Remote Sensing Image Characteristics and Typical Area Analysis of Taiyuan Xishan Ecological Restoration Area

Abstract

The Taiyuan Xishan Ecological Restoration Zone is located in the west of Taiyuan City and belongs to the Xishan Coalfield. Due to the resource development activity of coal mining, which is caused by coal gangue accumulation, surface vegetation degradation, bare surfaces, and other phenomena, it is most common in this area. These have an impact on the surface ecology; however, after ecological restoration, the surface ecology has been greatly improved. There are many extraction models of vegetation coverage based on pixel dichotomology combined with multispectral vegetation index, but we believe that the combination of visible light vegetation index to construct models is relatively unexplored. The main problem of how to use the RGB image data in order to quickly and accurately extract vegetation coverage information is still under investigation and needs researchers’ attention. In this paper, through selecting the vegetation coverage as the evaluation index of ecological restoration effect, a new RGB vegetation coverage CIVE calculation model is innovatively proposed to solve the above problem, and on the basis of this model, the vegetation cover change analysis is carried out in the Xishan ecological restoration area of Taiyuan. According to the analysis of vegetation coverage change, relevant paper data, and the characteristics of multiple historical remote sensing images, the ecological restoration area of Taiyuan Xishan is divided into six typical areas. Through empirical evaluation, we summarize and analyze these six typical areas, which can provide typical demonstration roles for other ecological restoration areas. Our findings suggest that the proposed CIVE model realizes the extraction of vegetation cover information and long-term series dynamic monitoring of vegetation coverage.

Analysis of Eco-Environmental Quality and Driving Forces in Opencast Coal Mining Area Based on GWANN Model: A Case Study in Shengli Coalfield, China

“…GWANN is a nonlinear GWR approach based on ANN, and it performs training in addition to obtaining neighborhood ratios, which makes it computationally more complex than an ANN model (Figure 4b) [26][27][28]. On the basis of maintaining the structure of the traditional ANN model, the GWANN closely combines the output neurons with the geospatial location to drive each weight factor (ω) to be a weight kernel function about the geospatial distance (d ij ) and bandwidth (b) [29] using Equation (4).…”

“…With the rapid development of the economy and society in China, the scale of mining activity in opencast coal mines is increasing year by year, which leads to the serious degradation of regional natural ecosystems and environmental quality and causes many ecoenvironmental problems, such as soil and water loss, land desertification, soil salinization, and changes in vegetation communities and characteristics, thus making the ecosystems in mining areas some of the most degraded ecosystems in the world [1][2][3][4][5][6][7]. In recent years, the retrieval of eco-environmental quality based on remote sensing technology has become the main means of evaluating regional eco-environmental quality [8].…”