Forest leaf area index (LAI) inversion using airborne LiDAR data

Leaf Area Index (LAI) is one of the most important parameters, which controls biological and physical processes associated with vegetation on the Earth' s surface, such as photosynthesis, respiration, transpiration, carbon and nutrient cycle, and rainfall interception. Therefore, rapid, reliable and objective estimations of LAI are essential. In this study, we developed a new approach for laser penetration index (LPI) estimation from LiDAR data, and first computed LPI based on corrected echoes intensity. Using the variable of LPI, we built LAI estimation model based on Beer-Lambert law. This approach was applied on a forest area in Dayakou, Gansu Province. The accuracy of the corrected intensity-derived LAI inversion model was compared with that of uncorrected intensity-derived and echoes counts-derived model. The study found that corrected echoes intensity can improve the accuracy of LAI inversion. To assess validity and generalization of the model, we validated the optimum model via the Leave-One-Out Cross-Validation (LOOCV) procedure, and the result showed that the model had no overfitting and was more general. Finally, we validated the accuracy of predicted LAIs with 16 field-measured LAIs which were not involved in the modeling process and found that LAI estimation accuracy is high in mountains area by corrected echoes intensity. The LiDAR-derived LAI (R-2 = 0. 825, RMSE = 0. 165) was compared with the LAI from Landsat TM images (R-2 = 0. 605, RMSE = 0. 257), the accuracy of the former is far higher than that of the latter. This study indicates that airborne LiDAR data can be used to obtain high-accuracy LAI estimation and can provide reliable data for ecological environment research.