Mining and Restoration Monitoring of Rare Earth Element (REE) Exploitation by New Remote Sensing Indicators in Southern Jiangxi, China

Abstract

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km2 in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area.

Assessment of the Effectiveness of Sand-Control and Desertification in the Mu Us Desert, China

“…The dryland-tailored vegetation index, GDVI, is shown in Equation (1). When n = 1, GDVI = NDVI; when n = 2, it is suitable for characterizing dryland biomes including shrubland, woodland and forest; when n = 3, it can be used for monitoring the degradation and desertification in sparsely vegetated area [22,23]. Compared with NDVI and other vegetation indices, GDVI has higher sensitivity and dynamic range, and is able to identify more effectively subtle differences in vegetation greenness in low vegetated areas [22].…”

“…However, it is difficult to discern subtle changes in vegetation when applied to desert areas with sparse vegetation. Recently, Wu (2014) proposed the generalized difference vegetation index (GDVI) for arid areas [22], and Xie et al (2020) developed a set of monitoring and restoration assessment indicators (MRAIs) for assessing the mining impacts and restoration effectiveness [23]. Since these two indicators were developed for sparse vegetation areas, both of them are promising for land degradation and desertification study.…”

Multi-scenario LUCC modeling based on a new LMCM model: A case study of Hefei, China

“…Considering the actual land cover characteristics of Hefei, three types of vegetation indices were selected, namely NDVI, EVI and GDVI (n = 2) (Wu, 2014;Xie et al, 2020), the formulae for the three types of vegetation index are shown as follows:…”

Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

“…Sensors 2022, 22,1948 2 of 22 Also, Felipe de Lucia Lobo et al graphed the landscape of a mining area in Amazon, Brazil, based on multispectral data obtained from supervised classification with a kappa coefficient of 0.7 [8]. Using Landsat multispectral data, Lifeng Xie et al extracted multiple ecological index and established mining and restoration assessment indicators (MRAIs), which are applicable in data collection and supervision of restoration in mining areas of southern JiangXi province, China [9]. Li Hengkai et al applied Sentinel-2 images to research the devastation and restoration of rare earths regions [10].…”