自然资源学报 ›› 2016, Vol. 31 ›› Issue (11): 1949-1957.doi: 10.11849/zrzyxb.20151393

• 资源研究方法 • 上一篇    下一篇

基于植被指数的GF-1与Landsat-OLI石漠化识别能力对比评价

朱大运, 熊康宁*, 肖华, 蓝家程   

  1. 贵州师范大学喀斯特研究院/国家喀斯特石漠化防治工程技术研究中心,贵阳 550001
  • 收稿日期:2015-12-16 修回日期:2016-04-23 出版日期:2016-11-20 发布日期:2016-11-20
  • 作者简介:朱大运(1984- ),男,河南信阳人,博士,主要从事石漠化综合治理与气候变化适应性、喀斯特水土保持研究。E-mail:zhudayun163@163.com *通信作者简介:熊康宁(1958- ),男,贵州威宁人,教授,博士生导师,主要从事喀斯特与洞穴、资源与环境及石漠化生态治理研究。E-mail:xiongkn@163.com
  • 基金资助:
    国家“十三五”重点研发计划(2016YFC0502601); 贵州省科学技术基金(黔科合基础〔2016〕1101,〔2015〕2111); 贵州师范大学2016年博士科研启动基金

Comparison of Rocky Desertification Detection Ability of GF-1 and Landsat-OLI Based on Vegetation Index

ZHU Da-yun, XIONG Kang-ning, XIAO Hua, LAN Jia-cheng   

  1. School of Karst, Guizhou Normal University/State Key Engineering Technology Research Center for Karst Rocky Desertification Rehabilitation, Guiyang 550001, China
  • Received:2015-12-16 Revised:2016-04-23 Online:2016-11-20 Published:2016-11-20
  • Supported by:
    “13th Five-Year” National Key Research and Development Plan, No.2016YFC0502601; Science and Technology Fund of Guizhou, No.〔2016〕1101 and 〔2015〕2111; Doctor Research Fund of Guizhou Normal University in 2016

摘要: 植被指数是运用多源遥感影像提取石漠化过程中的主要参考指标之一。为了在石漠化提取中选择最优植被指数,论文以GF-1和Landsat-OLI为源数据,运用欧氏距离对多种植被指数在石漠化提取过程中的可分性和类型识别能力进行了定量的对比评价。结果表明:Landsat-OLI在石漠化与非石漠化、不同等级石漠化信息提取的可分性上略优于GF-1,共有71个参数的欧氏距离大于等于阈值1.56;通过植被指数光谱特征,可以对非岩溶区与石漠化地区进行较好的区分,其类型间欧氏距离普遍高于阈值;然而由于相邻等级石漠化之间植被覆盖率存在渐近式过渡关系,在遥感影像上光谱反射率接近,比间隔等级石漠化更加难于区分。在石漠化类型识别能力方面,波段差和比方法优于单一光谱指数。对于GF-1和Landsat-OLI而言,石漠化信息提取中推荐使用的最优植被指数均为NDVI,其次为GRNDVI

关键词: GF-1, Landsat-OLI, 欧氏距离, 石漠化, 最优植被指数

Abstract: Vegetation index is one of the major parameters in rocky desertification information extraction by using multi-source satellite images. Based on GF-1 and Landsat-OLI, this paper compared the detachability and detection ability of multiple vegetation indices with Euclidean distance between them, so as to choose an optimal parameter from satellite images and acquire the accurate rocky desertification information. It is proved that Landsat-OLI is better than GF-1 in classifying different grades of rocky desertification and separating rocky desertification region from non-desertification region, and it has 71 parameters with Euclidean distance value greater than the threshold value of 1.56. It is easy to distinguish the rocky desertification area from non-karst area through the Euclidean distance. When classifying different grades of rocky desertification, band difference and ratio are better than single spectral index. Finally, the recommended optimal vegetation index of rocky desertification information extraction from GF-1 images and Landsat-OLI images is NDVI, followed by GRNDVI.

Key words: euclidean distance, GF-1, Landsat-OLI, optimal vegetation index, rocky desertification

中图分类号: 

  • Q948