Landslides seriously threaten human life and property. The rapid and accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention and mitigation. Traditional landslide susceptibility evaluation methods have disadvantages in terms of factor classification and subjective weight determination. Based on this, this paper uses a random forest model built using Python language to predict the landslide susceptibility of Muli County in western Sichuan and outputs the factor weight and model accuracy. The results show that (1) the three most important factors are elevation, distance from the road, and average annual rainfall, and the sum of their weights is 67.54%; (2) the model’s performance is good, with ACC = 99.43%, precision = 99.3%, recall = 99.48%, and F1 = 99.39%; (3) the landslide development and susceptibility zoning factors are basically the same. Therefore, this model can effectively and accurately evaluate regional landslide susceptibility. However, there are some limitations: (1) the landslide information statistical table is incomplete; (2) there are demanding requirements in terms of training concentration relating to the definition of landslide and non-landslide point sets, and the landslide range should be accurately delineated according to field surveys.

The purpose of this study was to construct a geological hazard susceptibility evaluation and analysis model using three types of machine learning models, namely, random forest (RF), support vector machine (SVM), and naive Bayes (NB), and to evaluate the susceptibility to landslides, using the Puge section of the Zemu River valley in the Liangshan Yi Autonomous Prefecture as the study area. First, 89 shallow landslide and debris flow locations were recognized through field surveys and remote sensing interpretation. A total of eight hazard-causing factors, namely, slope, aspect, rock group, land cover, distance to road, distance to river, distance to fault, and normalized difference vegetation index (NDVI), were selected to evaluate the spatial relationship with landslide occurrence. As a result of the analysis, the results of the weighting of the hazard-causing factors indicate that the two elements of rock group and distance to river contribute most to the creation of geological hazards. After comparing all the indices of the three models, the random forest model had a higher correct area under the ROC curve (AUC) value of 0.87, root mean squared error (RMSE) of 0.118, and mean absolute error (MAE) of 0.045. The SVM model had the highest sensitivity to geological hazards. The results of geological hazard prediction susceptibility analysis matched the actual situation in the study area, and the prediction effects were good. The results of the hazard susceptibility assessment of the three models are able to provide support and help for the prevention and control of geological hazards in the same type of areas.

Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoigê Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R² = 0.64, RMSE = 43.52 g/m², RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R² = 0.69, RMSE = 44.07 g/m², RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning.

Landslides are among the most frequent geological hazards, often resulting in casualties and economic losses, particularly in alpine valley areas characterized by complex topography and dense vegetation. Landslides in these regions are distinguished by their high altitude, concealment, and sudden onset, which render traditional monitoring methods inefficient. This study proposes a landslide monitoring method for complex environments that leverages multi-source remote sensing data, incorporating the radiative transfer model and Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology. The proposed method was implemented to monitor the instability of the Baige landslide in Tibet, China. The results show that the vegetation Canopy Water Content (CWC) estimated using the radiative transfer model indirectly reflects landslide susceptibility. Specifically, excessive soil moisture from rainfall reduces oxygen in plant roots, affecting growth and lowering canopy water content. The region with lower Canopy Water Content (CWC < 0.04) exhibited an increasing trend in the number of pixels, rising from 271 to 549 before the landslide event, indicating poorer vegetation conditions in the area. Additionally, the SBAS-InSAR technique was utilized to extract surface displacement, achieving a maximum displacement of 112 mm during the monitoring period. Ultimately, the spatial changes of the two monitoring signals exhibited a high consistency. This study enhances the reliability of landslide displacement monitoring in complex environments and provides substantial scientific support for future large-scale monitoring efforts.