Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR

Abstract

The aerodynamic roughness length (Z0) 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 Z0 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 Z0 maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding Z0 values from a nearby eddy covariance tower (Z0_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. Z0 from UAV-LiDAR-driven models converged with Z0_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in Z0 conditioned by vertical and horizontal vegetation density. The Z0 calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the Z0_EC. Deriving Z0 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.

Parameterization and Remote Sensing Retrieval of Land Surface Processes in the Gurbantunggut Desert, China

“…where x = [1-19z m /L] 1/4 ; L(m) and T * (K), respectively, are the Monin-Obukhov rough length and friction temperature, which can be calculated from Equations ( 5) and (6). The screening wind speed is greater than 1 m/s; the friction speed is greater than 0.01 m/s; the sensible heat flux is greater than 10 W/m 2 ; no rain; the acquisition time is daytime flux data.…”

“…A large number of studies have been carried out on the surface parameters, atmospheric boundary layer, and stability of each underlying surface in the arid and semi-arid zone [4], and Peng et al [5] combined machine learning techniques, wind profile equations, station observations, and MODIS remote sensing data to estimate the daily dynamic roughness on a global scale. Trepekli et al [6] used a UAV-mounted lidar system to estimate the dynamic roughness of the underlying surface under farmland. Ma et al [7] evaluated a variety of thermodynamic roughness schemes, and the results showed that the C97 scheme had the best effect on the underlying surface in the grassland of eastern Tibet.…”

Surface Roughness parameters at a Tropical Semi-Arid Site: 4 years of Atmosphere Boundary Layer observations in the Deccan Plateau region at Shadnagar, India

“…2021 generated a dataset of global aerodynamic parameters for the period 1982 to 2017, employing remote sensing measurements of Leaf Area Index (LAI), canopy height, and canopy morphological characteristics, with the canopy height estimated through a semi-empirical approach based on LAI. More recently, ultra-fine resolution (0.1 m) spatial maps of 𝑧 0 have been developed from LiDAR systems mounted on Unmanned Aerial Vehicles (Trepekli and Friborg 2021). Overall, it can be summarized that remote sensing / satellite-based methods for estimating roughness parameters are becoming more reliable and popular in recent years.…”