critical component of ecological restoration and land use planning in the Loess Plateau, aiming to advance soil and water conservation and foster sustainable ecological environment development. Despite progress in vegetation restoration, systematic evaluations of habitat suitability in complex geomorphic regions like the Loess Plateau remain scarce, particularly in balancing hydrological and ecological trade-offs. The Yanhe River Basin (7725 km²), a sediment-prone tributary of the Yellow River, exemplifies the challenges of soil erosion and semi-arid climatic constraints, making it a critical case for evaluating restoration strategies. This study employed a comprehensive approach utilizing Analytic Hierarchy Process (AHP), Geographic Detector, mathematical statistics, and other methods. An evaluation indicator system and methodology were established to assess the suitability of forest and grassland habitats in the Yanhe River Basin, evaluating the suitability and quality improvement potential under the current land use conditions. The results indicate: (1) The dominant factors influencing the suitable distribution of forests include photosynthetically active radiation (PAR), soil total phosphorus content, annual precipitation, and elevation. For grasslands, the dominant factors include photosynthetically active radiation, annual average temperature, elevation, and annual precipitation. (2) In the watershed, forestland and grassland areas classified as moderately suitable or higher cover 1064.9 km² and 4196.9 km², accounting for 91.9% and 94.7% of their total respective areas, indicating a generally rational spatial allocation of forest and grassland ecosystems. (3) The improvable area for forests measures 366 km² (34.4% of moderately or higher suitability zones), with most already meeting coverage thresholds. In contrast, grasslands have an improvable area of 2491.6 km² (59.4% of moderately or higher suitability zones), where over half of the area remains below coverage thresholds corresponding to their habitat conditions. (4) Forests can adopt natural restoration-focused low-intensity interventions through strengthened closure management, while grasslands require spatially tailored measures—such as precipitation interception and enhanced stewardship—targeting suitability-based potential grades, collectively achieving overall improvement in grassland vegetation coverage. This study represents the first systematic evaluation of forest–grassland habitat suitability in the Yanhe River Basin, elucidating its spatial distribution patterns and providing critical insights for watershed-scale ecological restoration.

The implementation of measures such as check dams and terraces in the Loess Plateau of China has had a groundbreaking impact on water and sediment conditions. The question of how to accurately simulate the runoff–sediment process under complex underlying surface conditions has become key to clarifying the water cycle law. This study focused on the Chenggou River basin, a small watershed located in the Loess Plateau, to examine the effect of the underlying surface characteristics on the runoff production process, and the spatial distribution of the dominant runoff process in the runoff generation mechanism was determined according to the land application, slope and vegetation coverage of the watershed. A runoff–sediment model was constructed that was combined with the traditional hydrological physical mechanism and a deep learning algorithm. Different parameters were calibrated depending on the spatial distribution of the dominant runoff process and we then ran the runoff–sediment simulation model to very its serviceability in the typical watershed of the Loess Plateau. Different parameters were calibrated for each type of hydrological response unit (HRU), according to the division of each HRU and the actual flood process, to calculate the runoff yield of each HRU. An LSTM algorithm was used for flow routing and a CSLE algorithm was used to simulate soil erosion. The results show that there were 29 flood events in the Chenggou River basin from 2013 to 2017. The average runoff depth had an 8.86% margin of error, while the peak flow had a slightly higher 9.44% deviation. The Nash efficiency coefficient was 0.84, and the relative error of soil erosion was 14.45%. The model simulation effect is good and can be applied to the typical watershed of the Loess Plateau. The model can provide a scientific basis for the highly efficient and sustainable utilization of water resources, ecological environment construction and the sustainable development of agriculture.

Habitat quality is critical for spatial planning strategies and ecological conservation initiative, evaluating the health of the natural environment that supports human survival. However, current approaches pay insufficient attention to revealing the evolution and spatial heterogeneity of the habitat quality simultaneously. In this study, a comprehensive and practical framework was therefore developed for mechanistic habitat quality analysis, which incorporates an adaptable evolutionary model alongside multiple spatial statistical methods. Ningxia, located in Northwest China, was selected as a case study area due to its fragile ecosystem. The proposed framework was then applied to characterize the evolutionary process and spatial heterogeneity of habitat quality in Ningxia. Key factors driving spatial heterogeneity were also found at the same time. From 2000 to 2024, habitat quality in Ningxia is characterized by good habitat and shows significant improvement, following a progressive trajectory. The proportion of poor habitat has been significantly reduced from 29.26% to 24.63%, while that of excellent habitat has been increased from 1.68% to 2.33% over the past two decades. Variation in habitat quality is more pronounced in northern and southern regions, while remaining relatively stable in the central Yellow River ecological corridor. Both natural and socioeconomic factors have an impact on the habitat change in this region, such as the Normalized Difference Vegetation Index (NDVI), Net Primary Productivity (NPP), and Gross Domestic Product (GDP). Vegetation factors play vital roles in spatial variation in habitat quality, while the influences of socioeconomic factors are relatively small. The spatial heterogeneity is driven by nonlinear synergistic effects among numerous factors. This paper developed a feasible framework to retrieve the evolution and spatial heterogeneity pattern of habitat quality, which provides a robust methodology for further habitat assessment at the ecologically fragile regions worldwide.