As global cities rapidly develop, the realization of sustainable urban development has become a pressing global concern. As a critical component of the urban built environment, urban green spaces are essential for maintaining the sustainability of urban ecosystems by offering a variety of ecological benefits. However, there are some problems in the design and construction of urban green framework, which often disregard the real needs of residents and hinder the sustainable development of urban areas. Therefore, urban planning that takes into account the supply and demand relationship of urban green space is considered to be a key technology that should help to improve residents’ happiness and promote urban sustainable development. This paper takes Zunyi City as an example; selects six indicators; combines UAV images, population, economic, and environmental data to evaluate ecosystem services valuation existing green space in the city; and analyzes its social and ecological needs. Affected by land utilization and development density, the supply value showed low spatial density characteristics in the central urban area and high spatial distribution characteristics in the surrounding urban area. The correlation between demand value and population density is the largest, and the distribution pattern is similar to that of population density. The supply-demand ratio in the study area is −0.54, indicating a severe imbalance between supply and demand. The correlation analysis between the comprehensive supply and demand ratio and each influencing factor shows that the correlation with population density is the largest. The combination of supply and demand maps addresses a major problem in planning, namely, ensuring that communities have priority in providing urban green space based on society’s need for ecosystem services. In addition, these maps will help develop the best green space planting plans to meet the specific needs of different communities. Taken together, the findings can help city planners and decision makers improve the layout of urban green areas, increase ecosystem service potential, and improve urban environmental quality and sustainability.

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.

Approximately 24% of the global land area consists of mountainous regions, with 10% of the population relying on these areas for their cultivated land. Accurate statistics and monitoring of cultivated land in mountainous regions are crucial for ensuring food security, creating scientific land use policies, and protecting the ecological environment. However, the fragmented nature of cultivated land in these complex terrains challenges the effectiveness of existing extraction methods. To address this issue, this study proposed a cascaded network based on an improved semantic segmentation model (DeepLabV3+), called Cascade DeepLab Net, specifically designed to improve the accuracy in the scenario of fragmented land features. This method aims to accurately extract cultivated land from remote sensing images. This model enhances the accuracy of cultivated land extraction in complex terrains by incorporating the Style-based Recalibration Module (SRM), Spatial Attention Module (SAM), and Refinement Module (RM). The experimental results using high-resolution satellite images of mountainous areas in southern China show that the improved model achieved an overall accuracy (OA) of 92.33% and an Intersection over Union (IoU) of 82.51%, marking a significant improvement over models such as U-shaped Network (UNet), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3+. This method enhances the efficiency and accuracy of monitoring cultivated land in mountainous areas and offers a scientific basis for policy formulation and resource management, aiding in ecological protection and sustainable development. Additionally, this study presents new ideas and methods for future applications of cultivated land monitoring in other complex terrain 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.