There are significant differences in the dominant driving factors of rocky desertification evolution in different historical periods in southwest karst mountainous areas. However, previous studies were mostly conducted in specific periods. In this study, taking Bijie City as an example, the spatial and temporal evolution pattern of rocky desertification in Bijie City in the recent 35 years was analyzed by introducing the feature space model and the gravity center model, and then the dominant driving factors of rocky desertification in the study area in different historical periods were clarified based on GeoDetector. The results were as follows: (1) The point-to-point B (bare land index)-DI (dryness index) feature space model has high applicability for rocky desertification monitoring, and its inversion accuracy was 91.3%. (2) During the past 35 years, the rocky desertification in Bijie belonged to the moderate rocky desertification on the whole, and zones of intensive and severe rocky desertification were mainly distributed in the Weining Yi, Hui, and Miao Autonomous Region. (3) During 1985–2020, the rocky desertification in Bijie City showed an overall weakening trend (‘weakening–aggravating–weakening’). (4) From 1985 to 2020, the gravity center of rocky desertification in Bijie City moved westward, indicating that the aggravating degree of rocky desertification in the western region of the study area was higher than that in the eastern region. (5) The dominant factors affecting the evolution of rocky desertification in the past 35 years shifted from natural factor (vegetation coverage) to human activity factor (population density). The research results could provide decision supports for the prevention and control of rocky desertification in Bijie City and even the southwest karst mountainous area.

A landslide disaster, especially a highway landslide, may greatly impact the transport capacity of nearby roads. Keeping highways open, in particular, is crucial for supporting the functioning of the economy, society and people. Therefore, evaluating the highway landslide susceptibility is particularly important. In this paper, the city of Laibin, in the Guangxi Zhuang Autonomous Region of China, was taken as the study zone. According to data on 641 highway landslide disaster points measured in the field and a basic evaluation of the study area, nine evaluation factors—the elevation, slope, aspect, height difference, plan curve, profile curve, precipitation, Topographic Wetness Index (TWI) and vegetation coverage—were selected. We coupled a Frequency Ratio (FR) model, Analytic Hierarchy Process (AHP), Logistic Regression (LR), Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) to evaluate the susceptibility to highway landslides, with a Receiver Operating Characteristic (ROC) curve used to analyze the precision of these models. The ROC curve showed that the accuracy of the five models was greater than 0.700 and thus had a certain reliability. Among them, the FR-LR model had the highest accuracy, at 0.804. The study protocol presented here can therefore provide a reference for evaluation studies on landslide susceptibility in other areas.

The traditional settlements are of paramount significance as indispensable elements of China’s cultural heritage, simultaneously functioning as prime assets for the enhancement of rural economic and social dynamics. Nestled within the comprehensive framework of China’s rural revitalization endeavor and Shandong Province’s proactive initiatives toward the amalgamation of cultural and tourism sectors, a meticulous exploration of the spatiotemporal evolution and connectivity of traditional villages in Shandong Province is indispensable for their preservation and forward-thinking evolution. For this study, 557 traditional villages across Shandong Province are identified as pivotal points, with the application of geographic information system (GIS) techniques to scrutinize their spatiotemporal transformation patterns and spatial characteristics. Additionally, a suite of analytical instruments, encompassing metrics for accessibility assessment, ordinary least squares (OLS) linear regression, and geographically weighted regression (GWR) models, are deployed to evaluate the accessibility levels and influential factors shaping traditional villages within the region. The analytical outcomes reveal the following: (1) Chronologically, approximately 80% of the traditional villages in the province of Shandong were established during the Ming and Qing epochs, and they demonstrate a migratory pattern that is spatially and temporally oriented from “southwest to northeast”; geographically, these traditional villages are characterized by pronounced clustering, predominantly situated at the confluence of Jinan and Zibo Cities, the Shantou District of Zaozhuang City, Zhaoyuan City of Yantai City, and Rongcheng City of Weihai City, forming a coherent “four-core” spatial distribution configuration. (2) Considering the criteria for village location, traditional villages in Shandong are predominantly found in areas with a predominantly flat landscape and a certain proximity to water bodies. (3) On the whole, the accessibility of traditional villages in Shandong is relatively high, with the average accessibility assessed at 199.92 min, a range spanning from 175 min, and approximately 57.99% of the villages falling within the 100 to 200 min accessibility bracket, indicating a systematic decline in accessibility from the central areas to the periphery. (4) The pivotal factors influencing the accessibility of traditional villages in Shandong are primarily altitude, slope, and road network density, with altitude and slope showing a negative correlation with accessibility, whereas road network density exhibits a positive correlation, and the proximity to water bodies has a relatively minor impact on accessibility.

Landslides are among the most prevalent geological hazards and are characterized by their high frequency, significant destructive potential, and considerable incident rate. Annually, these events lead to substantial casualties and property losses. Thus, conducting landslide susceptibility assessments in the regions vulnerable to such hazards has become crucial. In recent years, the coupling of traditional statistical methods with machine learning techniques has shown significant advantages in assessing landslide risk. This study focused on Sichuan Province, China, a region characterized by its vast area and diverse climatic and geological conditions. We selected 13 influencing factors for the analysis: elevation, slope, aspect, plan curve, profile curve, valley depth, precipitation, the stream power index (SPI), the topographic wetness index (TWI), the topographic position index (TPI), surface roughness, fractional vegetation cover (FVC), and slope height. This study incorporated the certainty factor method (CF), the information value method (IV), and their coupling with the decision tree C5.0 model (DT) and a logistic regression model (LR) as follows: IV-LR, IV-DT, CF-LR, and CF-DT. The results, validated by an ROC curve analysis, demonstrate that the evaluation accuracy of all six models exceeded 0.750 (AUC > 0.750). The IV-LR model exhibited the highest accuracy, with an AUC of 0.848. When comparing the accuracy among the models, it is evident that the coupling models outperformed the individual statistical models. Based on the results of the six models, a landslide susceptibility map was generated, categorized into five levels. High and very high landslide risk zones are mainly concentrated in the eastern and southeastern regions, covering nearly half of Sichuan Province. Medium-risk areas form linear distributions from northeast to southwest, occupying a smaller proportion of the area. Extremely low- and low-risk zones are predominantly located in the western and northwestern regions. The density of the landslide points increases with higher risk levels across the regions. This further validates the suitability of this research methodology for landslide susceptibility studies on a large scale. Consequently, this methodology can provide crucial insights for landslide prevention and mitigation efforts in this region.

Few studies of monitoring rocky desertification based on Sentinel-2 MSI image with higher spatial and temporal resolutions have been reported. In this paper, nine typical surface parameters that derived from Sentinel-2 MSI image including three red edge indices have been introduced to establish two types of feature space monitoring index: point-to-line type and point-to-point type. The results show that: (1) The feature of space monitoring index based on Sentinel-2 MSI image has a higher applicability in obtaining rocky desertification information for karst zones; (2) The point-to-line BSI (bare soil index)-NDre1 (red edge index 1)feature space monitoring index of rocky desertification has the best applicability with R²=95.1% and root mean square error (RMSE)=0.872; (3) The feature space monitoring indexes that composed of red edge indices have a higher inversion accuracy, which indicates that the red edge indices are sensitive indicators of the rocky desertification condition and process;(4) The intensive and severe rocky desertification zones are mostly located in the southeast and central parts. The 3-dimensional or multidimensional feature space model will be constructed to improve the inversion accuracy of rocky desertification in further studies. The research results can provide methodological supports for revealing the rocky desertification evolution laws.

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.