Estimation Method of Crop Leaf Area Index Based on Airborne LiDAR Data

Abstract:

Leaf area index (LAI) is an important parameter in crop growth monitoring and crop yield estimation. However, optical remote sensing cannot extract the structural information. Light detection and ranging (LIDAR) can provide accurate crop structural information, so LiDAR can make up the shortage of optical remote sensing. Therefore, the purpose of this research is to study the vertical structure information of crops which can be extracted by LiDAR, analyze the correlation of LiDAR vertical metrics and LAI of crop, and estimate LAI of the whole study area. First, the metrics were extracted based on LiDAR data, including mean height above ground of all first returns (Hmean), maximum height above ground of all first returns (Hmax), minimum height above ground of all first returns (Hmin), the percentiles of the canopy height distributions(H25th, H50th, H75th, H90th), laser penetration index (LPI), density of points, porosity and leaf angle. Then, Pearson correlation analysis was used to filter LiDAR metrics which are better related to LAI measured data. Last, regression analysis of selected sensitive parameters was carried out on setting up LiDAR-LAI estimation model, and the LAI estimated result of the whole study area was calculated. The result shows that correlation coefficient between estimated LAI and field measured LAI is 0.79, and RMSE is 0.47. It shows that crop canopy structure parameters extracted by LiDAR can be used to estimate the spatial continuous and large area of LAI of crops.