融合主被动遥感的乌海矿区土地损伤测度

doi:10.11934/j.issn.1673-4831.2018.08.002

摘要/Abstract

摘要：

煤炭资源开发战略加速西移，迫使西北矿区土地损伤迅速加剧。传统的矿区土地监测手段难以实现全空间覆盖的土地损伤诊断，该研究以乌海矿区为研究对象，采用Landsat TM/OLI影像和Sentinel-1A影像，利用3S、合成孔径雷达干涉测量（InSAR）技术，通过提取植被损伤指数、地表形变指数和土地覆被类型等关键参数，以土地结构损伤函数与土地功能损伤函数为主体构建矿区土地损伤测度模型。结果表明：矿区土地损伤在空间分布上呈现以开采工作面为中心向四周辐射衰减特征，土地损伤值为4.5~8.0的高损伤区面积为42.36 km²，占总面积的2.68%；中损伤区面积为544.21 km²，占比为34.43%；非损伤区面积为994.02 km²，占比为62.89%。损伤测度结果有助于矿区环境修复精细化设计，为乌海矿区土地整治与修复提供决策参考。

关键词: 土地损伤, 采矿扰动, 测度模型, 3S, 乌海矿区

Abstract:

The exploitation of coal resources is moving toward the west China acceleratively, which aggravate the land damage in northwest mining area. Considering the traditional monitoring methods can hardly achieve the goal of overall diagnosis of land damage, Wuhai mining area was taken as study area and a model for measuring the land damage in mining area was proposed. The model mainly combined the functions of land structure damage and function damage. Landsat TM/OLI images, Sentinel-1A SAR images, 3S technology and InSAR technology are used to extract the model's critical parameters such as vegetation damage index, ground deformation index, land cover, etc. The results show that the spatial distribution of land damage is featured by attenuation around the mining working face. 42.36 km² of land has high land damage value between 4.5 and 8.0, accounting for 2.68% of the total area. 544.21 km² of land has moderate land damage value, accounting for 34.43%, while 994.02 km² of land is non-damage area, accounting for 62.89%. The measurement of land damage can refine the design of environmental restoration and provide reference for the land consolidation and restoration of Wuhai mining area.

Key words: land damage, mining disturbance, measure model, 3S, Wuhai Mining Area

中图分类号:

郭山川, 汤傲, 李效顺, 卞正富, 侯湖平, 倪衡. 融合主被动遥感的乌海矿区土地损伤测度[J]. 生态与农村环境学报, 2018, 34(8): 678-685.

GUO Shan-chuan, TANG Ao, LI Xiao-shun, BIAN Zheng-fu, HOU Hu-ping, NI Heng. Measurement of Land Damage Based on Active and Passive Remote Sensing in Wuhai Mining Area[J]. Journal of Ecology and Rural Environment, 2018, 34(8): 678-685.

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