Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

Abstract

AbstractBi‐weekly National Oceanic and Atmospheric Administration‐advanced very high‐resolution radiometer (NOAA‐AVHRR) satellite data covering a fourteen‐year time period (1990–2003) were used to examine spatial patterns in the normalized difference vegetation index (NDVI) and their relationships with environmental variables covering tropical evergreen forests of the Western Ghats, India. NDVI values and corresponding environmental variables were extracted from 23 different forested sites using the NOAA‐AVHRR global inventory monitoring and modelling studies (GIMMS) dataset. We specifically used the partial least square (PLS) multivariate regression technique that combines features from principal component analysis and multiple regression to link spatial patterns in NDVI with the environmental variables. PLS regression analysis suggested the two‐component model to be the best model, explaining nearly 71% of the variance in the NDVI datasets with relatively good R2 value of 0.78 and a predicted R2 value of 0.74. The most important positive predictors for NDVI included Riva's continentality index, precipitation indicators summed over different quarters, average precipitation and elevation. Also, the results from PLS regression clearly suggested that bio‐climatic indicators that relied only on precipitation parameters had much more positive influence than indicators that combined both temperature and precipitation together. These results highlight the climatic controls of vegetation vigor in evergreen forests and have implications for monitoring bio‐spheric activity, developing prognostic phenology models and deriving land cover maps in the Western Ghats region of India. Copyright © 2008 Royal Meteorological Society