ANALYSIS OF SPATIO-TEMPORAL PATTERNS OF LEAF AREA INDEX IN DIFFERENT FOREST TYPES OF INDIA USING HIGH TEMPORAL REMOTE SENSING DATA

• Main Authors: A. Chhabra, S. Panigrahy
• Format: Article
• Language: English
• Published: Copernicus Publications 2012-08-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXVIII-8-W20/119/2011/isprsarchives-XXXVIII-8-W20-119-2011.pdf

Knowledge of temporal variations of Leaf Area Index (LAI) aids in understanding the climate-vegetation interaction of different vegetative systems. This information is amenable from high temporal remote sensing data. India has around 78.37 million hectare, accounting for 23.84% of the geographic area of the country under forest/tree cover. India has a diverse set of vegetation types ranging from tropical evergreen to dry deciduous. We present a detailed spatio-temporal and inter-seasonal analysis of LAI patterns in different forest types of India using MODIS 8-day composites global LAI/fPAR product for the year 2005 at 1-km spatial resolution. A forest cover mask was generated using SPOT 1-km landuse/landcover classification over the Indian region. The range of estimated LAI varied from 0.1–6.9 among the different forest types. Maximum LAI was observed in tropical evergreen forests in North-Eastern region and Western Ghats. Low LAI was observed in Central Indian region due to predominance of dry deciduous forests. The spatial patterns of seasonal variations detected that for most of the forest types, the peak LAI values were observed during September and October months of the autumn season in contrast to minimum LAI during summer season. The mean LAI and standard deviation for each 8-day LAI composite were also computed and mean monthly LAI profiles were derived for each forest type classified on the basis of their geographical locations. These results are useful indicators for detailed understanding of phenological sequence and may also serve as important inputs for deriving bioclimatic indices for different forest types of India.