Retrieving leaf area index of forests in red soil hilly region using remote sensing data

Remote sensing is an effective tool to retrieve leaf area index (LAI) at local, regional and global scales. Two approaches are currently employed for this purpose. The first is the empirical relationship approach. Map of LAI is produced according to the relationship between ground measured LAI and spectral vegetation index (VI) calculated from remote sensing signals. Inversion of radiation transfer or geometric optical models is another algorithm to retrieve LAI. The objective of this study is to investigate the ability of two approaches to retrieve forest LAI in red soil hilly region of Jian city, Jiangxi province. The applicability of empirical relationship approach was studied through analyzing the relationship between measured LAI and various vegetation indices calculated from Landsat-5 TM data, including SR (Simple Ratio), NDVI (Normalized Difference Vegetation Index), RSR (Reduced Simple Ratio), SAVI (Soil Adjusted Vegetation Index), EVI (Enhanced Vegetation Index). It was found that NDVI is the best predictor of LAI (R2=0.6811, N=47). A BRDF-based inversion algorithm was used to inverse LAI from MODIS 500m reflectance products. LAI derived using empirical relationship and BRDF-based inversion methods shows certain similarity and demonstrates that these two algorithms are both applicable for retrieving forest LAI in this region. The average value of inversed LAI and the MODIS LAI was about 12.2% and 16% lower compared with LAI retrieved using high resolution TM-5 data. Considerable difference existed between LAI estimated using the BRDF-based inversion approach and the MODIS LAI product although these LAI datasets were produced using same reflectance data.

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