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

^{1}

^{2}

^{*}

## Abstract

**:**

_{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.

## 1. Introduction

_{0}) is a key factor in micrometeorological and hydrological applications, since it can elucidate how surface geometry may lead to alterations in energy, gas, and water exchanges surface friction; and the deflection of airflow [1,2,3]. Many models have been developed to estimate the components of surface energy balance and evapotranspiration (ET) [4,5,6,7] using passive remote sensing observations and a set of algorithms to retrieve surface parameters such as the aerodynamic resistance to heat transfer (r

_{ah}), which is a function of Z

_{0}[8,9]. Obtaining an efficient parameterization for r

_{ah}has been a challenging task, and there is no single method to accurately estimate Z

_{0}over a wide range of land cover types [10,11], introducing further uncertainty in the modeling of energy fluxes and ET. As such, incremental advances in resolving Z

_{0}using canopy structure data at ultra-high resolution from drone-borne instrumentation have the potential to improve the accuracy of surface energy balance models in the context of energy, gas, and water exchange estimations in precision agriculture and related applications.

_{0}values. The most commonly used method is based on micrometeorological observations obtained by an eddy covariance (EC) system and the Monin–Obukhov similarity theory. A disadvantage of this approach is that Z

_{0}is restricted to single average values in the flux footprint of the EC system. Moreover, estimates of Z

_{0}cannot always rely on field-based experiments due to practical limitations and high costs. The second approach relies on the demarcation of surface objects using remote sensing observations and the establishment of empirical relationships between Z

_{0}and measurable characteristics of site-specific roughness elements. In this morphometric method, theoretical models of the boundary layer are combined with more sophisticated physical models of vegetation canopy to determine Z

_{0}(e.g., [12]). Following this approach, Z

_{0}is often associated with the frontal area index (fai), which is the area of windward vertical faces of the roughness elements to the total area under consideration, and the plan area index (pai), which equals the horizontal area occupied by roughness elements divided by the total area [13]. For agricultural and natural sites without density information, Z

_{0}is often simply related to canopy height (h) [14]. However, this relation is not always constant, since the density, the type of vegetation, and the micro- or macrotopographic characteristics can affect Z

_{0}variations as well [15].

_{0}/h of vegetation surfaces [16], and some of them have included the effects of vegetation indices (VIs) [17]. However, precise observations of vegetation height or VI in both high spatial and temporal resolution are difficult to obtain from publically available satellite datasets [18]. A major weakness of airborne/satellite imagery is its limitation in viewing beneath the canopy, leading to sparse points and low-density information on bare soil [19], whereas light detection and ranging (LiDAR) scanners can provide quantitative information on the 3D structure of a canopy because the laser pulses can partly penetrate vegetation cover. The technology of airborne LiDAR scanners (ALS) has now successfully been employed for the extraction of surface roughness characteristics in forests [20,21], urban areas [22], and low-vegetation areas [23]. The representation of a mixed grassland prairie by ALS datasets revealed that up to 76% of the variation in Z

_{0}was due to the height variability of vegetation and up to 65% of the variation could be explained by estimates of vegetation height [24]. Li et al. [25] found that the accuracy of the estimated Z

_{0}of a semi-arid shrubland using ALS data depends on the adopted morphometric models and the precise representation of shrub height in these models. In short and dense canopies, the estimation of vegetation height using ALS is prone to errors [26], mainly due to the lack of identifiable referenced objects and of detectable differences between first (i.e., vegetation) and last (i.e., ground) LiDAR returns [27,28].

_{0}in agricultural areas. Compared to manned ALS, the comparatively cost-effective UAV-LiDAR systems are more flexible in data sampling and produce higher point cloud density due to the larger field of view of the scanner and lower flight altitude and speed, allowing a larger number of LiDAR beams per scan [29]. These characteristics may limit the commonly observed underestimation of canopy heights and mitigate difficulties in deriving individual roughness element canopies from airborne LiDAR data [30]. Resop et al. [31] documented that higher-resolution UAV-LiDAR data facilitated the identification of small vegetation and micro-alterations in a heterogeneous terrain that were not detectable by ALS observations. In a similar study, the 3D characterization of individual plant species of a shrubland area was achievable at the submeter scale using a UAV-LiDAR system [32]. However, the technology of UAV-LiDAR is not currently used in precision farming, despite its ability to effectively monitor canopy density [33] and fine-scale variations in crops attributes compared to UAV-optical imagery [34,35,36], which is widely employed in such applications [37,38,39,40].

- An evaluation of the performance of three segmentation approaches (i.e., a morphological filter (MF), a progressive triangulated irregular network densification filter (TIN), and a combination of MF and TIN) to reliably partition the UAV-LiDAR-derived point cloud data into bare earth and vegetation and, consequently, to generate CHMs at centimeter resolution.
- A discussion of the challenges and further potential of UAV-LiDAR in precision agriculture and related applications.

## 2. Materials and Methods

#### 2.1. Site Description

#### 2.2. Data Collection

^{2}(max 400 points/m

^{2}). The swath width of a single pass was 89 m, and the overlap between two adjacent swaths was greater than 25%. The raw GNSS data files obtained by the INS were converted to position data in pos format using trajectory software (RT Post-process of the NAVsuite software package). The laser scanner’s data were initially produced in bin format and were converted to point clouds in las format using the position data (Geo-LAS software).

_{2}/H2O gas analyzer (LI-COR, Lincoln, NE, USA). During the sampling period, EC data were recorded at a nominal sampling frequency of 20 Hz and ancillary meteorological data at 1 Hz (for further details, see [44]).

#### 2.3. Evaluation Procedure

_{0}values by comparing them with the anemometric-based method for specific flux footprint areas.

#### 2.4. Point Cloud Processing

#### 2.5. Description of Morphometric Methods

_{0}of the subscenes, Plot 1 and Plot 2, surrounding the EC tower through surface morphology.

#### 2.5.1. Roughness Length Based on Vegetation Height

_{0}[14] as follows:

_{0}_RT = 0.1 h

#### 2.5.2. Roughness Length Based on Vegetation Geometry and Wind Conditions

_{s}= 0.003); the drag coefficient for the substrate surface at $\overline{h}$ (c

_{r}= 0.3); the roughness sublayer influence function (ψ

_{h}= 0.193, accounting for the correction to the logarithmic wind profile); the wind speed (U), the friction velocity, u

_{*}, (u

_{*}/U)max = 0.3; and a free parameter (c

_{d1}= 7.5). Z

_{d}(m) is the zero-plane displacement and k is the von Karman’s constant (= 0.4).

_{0}_RAP = h (1 − Z

_{d}/h) exp(−k U/u

_{*}+ ψ

_{h})

_{d}/h = 1 + {(exp [−(2c

_{d1}fai)

^{0.5}] − 1)/(2c

_{d1}fai)

^{0.5}}

u

_{*}/U = min [(c

_{s}+ c

_{r}fai)

^{0.5}, (u

_{*}/U)max]

_{0}from all directions were averaged into one value at each grid cell.

_{0}and Z

_{d}[15]. For instance, it was observed that as surface cover increases, the magnitude of Z

_{d}/$\overline{h}$ produces a convex curve asymptotically increasing from zero to unity, which is the maximum possible value of pai. The pai and fai for each wind direction were calculated using the UMEP plugin [57] in the open-source geographical information software QGIS [58].

#### 2.5.3. Roughness Length Based on Vegetation Height Variability

_{0}as a function of vegetation height variability for each grid cell that is segmented by subcells following:

_{0}_MR = (1/N) ∑ (σi,j/hi,j) h

_{avg}

_{avg}is the average vegetation height calculated from the LiDAR’s CHM. It was documented that coarser grid cells reduce the standard deviation of height regardless of the size of the subcells, while larger subcells lead to higher values of Z

_{0}[25]. Based on these observations, the size of each grid cell was chosen to be equal to 1 m and the segment size inside each grid was 0.25 m, reflecting the maximum expected variance in plant height within a 1 × 1 m cell. All the geometric parameters of vegetation from the CHMs were retrieved using QGIS.

#### 2.6. Description of the Anemometric Method

_{0}(Z

_{0}_EC) following the logarithmic wind law. For stable or unstable atmospheric conditions, the logarithmic wind profile [64] is given by:

_{0}_EC = (z − Z

_{d})/exp(kU/u

_{*}+ ψ

_{m})

_{d}was considered here to be equal to 0.7 h [14].

_{0}_EC from 25 to 27 June, from 13 to 15 July, from 12 to 13 of August, and for daytime hours (from 9:00 to 19:00 local time) were used as reference values for validating the morphometric-derived Z

_{0}. This EC dataset was selected for the comparison analysis of the different methods to calculate Z

_{0}in order to minimize the effect of the differences in the temporal and spatial resolution of the remote sensing data acquired on the 26 June, 14 July, and 12 August, and in situ EC data.

## 3. Results

#### 3.1. Segmentation of Point Cloud Data

#### 3.2. Validation of Canopy Height Models

_{LiDAR}) and measured canopy height for 21 plants (h

_{field}) exhibited a linear relationship (Equation (7)) with a coefficient of determination (R

^{2}) of 0.89 and root mean square error (RMSE) of 0.028 m.

^{2}around the EC tower were calculated for each UAV survey to quantitatively assess the validity of the CHMs (Table 4). The estimated CHMs indicated an increase in vegetation height and volume from June to August, but the standard deviation of vegetation height decreased in August. This pattern could be explained by the mechanical removal of potato vines and the ridging of soil to cover growing tubers that both occurred at the end of July to facilitate the harvest of the potato plants by the end of August.

#### 3.3. Source Turbulent Areas Using Morphometric Models

_{0}values as calculated for the cross-sections of the CHM that coincided with the wind direction that was considered as the input for each run of the footprint model. The footprint model requires the standard deviation of the lateral velocity component, the measurement height, the Obukhov length, the friction velocity, and wind direction (all derived by the EC system), an estimation of the boundary-layer height, and a minimum fetch around the EC tower (approximately 100 m). The 80% cumulative source area for each 30-min EC measurement was utilized to weight the fractional contribution of each grid square of the CHM. This allowed the calculation of a single value of Z

_{0}_EC (by weighting the values in the source area) and the calculation of the average morphometric-derived Z

_{0}for each turbulent source area (Figure 7). Unstable atmospheric conditions were defined as those corresponding to the ratio z/L < −0.032, while near-neutral atmospheric conditions reflected the relation −0.032 ≤ z/L ≤ 0.032 [67]. The source area climatology was biased toward the dominant west-southerly wind direction, as in Plot 1. During the experimental campaign, only in June did wind originate from the east-southerly direction, as in Plot 2.

#### 3.4. Comparison of Methods to Derive Roughness Length

_{0}as calculated by the anemometric and all morphometric methods gradually increased from June to August, following the progression of vegetation growth resulting from the increases in fai and Z

_{d}due to the higher vegetation density and height. On average, the morphometric-based Z

_{0}presented strong linear correlations with Z

_{0}_EC (Figure 8), and a standard deviation of less than 4.2 cm with averages ranging from an underestimation of 1.3 cm (Z

_{0}_RT) to an overestimation of 1.9 cm (Z

_{0}_MR) (Table 5).

_{0}obtained by the EC method and the mean Z

_{0}_RT (i.e., the height of the plants) during the vegetation-growing period indicated that an accurate representation of vegetation height derived by a LiDAR system could be effective for estimating Z

_{0}using the simple rule of thumb method (Figure 8). However, the correlations between Z

_{0}_RAP and Z

_{0}_MR with Z

_{0}_EC exhibited higher coefficients of determination (R

^{2}= 0.96 and 0.93, respectively) and smaller RMSEs compared to Z

_{0}_RT. Thus, the investigation of a suitable morphometric method may be crucial to improving the accuracy of canopy aerodynamic characteristics estimations.

_{0}derived by RAP and the anemometric method was less than 10% for June, July, and August. The RT method had a similar performance to RAP for July and August, while the estimated Z

_{0}_MR was 4% to 19% greater than the mean Z

_{0}_EC. The mean roughness length values under near-neutral conditions were higher than the Z

_{0}calculated for unstable conditions (Table 5), since the extent of the turbulent source areas was typically larger in the former case with smaller values of friction velocity or wind speed. Perhaps the inclusion of the effect of frontal surface U and u

_{*}in the RAP method as well as the inclusion of vegetation height variability in the MR method enabled the capture of Z

_{0}amplification under near-neutral conditions, whereas the dependency of Z

_{0}_RT to the averaged h per grid cell produced similar roughness lengths for unstable and near-neutral conditions. The mean Z

_{0}_EC, Z

_{0}_RAP, and Z

_{0}_MR in August and under unstable atmospheric conditions were smaller than the respective Z

_{0}in July. This could be attributed to the decreased standard deviation of vegetation height within the cumulative source areas observed in August ($\sigma h$ = 0.1 m), which may have translated to a higher density in foliage compared with the respective one for July ($\sigma h$ = 0.15 m).

_{0}variations retrieved in June. The average Z

_{0}in June calculated for the source areas corresponding to Plot 1 (mean h = 0.61 m) was smaller than that obtained in Plot 2 (mean h = 0.55 m) for both unstable and near-neutral conditions. The mean friction velocity in the direction of Plot 1 was also considerably higher, indicating turbulent disturbance (u

_{*}= 0.53–0.54 m/s in Plot 1 vs. u

_{*}= 0.38 m/s in Plot 2). The decreased Z

_{0}in Plot 1, even if the average plants’ h was shorter than the respective one in Plot 2, could be attributed to the concurrent lower roughness vegetation density (fai) of Plot 1 and the higher planar vegetation density (pai) compared to Plot 2 (Figure 9), which was the experimental field covered by short grass. The roughness density (fai) is related to the shapes of plant crowns and to the average density of the canopy elements (pai). The pai is related to the effect of intervening spaces between roughness elements in the overall drag efficiency of a canopy, where a higher pai has a smothering effect on the canopy that increases the Z

_{d}; therefore, Z

_{0}would decrease for a given value of fai, as in Plot 1.

_{0}/$\overline{h}$ also increases at some intermediate level of vegetation densities until it reaches a maximum value for a critical value of fai. The critical fai depends on the method used to determine Z

_{0}and can be interpreted as the level of homogeneity of the canopy at which adding further roughness elements to the surface does not affect the bulk drag because additional elements merely shelter one another [68]. Similarly, as the pai approximates unity, the surface elements are so densely packed that they merge to form a new surface with limited resistance to airflow. In this study, the spatial distribution of Z

_{0}exhibited maximum values for pixels with an fai of 0.08 (Figure 10), where the height of the momentum sink starts to move upward since a large fraction of the total drag is exerted by the outermost leaves and branches rather than the background. The drag Z

_{0}values for pixels with an fai higher than 0.08 are expected to be smaller than the maximum Z

_{0}. This pattern, however, cannot be described with the application of the RT method.

#### 3.5. Influence of Wind Orientation for Deriving Roughness Length

_{0}can be mapped for a larger area beyond the turbulent source areas of the EC tower by calculating the CHM and the wind directions for which the morphometric parameters are modeled. By considering an isotropic surface, the LiDAR-derived height metrics for each wind direction can be integrated into one value in each grid of the map [47]. Figure 11 illustrates the maps of Z

_{0}_RT, Z

_{0}_RAP, and Z

_{0}_MR for a subscene corresponding to the wind regime of 190° to 347° (130 × 250 m), which was surveyed in June. The RAP and MR tended to exhibit higher roughness length values than the RT. The averaged Z

_{0}_RAP over the homogeneous part of the crop field (e.g., Figure 11) resulted in similar values when different win directions were considered. However, when the area was heterogeneous, the spatial distribution of Z

_{0}varied significantly.

_{0}_RAP was mapped for a subscene (200 × 300 m) corresponding to the wind regime spanning from 90° to 190° (Figure 12). The orientation of the trees and lower vegetation compared to the wind direction altered the spatial distribution of Z

_{0}, with higher values of Z

_{0}occurring when the tree arrays and the longitudinal dimension of tramlines were perpendicular to the wind flow (105°). Lower Z

_{0}values were observed when crops were perpendicular to the wind flow (205°), reflecting the smaller frontal surface of the roughness objects opposed to the wind orientation compared to the frontal surfaces opposed to the wind direction of 105°.

## 4. Discussion

_{0}at both high temporal and spatial resolution using UAV-based observations is the appropriateness of the applied morphometric model. In agricultural studies that are based on remote sensing data to estimate turbulent heat and gas fluxes, Z

_{0}is usually calculated as a fraction of the mean height of roughness objects (e.g., [78]) or as a dependent variable of a VI assuming a homogeneous area to derive the resistance to heat transfer [4,5,6,7]. However, the temporal variations in Z

_{0}even in agricultural areas do not always simply correlate with the vegetation height, or the area is not always homogeneous; therefore, these approaches may introduce uncertainty to the estimated energy or gas fluxes [79]. In this study, Z

_{0}depended on the height-based geometric parameters and on the vegetation structure, expressed by the frontal and planar area indices (e.g., Figure 9 and Figure 10), resulting in better correlations between Z

_{0}_RAP and the Z

_{0}_EC averaged within each source area compared to Z

_{0}_RT (Table 5).

_{0}_MR could be considered as the upper limit of the mean Z

_{0}_EC using a 0.25 m subcell scale that can describe the height variation of a plant. This method can accurately capture the vegetation variability using high-resolution CHMs (e.g., 0.10 m) from UAV-LiDAR measurements, but it may be sensitive to the choice of the grid cell size and its subcell sizes. For example, it was observed that coarser subcell scales resulting from coarser ALS-derived CHMs can lead to less convergence between Z

_{0}_EC and Z

_{0}_MR [25]. The RAP method may be more appropriate to simulate the temporal variation in Z

_{0}in this type of landscape, since the formula describes an interplay between roughness length and alterations in airflow orientation (through fai) and wind speed for regular arrays of roughness elements that characterize our study site.

_{0}_RAP and Z

_{0}_MR values across different wind fields outside the turbulent source areas would necessitate the acquisition of anemometric-derived Z

_{0}across the whole field. Therefore, a precise statement about how these morphometric-based Z

_{0}respond to vegetation variability is difficult. However, differences in the spatial distribution of Z

_{0}between crop fields, bare soil, and trees were generated (e.g., Figure 11), and the effect of wind direction on the fai and Z

_{0}for the heterogeneous subscene of the agricultural site was evident using the RAP method (Figure 12). These observations are aligned with the findings of Colin and Faivere [23], who documented that the RAP approach could account for the heterogeneity of an area covered by grassland with staggered arrays of trees.

_{0}variations in low vegetation, probably because these models were designed to determine Z

_{0}in urban areas.

_{0}at centimeter-level resolution for an agricultural site and for selected prevailing wind directions, but the contribution of the upstream roughness elements cannot be quantified. The choice of an appropriate spatial scale of analysis for computing Z

_{0}could be derived from a general analysis of the landscape characteristics along the prevailing airflows.

## 5. Conclusions

_{0}can delineate how land cover alterations affect shear stress and turbulence, which, in turn, regulate the air–surface exchange of energy, water, and greenhouse gases.

_{0}, the selection of the appropriate morphometric method and the effective classification of UAV-LiDAR-derived point clouds into vegetation and terrain that generates precise CHMs are both critical.

_{0}values and those found through the UAV-LiDAR-driven models at the turbulent source area scale. All morphometric models showed a standard deviation of less than 4.2 cm with averages ranging from an underestimation of 1.3 cm (Z

_{0}_RT) to an overestimation of 1.9 cm (Z

_{0}_MR). The detailed comparison indicated that the Raupach roughness model is more suitable for simulating the temporal variations in Z

_{0}. The spatial distribution of zo_RAP for a heterogeneous subscene beyond the turbulent source areas was conditioned by the shape of the frontal surface opposed to wind direction, with a higher Z

_{0}occurring when the tree arrays were perpendicular to the wind flow.

_{0}values in agricultural fields with other crop species and different climatic conditions could be used to assess their adequacy in other contexts. Further research is needed to improve the morphometric models for Z

_{0}in vegetated landscapes that can benefit from canopy height models of ultra-high spatial resolution to account for surface drag effects of upstream roughness elements.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Stull, R.B. An Introduction to Boundary Layer Meteorology; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1988; p. 670. [Google Scholar]
- Minvielle, F.; Marticorena, B.; Gillette, D.A.; Lawson, R.E.; Thompson, R.; Bergametti, G. Relationship between the Aerodynamic Roughness Length and the Roughness Density in Cases of Low Roughness Density. Environ. Fluid Mech.
**2003**, 3, 249–267. [Google Scholar] [CrossRef] - Dickinson, R.E. Land surface processes and climate surface albedos and energy balance. Adv. Geophys.
**1983**, 25, 305–353. [Google Scholar] - Allen, R.G.; Tasumi, M.; Trezza, R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)–model. J. Irrig. Drain. Eng. ASCE
**2007**, 133, 380–394. [Google Scholar] [CrossRef] - Bastiaanssen, W.; Menenti, M.; Feddes, R.; Holtslag, A.A.M. A remote sensing surface energy balance algorithm for land (SEBAL), 1. Formulation. J. Hydrol.
**1998**, 212–213, 198–212. [Google Scholar] [CrossRef] - Roerink, G.; Su, Z.; Menenti, M. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth.
**2000**, 25, 147–157. [Google Scholar] [CrossRef] - Su, Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes at scales ranging from a point to a continent. Hydrol. Earth Syst. Sci.
**2002**, 6, 85–99. [Google Scholar] [CrossRef] - Massman, W.J. A model study of kBH-1 for vegetated surfaces using ‘localized near-field’ Lagrangian theory. J. Hydrol.
**1999**, 223, 27–43. [Google Scholar] [CrossRef] - Blumel, K.B. A simple formula for estimation of the roughness length for heat transfer over partly vegetated surfaces. J. Appl. Meteorol.
**1999**, 38, 814–829. [Google Scholar] [CrossRef] - Su, Z.; Schmugge, T.; Kustas, W.P.; Massman, W.J. An evaluation of two models for estimation of the roughness height for heat transfer between the land surface and the atmosphere. J. Appl. Meteorol.
**2001**, 40, 1933–1951. [Google Scholar] [CrossRef][Green Version] - Kustas, W.P.; Anderson, M.C.; Norman, J.M.; Li, F. Utility of radiometric–aerodynamic temperature relations for heat flux estimation. Bound.-Layer Meteorol.
**2007**, 122, 167–187. [Google Scholar] [CrossRef] - Macdonald, R.; Griffiths, R.; Hall, D. An improved method for the estimation of surface roughness of obstacle arrays. Atmos. Environ.
**1998**, 32, 1857–1864. [Google Scholar] [CrossRef] - Grimmond, C.S.B.; Oke, T.R. Aerodynamic properties of urban areas derived from analysis of surface form. J. Appl. Meteorol.
**1999**, 38, 1262–1292. [Google Scholar] [CrossRef] - Kustas, W.P.; Choudhury, B.J.; Moran, M.S.; Reginato, R.J.; Jackson, R.D.; Gay, L.W.; Weaver, H.L. Determination of sensible heat flux over sparse canopy using thermal infrared data. Agric. For. Meteorol.
**1989**, 44, 197–216. [Google Scholar] [CrossRef] - Garratt, J. The Atmospheric Boundary Layer; Cambridge University Press: Cambridge, UK, 1992; p. 336. [Google Scholar]
- Borak, J.S.; Jasinski, M.; Crago, R. Time series vegetation aerodynamic roughness fields estimated from modis observations. Agric. For. Meteorol.
**2005**, 135, 252–268. [Google Scholar] [CrossRef] - Schaudt, K.J.; Dickinson, R.E. An approach to deriving roughness length and zero-plane displacement height from satellite data, prototyped with BOREAS data. Agric. For. Meteorol.
**2000**, 104, 143–155. [Google Scholar] [CrossRef] - Tian, X.; Li, Z.Y.; Van der Tol, C.; Su, Z.; Li, X.; He, Q.; Bao, Y.; Chen, E.; Li, L. Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data. Remote Sens. Environ.
**2011**, 115, 2330–2341. [Google Scholar] [CrossRef] - Yilmaz, V.; Konakoglu, B.; Serifoglu, C.; Gungor, O.; Gökalp, E. Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos. Geocarto Int.
**2018**, 33, 310–320. [Google Scholar] [CrossRef] - Paul-Limoges, E.; Christen, A.; Coops, N.C.; Black, T.A.; Trofymow, J.A. Estimation of aerodynamic roughness of a harvested Douglas-fir forest using airborne LiDAR. Remote Sens. Environ.
**2013**, 136, 225–233. [Google Scholar] [CrossRef] - Floors, R.; Enevoldsen, P.; Davis, N.; Arnqvist, J.; Dellwik, E. From LiDAR scans to roughness maps for wind resource modelling in forested areas. Wind Energ. Sci.
**2018**, 3, 353–370. [Google Scholar] [CrossRef][Green Version] - Holland, D.E.; Berglund, J.A.; Spruce, J.P.; McKellip, R.D. Derivation of effective aerodynamic surface roughness in urban areas from airborne LiDAR terrain data. J. Appl. Meteorol. Clim.
**2008**, 47, 2614–2626. [Google Scholar] [CrossRef] - Colin, J.; Faivre, R. Aerodynamic roughness length from very high-resolution LIDAR observation. Hydrol. Earth Syst. Sci.
**2010**, 14, 2661–2669. [Google Scholar] [CrossRef][Green Version] - Brown, O.W.; Hugenholtz, C.H. Estimating aerodynamic roughness (zo) in mixed grassland prairie with airborne LiDAR. Can. J. Remote Sens.
**2012**, 37, 422–428. [Google Scholar] [CrossRef] - Li, A.; Zhao, W.; Mitchell, J.J.; Glenn, N.F.; Germino, M.J.; Sankey, J.B.; Allen, R.G. Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland. Photogramm. Eng. Remote S.
**2017**, 83, 415–427. [Google Scholar] [CrossRef][Green Version] - Hopkinson, C.; Chasmer, L.E.; Gabor, S.; Creed, I.F.; Sitar, M.; Kalbfleisch, W.; Treitz, P. Vegetation class dependent errors in LiDAR ground elevation and canopy height estimates in a boreal wetland environment. Can. J. Remote Sens.
**2005**, 13, 191–206. [Google Scholar] [CrossRef] - Rosso, P.H.; Ustin, S.L.; Hastings, A. Use of LiDAR to study changes associated with Spartina invasion in San Francisco Bay marshes. Remote Sens. Environ.
**2006**, 100, 295–306. [Google Scholar] [CrossRef] - Wang, C.; Menenti, M.; Stoll, M.P.; Feola, A.; Belluco, E.; Marani, M. Separation of ground and low vegetation signatures in LiDAR measurements of salt-marsh environments. IEEE Trans. Geosci. Remote
**2009**, 47, 2014–2023. [Google Scholar] [CrossRef] - Kellner, J.R.; Armston, J.; Birrer, M.; Cushman, K.; Duncanson, L.; Eck, C.; Falleger, C.; Imbach, B.; Král, K.; Krůček, M.; et al. New Opportunities for Forest Remote Sensing Through Ultra-High-Density Drone LiDAR. Surv. Geophys.
**2019**, 40, 959–977. [Google Scholar] [CrossRef] [PubMed][Green Version] - Mitchell, J.J.; Glenn, N.F.; Sankey, T.T.; Derryberry, D.R.; Anderson, M.O.; Hruska, R.C. Small-footprint LiDAR estimations of sagebrush canopy characteristics. Photogramm. Eng. Remote S.
**2011**, 77, 521–530. [Google Scholar] [CrossRef] - Resop, J.P.; Lehmann, L.; Hession, W.C. Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial LiDAR. Drones
**2019**, 3, 35. [Google Scholar] [CrossRef][Green Version] - Sankey, T.T.; McVay, J.; Swetnam, T.L.; McClaran, M.P.; Heilman, P.; Nichols, M. UAV hyperspectral and LiDAR data and their fusion for arid and semi-arid land vegetation monitoring. Remote Sens. Ecol. Con.
**2018**, 4, 20–33. [Google Scholar] [CrossRef] - Bates, J.S.; Montzka, C.; Schmidt, M.; Jonard, F. Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR. Remote Sens.
**2021**, 13, 710. [Google Scholar] [CrossRef] - Rogers, S.R.; Manning, I.; Livingstone, W. Comparing the spatial accuracy of digital surface models from four unoccupied aerial systems: Photogrammetry versus LiDAR. Remote Sens.
**2020**, 12, 2806. [Google Scholar] [CrossRef] - Cao, L.; Liu, H.; Fu, X.; Zhang, Z.; Shen, X.; Ruan, H. Comparison of UAV LiDAR and digital aerial photogrammetry point clouds for estimating forest structural attributes in subtropical planted forests. Forests
**2019**, 10, 145. [Google Scholar] [CrossRef][Green Version] - Sofonia, J.; Shendryk, Y.; Phinn, S.; Roelfsema, C.; Kendoul, F.; Skocaj, D. Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry. Int. J. Appl. Earth Observ. Geoinform.
**2019**, 82, 101878. [Google Scholar] [CrossRef] - Duan, T.; Zheng, B.; Guo, W.; Ninomiya, S.; Guo, Y.; Chapman, S.C. Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Funct. Plant. Biol.
**2017**, 44, 169–183. [Google Scholar] [CrossRef] - Shendryka, Y.; Sofonia, J.; Garrardc, R.; Rista, Y.; Skocajd, D.; Thorburn, P. Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging. Int. J. Appl. Earth Obs. Geoinf.
**2020**, 92, 102177. [Google Scholar] [CrossRef] - Adão, T.; Hruška, J.; Pádua, L.; Bessa, J.; Peres, E.; Morais, R.; Sousa, J. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens.
**2017**, 9, 1110. [Google Scholar] [CrossRef][Green Version] - Gano, B.; Dembele, J.S.B.; Ndour, A.; Luquet, D.; Beurier, G.; Diouf, D.; Audebert, A. Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions. Agronomy
**2021**, 11, 850. [Google Scholar] [CrossRef] - Chen, Q.; Gong, P.; Baldocchi, D.; Xie, G. Filtering airborne laser scanning data with morphological methods. Photogram. Eng. Remote Sens.
**2007**, 73, 175–185. [Google Scholar] [CrossRef][Green Version] - Raupach, M.R. Simplified expressions for vegetation roughness length and zero-plane displacement as functions of canopy height and area index. Bound.-Lay. Meteorol.
**1994**, 71, 211–216. [Google Scholar] [CrossRef] - Menenti, M.; Ritchie, J.C. Estimation of effective aerodynamic roughness of Walnet Gulch watershed with laser altimeter measurements. Water Resour. Res.
**1994**, 5, 1329–1337. [Google Scholar] [CrossRef] - Jensen, R.; Herbst, M.; Friborg, T. Direct and indirect controls of the interannual variability in atmospheric CO
_{2}exchange of three contrasting ecosystems in Denmark. Agric. For. Meteor.**2017**, 233, 12–31. [Google Scholar] [CrossRef] - Chang, C.; Habib, F.; Lee, C.; Yom, H. Automatic classification of lidar data into ground and nonground points. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2008**, 37, 463–468. [Google Scholar] - Axelsson, P. DEM generation from laser scanner data using adaptive TIN models. Int. Arch. Photogram. Remote Sens.
**2000**, 33, 111–118. [Google Scholar] - Zhang, K.; Chen, S.-C.; Whitman, D.; Shyu, M.-L.; Yan, J.; Zhang, C. A progressive morphological filter for removing non-ground measurements from airborne LIDAR data. IEEE Trans. Geosci. Remote Sens.
**2003**, 41, 872–882. [Google Scholar] [CrossRef][Green Version] - Zhao, X.; Guo, Q.; Su, Y.; Xue, B. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas. ISPRS J. Photogramm. Remote Sens.
**2016**, 117, 79–91. [Google Scholar] [CrossRef][Green Version] - Hutton, C.; Brazier, R. Quantifying riparian zone structure from airborne LiDAR: Vegetation filtering, anisotropic interpolation, and uncertainty propagation. J. Hydrol.
**2012**, 442–444, 36–45. [Google Scholar] [CrossRef] - Blue Marble Geographics. 2020. Available online: https://www.bluemarblegeo.com/ (accessed on 19 January 2021).
- LAStools. 2015. Available online: https://rapidlasso.com/lastools/ (accessed on 14 February 2021).
- GreenValley International Ltd. LiDAR360 Suite Software. 2019. Available online: https://greenvalleyintl.com (accessed on 7 April 2021).
- Sithole, G.; Vosselman, G. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS J. Photogram. Remote Sens.
**2004**, 59, 85–101. [Google Scholar] [CrossRef] - Shepard, D. A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference, New York, NY, USA; 1968; pp. 517–524. [Google Scholar]
- Krayenhoff, E.; Santiago, J.; Martilli, A.; Christen, A.; Oke, T. Parametrization of drag and turbulence for urban neighbourhoods with trees. Bound.-Layer Meteorol.
**2015**, 156, 157–189. [Google Scholar] [CrossRef] - De Vries, A.C.; Kustas, W.P.; Ritchie, J.C.; Klaassen, W.; Menenti, M.; Rango, A.; Prueger, J.H. Effective aerodynamic roughness estimated from airborne laser altimeter measurements of surface features. Int. J. Remote Sens.
**2003**, 24, 1545–1558. [Google Scholar] [CrossRef][Green Version] - Lindberg, F.; Grimmond, C.S.B.; Gabey, A.; Huang, B.; Kent, C.W.; Sun, T.; Theeuwes, N.E.; Järvi, L.; Ward, H.C.; Capel-Timms, I.; et al. Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. Environ. Model. Softw.
**2018**, 99, 70e87. [Google Scholar] [CrossRef] - QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2017. Available online: http://www.qgis.org/ (accessed on 23 May 2021).
- Vickers, D.; Mahrt., L. Quality control and flux sampling problems for tower and aircraft data. J. Atmos. Ocean. Technol.
**1997**, 14, 512–526. [Google Scholar] [CrossRef] - Nakai, T.; Van Der Molen, M.K.; Gash, J.H.C.; Kodama, Y. Correction of sonic anemometer angle of attack errors. Agric. For. Meteorol.
**2006**, 136, 19–30. [Google Scholar] - Schotanus, P.; Nieuwstadt, F.T.M.; De Bruin, H.A.R. Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Bound.-Lay. Meteorol.
**1983**, 26, 81–93. [Google Scholar] [CrossRef] - Webb, E.K.; Pearman, G.A.; Leuning, R. Correction of flux measurements for density effects due to heat and water vapor transfer. Q. J. R. Meteorol. Soc.
**1980**, 106, 85–100. [Google Scholar] [CrossRef] - Foken, T.; Göockede, M.; Mauder, M.; Mahrt, L.; Amiro, B.; Munger, W. Post-field data quality control. In Handbook of Micrometeorology; Lee, X., Massman, W., Law, B., Eds.; Atmospheric and Oceanographic Sciences Library, Springer: Dordrecht, The Netherlands, 2004. [Google Scholar]
- Tennekes, H.; Lumley, J.L. Wall-bounded shear flows. In A First Course in Turbulence, 16th ed.; The MIT Press: Cambridge, MA, USA, 2018. [Google Scholar]
- Högström, U. Review of some basic characteristics of the atmospheric surface € layer. Bound.-Layer Meteorol.
**1996**, 28, 215–246. [Google Scholar] [CrossRef] - Kljun, N.; Calanca, P.; Rotach, M.W.; Schmid, H.P. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP). Geosci. Model. Dev.
**2015**, 8, 3695–3713. [Google Scholar] [CrossRef][Green Version] - Arya, P. Thermally stratified surface layer. In Introduction to Micrometeorology, 2nd ed.; Academic Press: San Diego, CA, USA, 2001; p. 420. [Google Scholar]
- Shaw, R.H.; Pereira, A.R. Aerodynamic roughness of a plant canopy: A numerical experiment. Agric. Meteorol.
**1982**, 26, 51–65. [Google Scholar] [CrossRef][Green Version] - Tilly, N.; Hoffmeister, D.; Cao, Q.; Huang, S.; Lenz-Wiedemann, V.; Miao, Y.; Bareth, G. Multitemporal crop surface models: Accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J. Appl. Remote Sens.
**2014**, 8, 083671. [Google Scholar] [CrossRef] - Jimenez-Berni, J.A.; Deery, D.M.; Rozas-Larraondo, P.; Condon, A.G.; Rebetzke, G.; James, R.; Bovill, W.; Furbank, R.; Sirault, X. High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR. Front. Plant. Sci.
**2018**, 9, 237. [Google Scholar] [CrossRef][Green Version] - Walter, J.D.C.; Edwards, J.; McDonald, G.; Kuchel, H. Estimating Biomass and Canopy Height with LiDAR for Field Crop Breeding. Front. Plant. Sci.
**2019**, 10, 1145. [Google Scholar] [CrossRef] [PubMed] - Streutker, D.R.; Glenn, N.F. LiDAR measurement of sagebrush steppe vegetation heights. Remote Sens. Environ.
**2006**, 102, 135145. [Google Scholar] [CrossRef] - Su, J.G.; Bork, E.W. Characterization of diverse plant communities in Aspen Parkland rangeland using LiDAR data. Appl. Veg. Sci.
**2007**, 10, 407–416. [Google Scholar] [CrossRef] - Wang, D.; Xin, X.; Shao, Q.; Brolly, M.; Zhu, Z.; Chen, J. Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar. Sensors
**2017**, 17, 180. [Google Scholar] [CrossRef] [PubMed][Green Version] - Maas, H.G.; Vosselman, G. Two algorithms for extracting building models from raw laser altimetry data. ISPRS J. Photogramm. Remote Sens.
**1999**, 54, 153–163. [Google Scholar] [CrossRef] - Zhang, W.; Qi, J.; Wan, P.; Wang, H.; Xie, D.; Wang, X.; Yan, G. An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sens.
**2016**, 8, 501. [Google Scholar] [CrossRef] - Liu, X. Airborne LiDAR for DEM generation: Some critical issues. Prog. Phys. Geogr.
**2008**, 32, 31–49. [Google Scholar] - Wang, S.; Garcia, M.; Bauer-Gottwein, P.; Jakobsen, J.; Zarco-Tejada, P.J.; Bandini, F.; Paz, V.S.; Ibrom, A. High spatial resolution monitoring land surface energy, water and CO
_{2}fluxes from an Unmanned Aerial System. Remote Sens. Environ.**2019**, 229, 14–31. [Google Scholar] [CrossRef] - Colin, J.; Menenti, M.; Rubio, E.; Jochum, A. Accuracy vs. operability: A case study over barrax in the context of the idots. In Proceedings of the AIP Conference Proceedings, Naples, Italy, 10–11 November 2005; Volume 852, pp. 75–83. [Google Scholar]
- Yang, X.; Yu, Y.; Fan, W.A. A method to estimate the structural parameters of windbreaks using remote sensing. Agrofor. Syst.
**2017**, 91, 37–49. [Google Scholar] [CrossRef] - Kent, C.W.; Grimmond, S.; Gatey, D. Aerodynamic roughness parameters in cities: Inclusion of vegetation. J. Wind Eng. Ind. Aerod.
**2017**, 169, 168176. [Google Scholar] [CrossRef] - Millward-Hopkins, J.; Tomlin, A.; Ma, L.; Ingham, D.; Pourkashanian, M. Estimating aerodynamic parameters of urban-like surfaces with heterogeneous building heights. Bound.-Layer Meteorol.
**2011**, 141, 443–465. [Google Scholar] [CrossRef]

**Figure 1.**Illustration of the agricultural area (56.037644° N, 9.159383° E) surveyed by the Unmanned aerial vehicle-Light detection and ranging (UAV-LiDAR) system (30.68 ha), and the two subscenes with a range of roughness element densities: Plot 1 with more homogeneous heights corresponding to the wind regime 190° to 347° (yellow, left rectangle), and Plot 2 with more heterogeneous heights corresponding to the wind regime spanning from 90° to 190° (red, right rectangle). The orange label indicates the location of the Eddy covariance tower.

**Figure 2.**Illustrations of (

**a**) part of the UAV-surveyed area covered by potato plants and (

**b**) the LiDAR instrumentation mounted on a Matrice 600 Pro UAV.

**Figure 3.**Example of rasterized point clouds after interpolation representing part of the agricultural field.

**Figure 4.**Filter performance sensitivity in terms of total errors to: (

**a**) window size and threshold for the morphological filter (MF), (

**b**) iterative distance and angle for the progressive triangulated irregular network densification (PTD), and (

**c**) grid step and spikes for the triangulated irregular network densification (TIN). All filters were applied to the Plot 1 subscene of the agricultural site.

**Figure 5.**(

**a**) Canopy height model (CHM) of the agricultural field indicating the locations of the experimental plots (yellow points), and profile view of point clouds normalized to the terrain illustrating the height of (

**b**) vegetation in June (brown points), July (light green points), and August (dark green points), (

**c**) a building, and (

**d**) trees.

**Figure 6.**Anemometric-derived roughness length Z

_{0}_EC, corresponding to different turbulent source areas from: 25 to 27 June; 13 to 15 July; 12 to 13 August. The central mark indicates the median of Z

_{0}, while the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers represent the most extreme data points.

**Figure 7.**Canopy height model of the agricultural site as obtained by the UAV-LiDAR survey in June. The ellipsoid shapes indicate the probable surface areas contributing to turbulent flux measurements imposing the respective prevailing meteorological conditions.

**Figure 8.**Scatterplots of the anemometric roughness length (Z

_{0}_EC) and the morphometric-derived roughness length (Z

_{0}) using the Menethi and Ritchie (MR), Raupach (RAP), and the rule of thumb (RT) methods.

**Figure 9.**(

**a**) Frontal area index (fai) and (

**b**) planar area index (pai) in the form of wind rose for winds oriented from the west (Plot 1) and east (Plot 2) in June.

**Figure 10.**Contours of the anemometric roughness length for June, July, and August with frontal area index and planar area index as predictor variables, indicating that Z

_{0}depends on the fai and pai and is maximized for fai and pai values close to 0.08 and 0.75, respectively.

**Figure 11.**Maps of roughness length for a relative homogeneous agricultural site estimated by the (

**a**) RT, (

**b**) RAP, and (

**c**) MR methods. The CHM was acquired by a UAV-LiDAR survey conducted on 26 June at 12:00 local time.

**Figure 12.**Maps of Z

_{0}_RAP for a subset of the CHM covered by field crops and trees considering two different view angles that correspond to the wind directions of (

**a**) 205° and (

**b**) 105° from the north. The CHM was acquired by a UAV-LiDAR survey conducted on 12 August at 12:30 local time.

Methods | Parameter Set | |||||
---|---|---|---|---|---|---|

Window Size (m) | Elevation Threshold (m) | Iterative Distance (m) | Iterative Angle (°) | Grid Size (m) | Spike (m) | |

MF | 1 | 1 | ||||

PTD | 0.4 | 4 | ||||

TIN | 0.8 | 0.2 |

**Table 2.**Comparison of the ratio of incorrectly classified points to the total number of points tested (TE) of the filtering algorithms using their optimal set of parameters for the two subscenes, Plot 1 and 2, monitored in June, July, and August.

Subscene/Date | PTD | TIN | MF |
---|---|---|---|

Plot 1/26 June | 15.57 | 26.38 | 9.27 |

Plot 1/14 July | 28.75 | 46.10 | 18.28 |

Plot 1/12 August | 23.84 | 40.56 | 16.36 |

Plot 2/26 June | 14.75 | 34.73 | 19.62 |

Plot 2/14 July | 18.32 | 37.46 | 25.65 |

Plot 2/12 August | 16.84 | 32.39 | 20.52 |

Mean Error | 19.67 | 36.27 | 18.28 |

**Table 3.**Comparison between LiDAR-derived height of plants (h) and plants’ h measured manually in geolocated experimental plots. Number of plant samples = 21.

Date | Field h (m) | LiDAR h (m) |
---|---|---|

26 June | 0.52 | 0.44 |

14 July | 0.71 | 0.56 |

12 August | 0.78 | 0.72 |

**Table 4.**Vegetation dynamics as expressed by the statistics of height of plants (h) and the net volume of a subscene of the CHMs around the EC tower covering 850 m

^{2}.

Date | Mean h (m) | Mode h (m) | Standard Deviation h (m) | Increase in Vegetation (%) |
---|---|---|---|---|

26 June | 0.61 | 0.60 | 0.11 | |

14 July | 0.76 | 0.75 | 0.16 | 30.25 |

12 August | 0.91 | 1.00 | 0.15 | 44.36 |

**Table 5.**Comparison of Z

_{0}(m) derived by the morphometric methods with the anemometric Z

_{0}averaged over the source areas for daytime hours (from 9:00 to 19:00). The data were screened for wind speed higher than 2m/s and friction velocity higher than 0.2 m/s.

Z_{0}_RAP | Z_{0}_RT | Z_{0}_MR | Z_{0}_EC | fai | |
---|---|---|---|---|---|

Differences to Z_{0}_EC | |||||

June (n = 63) | 0.009 | 0.037 | −0.025 | 0.148 | 0.048 |

July (n = 62) | 0.018 | 0.008 | −0.023 | 0.171 | 0.048 |

August (n = 36) | 0.015 | 0.013 | −0.006 | 0.200 | 0.058 |

Average | 0.014 | 0.013 | −0.019 | ||

Standard deviation | 0.031 | 0.042 | 0.022 | ||

Unstable conditions Plot 1 | |||||

June (n = 8) | 0.028 | −0.003 | −0.043 | 0.117 | 0.039 |

July (n = 46) | −0.016 | −0.026 | −0.042 | 0.137 | 0.045 |

August (n = 3) | −0.022 | −0.065 | 0.022 | 0.123 | 0.042 |

Neutral conditions Plot 1 | |||||

June (n = 13) | 0.078 | 0.05 | 0.006 | 0.171 | 0.041 |

July (n = 16) | 0.027 | 0.018 | −0.053 | 0.182 | 0.042 |

August (n = 33) | 0.018 | 0.02 | −0.009 | 0.207 | 0.060 |

Unstable conditions Plot 2 | |||||

June (n = 36) | −0.020 | 0.036 | −0.035 | 0.141 | 0.054 |

Neutral conditions Plot 2 | |||||

June (n = 6) | 0.017 | 0.077 | −0.002 | 0.188 | 0.046 |

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**MDPI and ACS Style**

Trepekli, K.; Friborg, T.
Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR. *Remote Sens.* **2021**, *13*, 3538.
https://doi.org/10.3390/rs13173538

**AMA Style**

Trepekli K, Friborg T.
Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR. *Remote Sensing*. 2021; 13(17):3538.
https://doi.org/10.3390/rs13173538

**Chicago/Turabian Style**

Trepekli, Katerina, and Thomas Friborg.
2021. "Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR" *Remote Sensing* 13, no. 17: 3538.
https://doi.org/10.3390/rs13173538