Investigation of Vegetation Changes in Different Mining Areas in Liaoning Province, China, Using Multisource Remote Sensing Data

Abstract

Mining can provide necessary mineral resources for humans. However, mining activities may cause damage to the surrounding ecology and environment. Vegetation change analysis is a key tool for evaluating damage to ecology and the environment. Liaoning is one of the major mining provinces in China, with rich mineral resources and long-term, high-intensity mining activities. Taking Liaoning Province as an example, vegetation change in six mining areas was investigated using multisource remote sensing data to evaluate ecological and environmental changes. Based on MODIS NDVI series data from 2000 to 2019, change trends of vegetation were evaluated using linear regression. According to the results, there are large highly degraded vegetation areas in the Anshan, Benxi, and Yingkou mining areas, which indicates that mining activities have seriously damaged the vegetation in these areas. In contrast, there are considerable areas with improved vegetation in the Anshan, Fushun, and Fuxin mining areas, which indicates that ecological reclamation has played a positive role in these areas. Based on Sentinel-2A data, leaf chlorophyll content was inferred by using the vegetation index MERIS Terrestrial Chlorophyll Index (MTCI) after measurement of leaf spectra and chlorophyll content were carried out on the ground to validate the performance of MTCI. According to the results, the leaf chlorophyll content in the mines is generally lower than in adjacent areas in these mining areas with individual differences. In the Yingkou mining area, the chlorophyll content in adjacent areas is close to the magnesite mines, which means the spillover effect of environmental pollution in mines should be considerable. In the Anshan, Benxi, and Diaobingshan mining areas, the environmental stress on adjacent areas is slight. All in all, iron and magnesite open-pit mines should be monitored closely for vegetation destruction and stress due to the high intensity of mining activities and serious pollution. In contrast, the disturbance to vegetation is limited in resource-exhausted open-pit coal mines and underground coal mines. It is suggested that land reclamation should be enhanced to improve the vegetation in active open-pit mining areas, such as the Anshan, Benxi, and Yingkou mining areas. Additionally, environmental protection measures should be enhanced to relieve vegetation stress in the Yingkou mining area.

The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing

“… where NIR is the surface reflectance in the near-infrared band and R is the surface reflectance in the red band. The NDVI of the study area was divided into five grades to represent the different vegetation cover [ 13 , 34 ]: −1.0 ≤ NDVI < 0.35 (low-VC), 0.35 ≤ NDVI < 0.48 (slightly low-VC), 0.48 ≤ NDVI < 0.62 (medium-VC), 0.62 ≤ NDVI < 0.75 (slightly high-VC), 0.75 ≤ NDVI < 1.0 (high-VC).…”

“…These works provided insight into vegetation on monitoring the restoration effect [ 11 , 12 ]. Furthermore, Ma et al and Yang et al analyzed the variation trend of interannual vegetation cover in a mining area from the perspective of time [ 13 , 14 ]. The spatial pattern change of vegetation cover in a mining area was analyzed from the perspective of space by Mi et al and Dlamini et al [ 15 , 16 ].…”

The temporal variation of sediment connectivity from 1982 to 2020 in the Wei River basin, China

“…The C‐factor of the Revised Universal Soil Loss Equation (RUSLE) is widely used to quantify the effect of vegetation cover on soil loss; a higher C ‐factor indicates severe soil loss and the W ‐value can be determined using the C ‐factor. It can be calculated as follows (Cai et al, 2000): f is the vegetation cover, which can be calculated using the image dichotomy (Ma et al, 2001): …”

An Open-Source Platform for GIS Data Management and Analytics

“…Their ability to analyze and visualize agricultural environments and workflows has proven to be beneficial for the agricultural sector [24]. GIS technology is becoming an essential tool for combining different sources of data, such as data acquired by drones, airborne sensors, and satellites [25]. GIS tools are also playing an increasingly important role in precision agriculture, thus helping farmers to increase production, reduce costs, and manage their land more efficiently [26,27].…”