Gansu Desert Control Research Institute

The policy of ex situ poverty alleviation and relocation (PAR) effectively achieves socioecological sustainable development goals. However, crossregional socioecological system (SES) interactions and their spatial heterogeneity remain insufficiently explored, potentially constraining constrain the efficacy of subsequent support policies for migrant relocation. Based on the continuous monitoring data, this study constructs an evaluation index system of social adaptability (SA) using four dimensions, namely, economy, livelihood, psychology and development ability. This study applied the methods of ecosystem health (EH) index, coupling coordination degree, and generalized additive model and introduced the telecoupling theory of SES to construct an analysis framework connecting the EH of the migrant outflow sites (MOS) and the SA of the migrant inflow sites (MIS). Results revealed that the SA improved significantly from 2019 to 2022, increaseing by 7.22 % and 15.93 % in 2021 and 2022, respectively. The EH decreased initially and then increased suddenly before stabilizing. Moreover, 18.75 % of the emigration counties continued to increase spatially. Microanalysis revealed the contribution process of PAR to EH. The telecoupling of SA and EH demonstrated a synergistic developmental trend, and spatial distance was found to affect this relationship through complex nonlinear characteristics. Our research deliver insights facilitating the consolidating of the outcomes of ex situ PAR and provide practical support for efforts to reduce global poverty reduction and ensure the coordinated development of cross-regional SES.

Soil erosion poses a significant threat to the sustainability of land systems in karst mountainous regions, where steep slopes, shallow soils, and intensive human activities exacerbate land degradation, undermining both the productive functions and ecological services of land resources. This study evaluated soil erosion susceptibility in the karst-dominated Qingshui River watershed, Southwest China, and identified key drivers of land degradation to support targeted land management strategies. Four machine learning models, BPANN, BRTs, RF, and SVR were trained using twelve geo-environmental variables representing lithological, topographic, pedological, hydrological, and anthropogenic factors. Variable importance analysis revealed that annual precipitation, land use type, distance to roads, slope, and aspect consistently had the greatest influence on soil erosion patterns. Model performance assessment indicated that BRTs achieved the highest predictive accuracy (RMSE = 0.161, MAE = 0.056), followed by RF, BPANN, and SVR. Spatial susceptibility maps showed that high and very high erosion risk zones were mainly concentrated in the central and southeastern areas with steep slopes and exposed carbonate rocks, while low-risk zones were located in flatter, vegetated southwestern regions. These results confirm that hydrological conditions, topography, and anthropogenic activities are the primary drivers of soil erosion in karst landscapes. Importantly, the findings provide actionable insights for land and landscape management—such as optimizing land use, restoring vegetation on steep slopes, and regulating human activities in sensitive areas—to mitigate erosion, preserve land quality, and enhance the sustainability of karst land systems.

Unmanned Aerial Vehicle Remote Sensing (UAV-RS) has emerged as a transformative technology in high-resolution Earth observation, with widespread applications in precision agriculture, ecological monitoring, and disaster response. However, a systematic understanding of its scientific evolution and structural bottlenecks remains lacking. This study collected 4985 peer-reviewed articles from the Web of Science Core Collection and conducted a comprehensive scientometric analysis using CiteSpace v.6.2.R4, Origin 2022, and Excel. We examined publication trends, country/institutional collaboration networks, keyword co-occurrence clusters, and emerging research fronts. Results reveal an exponential growth in UAV-RS research since 2015, dominated by application-driven studies. Hotspots include vegetation indices, structure from motion modeling, and deep learning integration. However, foundational challenges—such as platform endurance, sensor coordination, and data standardization—remain underexplored. The global collaboration network exhibits a “strong hubs, weak bridges” pattern, limiting transnational knowledge integration. This review highlights the imbalance between surface-level innovation and deep technological maturity and calls for a paradigm shift from fragmented application responses to integrated systems development. Our findings provide strategic insights for researchers, policymakers, and funding agencies to guide the next stage of UAV-RS evolution.

Accurate permafrost mapping in mountainous regions is hindered by sparse in situ observations and heterogeneous terrain. This study develops a GPR-augmented machine learning framework to map mountain permafrost in the northeastern Qinghai–Tibet Plateau. A total of 1037 presence–absence samples were compiled from boreholes, soil pits, 128 GPR transects collected in 2009, and 22 additional empirical points above 4700 m, covering diverse topographic and thermal conditions. Thirteen classification algorithms were evaluated using 5-fold cross-validation repeated 40 times, with LightGBM, CatBoost, XGBoost, and RF achieving top performance (F1 > 0.98). Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. Aspect-controlled permafrost boundaries were captured, with modeled lower elevation limits varying by >200 m across slope directions. SHAP analysis showed that climate and soil variables contributed nearly 80% to model outputs, with LST, FDD, BD, and TDD being dominant. Several predictors exhibited threshold or nonlinear responses, reinforcing their physical relevance. Additional experiments confirmed that integration of GPR and high-elevation constraint samples significantly improved model generalization, especially in underrepresented terrain zones. This study demonstrates that a GPR-augmented machine learning framework can support cost-effective, physically informed mapping of frozen ground in complex alpine environments.

This study examines the distribution dynamics of the tribe Erythroneurini, a group of economically significant leafhoppers in China that pose threats to crops through sap feeding and virus transmission, while also serving as valuable ecological indicators due to their sensitivity to environmental changes. Through a systematic evaluation of 12 species distribution models (SDMs), we ultimately selected the Maximum Entropy (MaxEnt) model for predicting species distributions. The R-optimized MaxEnt model incorporated 11 environmental variables and 218 occurrence records to assess habitat suitability under historical, current, and future climate scenarios (SSP1-2.6 and SSP5-8.5). The model was configured with LQP features and a default regularization multiplier value of 1. Results reveal that temperature (BIO6, BIO2, BIO4) and precipitation (BIO12) are the primary drivers of habitat suitability, with tropical and subtropical regions identified as the most favorable. Future projections indicate a complex pattern of habitat contraction and expansion, with a notable northward shift toward higher latitudes under climate change. These findings highlight the profound impact of climate change on Erythroneurini distribution, underscoring the need for proactive management. Implementing long-term monitoring and targeted control in vulnerable regions mitigates ecological and agricultural risks, supporting sustainable pest management and fostering the integration of ecological conservation with agricultural development.

Karst yellow soil is one of the most important cultivated soils in southwest China. At present, only a few studies have dealt with rainfall erosivity and erosive rainfall thresholds in the karst yellow soil region. This paper utilizes statistical methods to identify erosive rainfall thresholds and slope erosion-prone areas in the Qianzhong region. This analysis is based on long-term experimental data from 10 experimental stations and 69 experimental plots within the region in 2006 to 2022. The findings show the following: The rainfall amount threshold was 12.66 mm for woodland plots, 10.57 mm for grassland plots, 9.94 mm for farmland plots, and 8.93 mm for fallow plots. Soil and water conservation measures in forestry and grassland effectively increase the rainfall amount thresholds. Compared to farmland, the rainfall threshold increased by 27.32% for woodland and 6.32% for grassland. Bare land and farmland are erosion-prone areas in the karst yellow soil region. The erosive rainfall thresholds for farmland plots with slopes of 13°, 15°, 20°, 23°, and 25° were 10.41 mm, 10.28 mm, 9.66 mm, 9.52 mm, and 9.15 mm, respectively. With the increase in the 13–25° slope gradient of farmland, the initial rainfall required for runoff generation leads to a reduction. The wrong selection indices (WSI) of all landcover plots were less than 10%, and the efficiency indices (EFF) were between 80.43% and 90.25%. The relative error index (REI) of the erosive rainfall thresholds for all landcover runoff plots was less than 0.50%, very close to 0, indicating that these thresholds have small errors and high accuracy. This study gained a better understanding of natural rainfall-induced erosion characteristics in the study area, determined rainfall thresholds for distinguishing erosive rainfall events from non-erosive across different landcover types, and reduced the workload of calculating rainfall erosivity while enhancing the accuracy of soil erosion forecasting and simulation in the karst mountain yellow soil area.