Abstract
The continuous development of mineral resources is increasingly damaging the ecological environment, so it is of great significance to ecological restoration and dynamic monitoring of the mining area. In this paper, dynamic monitoring and evaluation method of ecological restoration in the mining area are proposed, which integrates GNSS + RS (Global Navigation Satellite System + Remote Sensing) technology. According to the Precipitable Water Vapor (PWV) retrieved by GNSS and NDVI (Normalized Vegetation Index) can monitor the ecological environment and introduce machine learning to improve the accuracy of the model. The dynamic assessment of ecological restoration was carried out by using temperature, rainfall, NPP (Net Primary Productivity), NDVI and PWV. The results show that: (1) the modeling effect of machine learning is better than that of the least square regression. (2) The comprehensive ecological evaluation index proposed can better reflect the ecological situation of the mining area. Therefore, the environmental monitoring and assessment of mining area based on GNNS + RS technology proposed in this paper have important reference significance.
Highlights
As non-renewable resources, mineral resources are the material guarantee for the survival and development of human society [1]
Accurate dynamic monitoring and accurate assessment of ecological restoration in mining area is of great significance to further implement ecological restoration planning, prevent and avoid secondary damage to ecological environment caused by unreasonable planning, and promote sustainable development of mining area [5]
The atmospheric precipitable water can be retrieved through the foundation GNSS mainly consists of two steps: firstly, ZHD (Zenith Hydrostatic Delay)[7] is calculated by the Saastamoinen model, ZWD (Zenith Wet Delay) is obtained by ZTD (Tm), and ZWD is converted into Precipitable Water Vapor (PWV) by the conversion parameters obtained by the weighted average atmospheric temperature Tm
Summary
As non-renewable resources, mineral resources are the material guarantee for the survival and development of human society [1]. The ecological restoration and reconstruction after mining is an important guarantee for the sustainable development of mining area, but how to dynamically monitor and evaluate the ecological environment in mining area, determine the degree of restoration and reconstruction and give the specific quantitative index is a difficult problem that needs to be solved urgently at home and abroad [3]. Accurate dynamic monitoring and accurate assessment of ecological restoration in mining area is of great significance to further implement ecological restoration planning, prevent and avoid secondary damage to ecological environment caused by unreasonable planning, and promote sustainable development of mining area [5]. The ecological environment was monitored by the GNSS data indirectly through the regression model, and the temporal and spatial characteristics of NPP, temperature, rainfall and NDVI were analyzed. BP (Back Propagation) neural network and SVM (Support Vector Machine) support vector machine are used to model and analyze the multi-ecological factors and NDVI, , the trend of vegetation coverage and comprehensive ecological index in Shanxi Province and cities is analyzed
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