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Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR
| Content Provider | MDPI |
|---|---|
| Author | Trepekli, Katerina Friborg, Thomas |
| Copyright Year | 2021 |
| Description | The aerodynamic roughness length $(Z_{0}$) 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 $Z_{0}$ 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 $Z_{0}$ maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding $Z_{0}$ values from a nearby eddy covariance tower $(Z_{0}$_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. $Z_{0}$ from UAV-LiDAR-driven models converged with $Z_{0}$_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in $Z_{0}$ conditioned by vertical and horizontal vegetation density. The $Z_{0}$ calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the $Z_{0}$_EC. Deriving $Z_{0}$ 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. |
| Starting Page | 3538 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs13173538 |
| Journal | Remote Sensing |
| Issue Number | 17 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-06 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Unmanned Aerial Vehicles (uavs) Light Detection and Ranging (lidar) Aerodynamic Roughness Length Point Cloud Classification Precision Agriculture Morphometric Roughness Models |
| Content Type | Text |
| Resource Type | Article |
