Study on Remote Sensing Monitoring Method of Dead Alpine Meadow in Winter of Sanjiangyuan Region

doi:10.11873/j.issn.1004-0323.2022.6.1472

Remote Sensing Technology and Application

2022, Vol. 37

Issue (6): 1472-1481 DOI: 10.11873/j.issn.1004-0323.2022.6.1472

Study on Remote Sensing Monitoring Method of Dead Alpine Meadow in Winter of Sanjiangyuan Region

Xuhui Duan¹(

),Weixin Xu¹(

),Hao Liang¹,Juan Zhang²,na Dai¹,Qiangzhi Xiao¹,Qiyu Wang¹

^1.College of Resources and Environment，Chengdu University of Information Engineering，Chengdu 610225，China
^2.Qinghai Institute of Meteorological Sciences，Xining 810001，China

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Abstract

Winter dead grass is a blank field of remote sensing monitoring services， by revealing the unique spectral characteristics of dead grass， establish alpine dead grass monitoring technology and a series of research， can promote alpine region dead grass remote sensing monitoring new services and the development of service time， for the Qinghai-Tibetan plateau ecological environment protection and management to provide innovative technical support. Based on two field observation tests in August and November 2016 in area of Sanjiangyuan in the hinterland of the Qinghai-Tibet Plateau， 72 ground hyperspectry data were obtained including the samples of fresh grass and dead grass. A significant linear pattern of the reflect spectrums for dead grass showed during from 1.5% at 350nm to about 38% near 1 350 nm at range of visible and near-infrared spectrum band. There are evidently differences between dead grass and fresh grass in spectral reflectivity， dead grass completely lost spectral characteristics which shown in the green vegetation with a strong absorption in the red band and a weak absorption in the green band， also shown a high reflection in the 760—1 300 nm （near-infrared band）. The red light band reflectance is about 4.9 times that of fresh grass， while the green light band reflectivity is nearly 1.4 times. In this study， we provided a normalized Dead Grass Vegetation Index （DGVI） using the band 5 and band 3 according to the MODIS satellite data. It was found that the DGVI can effectively identify dead grass in winter， the correlation coefficient between the measured and estimated data by DGVI reaches 0.68 （P <0.05）， and DGVI is significantly better than the general vegetation index. Our study indicated that the DGVI can be used to monitoring for alpine dead grass in winter.

Key words: Dead alpine grass Vegetation index Sensitive spectrum Qinghai-Tibet Plateau

Received: 22 June 2021 Published: 15 February 2023

ZTFLH:

P964

Corresponding Authors: Weixin Xu E-mail: 2439701772@qq.com;weixin.xu@cuit.edu.cn

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	Xuhui Duan
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	Hao Liang
	Juan Zhang
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	Qiyu Wang

Cite this article:

Xuhui Duan,Weixin Xu,Hao Liang,Juan Zhang,na Dai,Qiangzhi Xiao,Qiyu Wang. Study on Remote Sensing Monitoring Method of Dead Alpine Meadow in Winter of Sanjiangyuan Region. Remote Sensing Technology and Application, 2022, 37(6): 1472-1481.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.6.1472 OR http://www.rsta.ac.cn/EN/Y2022/V37/I6/1472

Fig.1 Schematic diagram of the observation sample square setting and subatic of different coverage levels

Fig.2 Reflectivity spectral curve of different cover grades of alpine dead grass in Yushu， November 13，2016

Fig.3 The average curve of alpine grass， withered grass and bare soil in 2016

Fig.4 Spectrum and dispersion distribution of Longbao grass test site in November 2016

Fig.5 The dead grass cover prediction models and estimates

Fig.6 Estimated coverage value and measured value scatter map of different vegetation indexes

Fig.7 Remote Sensing Image Band of Sanjiang Source Region and DGVI value distribution diagram on October 26，2016

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