Inversion of Forest Leaf Area Index Based on Lidar Data

Abstract: Leaf area index (LAI) is an important parameter of vegetation ecosystems, which can reflect the growth status of vegetation, and its inversion result has important significance on forestry system. The inversion values of forest LAI exists a certain deviation using traditional method. The airborne LiDAR technology adopts a new type of aerial earth observation method and makes it possible to estimate forest structural parameters accurately. In order to improve the estimation precision of leaf area index (LAI) of forest canopies, an analyzing method based on Lidar data was proposed in this paper. Firstly it conducts data filtering and calibration techniques, then relevant flight experiment and LAI inversion principle are introduced. Finally the inversion model was optimized based on statistic analysis method. LAI map well reflected spatial distribution pattern of LAI in experiment fields. The coefficient of determination (R2) and root mean square error (RMSE) were selected as testing indicators to analyze the inversion results. According to our validation data, the related result showed that the established model was workable, forest LAI estimation are very close to the field-measured, And inversion results with measured LAI has a good consistency, shows high accuracy (R2=0.8848,RMSE=0.2213), which provides a new method to estimate LAI with large regional scale.
Keywords: Leaf area Index, Lidar data, discrete points cloud, laser penetrate index, statistic analysis
Author: Zuowei Huang, Yu Zou
Journal Code: jptkomputergg160126

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