UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds
| dc.contributor.author | Li, Minhui | en |
| dc.contributor.author | Shamshiri, Redmond R. | en |
| dc.contributor.author | Schirrmann, Michael | en |
| dc.contributor.author | Weltzien, Cornelia | en |
| dc.contributor.author | Shafian, Sanaz | en |
| dc.contributor.author | Laursen, Morten Stigaard | en |
| dc.date.accessioned | 2022-02-11T16:11:58Z | en |
| dc.date.available | 2022-02-11T16:11:58Z | en |
| dc.date.issued | 2022-01-26 | en |
| dc.date.updated | 2022-02-11T14:46:16Z | en |
| dc.description.abstract | Leaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 × 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R<sup>2</sup> = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH<sub>90</sub>. The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Li, M.; Shamshiri, R.R.; Schirrmann, M.; Weltzien, C.; Shafian, S.; Laursen, M.S. UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds. Remote Sens. 2022, 14, 585. | en |
| dc.identifier.doi | https://doi.org/10.3390/rs14030585 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/108300 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | leaf area index (LAI) | en |
| dc.subject | 3D point cloud from photogrammetry | en |
| dc.subject | UAV remote sensing | en |
| dc.subject | crop phenotyping | en |
| dc.subject | precision agriculture | en |
| dc.subject | decision making in crop production | en |
| dc.title | UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds | en |
| dc.title.serial | Remote Sensing | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |