Article
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Preserved in Portico This version is not peer-reviewed
Estimation of Rice Canopy Height and Density Research Using LiDAR Data
Version 1
: Received: 30 August 2023 / Approved: 31 August 2023 / Online: 31 August 2023 (13:19:37 CEST)
A peer-reviewed article of this Preprint also exists.
Jing, L.; Wei, X.; Song, Q.; Wang, F. Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data. Sensors 2023, 23, 8334. Jing, L.; Wei, X.; Song, Q.; Wang, F. Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data. Sensors 2023, 23, 8334.
Abstract
Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate rice canopy phenotypic parameters. To achieve non-destructive detection and estimation of essential phenotypic parameters in rice, a platform based on LiDAR point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy top point clouds were selected using a method based on the highest percentile, and the rice canopy surface model was calculated. Canopy height estimation was the difference between ground elevation and percentile value. To determine the optimal percentile defining the rice canopy top, testing was conducted incrementally from 0.8 to 1 with an increment of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between LiDAR-detected canopy height and manually measured canopy height was calculated. The prediction model based on canopy height (R2=0.941, RMSE=0.019) exhibited a strong correlation with actual canopy height. Linear regression analysis was conducted between gap fraction of different plots and manually detected average Leaf Area Index (LAI) of rice canopy. Prediction models for canopy LAI based on ground return counts (R2=0.24, RMSE=0.1) and ground return intensity (R2=0.28, RMSE=0.09) showed strong correlations but had lower correlation with rice canopy LAI. Regression analysis was performed between LiDAR-detected canopy height and manually measured rice canopy LAI. The results indicated that the prediction model based on canopy height (R2=0.77, RMSE=0.03) was more accurate.
Keywords
rice canopy height and density; Lidar; rice canopy LAI; regression analysis
Subject
Engineering, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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