Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland

The aerodynamic roughness length (Z0m) serves an important role in the flux exchange between the land surface and atmosphere. In this study, airborne lidar (ALS), terrestrial lidar (TLS), and imaging spectroscopy data were integrated to develop and test two approaches to estimate Z0m over a shrub dominated dryland study area in south-central Idaho, USA. Sensitivity of the two parameterization methods to estimate Zom was analyzed. The comparison of eddy covariancederived Z0m and remote sensing-derived Z 0m sho1iVed that the accuracy of the estimated Z0m heavily depends on the estimation model and the representation of shrub (e.g., Artemisia tridentata subsp. lryomingensis) height in the models. The geometrical method (RA1994) led to 9 percent (-0.5 cm) and 25% (1.1 cm) errors at site 1 and site 2, respectively, which performed better than the height variability-based method (MR1994) with bias error of 20 percent and 48 percent at site 1 and site 2, respectively. The RA1994 model resulted in a larger range of Zom than the MR1994 method. We also found that the mean, median and 75th percentiles of heights (H75) from ALS provides the best Z 0m estimates in the MR1994 model, while the mean, median, and MAD (Median Absolute Deviation from Median Height), as well as AAD (Mean Absolute Deviation from Mean Height) heights from ALS provides the best Z0m estimates in the RA1994 model. In addition, the fractional cover of shrub and grass, distinguished with ALS and imaging spectroscopy data, provided the opportunity to estimate the frontal area index at the pixel-level to assess the influence of grass and shrub on Z0m estimates in the RA1994 method. Results indicate that grass had little effect on Z 0m in the RA1994 method. The Z0m estimations were tightly coupled with vegetation height and its local variance for the shrubs. Overall, the results demonstrate that the use of height and fractional cover from remote sensing data are promising for estimating Zom• and thus refining land surface models at regional scales in semiarid shrublands. Aihua Li and Nancy F. Glenn are with the Department of Geoscience, Boise State University, 1920 University Drive, Boise ID 83725 ([email protected]). Jessica J. Mitchell is with the Depa1tment of Geography and Planning, Appalachian State University, Boone NC. Ivlatthew J. Germino is with the US Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, ID. Joel B. Sankey is with the US Geological Survey, Grand Canyon Monitoring and Research Center, Flagstaff, AZ. Wenguang Zhao and Richard Allen are with Biological Engineering, University of Idaho, Kimberly, ID. PHOTOGRAMMETRk: ENGINEERING & REMOTE SENSING Introduction The roughness of the land surface plays an important role in the flux exchange between the land surface and atmosphere (Sud et aL, 1988; Prueger et al., 2004). Land surface roughness can be characterized by the aerodynamic roughness length (Z0m), which is the height of roughness elements at which the mean wind speed approaches zero given the extrapolation of the logarithmic wind profile (Garratt, 1992; Kaimal and Finnigan, 1994). In dryland ecosystems, such as semiarid shrublands, the spatial distribution of roughness elements and specifically Z0m are key parameters for physical models of aeolian transport and for estimating dust emissions from wind erosion (Prigent et al., 2005; Sankey et al., 2010; Sankey et al., 2013; Nield et al., 2013; Pelletier and Field, 2016) and for land surface models (Dickinson and Henderson-Sellers, 1988; Jasinski and Crago, 1999). Traditionally, Zom is calculated using the Ivlonin-Obukhov similarity theory (MOST) applied to measurements of horizontal V\rind speed profiles (Garratt, 1994; Kustas et al., 1994). Therefore, Z0m can be obtained through observations by an eddy covariance (EC) system which provides meteorological measurements; however, estimating Zom from EC is restricted to a single value in the source area of the EC tower, and thus EC estimates are limited for regional land surface models (Paul-Limoges et al., 2013). To address this issue, studies have used remotely sensed information, such as scatterometer (Prigent et al., 2005) and bi-directional reflectance (Marticorena et al., 2004) data, along with laser altimeter measurements (Menenti and Ritchie, 1994; De Vries et al., 2003, Colin and Faivre, 2010, Weligepolage et al., 2012) for parameterizing Zom over a local or regional scale. Aerodynamic roughness is influenced by the height, geometry, density and pattern of roughness elements which include vegetation and microand macro-topographic features (Garratt, 1992; Lettau, 1969; Raupach, 1992 and 1994; Shaw and Pereira, 1982). Empirical relationships between Zom and measurable characteristics of roughness elements (e.g., vegetation height, normalized difference vegetation index (NDVI), leaf area index (LAI), frontal area index (FAI, A.1)) have been used to parameterize Z0m over a large sale. For example, NDVI and LAI derived from optical remote sensing have been correlated with Zom (Choudhury and Monteith, 1988; Bastiaanssen, 1995; Jia et al., 2003). In some previous studies, Z0m was assumed as a proportion of roughness element height (i.e., Kustas et al., 1989; Garratt, 1992). The three-dimensional (3D) structure of the land's surface and vegetation, as captured by laser altimetry (or light detection and ranging (lidar)) provides a straightforward measure of Photogrammetric Engineering & Remote Sensing Vol. 83, No. 6, June 2017, pp. 415-427. 0099-1112/17/415-427 © 2017 American Society for Photogrammetry and Remote Sensing doi: 10.14358/PERS.83.6.415