Aerodynamic Roughness Length Estimation
with Lidar and Imaging Spectroscopy in a
Shrub-Dominated Dryland
Aihua Li, Wenguang Zhao, Jessica J. Mitchell, Nancy F. Glenn, Matthew J. Germino, Joel B. Sankey, and Richard G. Allen
Abstract
The aerodynamic roughness length (Z
0
m
) serves an impor-
tant 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 Z
0
m
over a shrub
dominated dryland study area in south-central Idaho, USA.
Sensitivity of the two parameterization methods to estimate
Z
0
m
was analyzed. The comparison of eddy covariance-
derived Z
0
m
and remote sensing-derived Z
0
m
showed that the
accuracy of the estimated Z
0
m
heavily depends on the estima-
tion model and the representation of shrub (e.g., Artemisia
tridentata subsp. wyomingensis) 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 Z
0
m
than the MR1994 method. We also
found that the mean, median and 75th percentiles of heights
(H75) from
ALS
provides the best Z
0
m
estimates in the MR1994
model, while the mean, median, and
MAD
(Median Absolute
Deviation from Median Height), as well as
AAD
(Mean Abso-
lute Deviation from Mean Height) heights from
ALS
provides
the best Z
0
m
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 Z
0
m
estimates in the RA1994
method. Results indicate that grass had little effect on Z
0
m
in
the RA1994 method. The Z
0
m
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 Z
0
m
, and thus refining land surface models at
regional scales in semiarid shrublands.
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
(Z
0
m
), 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 Z
0
m
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, Z
0
m
is calculated using the Monin-Obukhov
similarity theory (
MOST
) applied to measurements of horizon-
tal wind speed profiles (Garratt, 1994; Kustas
et al.
, 1994).
Therefore, Z
0
m
can be obtained through observations by an
eddy covariance (EC) system which provides meteorological
measurements; however, estimating Z
0
m
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 (Pri-
gent
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
Z
0
m
over a local or regional scale. Aerodynamic roughness
is influenced by the height, geometry, density and pattern
of roughness elements which include vegetation and micro-
and macro-topographic features (Garratt, 1992; Lettau, 1969;
Raupach, 1992 and 1994; Shaw and Pereira, 1982). Empirical
relationships between Z
0
m
and measurable characteristics of
roughness elements (e.g., vegetation height, normalized dif-
ference vegetation index (
NDVI
), leaf area index (
LAI
), frontal
area index (
FAI
,
λ
f
)) have been used to parameterize Z
0
m
over
a large sale. For example,
NDVI
and
LAI
derived from optical
remote sensing have been correlated with Z
0
m
(Choudhury and
Monteith, 1988; Bastiaanssen, 1995; Jia
et al.
, 2003). In some
previous studies, Z
0
m
was assumed as a proportion of rough-
ness 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
Aihua Li and Nancy F. Glenn are with the Department of
Geoscience, Boise State University, 1920 University Drive,
Boise ID 83725 (
).
Jessica J. Mitchell is with the Department of Geography and
Planning, Appalachian State University, Boone NC.
Matthew 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.
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
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
June 2017
415
