Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing
Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (<i>Gossypium hirsutum</i> L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator app...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/9/1504 |
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| author | Anish Bhattarai Gonzalo J. Scarpin Amrinder Jakhar Wesley Porter Lavesta C. Hand John L. Snider Leonardo M. Bastos |
| author_facet | Anish Bhattarai Gonzalo J. Scarpin Amrinder Jakhar Wesley Porter Lavesta C. Hand John L. Snider Leonardo M. Bastos |
| author_sort | Anish Bhattarai |
| collection | DOAJ |
| description | Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (<i>Gossypium hirsutum</i> L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing yield. The main goal of this study was to determine the optimal unmanned aerial vehicle flight settings—altitude and speed—and assess specific processing parameters’ impact on data accuracy, processing time, and file size. Nine flight settings comprising three altitudes (12.2 m, 24.4 m, and 48.8 m) and three speeds (4.8 km/h, 9.6 km/h, and 14.4 km/h) were tested. LiDAR data were processed using DJI Terra software (v. 4.1.0), where two user-defined processing steps were examined: point-cloud thinning via grid size sub-sampling (0, 10, 20, 30, 40, and 50 cm) and slope classification (flat, gentle, and steep). The optimal flight altitude was 24.4 m, with no effect of flight speed. Grid sub-sampling up to 20 cm produced balanced accuracy, processing time, and file size. The choice of slope category had no significant effect on LiDAR-derived canopy height. These findings contribute to the development of standardized LiDAR data acquisition and processing guidelines for cotton to support crop management decision. |
| format | Article |
| id | doaj-art-36b5bfdd977347089db95ea18717bf68 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-36b5bfdd977347089db95ea18717bf682025-08-20T02:31:08ZengMDPI AGRemote Sensing2072-42922025-04-01179150410.3390/rs17091504Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data ProcessingAnish Bhattarai0Gonzalo J. Scarpin1Amrinder Jakhar2Wesley Porter3Lavesta C. Hand4John L. Snider5Leonardo M. Bastos6Department of Crop and Soil Sciences, University of Georgia, Miller Plant Sciences Building, 120 Carlton Street, Athens, GA 30602, USADepartment of Crop and Soil Sciences, University of Georgia, Miller Plant Sciences Building, 120 Carlton Street, Athens, GA 30602, USADepartment of Crop and Soil Sciences, University of Georgia, Miller Plant Sciences Building, 120 Carlton Street, Athens, GA 30602, USADepartment of Crop and Soil Sciences, University of Georgia, 2282 Rainwater Rd., Tifton, GA 31793, USADepartment of Crop and Soil Sciences, University of Georgia, 2282 Rainwater Rd., Tifton, GA 31793, USADepartment of Crop and Soil Sciences, University of Georgia, 2282 Rainwater Rd., Tifton, GA 31793, USADepartment of Crop and Soil Sciences, University of Georgia, Miller Plant Sciences Building, 120 Carlton Street, Athens, GA 30602, USALight Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (<i>Gossypium hirsutum</i> L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing yield. The main goal of this study was to determine the optimal unmanned aerial vehicle flight settings—altitude and speed—and assess specific processing parameters’ impact on data accuracy, processing time, and file size. Nine flight settings comprising three altitudes (12.2 m, 24.4 m, and 48.8 m) and three speeds (4.8 km/h, 9.6 km/h, and 14.4 km/h) were tested. LiDAR data were processed using DJI Terra software (v. 4.1.0), where two user-defined processing steps were examined: point-cloud thinning via grid size sub-sampling (0, 10, 20, 30, 40, and 50 cm) and slope classification (flat, gentle, and steep). The optimal flight altitude was 24.4 m, with no effect of flight speed. Grid sub-sampling up to 20 cm produced balanced accuracy, processing time, and file size. The choice of slope category had no significant effect on LiDAR-derived canopy height. These findings contribute to the development of standardized LiDAR data acquisition and processing guidelines for cotton to support crop management decision.https://www.mdpi.com/2072-4292/17/9/1504point cloudLASremote sensingrow croptechnologyagriculture |
| spellingShingle | Anish Bhattarai Gonzalo J. Scarpin Amrinder Jakhar Wesley Porter Lavesta C. Hand John L. Snider Leonardo M. Bastos Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing Remote Sensing point cloud LAS remote sensing row crop technology agriculture |
| title | Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing |
| title_full | Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing |
| title_fullStr | Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing |
| title_full_unstemmed | Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing |
| title_short | Optimizing Unmanned Aerial Vehicle LiDAR Data Collection in Cotton Through Flight Settings and Data Processing |
| title_sort | optimizing unmanned aerial vehicle lidar data collection in cotton through flight settings and data processing |
| topic | point cloud LAS remote sensing row crop technology agriculture |
| url | https://www.mdpi.com/2072-4292/17/9/1504 |
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