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中国沙漠 ›› 2019, Vol. 39 ›› Issue (1): 26-33.DOI: 10.7522/j.issn.1000-694X.2019.00001

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基于无人机遥感的高寒草原沙化模型及等级划分

花蕊, 周睿, 王婷, 许铭, 唐庄生, 花立民   

  1. 甘肃农业大学 草业学院/草业生态系统教育部重点实验室, 甘肃 兰州 730070
  • 收稿日期:2018-12-03 修回日期:2019-01-09 发布日期:2019-02-14
  • 通讯作者: 花立民(E-mail:hualm@gsau.edu.cn)
  • 作者简介:花蕊(1994-),女,甘肃临洮人,硕士研究生,研究方向为草地生物多样性。E-mail:huarui_gsau@163.com
  • 基金资助:
    川西北和甘南退化高寒生态系统综合整治项目(2017YFC0504803);三江源区退化高寒生态系统恢复技术及示范项目(2016YFC0501902);甘肃省高校协同创新科技团队支持计划项目

Desertification Model and Classification of Alpine Steppe Based on Unmanned Aerial Vehicle (UAV) Remote Sensing

Hua Rui, Zhou Rui, Wang Ting, Xu Ming, Tang Zhuangsheng, Hua Limin   

  1. College of Grassland Science/Key Laboratory of Grassland Ecosystem of the Ministry of Education, Gansu Agricultural University, Lanzhou 730070, China
  • Received:2018-12-03 Revised:2019-01-09 Published:2019-02-14

摘要: 高寒草原是青藏高原草地生态系统的主要组成,在防风固沙、野生动物保育等方面具有重要作用。近年来,在全球气候变化和人为干扰加剧的背景下,高寒草原沙化加剧,基于时空尺度监测范围及程度是防治高寒草地沙化的前提。以青海三江源区玛多县的高寒草原为研究区,结合大疆“精灵3”和“经纬M100”旋翼无人机和地面调查,探讨基于无人机遥测的植被指数在草地沙化调查方面的适宜性,以此为基础制定了高寒草原沙化模型及等级划分标准。结果显示:(1)通过对VDVI(Visible-Band Difference Vegetation Index)、ENDVI(Enhanced Normalized Difference Vegetation Index)和NGRDI(Normalized Green-Red Difference Index)指数与草地沙化指数GDI(Grassland Desertification Index)的相关分析,选取出高寒草地沙化研究最优植被指数为VDVIR=0.9055);(2)GDIVDVI的关系模型为VDVI=0.3024GDI2-0.0335GDI+0.0119(R2=0.9326)。模型相对误差为1.779%(RMSE=0.165,R2=0.7447),拟合精度较高;(3)基于无人机遥感植被指数的聚类分析,将研究区高寒草原沙化划分为5个等级,即无明显沙化(VDVI>0.2247)、轻度沙化(0.1493 < VDVI < 0.2246)、中度沙化(0.0924 < VDVI < 0.1492)、重度沙化(0.0692 < VDVI < 0.0923)和极度沙化(VDVI<0.0692)。

关键词: 无人机, 沙化, 植被指数, 高寒草原

Abstract: The alpine steppe is a major type of the grassland ecosystem in the Qinghai-Tibet Plateau and plays an important role in soil erosion control and wild animal conservation. In recent years, the desertification of alpine steppe is expanding because of the global climate change and human disturbance. Therefore, it is very important to monitor the area and extent of grassland desertification at a spatial-temporal scale for control. The study used two models of unmanned aerial vehicle (DJ Phantom 3 and Matrice100) and ground survey technology to investigate the desertification status of alpine steppe of Maduo County in Sanjiangyuan National Park, which located in Qinghai Province. The purpose of this study is to select the proper vegetation indices of UAV that suit to build desertification model and classification criteria for alpine steppe desertification. The results showed as following:(1) Based on the respective correlation between Visible-Band Difference Vegetation Index (VDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Normalized Green-Red Difference Index (NGRDI) and Grassland Desertification Index (GDI), the optimal vegetation index of UAV is VDVI (R=0.9055). (2) Built the grassland desertification model, VDVI=0.3024GDI2-0.0335GDI+0.0119(R2=0.9326), the relative error is 1.779% (RMSE=0.165, R2=0.7447), which means the higher fitting precision. (3) The desertification of the alpine steppe in the study area is divided into five grades, involving no obvious desertification(VDVI>0.2247), mild desertification(0.1493 < VDVI < 0.2246), moderately desertification(0.0924 < VDVI < 0.1492), severely desertification(0.0692 < VDVI < 0.0923), and extremely desertification(VDVI<0.0692).

Key words: UAV, desertification, vegetation index, alpine steppe

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