Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China.
Int J Environ Res Public Health. 2020 Mar 12;17(6):1846. doi: 10.3390/ijerph17061846.
Based on the results of an extensive literature research, we summarize the research progress of remote sensing monitoring in terms of identifying mining area boundaries and monitoring land use or land cover changes of mining areas. We also analyze the application of remote sensing in monitoring the biodiversity, landscape structure, vegetation change, soil environment, surface runoff conditions, and the atmospheric environment in mining areas and predict the prospects of remote sensing in monitoring the ecological environment in mining areas. Based on the results, the accurate classification of land use or land cover and the accurate extraction of environmental factors are the basis for remote sensing monitoring of the ecological environment in mining areas. In terms of the extraction of ecological factors, vegetation extraction is relatively advanced in contrast to the extraction of animal and microbial data. For the monitoring of environmental conditions of mining areas, sophisticated methods are available to identify pollution levels of vegetation and to accurately monitor soil quality. However, the methods for water and air pollution monitoring in mining areas still need to be improved. These limitations considerably impede the application of remote sensing monitoring in mining areas. The solving of these problems depends on the progress of multi-source remote sensing data and stereoscopic monitoring techniques.
基于广泛的文献研究结果,我们总结了遥感监测在识别矿区边界和监测矿区土地利用/土地覆盖变化方面的研究进展。我们还分析了遥感在监测矿区生物多样性、景观结构、植被变化、土壤环境、地表径流条件和大气环境方面的应用,并预测了遥感在监测矿区生态环境方面的前景。基于这些结果,准确的土地利用/土地覆盖分类和环境因素的准确提取是遥感监测矿区生态环境的基础。在生态因素的提取方面,与动物和微生物数据的提取相比,植被的提取相对先进。对于矿区环境条件的监测,已经有了识别植被污染水平和准确监测土壤质量的复杂方法。然而,矿区水和空气污染监测的方法仍有待改进。这些限制极大地阻碍了遥感监测在矿区的应用。这些问题的解决依赖于多源遥感数据和立体监测技术的进展。