Leaf area index estimation in Fujian province based on remotely sensed imagery

Abstract-Leaf area index (LAI) is a key parameter in carbon cycling models of forest ecosystem and acquiring LAI with a high spatial and temporal accuracy is of great importance to improve the performance of carbon cycling models. Remote sensing technology provides a promising and practical way to estimate LAI at a large area with high temporal coverage, and hence, considerable effort has been expended in developing LAI retrieval models from remotely sensed imagery. In the past two decades, much work has been done for LAI estimation in boreal forest based on remote sensing imagery. However, such studies performed in Asian subtropical monsoon climate region are relatively less. Therefore, this study has been conducted to retrieve LAI in the forested area of Yongan county, Fujian province, located in southeast of China, which has a typical subtropical monsoon climate. IPS P6 LISS 3 imagery acquired on 24 March 2008 in Yongan county was employed in this study. Firstly, a practical atmospheric correction algorithm combining MODIS imagery with conventional Dark Object Subtraction (DOS) technique was used in the atmospheric correction procedure. Then various vegetation indices (NDVI, SR, RSR, etc.) were formulated with the atmospherically corrected reflectance. Finally, LAI retrieval models for three major forest types (pinus, China fir, and broad-leaf forest) in Fujian province were determined through a comparative analysis.