Estimating Effective Leaf Area Index Using Li-Strahler Geometric-Optical Model, Landsat 7 ETM+, and Airborne LiDAR in the Greater Khingan Mountains of China

Accurate estimation of forest effective leaf area index (LAIe) is of great significance for regional carbon sequestration studies, forest management and monitoring. In this study, an advanced method was developed to predict LAIe based on Li-Strahler geometric-optical model, airborne LiDAR and Landsat 7 ETM+ data. More specifically, based on the Li-Strahler geometric-optical model, a reliable method was developed to solve the mixed pixel problem, and further to realize the prediction of regional LAIe from airborne LiDAR and multispectral data. First, based on the airborne LiDAR-derived canopy height product, the LAIe was estimated over airborne LiDAR coverage. Second, the sunlit background component was calculated based on the simplified relationship with canopy gap, and LAIe. Then, the reflectance of sunlit background was calculated based on the linear decomposition model. Finally, the forest LAIe was estimated by using Li-Strahler geometric-optical model over the study area. Results showed that the retrieval method proposed in this study could be used effectively in the inversion of regional LAIe, with the significant coefficient of determination (R
²
) was 0.81 and root mean square error (RMSE) was 0.23, as compared with field measurements.