利用移动窗口遥感生态指数分析矿区生态环境

Analyzing the Ecological Environment of Mining Area by Using Moving Window Remote Sensing Ecological Index

  • 摘要: 矿山环境问题日益加剧,矿区生态环境监测与评价是矿区管理必不可少的内容。前人通过遥感生态指数(remote sensing ecology index, RSEI)对矿区生态环境进行评价得到了有效的结果,但计算遥感生态指数时没有考虑到在自然条件下生态环境的影响是区域性的,且矿区所处地理位置不同会对矿区及其周边的环境造成不同的效应。针对大面积地物类型复杂的研究区域,提出了基于遥感生态指数和移动窗口评价单元的改进生态指数——基于移动窗口的遥感生态指数(moving window-based remote sensing ecological index,MW-RSEI)。结果显示,研究区域MW-RSEI均值为0.522,绿度和湿度对环境起正效应,干度和热度起负效应,其中矿山地貌景观破坏区域生态环境等级差和等级较差的占整体矿山地貌景观破坏区域面积的85.40%。提出的MW-RSEI符合地理学第一定律,其结果与RSEI结果具有一致性,同时显示了更多的矿区周边生态环境的渐变信息,印证了生态环境中的耗散结构理论,可以为矿区生态环境监测与评价提供有效的依据。

     

    Abstract:
      Objectives  Mine environmental problems are intensifying, and the environmental monitoring and evaluation of the mining area is an indispensable part of mine management. Previous researches have obtained effective results by evaluating the mining ecological environment through the remote sensing ecological index (RSEI). However, the calculation does not take into account that the environmental influence is regional under natural conditions of RSEI, and the geo-graphical location of the mining area has different effects on its surrounding ecological environment. The main purpose of this research is to propose a new remote sensing ecological index that is more in line with the ecological response mechanism based on the characteristics of the ecological environment of the mining area.
      Methods  Aiming at the complex research area of large area feature type, this paper presents an improved ecological index named moving window-based remote sensing ecological index (MW-RSEI) which based on RSEI and moving window evaluation unit. MW-RSEI divides the ecological environment into four influencing factors: Greenness, wetness, dryness and heat. Each pixel in the study area is taken as the research object, and the extended window of the pixel is taken as the calculation range of each factor. The experiment assigns weight to the center pixel of each window through principal component analysis.
      Results  The MW-RSEI and RSEI models were used to calculate in the study area, the results show that the mean value of MW-RSEI and RSEI in the study area were 0.522 and 0.459, the greenness and wetness had positive effect on the environment, and the dryness and heat had negative effects. Based on the MW-RSEI model, mine geomorphology destroys the regional ecological environment grade is worst and poor accounted for 85.40% of the overall area. MW-RSEI in line with the first law of geography. Its results are consistent with RSEI, and it shows more gradient information of the ecological environment around the mining area, which confirms the theory of dissipative structure in the ecological environment.
      Conclusions  MW-RSEI refines the local characteristics of the ecological environment, and it can provide an effective basis for ecological environment monitoring and evaluation in mining areas.

     

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