测绘通报 ›› 2020, Vol. 0 ›› Issue (11): 1-6.doi: 10.13474/j.cnki.11-2246.2020.0344

• 生态环境动态监测 •    下一篇

胜利矿区植被覆盖度时序变化的空间异质性监测

王科雯1, 李晶1, 王瑞国2,3, 付晓4   

  1. 1. 中国矿业大学(北京)地球科学与测绘工程学院, 北京 100083;
    2. 神华信息技术有限公司, 北京 100080;
    3. 神华地质勘查有限责任公司, 北京 100085;
    4. 中国科学院生态环境研究中心城市与区域生态国家重点实验室, 北京 100084
  • 收稿日期:2020-01-08 发布日期:2020-11-30
  • 作者简介:王科雯(1997-),女,硕士,主要研究方向为生态遥感、土地复垦与生态重建。E-mail:wangkewen0124@163.com
  • 基金资助:
    “十三五”国家重点研发计划(016YFC0501101-4);国家自然科学基金(41501564)

Spatial heterogeneity monitoring of temporal variation of vegetation coverage in Shengli mining area

WANG Kewen1, LI Jing1, WANG Ruiguo2,3, FU Xiao4   

  1. 1. College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China;
    2. Shenhua Information Technology Co., Ltd., Beijing 100080, China;
    3. Shenhua Geological Exploration Co., Ltd., Beijing 100085, China;
    4. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100084, China
  • Received:2020-01-08 Published:2020-11-30

摘要: 通过对胜利矿区的地理位置、气候条件等背景的分析,本文为实现获取时序性植被覆盖度的空间异质性的目的,使用ENVI、GIS、Matlab等软件,基于胜利矿区1985—2017年的Landsat TM/ETM+/OLI遥感数据计算NDVI,利用像元二分模型计算植被覆盖度,得到研究区植被覆盖度均值的时序变化情况。采用转移矩阵法和Sen+Mann-Kendall法对研究区域内不同等级的植被覆盖转移情况及变化趋势情况进行分析。研究表明:胜利矿区植被覆盖度均值波动较大,呈轻微下降趋势。在监测时段内68.36%的高植被覆盖区域植被发生了退化,只有3.2%左右的极低植被覆盖区域得到了良好的改善。此外,研究区植被覆盖度受到结构性因子和随机性因子的影响,空间异质性明显,灌溉区由于人为干涉,植被生长良好,极低植被覆盖面积维持在3%以下,植被覆盖显著下降区域主要集中在露天采坑、排土场等矿业景观区。

关键词: 植被覆盖度, 空间异质性, 像元二分模型, Sen+Mann-Kendall法

Abstract: By analyzing the geographical location and climatic conditions of the Shengli mining area, in order to get the spatial heterogeneity of the gain scheduling vegetation coverage, this paper uses ENVI, GIS, Matlab and other softwares, based on the Landsat TM/ETM+/OLI remote sensing data of the Shengli mining area from 1985 to 2017 to calculate NDVI, and the binary model is used to calculate the vegetation coverage to obtain the temporal changes of the average vegetation coverage in the study area. The transfer matrix method and Sen+Mann-Kendall method are used to analyze the transfer status and change trend of different levels of vegetation coverage in the study area. Conclusion: The average vegetation coverage in Shengli mining area fluctuates greatly and shows a slight downward trend. During the monitoring period, 68.36% of the areas with high vegetation coverage have degraded, and only about 3.2% of the areas with very low vegetation coverage have been improved. In addition, the vegetation coverage in the study area is affected by structural factors and randomness factors. The spatial heterogeneity is obvious. The vegetation in the irrigation area grows well due to human interference. The extremely low vegetation coverage area is maintained below 3%, and the areas that vegetation coverage decreases significantly is mainly concentrated in mining landscape areas such as open pits and dumps.

Key words: vegetation coverage, spatial heterogeneity, pixel binary model, Sen+Mann-Kendall method

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