A Method for Identifying the Spatial Range of Mining Disturbance Based on Contribution Quantification and Significance Test

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

Understanding the synergistic effect between topography and vegetation in the underground coal mine is of great significance for the ecological restoration and sustainable development of mining areas. This paper took advantage of unmanned aerial vehicle (UAV) remote sensing to obtain high-precision topographic factors (i.e., digital elevation model (DEM), slope, and aspect) in the Shangwan Coal Mine. Then, a normalized difference vegetation index (NDVI) was calculated utilizing Landsat images from 2017 to 2021, and the NDVI with the same spatial resolution as the slope and aspect was acquired by down-sampling. Finally, the synergistic effect of topography and vegetation in the underground mining area was revealed by dividing the topography obtained using high-precision data into 21 types. The results show that: (1) the vegetation cover was dominated by "slightly low-VC", "medium-VC", and "slightly high-VC" in the study area, and there was a positive correlation between the slope and NDVI when the slope was more than 5°. (2) When the slope was slight, the aspect had less influence on the vegetation growth. When the slope was larger, the influence of the aspect increased in the study area. (3) "Rapidly steep-semi-sunny slope" was the most suitable combination for the vegetation growth in the study area. This paper revealed the relationship between the topography and vegetation. In addition, it provided a scientific and effective foundation for decision-making of ecological restoration in the underground coal mine.

Unmixing the coupling influence from driving factors on vegetation changes considering spatio-temporal heterogeneity in mining areas: a case study in Xilinhot, Inner Mongolia, China

Considering the spatio-temporal heterogeneity, this study resolved the coupling influence of a variety of driving factors on vegetation changes in mining areas and discovered the influencing characteristics of the respective driving factors, especially mining activities. First, the spatio-temporal characteristics of FVC (fractional vegetation cover) variation were analyzed in the Sheng-Li mining area. Second, the quantitative relationships among the natural factors (temperature, precipitation, and elevation), artificial factors (mining activities, urban activities), and FVC were constructed by GTWR (geographically and temporally weighted regression) to quantify the contribution of each factor to the change in FVC. Third, the influencing characteristics of the respective driving factors, especially mining activities, were analyzed and summarized. The results show that (1) the FVC change was mainly influenced by natural factors in the areas far from mines and towns and artificial factors in the areas close to mines and towns. (2) The contribution of mining activities to vegetation change (C-Mine) was spatially characterized by two features: (a) distance attenuation characteristics: C-Mine showed logarithmic decrement with distance; (b) directional heterogeneity: C-Mine varied significantly in different directions. In particular, there was a high C-Mine area located near multiple mining areas, and the range of this area shifted to include the mine with more production over time. Overall, unmixing the coupling influence from driving factors with spatio-temporal heterogeneity and achieving a quantitative description of the influencing characteristics in mining areas were the main contributions of this study. The quantification methods and results in this paper provide important support for decision-making on ecological protection and restoration in mining areas.

Monitoring of Vegetation Disturbance and Restoration at the Dumping Sites of the Baorixile Open-Pit Mine Based on the LandTrendr Algorithm

Overstocked dumping sites associated with open-pit coal mining occupy original vegetation areas and cause damage to the environment. The monitoring of vegetation disturbance and restoration at dumping sites is important for the accurate planning of ecological restoration in mining areas. This paper aimed to monitor and assess vegetation disturbance and restoration in the dumping sites of the Baorixile open-pit mine using the LandTrendr algorithm and remote sensing images. Firstly, based on the temporal datasets of Landsat from 1990 to 2021, the boundaries of the dumping sites in the Baorixile open-pit mine in Hulunbuir city were extracted. Secondly, the LandTrendr algorithm was used to identify the initial time and duration of vegetation disturbance and restoration, while the Normalized Difference Vegetation Index (NDVI) was used as the input parameter for the LandTrendr algorithm. Thirdly, the vegetation restoration effect at the dumping sites was monitored and analyzed from both temporal and spatial perspectives. The results showed that the dumping sites of the Baorixile open-pit mine were disturbed sharply by the mining activities. The North dumping site, the South dumping site, and the East dumping site (hereinafter referred to as the North site, the South site, and the East site) were established in 1999, 2006, and 2010, respectively. The restored areas were mainly concentrated in the South site, the East site, and the northwest of the North site. The average restoration intensity in the North site, South site, and East site was 0.515, 0.489, and 0.451, respectively, and the average disturbance intensity was 0.371, 0.398, and 0.320, respectively. The average restoration intensity in the three dumping sites was greater than the average disturbance intensity. This study demonstrates that the combination of temporal remote sensing images and the LandTrendr algorithm can follow the vegetation restoration process of an open-pit mine clearly and can be used to monitor the progress and quality of ecological restoration projects such as vegetation restoration in mining areas. It provides important data and support for accurate ecological restoration in mining areas.