Estimation of birch forest LAI based on single laser penetration index of airborne LiDAR data.

Abstract

Forest leaf area index (LAI) is an important indicator to describe the forest canopy structure and growth status of trees. In this paper, the Yigen area of Inner Mongolia was selected as the study area. Taken full account of the differences among different echo types, the LiDAR point cloud data were split into different single lasers. Then, intensity normalization was implemented for LiDAR point cloud data with the range between sensor and target. Based on the normalized intensity data, a new laser penetration index, called single laser beam penetration index (LPIs), was calculated along with the calculation of traditional LPI. These two laser penetration indexes were used to estimate the forest LAI based on the theoretical model and empirical model on four different sampling scales (5, 10, 15, and 20 m), respectively, which aimed to improve the retrieval accuracy of forest LAI through laser beam splitting. The results showed that the forest LAI estimated from mean LPIs (LPImean) was obviously better than that from traditional LPI. In addition, both of the empirical [R2=0.80, mean absolute deviation (MAD)=0.11] and theoretical models (R2=0.77, MAD=0.16) achieved the best performances with sampling scale of 15 m. The mapping of birch forest LAI for the study area was derived by integrating both the advantages of best empirical and theoretical models.