首页|基于可见光影像的木里矿区排土场植被覆盖度提取研究

基于可见光影像的木里矿区排土场植被覆盖度提取研究

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为了快速准确提取木里矿区排土场的植被覆盖度,基于无人机可见光影像,选择过绿指数、过绿减过红指数、可见光波段差异植被指数和归一化绿红差异指数,将植被指数时序图交点法和样本统计法相结合进行阈值分割,从而提取植被覆盖度,并对提取结果的精度进行验证.结果表明:可见光波段差异植被指数的提取精度最高,达到95.83%,Kappa系数为0.92.由此可见,利用可见光波段差异植被指数,将植被指数时序图交点法和样本统计法相结合的方法适用于提取木里矿区排土场的植被覆盖度且精度较高.研究结果可为高寒矿区排土场植被覆盖度的精确提取提供一定的参考.
Research on the fractional vegetation coverage of Muli mining dump based on visible light images
Based on the UAV visible light images,excess green(EXG),excess green-excess red(EXG-EXR),visible-band difference vegetation index(VDVI)and normalized green-red diffe-rence index(NGRDI)are adopted to quickly and accurately extract the fractional vegetation coverage(FVC)of Muli mining dump.The threshold segmentation is carried out by combining vegetation in-dex time series intersection method and sample statistical method so as to extract FVC.The accuracy of the extraction results is verified.The results show that the extraction accuracy of VDVI is the high-est,reaching 95.83%,with a Kappa coefficient of 0.92.Therefore,it can be seen that by adopting VDVI,the combination of vegetation index time series intersection method and sample statistical method is suitable for extracting FVC of Muli mining dump with high accuracy.The research results can provide certain reference for the accurate extraction of FVC in the vegetation reconstruction area of high-altitude mining dump.

visible light imageMuli mining dumpfractional vegetation coverage(FVC)vegeta-tion index

王克钰、李希来、李国荣、王祎明、把熠晨

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青海大学地质工程学院,青海 西宁 810016

青海大学农牧学院,青海 西宁 810016

可见光影像 木里矿区 植被覆盖度 植被指数

中央林业改革发展资金天峻县木里矿区种草复绿区合理利用试验研究项目中央财政林草科技推广示范项目青海省科技创新创业团队项目高等学校学科创新引智计划(111计划)

E6301000076043631001001青[2021]TG01号D18013

2024

10.13901/j.cnki.qhwxxbzk.2024.03.003

青海大学学报(自然科学版)
青海大学

青海大学学报(自然科学版)

影响因子：0.355
ISSN：1006-8996
年,卷(期)：2024.42(3)
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