Open AccessResponses of NDVI to climate factors in Inner Mongolia using geographically weighted regression
Author(s) -
Yuwei Wang,
Wang Gao
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/568/1/012012
Subject(s) - normalized difference vegetation index , geographically weighted regression , ordinary least squares , inner mongolia , precipitation , regression analysis , regression , vegetation (pathology) , physical geography , environmental science , geography , climatology , climate change , china , statistics , meteorology , mathematics , geology , medicine , oceanography , archaeology , pathology
Using the MODIS normalized difference vegetation index (NDVI) datasets along with the climate data (precipitation and temperature), geographically weighted regression (GWR) was constructed to explore the spatial nonstationarity and heterogeneity relationships between NDVI and climate factors in Inner Mongolia, China. Our work compared the results of GWR model accuracy with ordinary least squares (OLS) model. The results indicated that GWR method yielded better goodness of fit and higher model accuracy than OLS. Moreover, the GWR model could deeply reveal the complex relationship between NDVI and climate factors. At the same time, the research results could also provide scientific basis for vegetation modeling in Inner Mongolia and similar areas.