Cargando…
Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data
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 these phenotypic parameters. To achieve the non-destructive detection and estimation of these essential parameters in rice,...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575206/ https://www.ncbi.nlm.nih.gov/pubmed/37837163 http://dx.doi.org/10.3390/s23198334 |
_version_ | 1785120871809024000 |
---|---|
author | Jing, Linlong Wei, Xinhua Song, Qi Wang, Fei |
author_facet | Jing, Linlong Wei, Xinhua Song, Qi Wang, Fei |
author_sort | Jing, Linlong |
collection | PubMed |
description | 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 these phenotypic parameters. To achieve the non-destructive detection and estimation of these essential parameters in rice, a platform based on LiDAR (Light Detection and Ranging) 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 a surface model of the canopy was calculated. The canopy height estimation was the difference between the ground elevation and the percentile value. To determine the optimal percentile that would define the rice canopy top, testing was conducted incrementally at percentile values from 0.8 to 1, with increments of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between the LiDAR-detected and manually measured canopy heights for each case was calculated. The prediction model based on canopy height (R(2) = 0.941, RMSE = 0.019) exhibited a strong correlation with the actual canopy height. Linear regression analysis was conducted between the gap fractions of different plots, and the average rice canopy Leaf Area Index (LAI) was manually detected. Prediction models of canopy LAIs based on ground return counts (R(2) = 0.24, RMSE = 0.1) and ground return intensity (R(2) = 0.28, RMSE = 0.09) showed strong correlations but had lower correlations with rice canopy LAIs. Regression analysis was performed between LiDAR-detected canopy heights and manually measured rice canopy LAIs. The results thereof indicated that the prediction model based on canopy height (R(2) = 0.77, RMSE = 0.03) was more accurate. |
format | Online Article Text |
id | pubmed-10575206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105752062023-10-14 Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data Jing, Linlong Wei, Xinhua Song, Qi Wang, Fei Sensors (Basel) Article 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 these phenotypic parameters. To achieve the non-destructive detection and estimation of these essential parameters in rice, a platform based on LiDAR (Light Detection and Ranging) 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 a surface model of the canopy was calculated. The canopy height estimation was the difference between the ground elevation and the percentile value. To determine the optimal percentile that would define the rice canopy top, testing was conducted incrementally at percentile values from 0.8 to 1, with increments of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between the LiDAR-detected and manually measured canopy heights for each case was calculated. The prediction model based on canopy height (R(2) = 0.941, RMSE = 0.019) exhibited a strong correlation with the actual canopy height. Linear regression analysis was conducted between the gap fractions of different plots, and the average rice canopy Leaf Area Index (LAI) was manually detected. Prediction models of canopy LAIs based on ground return counts (R(2) = 0.24, RMSE = 0.1) and ground return intensity (R(2) = 0.28, RMSE = 0.09) showed strong correlations but had lower correlations with rice canopy LAIs. Regression analysis was performed between LiDAR-detected canopy heights and manually measured rice canopy LAIs. The results thereof indicated that the prediction model based on canopy height (R(2) = 0.77, RMSE = 0.03) was more accurate. MDPI 2023-10-09 /pmc/articles/PMC10575206/ /pubmed/37837163 http://dx.doi.org/10.3390/s23198334 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jing, Linlong Wei, Xinhua Song, Qi Wang, Fei Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data |
title | Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data |
title_full | Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data |
title_fullStr | Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data |
title_full_unstemmed | Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data |
title_short | Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data |
title_sort | research on estimating rice canopy height and lai based on lidar data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575206/ https://www.ncbi.nlm.nih.gov/pubmed/37837163 http://dx.doi.org/10.3390/s23198334 |
work_keys_str_mv | AT jinglinlong researchonestimatingricecanopyheightandlaibasedonlidardata AT weixinhua researchonestimatingricecanopyheightandlaibasedonlidardata AT songqi researchonestimatingricecanopyheightandlaibasedonlidardata AT wangfei researchonestimatingricecanopyheightandlaibasedonlidardata |