Earth Observation Systems in Geology Mass Identification, Investigation and Inventory Mapping

Surface waterlogging disasters due to underground mining and geological status have caused the abandonment of fertile land, seriously damaged the ecological environment, and have influenced the sustainable development of coal resource-based cities, which has become a problem that some mining areas need to face. However, the traditional underwater terrain measurement method using sonar encompasses a time-consuming and labor-intensive process. Thus, an inversion method for obtaining the underwater spatial information of subsidence waterlogging in coal mining subsidence waterlogging areas is proposed, based on differential interferometric synthetic aperture radar (D-InSAR) and the probability integral prediction method. First, subsidence values are obtained in the marginal area of the subsidence basin using D-InSAR technology. Then, the subsidence prediction parameters of the probability integral method (PIM) are inverted by a genetic algorithm (GA) based on the subsidence values. Finally, the underwater spatial information of subsidence waterlogging is calculated on the basis of the prediction parameters. The subsidence waterlogging area in the Wugou coal mine was adopted as the study area, and the underwater spatial information of subsidence waterlogging was inverted by the proposed method. The results show that this method can effectively provide the underwater spatial information of subsidence waterlogging, including the maximum subsidence value, waterlogging volume, subsidence waterlogging area, and underwater terrain in the subsidence waterlogging area. Compared with field-measured data from the same period, the RMSE of water depth is 99 mm, and the relative error is 9.9%, which proves that this inversion method is accurate and can meet engineering precision requirements. Full article

To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research using the catalogs of rainfall and seismic landslides selected nine background factors those affect the occurrence of landslides through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslide susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area were 0.9997, 0.9822, and 0.9207. Both of the confusion matrix and the ROC curve indicated that the accuracy of the coupling model is high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area, and through the maps, it was found that the extremely high susceptibility area of seismic landslides is located at a higher elevation than rainfall landslides by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of evaluating landslide susceptibility were significantly different. As for a same background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types. Full article

This study explored the applicability of TRMM, TRMM nonlinear downscaling, and ANUSPLIN (ANU) interpolation of three different types of precipitation data to define regional-scale rainfall-triggered landslide thresholds. The spatial resolution of TRMM precipitation data was downscaled from 0.25° to 500 m by the downscaling model considering the relationship between humidity, NDVI, and numerous topographic factors and precipitation. The rainfall threshold was calculated using the rainfall intensity–duration threshold model. The calculation showed that TRMM downscaled precipitation data have better detection capability for extreme precipitation events than the other two, the TRMM downscaling threshold was better than the ANU interpolation, and the cumulative effective rainfall of TRMM downscaling was preferred as the macroscopic critical rainfall-triggered landslide threshold for the early warning of the Wudu. The predictive performance of the rainfall threshold of 50% was better than the other two (10% and 90%). When the probability of landslide occurrence was 50%, the TRMM downscaled threshold curve was given by $I_{50}=21.03\times D^{-1.004}$. The authors also analyzed the influence of factors such as topography landform and soil type on the rainfall threshold of landslides in the study area. The rainfall intensity of small undulating mountains was higher than that of medium and large undulating mountains, and the rainfall intensity of landslides peaks at high altitude mountains of 3500–5000 m. Full article

In order to identify giant ancient landslides more effectively and to quantify the risk of giant ancient landslides, this study takes a Lengqu River section located on the Qinghai–Tibet Plateau as an example and then uses the red relief image map (RRIM) method to enhance the digital elevation model (DEM) for topographic 2D visualization to identify giant ancient landslides. Then, the relationships between giant ancient landslides (GALs), resurgent GALs, the deposition of inactive GALs and the excess topography of hillslopes under 30° threshold are analyzed separately. A total of 54 GALs are identified at last by using the RRIM method; 77.75% of GALs are still located on excess topography, 68.38% of resurgent GALs occurred on excess topography, and 62.21% of the deposition of inactive GALs are on non-excess topography. The RRIM method provides a new way to identify giant ancient landslides. The excess topography provides an indication of the risk of new landslides through the destructive effect of GALs on the threshold hillslope, and the preliminary investigation of the quantitative relationship between the resurrection of GALs and excess topography also shows that there is a certain pattern between the resurrection of GALs and the excess topography under the natural state, so the excess topography has a certain indication of the generation of new landslides and secondary resurrection at the original GAL positions. Full article