Remote-Sensing Evaluation and Temporal and Spatial Change Detection of Ecological Environment Quality in Coal-Mining Areas

Abstract

The large-scale development and utilization of coal resources have brought great challenges to the ecological environment of coal-mining areas. Therefore, this paper has used scientific and effective methods to monitor and evaluate whether changes in ecological environment quality in coal-mining areas are helpful to alleviate the contradiction between human and nature and realize the sustainable development of such coal-mining areas. Firstly, in order to quantify the degree of coal dust pollution in coal-mining areas, an index-based coal dust index (ICDI) is proposed. Secondly, based on the pressure-state-response (PSR) framework, a new coal-mine ecological index (CMEI) was established by using the principal component analysis (PCA) method. Finally, the coal-mine ecological index (CMEI) was used to evaluate and detect the temporal and spatial changes of the ecological environment quality of the Ningwu Coalfield from 1987 to 2021. The research shows that ICDI has a strong ability to extract coal dust with an overall accuracy of over 96% and a Kappa coefficient of over 0.9. As a normalized difference index, ICDI can better quantify the pollution degree of coal dust. The effectiveness of CMEI was evaluated by four methods: sample image-based, classification-based, correlation-based, and distance-based. From 1987 to 2021, the ecological environment quality of Ningwu Coalfield was improved, and the mean of CMEI increased by 0.1189. The percentages of improvement and degradation of ecological environment quality were 71.85% and 27.01%, respectively. The areas with obvious degradation were mainly concentrated in coal-mining areas and built-up areas. The ecological environment quality of Pingshuo Coal Mine, Shuonan Coal Mine, Xuangang Coal Mine, and Lanxian Coal Mine also showed improvement. The results of Moran’s Index show that CMEI has a strong positive spatial correlation, and its spatial distribution is clustered rather than random. Coal-mining areas and built-up areas showed low–low clustering (LL), while other areas showed high–high clustering (HH). The utilization and popularization of CMEI provides an important reference for decision makers to formulate ecological protection policies and implement regional coordinated development strategies.

Evaluation and dynamic monitoring of ecological environment quality in mining area based on improved CRSEI index model

“…For example, Nie et al [ 16 ] evaluated the ecological environment quality of Yangquan coal mine based on the RSEI index after terrain correction, and the results showed that the terrain effect of the optimized RSEI index was significantly reduced and the ecological environment was improved. It also established the coal mine Ecology Index (CMEI) based on PSR framework [ 17 ]. Xu et al [ 18 ] established ecological index (RE-RSEI) applicable to rare earth mining area based on Landsat images, and achieved positive results.…”

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

“…An appropriate, reasonable, and representative index system is the key to objectively reflecting EEQ in the study area [28]. Based on the concept of eco-geological environment system and research progress [29][30][31], combining the influence of internal system factors of geological and ecological conditions on the eco-geological environment and the influence of external system factors of mining activities and social economy the on eco-geological environment, and through communication with experts, 14 evaluation indexes were finally summarized, and the principles of selecting indexes are shown in Table 1.…”

Ecological environment quality assessment and spatial autocorrelation of northern Shaanxi mining area in China based-on improved remote sensing ecological index

“…Secondly, based on multi-temporal remote sensing images, the changes of ecological environment quality in mining areas can be quantitatively analyzed through image classification and image change detection. Secondly, based on multi-temporal remote sensing images, the changes of ecological environment quality in mining areas can be quantitatively analyzed through image classification and image change detection (Han et al, 2021;Bai et al, 2022;Liu et al, 2022;Nie et al, 2022). As early as 2006, China's Ministry of Ecology and Environment promulgated the Technical Specifications for the Assessment of Ecological Environment Status, which is used to evaluate the ecological environment status and change trend in counties, provinces and ecological regions (China MoEaEotPsRo, 2015).…”