A hypsometric integral (HI) is a topographical index with important geomorphological meaning. It can both describe the development state of the watershed and reflect the strength of activity in the geological structure. Based on this index, the integral curve method was used to analyze the HI characteristics of watersheds in the region west of the Lvliang Mountains on the Loess Plateau of Shanxi Province, China, in combination with 30-m resolution Shuttle Radar Topography Mission data. In order to accurately characterize erosion development within the watershed, the HI characteristics of each topographic feature object in the watershed were comprehensively analyzed. The results of HI scale effects show that HI was not strongly dependent on the spatial resolution of Digital Elevation Model (DEM) data, but had some scale dependence on the stable area of the watershed. The spatial distribution of the HI shows that the HI values of the watersheds in the southern loess residual tableland were the largest, and the degree of erosion was the weakest; the HI values of the watersheds in the north-central part of the loess hilly-gully region were relatively small, and the degree of erosion was stronger. This result is consistent with the results of the spatial distribution of sediment yield in literatures. The reasons for the spatial distribution of the HI were analyzed in terms of geological formations and climatic environments. The results show that for the study area, tectonic activity, formation lithology, rainfall, and agrotype were not the main factors affecting the HI values. The structure of the paleotopography and erosion cutting due to the uplift of the Lvliang Mountains and later water erosion controlled the spatial distribution state of HI. Thus, for the study area, the HI value cannot reflect the strength of tectonic activity. The combination of topographic feature object method and the HI index further quantified the erosion development state of the watershed.

Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development.

The 2014 Mw 6.2 Ludian earthquake exhibited a structurally complex source rupture process and an unusual spatial distribution pattern of co-seismic landslides. In this study, we constructed a spatial database consisting of 1470 co-seismic landslides, each exceeding 500 m². These landslides covered a total area of 8.43 km² and were identified through a comprehensive interpretation of high-resolution satellite images taken before and after the earthquake. It is noteworthy that the co-seismic landslides do not exhibit a linear concentration along the seismogenic fault; instead, they predominantly extend along major river systems with an NE–SW trend. Moreover, the southwest-facing slopes have the highest landslide area ratio of 1.41. To evaluate the susceptibility of the Ludian earthquake-triggered landslides, we performed a random forest model that considered topographic factors (elevation, slope, aspect, distance to rivers), geological factors (lithology), and seismic factors (ground motion parameters, epicentral distance, distance to the seismogenic fault). Our analysis revealed that the distance to rivers and elevation were the primary factors influencing the spatial distribution of the Ludian earthquake-triggered landslides. When we considered the directional variation in ground motion parameters, the AUC of the model slightly decreased. However, incorporating this variation led to a significant reduction in the proportion of areas classified as “high” and “very high” landslide susceptibility. Moreover, SED^d emerged as the most effective ground motion parameter for interpreting the distribution of the co-seismic landslides when compared to PGA^d, PGV^d, and Ia^d.