Study on the Method of Grass Yield Model in the Source Region of Three Rivers with Multivariate Data

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

You, Haoyan^1,2 ; Luo, Chengfeng^1 ; Liu, Zhengjun^1 ; Wang, Jiao^1

Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing 100830, China^1

School of Geometrics, Liaoning Technical University, Fuxin, 123000, China^2

关键词: Geographically weighted regression;    Meteorological data;    Multiple linear regression models;    Multiple linear regressions;    Multivariate data;    Remote sensing and GIS;    Remote sensing data;    Vegetation index;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012031/pdf
DOI  :  10.1088/1755-1315/17/1/012031

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

This paper uses remote sensing and GIS technology to analyse the Source Region of Three Rivers (SRTR) to establish a grass yield estimation model during 2010 with remote sensing data, meteorological data, grassland type data and ground measured data. Analysis of the correlation between ground measured data, vegetation index based HJ-1A/B satellite data, meteorological data and grassland type data were used to establish the grass yield model. The grass yield model was studied by several statistical methods, such as multiple linear regression and Geographically Weighted Regression (GWR). The model's precision was validated. Finally, the best model to estimate the grass yield of Maduo County in SRTR was contrasted with the TM degraded grassland interpretation image of Maduo County from 2009. The result shows that: (1) Comparing with the multiple linear regression model, the GWR model gave a much better fitting result with the quality of fit increasing significantly from less than 0.3 to more than 0.8; (2) The most sensitive factors affecting the grass yield in SRTR were precipitation from May to August and drought index from May to August. From calculation of the five vegetation indices, MSAVI fitted the best; (3) The Maduo County grass yield estimated by the optimal model was consistent with the TM degraded grassland interpretation image, the spatial distribution of grass yield in Maduo County for 2010 showed a "high south and low north" pattern.