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Currently, the inversion of remote sensing satellite images of water environment indicators mostly stays in the indicators with active optical characteristics, while there is less research on the inversion of most water quality indicators with non-optical activity properties, weak scattering and absorption of optical radiation, the size of their concentration has little effect on the spectral characteristics of the water body, such as TOC(Total Organic Carbon). In this paper, based on Pingzhai Reservoir, a dammed river in the karst mountainous area, the inversion model of TOC concentration was established based on BP neural network (BPNN) and sentinel-2 satellite remote sensing images. The results showed that the single bands with high correlation with the measured TOC concentration data were two vegetation red-edge bands B6 (740 nm) and B7 (783 nm) and one NIR band B8 (842 nm), and finally b7, b6 + b7, b7 + b8, b7 ×b8 were selected as the input layers of BPNN for modeling through the combination of the bands, and their Pearson's coefficients were -0.667, -0.656, -0.655, - 0.675. The inverse model established could reach a minimum RMSE of 0.235 mg/L and a maximum R2 of 0.889, which was superior to that of the conventional empirical model. Demonstrate the feasibility of a TOC inversion method based on Sentinel-2 data and BPNN to monitor TOC concentrations in Pingzhai Reservoir. The study successfully established a BP neural network inversion model of TOC concentration in Pingzhai Reservoir with low error, meanwhile, we analyzed the correlation between common water quality indicators and TOC in the reservoir, in which TOC showed significant positive correlation with WT and significant negative correlation with TN and EC, with Pearson's coefficients of 0.655, -0.666, and -0.393, respectively. The article provides scientific theoretical foundation and technical support for water quality protection of water sources. [ABSTRACT FROM AUTHOR]

The karst plateau is dominated by mountainous and hilly landforms, with low mechanization level of cropland, high difficulty of cultivation, and obvious phenomenon of cropland abandonment, which threatens regional food security. This study aims to analyze the spatial-temporal variation and its driving mechanisms of abandoned cropland in the Karst Plateau in Eastern Yunnan, China (KPEYC) between 2001 and 2020. To achieve this goal, 18 key factors from population, economic environment, cropland attributes, and farming conditions are selected. Moreover, correlation analysis, geodetector, and regression analysis methods are applied from three perspectives: temporal change, spatial distribution and spatial-temporal change. The results show that: (i) The cropland abandonment rate (CAR) in the KPEYC shows a fluctuating trend, with an average value of 9.78%, and the spatial distribution shows a pattern of "high in the center and low in the south and north". (ii) From the perspective of temporal change, gross value of agricultural production, and gross value of industrial production have the largest correlation coefficients with CAR. (iii) The explanatory power of gross tertiary industrial production, gross value of industrial production, followed by soil thickness. (iv) Gross value of agricultural production, and gross tertiary industrial production are the core driving forces for the spatial-temporal change of CAR. The higher the gross value of agricultural production and gross tertiary industrial production, the lower the CAR. elevation, soil thickness, and traffic mileage are the main driving factors for the spatial-temporal change of CAR. The study indicates that economic factors are decisive for cropland abandonment in the KPEYC. Based on the results, this study can provide decision-making support for local prevention and control of cropland abandonment, and the local community needs to promote land transfer and concentration and local urbanization according to local conditions, improve agricultural policies, improve farming conditions, etc. in order to increase farmers' enthusiasm for production, promote the rational use of cropland, and solidly push forward ecological restoration and management, optimize ecological spatial patterns, manage serious areas of rocky desertification, and appropriately alleviate the contradiction between people and land. [ABSTRACT FROM AUTHOR]

Regional ecosystem service value (ESV) is significantly influenced by factors such as land use/cover change (LUCC). In this study, from the perspective of spatio-temporal heterogeneity, we constructed a dynamic and zonal equivalence table of ecosystem service values using the equivalence factor method and analyzed the spatio-temporal changes in ecosystem service values of different agricultural plantation regions of the karst mountainous areas of southwestern China (Yunnan Province, YP) in the years from 1990 to 2020. Also, the ESV of YP in 2030 was simulated using the Patch-generating Land Use Simulation (PLUS) model. The results showed the following: (1) land use/land cover (LULC) in YP from 1990 to 2020 was dominated by needle-leaved forestland, broadleaved forestland, grassland, and rainfed cropland. (2) The total ESV in YP fluctuated between CNY 876.74 and 1323.68 B from 1990 to 2020, expanding at a rate of 50.98%. The largest portion of the total ESV comes from climate regulation. The ESV increased from east to west, and the positive spatial correlation of the ESV gradually weakened. (3) The ESV in YP was projected to reach CNY 1320.70 B by 2030, representing a decrease of ~CNY 2.98 B since 2020. The results showed a decline in the ecological environment's quality in YP. [ABSTRACT FROM AUTHOR]

The karst strata in the southwestern mountainous areas of China are extensively developed, creating a fragile geological environment. Landslide geological disasters occur frequently in these areas due to high annual rainfall, concentrated time periods, and frequent human engineering activities. Conventional ground geophysical methods face challenges in complex terrains, making it difficult to quickly and reliably obtain underground structures in landslide-prone areas. The semi-airborne transient electromagnetic method (SATEM) combines ground-based transmission and airborne reception of electromagnetic responses. This method, characterized by large emission magnetic moment and rapid data collection in the air, offers advantages in detecting deep geological structures in complex terrain areas. This article presents the application of a newly developed loop source SATEM system, integrated with multi-rotor unmanned aerial vehicles (UAVs), to conduct deep geological structure detection experiments on a large-scale landslide. The inversion results clearly depict the spatial distribution of the Feixianguan Formation and Longtan Formation, as well as vertical fractures developed in the strata due to underground coal mining. The detection test demonstrates the applicability of the SATEM method in detecting underground structures in Karst Plateau special landform areas, providing a new detection approach for similar regions. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of vegetation restoration on soil humus and aggregate stability within the karst region of Southwest China. This study focused on soils at five vegetation succession stages (abandoned land, grassland, shrub rangeland, shrubland, and secondary forest) in the typical karst region, and the aggregate stability was determined using wet sieving and the Le Bissonnais method. Simultaneously, the Pallo method and separation extraction were used to determine the humus composition, aiming to analyze the distribution of humus content in the soil aggregates and its effect on aggregate stability. The results revealed the following: (1) The mean weight diameter of soil aggregates significantly increased with vegetation restoration stages. Soil water-stable aggregates at each vegetation stage mainly included particles over 2 mm in size. (2) The humic acid and fulvic acid contents consistently increased with vegetation restoration, and the precipitation quotient value of the humification degree showed an increasing trend. At each vegetation restoration stage, the percentage of each humus component was, from highest to lowest, as follows: insoluble HM, fulvic acid, humic acid, clay-bound HM, and iron-bound HM. (3) Through stepwise regression analysis, humic acid content in >2 mm aggregates, fulvic acid and clay-bound HM contents in 1–2 mm aggregates, and insoluble HM content in <0.25 mm aggregates were the dominant factors affecting soil aggregate stability in the karst region. These results aim to provide novel insights for a more in-depth comprehension of the restoration and rehabilitation of vegetation within the karst region of Southwest China, thereby laying a robust foundation for scientific theories and further investigations. [ABSTRACT FROM AUTHOR]

Rocky desertification (RD), a natural and human-induced process of land degradation in karst areas, has become the primary ecological disaster and one of the obstacles to sustainable ecological development in southwest China. Nevertheless, the variation of soil physical and chemical properties, bacterial and fungal communities, and their relationships in RD forests remains limited. Therefore, soil samples were collected from forests under four degrees of RD (NRD, non-RD; LRD, light RD; MRD, moderate RD; and SRD, severe RD) and subjected to high-throughput sequencing of 16S rRNA and ITS1 genes. The results showed a significant reduction in bacterial richness and diversity, while fungal richness and diversity decreased markedly and then showed a balanced trend with the increase in RD degree, indicating that bacteria and fungi did not present the same dynamics in response to the process of RD. The bacterial communities were dominated by Proteobacteria, Actinobacteria, Acidobacteria, and Chloroflexi, while the fungal communities were dominated by Basidiomycota, Ascomycota, and Mortierellomycota. The PCoA and NMDS demonstrated significant differences in microbial communities in study sites, among which the fungal communities in non-RD forest and LRD forest clustered together, suggesting that fungal communities were more stable than bacteria in RD forest. The db-RDA, Mantel test, and random forest model confirmed the important role of soil BD, pH, SOC, AN, and AP in driving microbial diversity and communities. The IndVal analysis suggested that Chloroflexi, Patescibacteria, Atheliales, and Cantharellales with high indicator values were identified as potential bio-indicators for RD forests. This study could not only improve our understanding of bacterial and fungal community dynamics across RD gradients, but also could provide useful information for the further use of microorganisms as indicators to reflect the environmental changes and ecosystem status during forest RD. [ABSTRACT FROM AUTHOR]