A Remote Sensing Based Forage Biomass Yield Inversion Model of Alpine-cold Meadow during Grass-withering Period in Sanjiangyuan Area

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Song, Weize^1 ; Jia, Haifeng^1 ; Liu, Shujie^2 ; Liang, Shidong^1 ; Wang, Zheng^1 ; Hao, Lizhuang^2 ; Chai, Shatuo^2

School of Environment, Tsinghua University, Beijing, 100084, China^1

Academy of Animal Husbandry and Veterinary Medicine, Qinghai University, Xining 810086, China^2

关键词: Alpine-cold meadow grassland;    Correlation analysis;    Grass-withering period;    Landsat Thematic Mapper;    NDVI;    Normalized difference vegetation index;    Prediction accuracy;    Sanjiangyuan area;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012042/pdf
DOI  :  10.1088/1755-1315/17/1/012042

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Estimating forage biomass yield remotely from space is still challenging nowadays. Field experiments were conducted and ground measurements correlated to remote sensing data to estimate the forage biomass yield of Alpine-cold meadow grassland during the grass and grass-withering period in Sanjiangyuan area in Yushu county. Both Shapiro-Wilk and Kolmogorov-Smirnov two-tailed tests showed that the field training samples are normally distributed, the Spearman coefficient indicated that the parametric correlation analysis had significant differences. The optimal regression models were developed based on the Landsat Thematic Mapper Normalized Difference Vegetation Index (TM-NDVI) and the forage biomass field data during the grass and the grass-withering periods, respectively. Then an integration model was used to predict forage biomass yield of alpine-cold meadow in the grass-withering period. The model showed good prediction accuracy and reliability. It was found that this approach can not only estimate forage yield in large scale efficiently but also overcome the seasonal limitation of remote sensing inversion. This technique can provides valuable guidance to animal husbandry to resource more efficiently in winter.