Short-term high-resolution quantitative precipitation forecasting (QPF) is very important for flash-flood warning, navigation safety, and other hydrological applications. This paper proposes a subpixel-based QPF algorithm using a pyramid Lucas–Kanade optical flow technique (SPLK) for short-time rainfall forecast. The SPLK tracks the storm on the subpixel level by using the optical flow technique and then extrapolates the precipitation using a linear method through redistribution and interpolation. The SPLK compares with object-based and pixel-based nowcasting algorithms using eight thunderstorm events to assess its performance. The results suggest that the SPLK can perform better nowcasting of precipitation than the object-based and pixel-based algorithms with higher adequacy in tracking and predicting severe storms in 0–2 h lead-time forecasting.

Although clay mineral content in ion-absorbed rare earth ores is crucial for migrating and releasing rare earth elements, the formation, distribution, and migration of clay minerals in supergene rare earth ores have not been fully understood. Therefore, this study analyzes the characteristics of clay mineral type and content, soil particle size, pH value, leaching solution concentration, and leaching rate. This analysis was performed using different methods, such as regional rare earth mine soil surveys, in situ leaching profile monitoring, and indoor simulated leaching. The results showed that the grain size and volume curve of rare earth ore have unimodal and bimodal shapes, respectively. X-ray diffraction showed the differences in clay mineral types formed by different weathered bedrocks. The principal clay minerals were kaolinite, illite, chlorite, and vermiculite, with their relative abundance varying with parent rock lithology (granite and low-grade metamorphic rocks). In the Ganxian granite weathering profile, the kaolinite content increased from top to bottom. The decomposition of feldspar minerals to kaolinite was enhanced with an increase in the SiO₂ content during weathering. The in situ leaching profile analysis showed that the kaolinite content increased initially and then decreased, whereas the illite/mica content exhibited the opposite trend. Under stable leaching solution concentration and leaching rate, clay mineral formation is favored by lower pH. Low pH, low leaching rate, and highly-concentrated leaching solution (12 wt%) resulted in a slow increase in kaolinite content in the upper part of the profile (30 cm). A lower concentration of the leaching solution (4 wt%) resulted in rapid enrichment of kaolinite after 15 days. Low pH, leaching solution concentration, and leaching rate promoted the formation of distinct kaolinite horizons. We suggest that by disregarding other control factors, rare earth recovery of over 90% can be achieved through leach mining with solutions of 8 wt% and a pH of 5 at a leaching rate of 5 mL/min.

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.

Sichuan Province, China, is a typical ecologically fragile area that is sensitive to global climate change. Studies regarding the spatial-temporal variations and driving factors of FVC (Fractional Vegetation Coverage) in Sichuan Province’s vegetation ecosystem are of important theoretical and practical significance for revealing the relationship between global climate change and vegetation ecosystems. These studies are also important theoretical and practical significance for the evaluation of environmental quality and service function adjustment of terrestrial ecosystems. In existing studies, there is a lack of detailed depictions of the FVC response to climatic factors in the context of different vegetation types and different landform features in Sichuan Province. In this study, the spatial-temporal patterns and change trends for the FVC of the growing seasons during the 2000–2017 period for Sichuan Province were analysed based on the FVCs that were inversely determined from MODIS (MODerate-resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) remote sensing data, and they were combined with air temperature, relative humidity and precipitation data. Moreover, the GRA (Grey Relational Analysis) method was used to study the response of the FVC to climate changes. Based on the results of the GRA, the zoning of climatic factors as driving forces for the FVC was performed, and the differences in the spatial-temporal characteristics of the FVC response to different climatic factors were presented in quantitative form. Here, we found that the vegetation coverage in Sichuan province showed a slight degradation trend, and that the medium to low altitude woody plants were significantly degraded. The proportion of regions in which relative humidity (17.3%) and precipitation (17.4) were strong drivers for FVC changes, was much greater than the regions in which air temperature (1.8%) and other co-drivers were the force.