[Application of regression tree in analyzing the effects of climate factors on NDVI in loess hilly area of Shaanxi Province].
Based on the 10-day SPOT VEGETATION NDVI data and the daily meteorological data from 1998 to 2007 in Yan' an City, the main meteorological variables affecting the annual and interannual variations of NDVI were determined by using regression tree. It was found that the effects of test meteorological variables on the variability of NDVI differed with seasons and time lags. Temperature and precipitation were the most important meteorological variables affecting the annual variation of NDVI, and the average highest temperature was the most important meteorological variable affecting the inter-annual variation of NDVI. Regression tree was very powerful in determining the key meteorological variables affecting NDVI variation, but could not build quantitative relations between NDVI and meteorological variables, which limited its further and wider application.
Liu
Yang
Y
Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361003, Fujian, China. ly2002wo20@126.com
Lü
Yi-he
YH
Zheng
Hai-feng
HF
Chen
Li-ding
LD
D000531:Altitude; D002681:China; D002980:Climate; D016208:Databases, Factual; D017753:Ecosystem; D008685:Meteorological Concepts; D063245:Plant Development; D012044:Regression Analysis