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.
It is important to analyze the trend in land use changes and assess the suitability of resource development for protecting natural resources, developing ecological industries, and land use planning issues. Ruijin City is located in South Jiangxi and has abundant resources for red tourism development. By analyzing the landscape changes in land use and the spatial distribution characteristics of local red culture resources, a supervised machine learning-based prediction model was constructed to quantitatively assess the suitability of red tourism development in a geographic information system (GIS) and the R language environment using geographical, economical, and human-related datasets. The results revealed that: (i) the increasing of human activities and economic vitality provide a beneficial social environment for the development of tourism resources; (ii) highly concentrated red resources, or those with special significance, are conducive to developing red tourism resources; (iii) preferentially, central–eastern Ruijin was followed by the extension areas to peripheral towns, which are potentially suitable areas for the development of red scenic spots. Generally, the findings of this study were consistent with the conventional cognitions and lessons on tourism development, and the constructed evaluation system is expected to be promoted to similar research.
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