High-Resolution Mining-Induced Geo-Hazard Mapping Using Random Forest: A Case Study of Liaojiaping Orefield, Central China

Abstract

Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, using Gaofen-1 panchromatic and multispectral (PMS) sensor data and Random Forest (RF) non-parametric ensemble classifier, a seven-class land cover map was generated for the study area with an overall accuracy (OA) and Kappa coefficient (KC) of 99.69% and 98.37%, respectively. Next, several environmental drivers including land cover, topography (aspect and slope), lithology, distance from fault, elevation difference between surface and underground excavation, and the difference of spectral information from PMS multispectral data of different years were integrated as predictors to construct an RF-based MGM model. The constructed model showed an excellent prediction performance, with an OA of 98.53%, KC of 97.06%, and AUC of 0.998, and the 85.60% of the observed geo-disaster that have occurred in the predicted high susceptibility class (encompassing 2.82% of the study area). The results suggested that the changes in environmental factors in the high susceptibility areas can be used as indicators for monitoring and early-warning of the geo-disaster occurrence.

Comprehensive Evaluation of the Eco-Geological Environment in the Concentrated Mining Area of Mineral Resources

“…The subjective empowerment method uses the Analytic Network Process (ANP), which is a further extension of the Analytic Hierarchy Process (AHP) [43]. The traditional hierarchical analysis method is widely used, but in the eco-geological environment system, the indicators are not completely independent of each other [44]. Therefore, compared with the hierarchical analysis method, the network analysis method more reasonably considers the interaction between factors or adjacent levels.…”

“…Geological hazards are representative factors that are directly related to the EEQ of a region [44]. Only geological hazards caused by mining were counted in this research, which included 37 landslides, 3 landslides, 4 debris flows, and 12 ground collapses.…”

Mapping the suitable areas for future urbanization in Jiangxi, China

“…And there are four deep crustal fault zones (e.g., > 400 km in length in the study area), 9 deep fault zones (e.g., 100-400 km) and 38 big fault belts (e.g., 10-100 km). The distribution and name (serial number) of major faults in the study area are shown in Figure 1 (Right)：Deep Crustal Fault Zones include Yifeng-Jingdezhen(1), Pingxiang-Guangfeng( 5), Ganjiang( 6) and Baitu-Dexing (11); Deep Fault Zones comprehend Qibaoshan-Fuchun(2), Qingyun-Xiangtun(3), Shangrao-Xiaoshan(4), Suichuan-Linchuan(7), Dayu-Nancheng(8), Yingtan-Anyuan(9), Huichang-Yunxiao(10), Yongping-Xunwu (12), and Ezhou-Jiujiang(13).…”

Suitability Assessment Method of Red Tourism Development Using Geospatial and Social Humanity Data: A Case Study of Ruijin City, East China

“…The samples for constructing and testing classifier were obtained from Gaofen satellite images, Google Earth high resolution imagery (2013 and 2017), and a field survey (2021). Nine spectral indices, including soil-adjusted atmospherically-resistant vegetation index (SARVI) [69], normalized difference water index (NDWI) [70], normalized difference buildup soil index (NDBSI) [71], brightness-greenness-wetness from Tasseled Cap Transform (TCT) [72], and hue-saturation-value from HSV transformation [53] were used as classifier feature variables. Table 2 shows the parameters of the RF classifier and corresponding validation metrics for three investigation years (2013, 2017, and 2021).…”

“…Remote sensing (RS) image classification is a common tool for land use survey, which has become more robust with the introduction of machine learning algorithms [41][42][43][44][45][46]. Supervised machine learning algorithms have obtained promising results in mineral prospectivity mapping [47][48][49][50], geo-hazard mapping and geo-risk assessment [51][52][53][54], biomass estimation [55][56][57][58], and dust source susceptibility mapping [59,60].…”