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.

Yellow soils developed in limestone weathering materials are representative on Guizhou Plateau, Southwest China. As one of the most important cultivated soils in Guizhou Province, karst yellow soils are generally thin and can be significantly damaged by mild soil erosion. This work used the structural equation model (SEM) to analyze the influence of various factors on runoff generation and sediment loss based on a long time series (2015–2020) of natural rainfall-runoff data and soil erosion data collected from 18 runoff plots in the karst yellow soil region of Southwest China, which reflects the erosion dynamics under natural conditions. Slope runoff plots are the most popular and efficient way to collect data on runoff generation and soil erosion. The findings show that: (1) There were 139 rainfall-runoff occurrences between 2015 and 2020, with moderate rain (10–25 mm) and heavy rain (25–50 mm) events making up the majority. Due to its high frequency and prolonged duration, heavy rain had the greatest impact on the overall rainfall erosivity R value (R). (2) Mean surface runoff (SR) values ranged from 17.37 mm to 133.90 mm, while mean sediment loss rates (SLR) ranged from 1.36 t·ha⁻¹·a⁻¹ to 23.49 t·ha⁻¹·a⁻¹. SR and SLR can be successfully reduced by the forest floor. Broadleaf forest, coniferous forest, mixed forest, and orchard had mean SR values of 19.33%, 12.97%, 16.10%, and 33.38% of fallow land, respectively, and had mean SLR values of 6.90%, 5.79%, 6.34%, and 12.64% of fallow land, respectively. (3) SR generation at the plot scale was substantially linked with 30-min maximum rainfall intensity (I₃₀), while vegetation coverage (VC) and antecedent soil water content (ASW) showed negative direct inference on SR and runoff sediment concentration (RSC) according to SEM analysis. The VC and ASW showed the highest indirect impact on SLR. This study will serve as a scientific reference for the water and soil erosion management in karst yellow soil region and serve as a scientific guidance for regional land use in Southwest China.

Suburban forest ecosystems have a great influence on local climate, especially for mitigating urban heat island effects and dry island effects. To quantify the climate regulation value of forest ecosystem, and provide a reference for regional ecosystem accounting and scientific land management, a new estimation method based on actual evapotranspiration (AET) is proposed and applied in this work. Based on remote sensing, meteorological, and soil data in the years 2000, 2010, and 2020, the annual AET of the forest ecosystem and its dynamic changes were calculated in the new urban area, Guanshanhu District, Guiyang, Guizhou Province, SW China. The climate regulation value is derived from differences in the annual AET of forest ecosystems relative to impervious surfaces. The results showed that: (1) the area of forest ecosystem in Guanshanhu District increased from 2000 to 2010 as a result of ecological engineering but decreased from 2010 to 2020 due to the establishment and expansion of the new urban area, while the area of the impervious surface increased rapidly; (2) the differences in annual AET of forest ecosystem relative to the impervious surface were calculated and subdivided according to different forest types. In 2000, 2010, and 2020, coniferous forests contributed the most to the annual AET difference, followed by coniferous and broad-leaved mixed forests, broad-leaved forests, shrubs, and other forests, respectively; (3) the total climate regulation value of forest ecosystem showed an increasing trend, on the whole, the estimation results were ¥8.78 × 10⁸ in 2000, ¥12.62 × 10⁸ in 2010, and ¥14.75 × 10⁸ in 2020; (4) The average per unit area climate regulation value of all types of forests in the area, based on electricity price in the year 2000, was ¥8.06 × 10⁴/ha in 2000, ¥8.11 × 10⁴/ha in 2010, and ¥10.58 × 10⁴/ha in 2020, the highest portion of per unit area climate regulation value was of coniferous forest, as ¥8.59 × 10⁴/ha in 2000, ¥9.28 × 10⁴/ha in 2010, and ¥11.05 × 10⁴/ha in 2020. This study is a beneficial exploration of forest ecosystem climate regulation value estimation in Guanshanhu District, and the results can provide references for ecological construction in new urban areas.

The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops’ yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions.

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.

Climate regulation ecosystem services (CRESs) play a crucial role in maintaining ecological balance and promoting regional sustainability. Previous studies have primarily focused on the total volume or per-unit-area quantity of CRESs, with limited attention given to their underlying driving mechanisms. This neglect overlooks their multidimensional attributes and dynamic complexity. Such simplifications often overlook the multidimensional attributes and dynamic complexity inherent in these services. Therefore, this study introduces a multidimensional evaluation framework to reveal the characteristic of the spatiotemporal evolution of CRESs. By integrating a multiscale geographically weighted regression (MGWR) model, the intensity and effective distance of theireffects are quantitatively identified, thereby providing a scientific and refined cognitive foundation for regional sustainable development. The results showed the following: (1) Between 2002 and 2022, CRESs in Guizhou Province showed an upward trend, with 64% of counties experiencing positive trends, whereas 51% of counties remained below average in terms of output and efficiency. (2) The spatial pattern of CRESs varied significantly, with stabilization in hotspots, improvement in coldspots, and the highest proportion of “A progress zones” in the east (45%). (3) Vegetation cover and annual precipitation were the two mainpositive factors that most strongly influenced the intensity of the CRESs, with values of 1.494 and 1.196, respectively; GDP had the most significant negative effect, with a value of −0.189; and population density had the largest range of effects, with a bandwidth of 1629. (4) Except for annual rainfall and aspect, the remaining eight influencingfactors, including population density, GDP, altitude, NPP, vegetation cover, annual temperature, and annual humidity, had positive and negative bidirectional effects on CRESs. Overall, this study emphasizes the need for differentiated, sustainability-oriented management strategies to better integrate ecosystem service evaluations into regional planning and sustainable policy development.