草地学报

草地学报 ›› 2019, Vol. 27 ›› Issue (5): 1431-1440.DOI: 10.11733/j.issn.1007-0435.2019.05.040

• 技术研发 • 上一篇    下一篇

基于无人机大样方数据及国产卫星反演草地植被覆盖度方法研究

蔡宗磊1, 苗正红1, 常雪2, 刘艳慧3, 郝刚4, 何龙涛4   

  1. 1. 吉林省水利水电勘测设计研究院, 吉林 长春 130021;
    2. 吉林省建筑科学研究设计院, 吉林 长春 130021;
    3. 东北大学测绘遥感与数字矿山研究所, 辽宁 沈阳 110819;
    4. 吉林省老龙口水库管理局, 吉林 珲春 133300
  • 收稿日期:2019-04-28 修回日期:2019-07-29 出版日期:2019-10-15 发布日期:2019-11-09
  • 通讯作者: 苗正红
  • 作者简介:蔡宗磊(1990-),男,河北唐山人,硕士,助理工程师,主要从事水利测量和植被遥感方面的研究,E-mail:caizonglei@qq.com
  • 基金资助:
    吉林省科技发展计划项目（20190303067SF）；吉林省水利厅项目（12600220190012）资助

Research on Grassland Fractional Vegetation Coverage Inversion Method Based on Drone Large Quadrat Data and Domestic Satellite

CAI Zong-lei1, MIAO Zheng-hong1, CHANG Xue2, LIU Yan-hui3, HAO Gang4, HE Long-tao4   

  1. 1. Jilin Water Resource and Hydropower Consultative Company of P. R. China, Changchun, Jilin Province 130021, China;
    2. Jilin Research and Design Institute of Building Science, Changchun, Jilin Province 130021, China;
    3. Institute for Geo-informatics & Digital Mine Research, Northeastern University, Shenyang, Liaoning Province 110819, China;
    4. Laolongkou Reservoir Management Bureau, Huichun, Jilin Province 133300, China
  • Received:2019-04-28 Revised:2019-07-29 Online:2019-10-15 Published:2019-11-09

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摘要： 受开垦、采矿等人类活动影响，草原出现退化甚至沙化，监测其植被覆盖度对于揭示草地的分布状况与空间变化规律具有重要意义。本文以无人机大样方数据与国产高分一号（GF-1）数据作为数据源，结合野外同步数码相机获取的数据，应用支持向量机（Support vector machine，SVM）构建不同数据源之间的植被覆盖度反演模型（数码相片—无人机大样方数据植被覆盖度估算模型，无人机大样方数据—GF-1数据植被覆盖度估算模型），探讨国产GF-1卫星结合无人机大样方估算草原植被覆盖度的方法。结果表明，基于SVM模型的GF-1数据结合无人机大样方计算的土壤调节植被指数（Soil-adjusted vegetation index，SAVI）具有较高的精度（判定系数R2=0.97，相对分析误差RPD=4.86，均方根误差RMSE=3.23），因此基于无人机大样方数据结合GF-1数据可以准确、快速地反演草地覆盖度，利用这种方法可以估算整个草原的植被覆盖度。

关键词: 草地植被覆盖度, 高分一号数据, 无人机, 数码相机, 支持向量机

Abstract: Hulun Buir Prairie has undergone some desertification and degradation due to the overgrazing,farming and mining. It is of great significance to monitor grassland coverage of Hulun Buir Prairie to uncover the distribution and spatial variety regulation of grassland. The Support Vector Machine (SVM) models is established based on the different vegetation indices from GF-1 multi-spectral data and ground data from unmanned aerial vehicle (UAV)to retrieve grassland coverage. The aim of this study is to evaluate the potential ability of Chinese GF-1 satellite imagery combining with UAV photogrammetry in grassland FVC retrieval at the surface mine area of north prairie in China. The results showed that soil-adjusted vegetation index (SAVI) from GF-1 can produce high accuracy estimation (R2=0.956,RPD=4.857,RMSE=3.232) based on SVM model. Therefore,the Chinese GF-1 data can provide grassland coverage with high accuracy based on SVM model. It was found that this method was spatially consistent,allowing accurate vegetation mapping over the entire grassland.

Key words: Fractional vegetation coverage of grassland, GF-1 data, Unmanned aerial vehicle, Digital photo, Support vector machine

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