Understanding spatiotemporal shifts in vegetation and their climatic and anthropogenic regulatory factors can offer a crucial theoretical basis for environmental conservation and restoration. In this article, the normalized difference vegetation index (NDVI) of the Miaoling area from 2000 to 2020 is studied using a trend analysis and the Mann–Kendall mutation test (MK test) to review the vegetation’s dynamic changes. Our study uses the Hurst index, a partial correlation analysis, and a geographic detector to investigate the contributions of climate change and human activities to regional vegetation changes and their drivers. We found that Miaoling’s annual average NDVI was between 0.66 and 0.83 in 2000–2020, with a mean of 0.766. The overall trend was slow upward (0.0009/year), and 53.82% of the region continued to grow and gradually increased from west to east in the spatial domain, among which the karst regional NDVI distribution area and its growth rate were higher than those of non-karst sites. Based on correlations between climatic factors and NDVI, precipitation seasonality (coefficient of variation, CV) had the strongest correlation (positive correlation) with NDVI, while vapor pressure deficit (VPD) had a negative correlation with NDVI. In the interaction, human activities played a dominant role in the influence of NDVI on the vegetation of Miaoling. The night light index had the most explanatory power on the NDVI (q = 0.422), and the interaction between anthropogenic factors and other factors dominated its explanatory power. This study has academic and practical importance for the management, protection, and sustainable development of karst basins.

In recent decades, China has exhibited the fastest and most remarkable social-economic development in the world. As a result of such development, the forest cover of the country has undergone radical changes. This paper aims to develop a method for analyzing long-term and spatial changes in forest cover based on historical maps and remote sensing images. Moreover, we will focus on the reduction or restoration of forests distributed at different altitudes, slopes, soils, and lithologic types in different periods, to reveal the problems that should be paid attention to in forest restoration in karst areas. A typical county of China was selected as the study area. A historical military operation map was considered the principal source of basic data. These data were then combined with Landsat satellite images to conduct quantitative analysis on changes in the spatial area and location of forest cover with a long time series. The findings are as follows: in terms of time series, the forest area in the study area showed a trend of decreasing at first and then increasing, with the year 1986 as the turning point. In terms of spatial patterns, a considerable difference is observed in regions with changes in forest cover under different historical periods. Changes are obvious in limestone areas, rock soil areas, and areas with an elevation of 2000–2500 m and a slope gradient of 6°–15°. Spatial–temporal changes in forest cover reflect the effects of the war, national policy, and economic development to some extent. All these results indicate that, despite its limitations, a historical map is a valuable document for studying an ecological environment.

Spatialization of soil formation rate (SFR) is always a difficult problem in soil genesis. In this study, the dissolution rate in karst areas of China during the period 1983–2015 was estimated on the basis of geospatial analysis techniques and detection of variation via the law of chemical thermodynamics in conjunction with long-term serial ecohydrology data. SFR at different lithological backgrounds was calculated on the basis of the content of acid-insoluble substances. Results showed that the spatial dissolution rate of carbonate rock ranges between 0 and 106 mm/ka, averaged at 22.51 mm/ka, and the SFR ranges between 10 and 134.93 t km⁻² yr⁻¹, averaged at 18.59 t km⁻² yr⁻¹. The dissolution rate and SFR exhibit a slight increasing trend with 0.04 mm/ka and 0.003 t km⁻² yr⁻¹, respectively. The risk for soil erosion was reevaluated on the basis of the SFR results, and the area with erosion risk and the ecologically safe area were corrected. Results indicated that the area with erosion risk is four times higher than the ecologically safe area. This study will hopefully instigate and facilitate the application and popularization of geospatial analysis technology to the research field of rock weathering and soil formation.

Rural settlements are the basic spatial units of rural geography research, and it is essential to explore the dynamic changes in land use on a rural settlement scale to promote the development of the rural revitalization strategy. The study took different rural settlement types in karst trough valleys as examples and applied geographic information mapping trajectory models, buffer zone spatial analysis, the nearest neighbor index, and other research methods. We explored the land use dynamic change in the buffer zone of different settlement types in the karst trough valley from 1964 to 2021 in the long time series and micro-spatial dimensions. We analyzed the homogeneity, variability, and coupling characteristics of land use evolution in typical settlements. The results indicate the following: (1) From 1964 to 2021, the karst trough valley settlements as a whole showed an aggregation state, and the settlements could be classified into four categories: expanding settlements (ES), atrophic settlements (AS), balancing rural settlements (BS), and decreasing settlements (DS) according to the settlement life cycle theory and settlement development index measurement. (2) Different expansion and shrinkage of land use buffer changes exist for different settlement types. The closer the ES is to the location of the settlement center, the richer the land use type; the further the AS from the settlement center, the richer the land use type; the BS is not affected by the distance; and the DS settlement shows dynamic changes. (3) Land use dynamic change in settlements is driven by multiple integrated factors, and there is variability in the driving factors of different settlement types. (4) In this paper, through a case study, we propose the research idea that land use change (LUCC) reflects land use transformation (LUT) in different rural settlement types from a settlement-scale perspective, and land use transformation further causes the development of rural settlement transformation (RUT). Our study revealed the LUCC—LUT—RUT interaction feedback mechanism of karst trough valley settlements in Southwest China. This study aims to enrich the theoretical research framework of rural transformation at the settlement scale, on the one hand, and to provide case studies for developing countries with karstic mountain valley landscapes, such as China, on the other.

The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.

As a model based on the harmonious development of society, economy, population, and resources, sustainable development is an essential driving force for a country’s social and economic development. The countryside is an important part of the country, and its sustainable development has been given attention, but existing research lacks a focus on the sustainable development of rural settlements in mountainous areas. In this paper, the rural settlements in the Miaoling Mountains of China are taken as the research object, a geographic detector is used to determine the weight of rural settlement sustainable development factors, a rural settlement sustainable development model is constructed, and a local spatial autocorrelation method is used to quantitatively analyse the level of sustainable development of the rural settlements and the influencing factors related to both the natural environment and socio-economic levels. The results show that (1) 78% of rural settlements in the Miaoling Mountains have a medium to low level of sustainable development and are weak in overall sustainability. (2) The spatial differentiation of the sustainable development levels of rural settlements in the Miaoling Mountains are apparent, and the high sustainable development level settlements are mainly distributed around the urban areas. In contrast, the low sustainable development level settlements are scattered. (3) The level of sustainable development is positively correlated spatially with gross domestic product (GDP), arable land, elevation, normalised difference vegetation index (NDVI), water systems, and roads, and negatively correlated spatially with slope. Of these, GDP, arable land, and elevation have a more substantial influence on the level of sustainability of rural settlements. According to the local autocorrelation analysis, the positively correlated settlement types are mainly clustered in distribution, while the negatively correlated settlement types are sporadic.