Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR
The aerodynamic roughness length (Z<sub>0</sub>) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the poten...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3538 |
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| author | Katerina Trepekli Thomas Friborg |
| author_facet | Katerina Trepekli Thomas Friborg |
| author_sort | Katerina Trepekli |
| collection | DOAJ |
| description | The aerodynamic roughness length (Z<sub>0</sub>) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the potential of unmanned aerial vehicle (UAV)-borne LiDAR systems to provide Z<sub>0</sub> maps with the level of spatiotemporal resolution demanded by precision agriculture by generating the 3D structure of vegetated surfaces and linking the derived geometry with morphometric roughness models. We evaluated the performance of three filtering algorithms to segment the LiDAR-derived point clouds into vegetation and ground points in order to obtain the vegetation height metrics and density at a 0.10 m resolution. The effectiveness of three morphometric models to determine the Z<sub>0</sub> maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding Z<sub>0</sub> values from a nearby eddy covariance tower (Z<sub>0</sub>_EC). A morphological filter performed satisfactorily over a homogeneous surface, whereas the progressive triangulated irregular network densification algorithm produced fewer errors with a heterogeneous surface. Z<sub>0</sub> from UAV-LiDAR-driven models converged with Z<sub>0</sub>_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in Z<sub>0</sub> conditioned by vertical and horizontal vegetation density. The Z<sub>0</sub> calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the Z<sub>0</sub>_EC. Deriving Z<sub>0</sub> in this manner could be highly useful in the context of surface energy balance and wind profile estimations for micrometeorological, hydrologic, and ecologic applications in similar sites. |
| first_indexed | 2024-03-10T08:05:01Z |
| format | Article |
| id | doaj.art-7f57d5e5c14c4976b610198dc6c10772 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:05:01Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-7f57d5e5c14c4976b610198dc6c107722023-11-22T11:10:28ZengMDPI AGRemote Sensing2072-42922021-09-011317353810.3390/rs13173538Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDARKaterina Trepekli0Thomas Friborg1Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, DenmarkDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, DenmarkThe aerodynamic roughness length (Z<sub>0</sub>) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the potential of unmanned aerial vehicle (UAV)-borne LiDAR systems to provide Z<sub>0</sub> maps with the level of spatiotemporal resolution demanded by precision agriculture by generating the 3D structure of vegetated surfaces and linking the derived geometry with morphometric roughness models. We evaluated the performance of three filtering algorithms to segment the LiDAR-derived point clouds into vegetation and ground points in order to obtain the vegetation height metrics and density at a 0.10 m resolution. The effectiveness of three morphometric models to determine the Z<sub>0</sub> maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding Z<sub>0</sub> values from a nearby eddy covariance tower (Z<sub>0</sub>_EC). A morphological filter performed satisfactorily over a homogeneous surface, whereas the progressive triangulated irregular network densification algorithm produced fewer errors with a heterogeneous surface. Z<sub>0</sub> from UAV-LiDAR-driven models converged with Z<sub>0</sub>_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in Z<sub>0</sub> conditioned by vertical and horizontal vegetation density. The Z<sub>0</sub> calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the Z<sub>0</sub>_EC. Deriving Z<sub>0</sub> in this manner could be highly useful in the context of surface energy balance and wind profile estimations for micrometeorological, hydrologic, and ecologic applications in similar sites.https://www.mdpi.com/2072-4292/13/17/3538unmanned aerial vehicles (UAVs)light detection and ranging (LiDAR)aerodynamic roughness lengthpoint cloud classificationprecision agriculturemorphometric roughness models |
| spellingShingle | Katerina Trepekli Thomas Friborg Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR Remote Sensing unmanned aerial vehicles (UAVs) light detection and ranging (LiDAR) aerodynamic roughness length point cloud classification precision agriculture morphometric roughness models |
| title | Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR |
| title_full | Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR |
| title_fullStr | Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR |
| title_full_unstemmed | Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR |
| title_short | Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR |
| title_sort | deriving aerodynamic roughness length at ultra high resolution in agricultural areas using uav borne lidar |
| topic | unmanned aerial vehicles (UAVs) light detection and ranging (LiDAR) aerodynamic roughness length point cloud classification precision agriculture morphometric roughness models |
| url | https://www.mdpi.com/2072-4292/13/17/3538 |
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