Please wait a minute...


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
Download:  HTML  PDF (4755KB) 
Export:  BibTeX | EndNote (RIS)      

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:;
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
Articles by authors
Xuhui Duan
Weixin Xu
Hao Liang
Juan Zhang
na Dai
Qiangzhi Xiao
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.

URL:     OR

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
1 Wang Caie, Huang Mei, Wang Wenyin, et al. Characteristics of degraded plant community diversity and aboveground biomass changes along the elevational gradient in the alpine slope area of the Sanjiangyuan region[J]. Ecological Journal, 2022,42(9):3640-2655.
1 王采娥, 黄梅, 王文银, 等. 三江源区高寒坡地退化植物群落多样性和地上生物量沿海拔梯度的变化特征[J]. 生态学报, 2022, 42(9):3640-3655.
2 Aase J, Tanaka D. Reflectances from four wheat residue cover densities as influenced by three soil backgrounds[J]. Agronomy Journal, 1991, 83(4): 753-757.
3 Ma Lin, Li Xuebin, Xie Yingzhong. Litter decomposition and function of grassland ecosystem[J]. Grassland and Animal Husbandry, 2011(12):7-12,24.
3 马琳, 李学斌, 谢应忠. 草地生态系统枯落物分解及功能研究[J]. 草业与畜牧,2011(12):7-12,24.
4 Ma Zhouwen, Wang Yingxin, Wang Hong, et al. Litter and its function in grazing ecosystem[J]. Acta prataculturae Sinica, 2017,26(7):201-212
4 马周文,王迎新,王宏,等.放牧生态系统枯落物及其作用[J]. 草业学报,2017,26(7):201-212.
5 DEáK B, VALKó O, Kelemen A, et al. Litter and graminoid biomass accumulation suppresses weedy forbs in grassland restoration[J]. Plant Biosystems-An International Journal Dealing with All Aspects of Plant Biology,2011,145(3):730-737.
6 Questen H, Eriksson O. Litter species composition influences the performance of seedlings of grassland herbs[J]. Functional Ecology, 2006, 20(3): 522-532.
7 Wang J, Zhao M, Willms W D, et al. Can plant litter affect net primary production of a typical steppe in Inner Mongolia?[J]. Journal of Vegetation Science, 2011, 22(2): 367-376.
8 Li M, Guo X. Long term effect of major disturbances on the Northern Mixed Grassland Ecosystem—A review[J]. Open Journal of Ecology, 2014, 4(4): 214-233.
9 Deutsch E S, Bork E W, Willms W D. Separation of grassland litter and ecosite influences on seasonal soil moisture and plant growth dynamics[J]. Plant Ecology,2010,209(1):135-145.
10 Yao Fuqi, CAI Huanjie, Wang Haijiang, et al. Correlation between canopy height spectral characteristics and coverage of winter wheat[J]. Journal of Agricultural Machinery, 2012,43 (7):156-162.
10 姚付启, 蔡焕杰, 王海江, 等. 冬小麦冠层高光谱特征与覆盖度相关性研究[J]. 农业机械学报,2012,43(7):156-162.
11 Liang Hui, He Jing, Lei Junjie. UAV hyperspectral maize canopy macrospot surveillance[J]. Spectroscopy and Spectroscopic Analysis,2020,40(6):1965-1972.
11 梁辉, 何敬, 雷俊杰. 无人机高光谱的玉米冠层大斑病监测[J]. 光谱学与光谱分析, 2020,40(6):1965-1972.
12 Luo Shanjun, He Yingbin, Duan Dingding, et al. Continuous removal method for the analysis of different potato varieties[J]. Spectroscopy and Spectral Analysis, 2018,38(10):3231-3237.
12 罗善军, 何英彬, 段丁丁, 等. 连续统去除法分析不同马铃薯品种高光谱差异性[J]. 光谱学与光谱分析, 2018, 38(10): 3231-3237.
13 Zhu Mengyuan, Yang Hongbing, Li Zhiwei. Hyperspectral images and early detection and identification of rice sheath blight with chlorophyll content[J]. Spectroscopy and Spectral Analysis, 2019,39(6): 1898-1904.
13 朱梦远, 杨红兵, 李志伟. 高光谱图像和叶绿素含量的水稻纹枯病早期检测识别[J]. 光谱学与光谱分析, 2019, 39(6): 1898-1904.
14 Mao Hengchang. Research progress in the application of hyperspectral technology in agricultural remote sensing[J]. Southern Agricultural Machinery, 2017,48(22): 88.
14 茅恒昌. 高光谱技术在农业遥感中的应用研究进展[J]. 南方农机, 2017, 48(22): 88.
15 Zhang Chunmei, Zhang Jianming. Analysis of grassland in arid areas based on hyperspectral images[J]. Spectroscopy and Spectral Analysis,2012,32(2):445-448.
15 张春梅, 张建明. 基于高光谱影像的干旱区草地光谱特征分析[J]. 光谱学与光谱分析, 2012,32(2):445-448.
16 Overholt K, Cabrera J, Kurzawski A, et al. Characterization of fuel properties and fire spread rates for little bluestem grass[J]. Fire Technology, 2014, 50(1): 9-38.
17 Overholt K, Kurzawski A, Cabrera J, et al. Fire behavior and heat fluxes for lab-scale burning of little bluestem grass[J]. Fire Safety Journal, 2014, 67(1): 70-81.
18 Varga T A, Asner G P. Hyperspectral and LiDAR remote sensing of fire fuels in Hawaii Volcanoes National Park[J]. Ecological Applications, 2008, 18(3): 613-623.
19 Murphy B P, Bradstock R A, Boer M M, et al. Fire regimes of Australia:A pyrogeographic model system[J]. Journal of Biogeography, 2013, 40(6): 1048-1058.
20 Zheng X Z, Hongyan Z, Zhiqiang F, et al. A method for estimating the amount of dead grass fuel based on spectral reflectance characteristics[J]. International Journal of Wildland Fire, 2015, 24(7): 940-948.
21 Wu Qiaoyan, Tong Zhijun, Zhang Jiguang, et al. Optimization layout of Xilin Gol League Grassland based on GIS[J]. Disaster Science, 2010,25(3): 86-89.
21 武巧彦, 佟志军, 张继权, 等. 基于GIS的锡林郭勒盟草原火灾救援物资库优化布局[J]. 灾害学, 2010, 25(3): 86-89.
22 Zhang Jiquan, Cui Liang, Tong Zhijun, et al. Study on hulunbuir grassland fire risk warning threshold based on grid GIS and optimal segmentation method[J]. System Engineering Theory and Practice,2013,33(3):770-775.
22 张继权,崔亮,佟志军, 等. 基于格网GIS与最优分割法的呼伦贝尔草原火灾风险预警阈值研究[J]. 系统工程理论与实践,2013,33(3):770-775.
23 Zhang Jiquan, Fan Jiubo, Liu Xingpeng, et al. Evaluation and prediction of grassland fire hazards in Hulunbuir, Inner Mongolia[J]. Disaster Science, 2010,25(1):35-38.
23 张继权, 范久波, 刘兴朋, 等. 内蒙古呼伦贝尔市草原火灾危害度评价及预测[J]. 灾害学, 2010, 25(1):35-38.
24 Zhang Jiquan, Zhang Hui, Tong Zhijun, et al. Fire disaster evaluation and classification of the grassland in northern China[J]. Grass Industry Journal, 2007,16(6): 121-128.
24 张继权, 张会, 佟志军, 等. 中国北方草原火灾灾情评价及等级划分[J]. 草业学报, 2007,16(6): 121-128.
25 Cui Liang, Zhang Jiquan, Bao Yulong, et al. Hulun Buir grassland fire risk early warning research[J]. Journal of Grass Industry, 2012,21(4):282-292.
25 崔亮, 张继权, 包玉龙, 等. 呼伦贝尔草原火灾风险预警研究[J]. 草业学报, 2012,21(4):282-292.
26 Cui Qingdong. Research on existing estimation technology of natural grass in cold season[D]. Beijing: Chinese Academy of Agricultural Sciences, 2009.
26 崔庆东. 冷季天然草地牧草现存量估测技术研究[D].北京:中国农业科学院, 2009.
27 Wang Xun, Liu Shujie, Zhang Xiaowei, et al. Dynamic monitoring model of forage nutrition in alpine grassland based on HJ-1A/1B data[J]. Remote Sensing of Land and Resources, 2013,25(3):183-188.
27 王迅, 刘书杰, 张晓卫, 等. 基于HJ-1A/1B数据的高寒草地牧草营养动态监测模型[J]. 国土资源遥感, 2013,25(3):183-188.
28 Luo Xiaolong, Tong Zhijun, Zhao Yunsheng, et al. Research on the field spectral identification of different grassland combustible materials and bare soil based on the fractal theory[J]. Spectroscopy and Spectral Analysis, 2016(8):2553-2557.
28 骆晓龙, 佟志军, 赵云升, 等. 基于分形理论的不同草原可燃物及裸土野外光谱识别研究[J]. 光谱学与光谱分析, 2016(8): 2553-2557.
29 Xu Hui, Bao Yuhai, Bao Gang, et al. Hyperspectral remote sensing estimation study of typical grassland hay biomass in Inner Mongolia[J]. Yinshan Journal (Natural Science edition), 2014,28(4): 22-27.
29 胥慧, 包玉海, 包刚, 等. 内蒙古典型草原干草生物量高光谱遥感估算研究[J]. 阴山学刊(自然科学版), 2014, 28(4): 22-27.
30 Liang Hao, Xu Weixin, Duan Xuhui, et al. The threshold rate of the key parameters of the alpine winter dry grass is determined based on the PROSAIL model[J]. Spectroscopy and Spectral Analysis, 2022,42(4):1144-1149.
30 梁好, 徐维新, 段旭辉, 等. 基于PROSAIL模型的高寒冬季枯草关键参数阈值率定[J]. 光谱学与光谱分析, 2022,42(4):1144-1149.
31 Ye Haimin, Rong Mengtian, Deng Xiaodong, et al. A normalized waveform-matching hybrid algorithm based on the absolute value difference[J]. Information Technology,2011,35 (8): 89-93.
31 叶海民, 戎蒙恬, 邓晓东, 等. 基于绝对值差的归一化波形匹配混合算法[J]. 信息技术, 2011,35(8): 89-93.
32 Le Tao, Zhang Hui, Li Guangyu, et al. Study on the spring vegetation dry fall floor cover in eastern Mongolia based on the MODIS band[J]. Science and Technology Bulletin, 2018,34 (10): 269-274.
32 乐涛, 张慧, 李广宇, 等. 基于MODIS波段的蒙东地区春季植被枯落层盖度研究[J]. 科技通报, 2018, 34(10): 269-274.
[1] DING Jing, TANG Jun-wu, LIN Ming-sen. Acquisition of MODIS Ocean Color Satellite Data and Its Data Processing[J]. Remote Sensing Technology and Application, 2003, 18(4): 263 -268 .
[2] WU Chuan-Qiang, WANG Qiao, YANG Zhi-Feng, WEI Bin, SUN Zhong-ping, LIU Xiao-Man. Remote Sensing Analysis in Yangtzi River Estuary and the Inshore Area[J]. Remote Sensing Technology and Application, 2007, 22(6): 707 -709 .
[3] XU Xiao-jun,DU Hua-qiang,ZHOU Guo-mo,FAN Wen-yi. Review on Correlation Analysis of Independent Variables in Estimation Models of Vegetation Biomass Based on Remote Sensing[J]. Remote Sensing Technology and Application, 2008, 23(2): 239 -247 .
[4] ZHANG Duo-kun,TIAN Zhao-shen,LONG Hui,WANG Hong-qi. A New Method for Automatic Geometric Rectification Based on Image Matching in Remote Sensing Image[J]. Remote Sensing Technology and Application, 2008, 23(5): 545 -550 .
[5] GUAN Min,GU Song-yan,YANG Zhong-dong. Geolocation Method for FY-3 MWHS&rsquo|Remote Sensing Image[J]. Remote Sensing Technology and Application, 2008, 23(6): 712 -716 .
[6] ZHU Shan-you,ZHANG Gui-xin,YIN Qiu,KUANG Ding-bo. The Study on the Retrieval of the Air Temperature Based onMulti-sources Polar Orbit Meteorological Satellite Data[J]. Remote Sensing Technology and Application, 2009, 24(1): 27 -31 .
[7] WANG Xu-feng,MA Ming-guo,YAO Hui. Advance in Dynamic Global Vegetation Models[J]. Remote Sensing Technology and Application, 2009, 24(2): 246 -251 .
[8] PAN Jing-hu, LIU Chun-yu. Retrieving Evapotranspiation of Loess Hilly-gully Region Using TSEB Parallel Model Based on Remote Sensing Image[J]. Remote Sensing Technology and Application, 2010, 25(2): 183 -188 .
[9] WANG Jiang-hao,GE Yong. Simulation Analysis of GCP Residuals in the Remote Sensing Image Registration[J]. Remote Sensing Technology and Application, 2011, 26(2): 226 -232 .
[10] Yu Wenping,Ma Mingguo. Validation of the MODIS Land Surface Temperature Products—A Case Study of the Heihe River Basin[J]. Remote Sensing Technology and Application, 2011, 26(6): 705 -712 .