A Method for Estimating Effective Leaf Area Index Using UAV 3D Point Cloud Data

Leaf area index (LAI) is a critical plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly significant role in providing the data source needed for LAI extraction. This study proposed a method that automatically calculate crop effective LAI (LAIe) using UAV-based 3-D point cloud. The porosity and projection function of crops from different zenith perspectives was estimated using three-dimensional perspective. Then the LAIe was calculated using the Beer Lambert law. The result shows a good linear correlation between the calculated LAIe and the field LAI measured by digital hemispherical photography method, and R
²
is 0.64. The method presented in this paper performs well in LAIe estimation of main leaf development stages of winter wheat growth period. It offers an effective means for mapping crop LAIe without reference data and saves time and cost.