Evaluation and Prediction of Ecological Restoration Effect of Beijing Wangping Coal Mine Based on Modified Remote Sensing Ecological Index

Abstract

As the construction of ecological civilization has become more and more important in recent years, ecological restoration and its effect assessment have also received increasing attention. Taking the Wangping coal mine in Beijing as an example, based on Landsat TM/OLI series remote sensing data, we chose five metrics, i.e., fraction vegetation coverage, humidity, heat, dryness, and black particulates, to construct the model for the modified remote sensing ecological index (MRSEI). It was also combined with the Hurst index to conduct dynamic monitoring, spatiotemporal analysis, and prediction studies of the ecological environment quality in the study area. The results showed that: (1) Compared with the RSEI, the first principal component of the MRSEI better integrates the information of each component indicator, has a better average correlation with each indicator, and better reflects the habitat condition in the study area. (2) The mean value of the MRSEI in the study area increased from 0.433 in 1990 to 0.722 in 2021, an increase of 40.03%. (3) From 1990 to 2001, the poor and fair MRSEI-grade areas in the study area were concentrated in the northeastern and southwestern parts of the area. After the ecological restoration project was carried out, the environmental quality of the Wangping coal mine improved year by year, and a small number of poor areas were concentrated in the northeastern border area. (4) The MRSEI of the Wangping coal mine predicted that the future ecological environment of the study area would show a general trend of continuous improvement, but a certain percentage of the area in the northeast had a weak antisustainability trend. The results of the study could provide a reference for ecological restoration planning, sustainable development, and the management of mining areas.

Evaluation of Chabuga Mountain mine after ecological restoration based on remote sensing information processing

“…The RSEI remote sensing ecological index in this paper is calculated by fusing the NDVI index, WET index, LST index and NDBSI index by principal component analysis method, which is an important method in calculating the RSEI remote sensing ecological index at present [6]. The index is entirely based on the remote sensing image values, and the load values of each index are completely determined objectively by the principal component analysis method, which has been applied in various scientific research works.…”