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Geomorphology 119 (2010) 135–145
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Geomorphology
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o m o r p h
Relationships of aeolian erosion and deposition with LiDAR-derived landscape
surface roughness following wildfire
Joel B. Sankey a,⁎, Nancy F. Glenn a,1, Matthew J. Germino b,2, Ann Inez N. Gironella c,3, Glenn D. Thackray d,4
a
Department of Geosciences, Idaho State University-Boise, 322 E. Front St., Suite 240, Boise, ID 83702, USA
Department of Biological Sciences, Idaho State University, 921 S 8th Ave, Stop 8007, Pocatello, ID 83209-8007, USA
Department of Mathematics, Idaho State University, 921 S. 8th Ave, Stop 8085, Pocatello, ID 83209-8085, USA
d
Department of Geosciences, Idaho State University, 921 S 8th Ave, Stop 8072, Pocatello, ID 83209-8072, USA
b
c
a r t i c l e
i n f o
Article history:
Received 18 December 2009
Received in revised form 8 March 2010
Accepted 11 March 2010
Available online 18 March 2010
Keywords:
Aeolian transport
Wildfire
LiDAR
Surface roughness
Remote sensing
Shrub steppe
a b s t r a c t
The reduction of vegetation by wildfire can subject stable soil surfaces to increased aeolian transport.
Vegetation and associated microtopography function as surface roughness elements that influence the
entrainment, transport, and deposition of sediment by wind in burned and unburned semiarid shrublands. We
examined whether surface roughness derived from LiDAR can explain variability in aeolian surface change
following wildfire in loess soils of a cold desert shrub steppe in SE Idaho, USA. Erosion bridges were installed in
Fall 2007, following a late summer wildfire to monitor soil surface change at sites in burned and downwind
unburned areas. Surface elevation measurements were made when the erosion bridges were installed and
again in Fall 2008. Surface change was determined from the difference in relative elevation between the two
dates. Airborne LiDAR data were acquired in Fall 2007 following erosion bridge installation. Surface roughness
was calculated at 2-m raster resolution using the standard deviation of all LiDAR elevations within the 2-m
cells, after elevations were detrended to remove the effects of topographic slope. Surface change varied as a
function of surface roughness among burned and unburned surfaces, with net erosion occurring on the
relatively smooth, burned surfaces and net deposition occurring on the rough, unburned surfaces. Site mean
surface change decreased linearly as a function of the inverse of site mean surface roughness (r2 = 0.77,
p b 0.00). Quantile regression analysis indicated that changes in surface roughness were related to
proportionally greater changes in erosion compared to deposition. Analysis of surface change at finer spatial
scales suggested that aeolian processes occurred with strong spatial patterns on burned, but not unburned
surfaces. Future research to examine relationships between aeolian transport and fine spatial resolution
topographic variability, for example from ground-based LiDAR systems, is recommended.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
Aeolian transport is an important biogeomorphic agent that can
mobilize ecologically sequestered sediment, minerals, nutrients, and
pollutants at local to global spatial scales (Okin et al., 2006, 2009). The
reduction of vegetation by wildfire is one particularly ubiquitous
process that can subject otherwise stable surfaces to increased
potential for aeolian transport. Increases in wind erosion following
wildfire have been reported for a large range of biomes, including cold
and warm deserts, grasslands, shrublands, and forests (Ash and
Wasson, 1983; Wasson and Nanninga, 1986; Zobeck et al., 1989;
⁎ Corresponding author. Tel.: +1 208 220 6571; fax: +1 208 345 8353.
E-mail addresses: [email protected] (J.B. Sankey), [email protected] (N.F. Glenn),
[email protected] (M.J. Germino), [email protected] (A.I.N. Gironella),
[email protected] (G.D. Thackray).
1
Tel.: +1 208 345 1994; fax: +1 208 345 8353.
2
Tel.: +1 208 282 3285; fax: +1 208 282 4570.
3
Tel.: +1 208 282 3465; fax: +1 208 282 2636.
4
Tel.: +1 208 282 3560; fax: +1 208 282 4414.
0169-555X/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.geomorph.2010.03.013
Wiggs et al., 1994, 1995, 1996; Whicker et al., 2002, 2006a,b;
Vermeire et al., 2005; Ravi et al., 2007, 2009; Breshears et al., 2009;
Sankey et al., 2009a). Particularly in semiarid shrublands, aeolian
transport is recognized as a predominant biogeomorphic process that
shapes contemporary pattern and form of vegetation and soil surfaces
(Breshears et al., 2003; Okin et al., 2006). Cold desert shrub steppe is
one type of semiarid shrubland in which aeolian transport following
wildfire has been shown to be especially influential (Sankey et al.,
2009a,b).
Aeolian transport has two functional components: (i) the wind's
ability to entrain soil particles (erosivity) that is impeded by surface
roughness components including micro- and macrotopography and
vegetation; and (ii) the soil's susceptibility to this entrainment
(erodibility) (Bagnold, 1941; Cornelis, 2006; Okin et al., 2006).
Erosivity and erodibility interact in complex ways involving potentially nonlinear, recursive and/or self-reinforcing relationships; and
the study of aeolian transport in biogeomorphic systems therefore has
many existing challenges (Baas, 2007, 2008). One challenge is the
development of a more clear understanding of spatial dynamics of
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aeolian processes: (i) on landscapes that span a wide range of surface
roughness (e.g., bare to densely vegetated), and (ii) at multiple spatial
scales that range from entire landscapes to that of individual plants
and/or associated surface microtopography, such as raised mounds
often located beneath woody vegetation and adjacent lower elevation
interspaces (Okin et al., 2006; Ravi et al., 2007, 2009; Breshears et al.,
2009). Conceptual models and a field experiment for warm desert
shrublands suggest that, when vegetation is reduced by disturbance,
wind removes sediment from coppices and deposits it in interspaces,
which serves to redistribute biogeochemical resources and result in a
more physically homogenous surface (Ravi et al., 2007, 2009; Li et al.,
2008; Ravi and D'Odorico, 2009). Disturbance of vegetation by fire in
cold desert shrub steppe has been suggested to homogenize the
microtopography of the soil surface as well (Hilty et al., 2003), but the
contributions of wind erosion and deposition to this process have not
been explicitly examined to our knowledge.
In semiarid shrublands, vegetation and associated microtopography function as surface roughness elements that influence aeolian
transport (Wolfe and Nickling, 1993) by: (i) providing protective
cover to the soil surface, (ii) altering wind flow by extracting
momentum from the wind, and (iii) trapping soil particles. The
protective cover provided by vegetation in undisturbed shrublands
has been hypothesized to be less effective compared to other
undisturbed landscapes (e.g., grasslands and forests) because (i) the
relatively sparse spatial distribution of shrubland vegetation permits
wind flow (in instances) to penetrate and even increase in erosive
potential below the height of the mean vegetation canopy, and (ii)
intershrub spaces tend to have relatively high amounts of exposed soil
and relatively low cover of herbaceous vegetation (Breshears et al.,
2009). Disturbance, such as wildfire, decreases the density and stature
of herbaceous and woody vegetation, which increases the propensity
for more erosive regimes of wind flow (Wolfe and Nickling, 1993;
Breshears et al., 2009). In disturbed and undisturbed shrublands,
sediment entrained by aeolian processes can be removed from
transport (trapped) by surface roughness elements (Grant and
Nickling, 1998; Raupach et al., 2001; Lee et al., 2002; Okin et al.,
2006). Physical processes of sediment entrainment, transport, and
deposition are well defined for surfaces that are vegetated (or
unvegetated) with a homogenous and/or regular spatial pattern.
Shrubland surfaces are often heterogeneous, however, and effects of
surface roughness on aeolian processes are therefore not well
understood across the continuum of disturbed to undisturbed
surfaces in these environments (Okin et al., 2006).
1.1. LiDAR surface roughness
Light detection and ranging (LiDAR) remote sensing technology is
particularly well suited for inquiry of spatial variability of surface
roughness and aeolian processes and is recognized as having great
utility for the quantitative characterization of a wide range of
biogeomorphic processes and systems (Tratt et al., 2008; Bauer,
2009; Pelletier et al., 2009). Digital elevation models (DEM)
constructed from LiDAR point data have been used to examine the
morphology and migration of desert and coastal sand dunes, for
example (Bourke et al., 2009; Pelletier et al., 2009; Ewing and
Kocurek, 2010). Specific to semiarid shrublands, LiDAR remote
sensing has been used successfully to describe variability in microtopographic and shrub vegetation height and morphology (Menenti
and Ritchie, 1994; Ritchie, 1995; Rango et al., 2000; Mundt et al.,
2006; Streutker and Glenn, 2006).
The term surface roughness implies a measure of topographic
variability. A variety of approaches for quantitatively describing the
roughness of biogeomorphic surfaces using LiDAR data have been
reported. Determination of surface roughness using LiDAR has been
most commonly performed by calculating a measure of the variability,
often the standard deviation, of point elevations within a moving
window or pixel (Ritchie, 1995; Davenport et al., 2004; Glenn et al.,
2006). LiDAR returns can be first classified as having reflected from
ground or vegetation surfaces; and then roughness of bare earth,
vegetation, and/or combined bare earth–vegetation calculated accordingly (Glenn et al., 2006; Streutker and Glenn, 2006). Prior to the
roughness calculation, LiDAR point elevations can be detrended to
remove variability because of macrotopographic slope (Davenport et
al., 2004). In lieu of LiDAR point elevation, variability in derivatives of
elevation (e.g., slope and aspect) can be examined using a LiDARderived DEM (McKean and Roering, 2004; Frankel and Dolan, 2007).
Pelletier et al. (2009) calculated surface roughness from a LiDARderived DEM as the difference between maximum and minimum
elevations within a neighborhood of pixels, for example.
Surface roughness estimates derived from LiDAR were presented
for vegetated surfaces in several landscapes by Ritchie (1995), who
noted their potential utility in the prediction of aeolian processes. In
semiarid shrublands, surface roughness from LiDAR has been used to
estimate aerodynamic roughness, an important parameter for
physical models of aeolian transport (Menenti and Ritchie, 1994).
Recently, an inverse relationship between surface roughness derived
from a LiDAR DEM and the entrainment of sand particles was
employed to model the migration of coastal sand dunes (Pelletier et
al., 2009).
1.2. Objectives
Surface roughness elements (e.g., vegetation and microtopography) vary spatially as a function of landscape evolution processes in
natural environments. As a result, wind flow and aeolian transport
vary spatially. Spatial patterns associated with aeolian transport and
the interaction of roughness elements with aeolian processes, in
general, are not well understood in heterogeneous environments at
landscape scales (i.e., spanning a continuum of disturbed and
undisturbed surfaces) (Okin et al., 2006). The objectives of our
study were therefore to (i) describe spatial patterns of surface change
(i.e., temporal variability in the relative elevation of the soil surface
indicative of deflation, inflation, or no change), and (ii) determine
relationships between LiDAR-derived surface roughness and surface
change, in recently burned cold desert shrub steppe. We hypothesized
that (i) surface change would be significantly related to surface
roughness, and (ii) the relationship would indicate that smooth,
burned surfaces are characterized by erosion and that adjacent, rough
unburned surfaces are characterized by deposition at the landscape
scale. Additional analysis was conducted to determine whether
patterns of surface change and the relationship between surface
roughness and surface change, observed at a landscape scale, were
consistent with observations at finer spatial scales.
2. Study area
This study was conducted in shrub steppe rangelands of the
eastern Snake River plain (SRP), Idaho, USA (Lat. 43°30′ N., Lon.
112°38′ W., 1650 m elevation) from Fall 2007 through Fall 2008
(Fig. 1). Geomorphology of the SRP is characterized by basalt lava
flows and calderas that lie along a migratory path of volcanism that
originated ∼ 16 ma in eastern Oregon and currently resides in the
Yellowstone plateau (Pierce and Morgan, 1992). Surface soils have
predominantly developed in aeolian sediments, which include loess
deposited ∼12,000–70,000 years ago, and (to a much lesser extent)
sand dunes as well as fluvial sands adjacent to the Snake River that
have been reworked by wind (Busacca et al., 2004). The geomorphic
setting of the study area was described in further detail in Sankey et al.
(2009b).
The aeolian surfaces studied included areas burned by one of
two wildfires (Twin Buttes fire — 3819 ha July 2007, and Moonshiner
fire — 1081 ha August 2007) and an adjacent, predominantly
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137
Fig. 1. (A) Location of study relative to Idaho, with inset showing location of Idaho relative to USA. (B) Aerial photograph of the eastern Snake River plain, Idaho, with outline of Twin
Buttes and Moonshiner wildfire boundaries, study site locations (S = severely burned, M = moderately burned, and U = unburned), and bare-earth digital elevation model for
LiDAR data collection area. (C) Schematic of erosion bridge locations within a 100-m radius hypothetical study site. At each site, erosion bridge transects 3 and 1 were oriented along
an axis from SW–NE, and transects 2 and 4 were oriented from SE–NW. Interval values denote distance of erosion bridge inner post from the center of the study site.
downwind unburned area. The Twin Buttes fire produced a severely
burned landscape with almost no vegetation remaining. The area
burned by the Moonshiner fire was less severely burned, with little
herbaceous vegetation but a greater presence of burned sagebrush
and juniper remaining following fire. All surfaces studied were
characterized by silt loam-textured soils originally deposited as
loess (NCSS Web Soil Survey, 2008).
Threshold wind speeds (the minimum windspeed required to
initiate saltation) were measured using the method developed by
Stout (2004, 2007) during Fall 2007 on severely and moderately
burned surfaces and could not be determined at an unburned surface
because of very low amounts of saltation (Sankey et al., 2009b).
Winds exceeding the approximate measured lower limit of threshold
(5 m s− 1) and middle value of threshold (8 m s− 1) predominantly
trended from the SW to the NE from Fall 2007 to Fall 2008 according
to windspeed and orientation data (10 m height) acquired from a
weather station located 5 km NE of the study location (Fig. 2).
A major source of surface variability within burned and unburned
areas was the pattern of playette and coppice surface microtopography, which is typical of shrub steppe rangelands of the SRP (Hilty
et al., 2003). Playettes are intershrub spaces that are generally
unvegetated and have a crusted, vesicular soil surface. Coppices are
Fig. 2. Wind direction for wind speeds N5 and 8 m s− 1 from Fall 2007 to Fall 2008. Chart
demonstrates that winds predominantly trended SW–NE. Wind data were acquired
from the Idaho National Laboratory (collected at 10 m height); 5 and 8 m s− 1
correspond to approximate minimum and middle values, respectively, of threshold
wind speed for aeolian transport previously measured by Sankey et al. (2009b) on
burned surfaces at the study location.
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small, vegetated mounds that have a less cohesive surface soil
structure. Following wildfire, coppices in the study area were
vegetated with shrub and herbaceous plants: commonly in the
unburned area, occasionally in the moderately burned area (Moonshiner), and rarely in the severely burned area (Twin Buttes). A survey
conducted in summer 2008 estimated that approximately half of the
landscape was covered by coppice and half by playette and indicated
that both microtopographic units were generally anisotropic in shape
(Amber Hoover, M.S. thesis in preparation, Idaho State University).
Mean (SE) (n = 24) playette dimensions were 1.66 (0.16) m along the
longest axis and 0.86 (0.12) m along the perpendicular axis (Amber
Hoover, M.S. thesis in preparation, Idaho State University). In a study
on the SRP, Hilty et al. (2003) estimated that the diameter of playettes
ranged from 0.5 to 5 m, and that coppices were up to 3 m in diameter
depending on whether they developed beneath one or more tightly
spaced shrubs.
3. Methods
3.1. Surface change measurements
Erosion bridges were installed beginning in August 2007 to monitor
soil surface change at five locations in the area burned by the Twin
Buttes fire (henceforth collectively referred to as severely burned study
sites), three locations in the area burned by the Moonshiner fire
(henceforth moderately burned study sites), and six locations in a
nearby unburned area (henceforth unburned study sites) (Fig. 1).
Twenty erosion bridges were installed at each study site, oriented such
that samples were collected on axes perpendicular and parallel to
predominant wind direction (Figs. 1 and 2). Erosion bridge positions
were recorded using a Trimble GeoXT GPS receiver. Position coordinates
were subsequently differentially corrected and estimated to average
b1 m horizontal accuracy. Each erosion bridge consisted of two 0.7-m
lengths of rebar driven ∼0.4 m into the ground, 1.2 m apart. Using a 1.2m-long carpenter's level, the height of each rebar post was carefully
adjusted to ensure that a level plane existed between the tops of the two
posts. Eleven measurements of the height of the carpenter's level above
the ground surface were taken at 0.1-m increments along each erosion
bridge (i.e., the first and eleventh measurement were separated by
1.0 m) on the installation date in Fall 2007. Basal cover, a measure of
cover at the soil surface (i.e., either soil or the rooted portion of
herbaceous or shrub vegetation) was recorded at each erosion bridge
measurement. Erosion bridge height and basal cover measurements
were repeated during 16–19 October 2008. Rate of surface change
(mm y− 1) for each measurement location was estimated by subtracting
the 2008 height value from the 2007 height value, dividing by the
number of days between the two measurement dates, and multiplying
by 365 days.
In order to minimize the effects of physical and biological
processes (e.g., cryoturbation, shrink/swell due to cycles of wetting
and drying, bioturbation) that might have impacted relative surface
elevations during the study period, measurements in Fall 2008 at
several individual locations and/or entire erosion bridges were either
not performed or subsequently removed from analysis because of: (i)
rebar posts that were no longer in level alignment, (ii) posts that
could not be located in unburned areas of dense vegetation, or (iii)
observation of an animal hole or mound at the measurement location.
Additionally, individual estimates of surface change indicating an
absolute value N50 mm were removed from analysis because they
were identified as extreme values likely to have been either
erroneously recorded in the field or indicative of newly formed
animal mounds, holes, and other forms of bioturbation that were not
properly noted when the measurements were made. The number of
measurements (and erosion bridges) in the final data set analyzed
were 1094 (97) at the severely burned area, 659 (59) at the
moderately burned area, 1278 (111) at the unburned area.
In order to estimate measurement error associated with our
erosion bridge measurements, the repeatability of surface change
measurements was assessed on the second measurement date for 10
bridges (i.e., N = 110 measurements) located in burned sites and 10
bridges located in unburned sites by calculating a standard error of the
lab (SEL):
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
2
∑ðY1 −Y2 Þ
SEL =
2N
where (Y1, Y2) are duplicate reference analysis and N is the number of
replicate pairs.
3.2. LiDAR data and processing
LiDAR data were acquired in November 2007 for an ∼60-km2 area
covering portions of the Twin Buttes fire, Moonshiner fire, adjacent
unburned area, and including all of the surface change measurement
locations (Fig. 1). LiDAR acquisition was performed by the National Center
for Airborne Laser Mapping (NCALM) using a Gemini ALTM© laser
scanner mounted on a fixed-wing aircraft flying at ∼700 m AGL. The
sensor acquired data with a pulse rate frequency of 71 kHz and a scan
frequency of 40 Hz. Average downtrack and crosstrack point spacing were
0.75 and 0.74 m, respectively. The data were collected in 21 flight lines
that averaged 537 m width with ∼50% overlap. Average point density of
the entire data set (i.e., overlapping flight lines) was 3.63 points m− 2 and
ranged from ∼1 point m− 2 in locations of no overlap to N6 points m− 2 in
locations with multiple overlapping flight lines.
Vertical accuracy of the LiDAR data were evaluated by NCALM using
2161 check points collected along the paved surface of U.S. Highway 20
located near the northern boundary of the LiDAR collection area. Check
points were collected with a vehicle-mounted GPS antenna (Ashtech
700700.c Marine antenna). Comparison of check points with nearest
neighbor LiDAR points indicated that the LiDAR data had relative
vertical bias of −0.050 m with a scatter of 0.051 m.
The LiDAR data consisted of up to four returns per LiDAR pulse. The
first two returns were included in the analysis for the sake of
processing efficiency, and the third and fourth returns comprised a
small and likely insignificant fraction (≤0.1%) of the data sets. LiDAR
data were spatially subset by study site, with one 200-m by 200-m
subset centered on each study site. Surface roughness was characterized using previously developed and described methods and LiDAR
tools (http://bcal.geology.isu.edu/envitools/index.html; Glenn et al.,
2006; Mundt et al., 2006; Streutker and Glenn, 2006). In this study,
surface roughness is defined as the standard deviation of all LiDAR
point elevations (i.e., reflected from ground and vegetation) within an
area (raster cell) of specified dimensions. LiDAR elevations were first
detrended to remove the effects of coarser scale topographic slope
(Davenport et al., 2004). Surface roughness was determined for each
subset at 1-, 2-, 3-, and 5-m raster cell resolution. A single surface
roughness value was estimated for each raster cell.
3.3. Scales of analysis
Comparison of data amongst severely burned, moderately burned,
and unburned areas represented the predominant and coarsest scale
of analysis for this study, which is henceforth referred to as landscape
scale. At the landscape scale, analyses were performed for surface
change data aggregated by erosion bridge as well as aggregated by
individual severely burned, moderately burned, and unburned sites
and by severely burned, moderately burned, and unburned areas.
Surface change data were also analyzed at two spatial scales that were
finer than landscape scale. These finer spatial scales are henceforth
termed among-playette/coppice scale and within-playette/coppice
scale. Analyses at among-playette/coppice scale focused on individual
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J.B. Sankey et al. / Geomorphology 119 (2010) 135–145
burned or unburned areas (i.e., either severely burned, moderately
burned, or unburned) and were generally performed for surface
change data aggregated by erosion bridge. At within-playette/coppice
scale, analyses focused on surface change measurements with
separation distances of 0.1–1.0 m (i.e., measurements made within
individual erosion bridges). The major source of surface variability at
within-playette/coppice scale was that of individual coppice or
playette units or the transition zone between the two types of
microtopographic units.
LiDAR surface roughness and relationships between LiDAR surface
roughness and surface change were analyzed at the landscape and
among-playette/coppice scales. The LiDAR data collected for this
project did not have a sufficiently fine spatial resolution for
application to within-playette/coppice scales.
3.4. Analysis of surface change and LiDAR surface roughness
The inverse of LiDAR-derived surface roughness values were
related to corresponding field-based surface change measurements at
the landscape and among-playette/coppice scales using correlation
and least squares regression (SPSS 15.0 for Windows) as well as
quantile regression analysis (QUANTREG package, R: A language and
environment for statistical computing). The inverse transformation
was used on surface roughness to linearize its relationship with
surface change as well as to stabilize the variability in surface change.
Quantile regression is a useful analysis technique when a response
variable is expected to vary at different rates as a function of the
predictor variable, depending on what portion of the response
variable distribution is analyzed (Cade and Noon, 2003). Because
the response variable (surface change) encapsulated two different
physical processes (i.e., erosion and deposition), we anticipated that
values representative of the higher quantiles (τ) of surface change
(values predominantly indicating deposition) might vary differently
as a function of surface roughness compared to values representative
of the lower quantiles (values predominantly indicating erosion) of
surface change conditional on surface roughness.
Plots of surface change and LiDAR-derived surface roughness vs.
corresponding basal cover measurements were examined qualitatively. Directional semivariograms were constructed along the SW–
NE and SE–NW axes (perpendicular and parallel to predominant wind
direction; Figs. 1 and 2) of surface change measurements aggregated
for each of the severely burned, moderately burned, and unburned
areas (Geostatistical Analyst, ArcGIS 9.3). The directional semivariograms were constructed with (i) a 5-m lag spacing and maximum
separation distance of 40 m and (ii) a 0.1-m lag spacing and maximum
separation distance of 1.0 m, as representations of the spatial
autocorrelation structure of surface change at the among- and
within-playette/coppice scales, respectively.
4.2. Spatial patterns of surface change
Experimental semivariograms demonstrated a random spatial
pattern of surface change at the among-playette/coppice scale in
severely and moderately burned areas (Fig. 5). In unburned areas,
surface change at among-playette/coppice and within-playette/
coppice scales occurred with a spatially random pattern as well,
indicated by experimental semivariograms that appeared to have a
relatively linear-flat structure (Figs. 5 and 6). Surface change in
burned areas demonstrated strong spatial autocorrelation structure at
the within-playette/coppice scale (Fig. 6). The spatial patterns were
directional in the severely burned area with a larger range for
measurements oriented perpendicular (in comparison to parallel) to
predominant wind directions. At the within-playette/coppice scale,
Site
4.1. Surface change
Vegetation in the burned areas in Fall 2007 consisted of burned
shrubs, with no herbaceous vegetation detected at the landscape scale
Table 1
Vegetation basal cover aggregated for burned and unburned areas.
Severe burn
Moderate burn
Unburned
(Table 1). Some vegetation, predominantly herbaceous, had regrown
in the burned areas by Fall 2008 (Table 1). Mean basal shrub cover
ranged from 0 to 2% at the severely and moderately burned sites,
respectively, in Fall 2007 (Table 2). Unburned sites spanned a wider
range of vegetation with mean total basal cover (shrub + herbaceous)
ranging from 9 to 28% amongst sites in Fall 2007 (Table 2).
The severely burned area had mean (SE) rate of surface change
of −2.1 (0.2) mm y− 1, indicating net deflation of the soil surface;
while the moderately burned area had mean (SE) rate of surface
change of −0.1 (0.2) mm y− 1 (Fig. 3). The unburned area, which was
downwind of the burned areas, had mean (SE) rate of surface change
of 1.5 (0.1) mm y− 1, indicating net inflation (Fig. 3). Standard error of
the lab values, a measure of repeatability for surface change
measurements, were smaller for burned surfaces (SEL = 1.3 mm)
likely because the lack of vegetation afforded greater accuracy in
repeat measurements compared to the unburned surfaces
(SEL = 2.3 mm). Some of the variability in surface change observed
at the among-playette/coppice scale (Fig. 3) might have been
influenced by the installation date of study sites (Table 2). For
example, substantially large deflation was observed for the first
severely burned site to be installed (S1), whereas substantially large
inflation was observed for the first installed unburned site (U1)
(Fig. 3). Plots of surface change with basal cover measured in Fall 2007
demonstrate a trend from deflation to inflation with increasing shrub
vegetation, in general, though the largest values of mean inflation
were observed for sites with low-intermediate shrub basal cover
(Fig. 4). A similar relationship was observed between surface change
and basal cover when total (shrub + herbaceous) vegetation basal
cover was considered for the unburned sites (Fig. 4).
Table 2
Install date and vegetation aggregated for severe (S), moderate (M), or unburned
(U) sites.
4. Results
Area
139
Mean (SE) basal cover %
fall 2007
Mean (SE) basal cover %
fall 2008
Shrub
Herbaceous
Shrub
Herbaceous
0.5 (0.2)
1.4 (0.4)
7.6 (0.7)
0.0 (0.0)
0.0 (0.0)
8.6 (0.8)
0.8 (0.3)
1.4 (0.4)
8.2 (0.7)
5.4 (0.7)
5.3 (0.9)
10.1 (0.8)
S1
S2
S3
S4
S5
M1
M2
M3
U1
U2
U3
U4
U5
U6
Install date
8/23/2007
8/29/2007
9/18/2007
9/19/2007
10/4/2007
8/28/2007
8/29/2007
9/19/2007
8/28/2007
8/30/2007
8/31/2007
9/6/2007
10/2/2007
10/3/2007
Mean (SE) basal cover % fall 2007
Shrub
Herbaceous
0.0 (0.0)
0.4 (0.4)
0.0 (0.0)
2.3 (1.0)
0.0 (0.0)
0.4 (0.4)
1.4 (0.8)
2.3 (1.0)
4.1 (1.3)
2.2 (1.0)
14.5 (2.4)
11.8 (2.2)
6.4 (1.6)
6.8 (1.7)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
0.0 (0.0)
7.3 (2.0)
6.4 (1.6)
10.0 (2.0)
16.4 (2.5)
6.8 (1.7)
4.5 (1.4)
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Fig. 3. Mean (with standard error bars) surface change and LiDAR-derived surface roughness measurements for severely burned, moderately burned, and unburned areas and for
individual study sites within these areas. Surface roughness was calculated as the standard deviation of all LiDAR point heights (ground + vegetation) per raster cell (2-m resolution)
after the point heights were detrended for topographic slope.
semivariogram models for burned surfaces had ranges b0.85 m
(Fig. 6). This suggests that patterns of surface change occurred within
landscape units (i.e., length scales) b0.85 m in length and/or width on
burned surfaces.
4.3. LiDAR surface roughness
At the landscape scale, the severely burned area had (on average)
small surface roughness values, the unburned area had large surface
roughness values, and the moderately burned area had intermediate
values (Fig. 3). Roughness increased with increasing shrub basal cover
at the landscape scale, in general, though the largest mean roughness
values were observed for unburned sites with low-intermediate shrub
basal cover (Fig. 4). A similar relationship was observed between
surface roughness and basal cover when total (shrub + herbaceous)
vegetation basal cover was considered for the unburned sites (Fig. 4).
Site S1 had small roughness values relative to the other four severely
burned sites, and site U2 had large roughness values relative to the
other unburned sites, providing examples of variability in LiDAR
roughness at the among-playette/coppice scale (Fig. 3). Relationships
of surface roughness values amongst sites and amongst the three
surface types were consistent for rasters processed at 1-, 2-, 3-, and 5m cell size.
4.4. LiDAR surface roughness and surface change
Fig. 4. Mean (with standard error bars) surface change and LiDAR-derived surface
roughness (refer to Fig. 3) vs. shrub vegetation basal cover (Fall 2007) for severely
burned, moderately burned, and unburned study sites.
Surface change was strongly, linearly and negatively related to the
inverse of surface roughness derived from LiDAR at the landscape
scale (Fig. 7). The strongest relationship (r2) using least squares
regression was observed with raster cell size of 2 m, though results
were very similar at 1-, 3-, and 5-m raster cell size (Table 3). The
relationship implies that smoother surfaces (burned areas) were
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141
Fig. 5. Autocorrelation structure of surface change measurements at the among-playette/coppice scale, as depicted by directional semivariograms constructed for erosion bridge data
aggregated by severely burned, moderately burned, and unburned areas with 5-m lag and 40-m maximum separation distance. Semivariograms depict the spatial dependence
(semivariance: y-axis) of samples as a function of separation distance (distance: x-axis). Smaller semivariance indicates greater relative spatial dependence (autocorrelation).
characterized by deflation, while rougher surfaces (unburned) were
characterized by inflation. The modeled relationship suggests that a
transition from deflation (i.e., erosion) to inflation (i.e., deposition)
occurred at a surface roughness of ∼ 0.05 m (i.e., inverse surface
roughness ∼ 20 m− 1; Fig. 7). Although the first sites installed in the
severely burned and unburned areas appeared to be potentially
influential and outlying points, when these points were removed from
analysis the model fit only improved very slightly (r2 = 0.78, p b 0.00,
for 2-m raster cell size).
At the landscape scale, quantile regression analysis of erosion
bridge mean surface change versus inverse surface roughness
indicated that changes in surface roughness corresponded to
proportionally greater changes in surface change for lower τ values
(i.e., erosion) in comparison to higher τ values (i.e., deposition),
where τ values signify quantiles of surface change conditional on
surface roughness (Fig. 8). The estimated slopes (β1) for τ = 0.10 and
0.25 (β1 = −0.27 and −0.21, respectively) were 2–4 times as large as
the estimated slopes for τ = 0.50, 0.75, and 0.90 (β1 = −0.10, −0.08,
and − 0.06, respectively), for example (Fig. 8). Quantile regression
slope and intercept coefficients were significant for τ = 0.1, 0.25, 0.5,
0.75, 0.90 [all p b 0.05 and SE(β1) = 0.06, 0.03, 0.03, 0.02, 0.02,
respectively]. Overall, findings suggested that the effect of surface
roughness on aeolian transport was greater for erosion versus
deposition processes.
Relationships between surface roughness and surface change at
the among-playette/coppice scale were consistent with, though not as
strong as, observations at the landscape scale. Mean surface change
(aggregated by erosion bridge) was negatively related with inverse
surface roughness; and the relationship was significant within the
unburned and severely burned areas, but not the moderately burned
area [severe burn: Pearson correlation coefficient (R) = − 0.24,
p = 0.01; moderate burn: R = − 0.21, p = 0.11; unburned: R =
−0.22, p = 0.02]. When performed within the burned and unburned
areas, quantile regression analysis of surface change versus inverse
surface roughness produced models with intercept and/or slope
coefficients that were not statistically significant (p N 0.05).
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Fig. 6. Autocorrelation structure of surface change measurements at the within-playette/coppice scale, as depicted by directional semivariograms (refer to Fig. 5) constructed for
erosion bridge data aggregated by severely burned, moderately burned, and unburned areas with 0.1-m lag and 1.0-m maximum separation distance.
5. Discussion
5.1. Surface change — landscape scale
A simple calculation suggests that 1.0 × 108 and 1.3 × 105 kg of soil
might have been mobilized by wind from burned surfaces at the Twin
Buttes and Moonshiner fires, respectively (mean surface change × area burned × assumed bulk density of 1.25 g/cm3). Results
imply that a net loss of soil occurred from the burned areas and that
Table 3
Site mean surface change (y) vs. site mean inverse LiDAR surface roughness (x).
Fig. 7. Relationship of mean surface change aggregated by site with the inverse of
LiDAR-derived surface roughness (refer to Fig. 3) amongst severely burned, moderately
burned, and unburned sites.
Raster cell size
Model
r2 (p)
MSE
SE (estimate)
1m
2m
3m
5m
y = − 0.3x + 4.9
y = − 0.3x + 5.6
y = − 0.3x + 4.8
y = − 0.3x + 4.2
0.76
0.77
0.75
0.73
1.03
0.99
0.88
1.18
1.02
0.99
0.94
1.09
(0.00)
(0.00)
(0.00)
(0.00)
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143
5.2. Surface change — finer scales
Fig. 8. Mean surface change aggregated by erosion bridge vs. the inverse of surface
roughness (refer to Fig. 3) for corresponding nearest neighbor LiDAR pixels amongst
severely burned, moderately burned, and unburned areas. Values for τ represent the
quantile of the surface change distribution analyzed conditional on the inverse of
surface roughness; values for β are the quantile regression slope coefficients.
some soil particles were likely transported to the unburned area that
was located downwind over the course of one year, post-fire. Surface
change measurement locations in the severely burned area were
positioned north of measurement locations in the unburned area due
to logistical constraints, which limited our scope of inference
concerning the transport of sediment from the burned to unburned
areas. We did visually observe clouds of dust that traveled from the
burned to the unburned areas during the course of the year-long
study, however. Localized erosion and deposition occurred within
burned and unburned areas, as well (Fig. 8). The small net deflation
observed within the Moonshiner fire and net inflation observed
within the unburned area might not have been different than no
change when considered with regard to measurement error (SEL).
The mean rate of surface deflation observed in the severely burned
area (− 2.1 mm y− 1) appeared smaller than that measured by
Whicker et al. (2002) who estimated a mean of − 5.8 mm 162 d− 1
from six erosion bridges in a burned semiarid shrubland in New
Mexico. A mean inflation of 1.9 mm 162 d− 1 was estimated by
Whicker et al. (2002) from six erosion bridges in an unburned area,
which appears larger than the range observed at the unburned sites
we studied (0.81–3.54 mm y− 1). To what extent inflation at the
unburned site studied by Whicker et al. (2002) was resultant of
background atmospheric deposition versus having been transported
from the burned area is unclear. The net inflation we observed in the
unburned area appears to be substantial relative to background
atmospheric deposition that might be expected in the absence of local
wildfire and subsequent aeolian transport. Analyses and reviews of
dust deposition rates in the western USA measured with sediment
traps have found ranges of 1–48 g m− 2 y− 1 for suspension-sized
particles at sites across seven states (Reheis and Kihl, 1995), and 2–
20 g m− 2 y− 1 for long-term monitoring sites in the southern Great
Basin and Mojave desert, USA (Reheis, 2006). Dividing the rates
presented by Reheis and Kihl (1995) by a particle density of
2.65 × 106 g m− 3 suggests a range of background atmospheric
deposition of 0.0004–0.02 mm (in units of thickness) per year. In
contrast, the net rate of inflation we observed in the unburned area
was 1.5 mm y− 1. How deposition rates measured with sediment traps
such as those used by Reheis and Kihl (1995) translate to vertical
accumulation of sediment across an actual landscape surface,
however, is uncertain as such measurements represent a lower limit
of background atmospheric dust that might accumulate in soil (M.C.
Reheis, United States Geological Survey, personal communication,
2009).
Aeolian transport resulted in zones of erosion and deposition that
were comparable in size or smaller (average dimensions
b0.85 × 0.85 m as indicated by semivariogram ranges; Fig. 6) than
the surface microtopography in burned areas (e.g., mean playette
dimensions = 1.66 × 0.86 m). The zones of erosion and deposition
were anisotropic in shape on the severely burned surfaces, with a long
axis oriented perpendicular to prevailing winds. In contrast, erosion
and deposition occurred in unburned areas with a random spatial
distribution. Based on these results, erosion and deposition appeared
to occur with a strong spatial pattern at scales comparable to and finer
than the microtopography on burned, but not unburned surfaces.
Our findings at finer scales appear analogous to those of Whicker
et al. (2002) who found differences in surface change resultant from
wind erosion in shrub versus intershrub patches, in burned but not
unburned areas. Our findings at finer scales appear counter to the
conceptual model for aeolian transport in burned shrublands and
shrub-encroached grasslands presented by Ravi et al. (2007, 2009)
and Ravi and D'Odorico (2009). In their model, the microtopography
of burned landscapes was homogenized by the removal of sediment
by wind from the raised coppice surfaces and deposition in the lower
interspaces, whereas our findings suggested that substantial erosion
and deposition might have occurred within burned coppices and
interspaces in our study environment. Similarly, our findings also
appear counter to those of Hilty et al. (2003) who found that the
regular pattern of coppice and playette microtopographic units
evolved to a flatter, disturbed surface following burning in a shrub
steppe landscape in Idaho, similar to our study area. Hilty et al. (2003),
however, analyzed the evolution of surfaces over timescales of
multiple years, post-fire; and the evolutionary mechanism they
proposed included a combination of burning, livestock trampling,
cheatgrass (Bromus tectorum L.) invasion, and water erosion, though
not aeolian transport. The areas we examined were not grazed during
the course of the study, and cheatgrass has not invaded the study
areas yet. The degree to which post-fire aeolian transport serves to
increase either the heterogeneity or homogeneity of surface microtopography through the redistribution of sediment is important, as it
can directly influence the cycling and spatial distribution of
biologically important nutrients, as well as the evolution of plant
communities that affects surface roughness and provides feedback to
aeolian processes (Su et al., 2006; Ravi et al., 2007; Li et al., 2008).
5.3. Surface change and LiDAR surface roughness
Characterization of surface (ground + vegetation) roughness with
LiDAR indicated that severely burned surfaces were generally
smoother than moderately burned surfaces, which were in turn
smoother than unburned surfaces. Results suggested that LiDAR
detected variability in roughness that existed largely because of the
degree of vegetation present and/or absent amongst the three types of
surfaces. Surface change varied as a function of surface roughness at
the landscape scale, and the modeled relationship supported the
hypothesis that smooth, burned surfaces are characterized by
deflation and that adjacent rough, unburned surfaces are characterized by inflation. The transition from erosion to deposition in the
modeled relationship (Fig. 7) occurred at surface roughness ∼0.05 m
(i.e., inverse surface roughness ∼ 20 m− 1) that approximately
corresponded with the threshold in roughness between burned and
unburned surfaces. Streutker and Glenn (2006) found burned and
unburned shrub steppe to have mean vegetation roughness of ∼ 0.05
and 0.09 m, respectively, in a nearby study location. The modeled
relationship appeared in accord with the inverse relationship
between surface roughness and sand entrainment that Pelletier et
al. (2009) employed for modeling coastal dune migration.
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Quantile regression analysis indicated that incremental changes in
surface roughness were related to proportionally greater
corresponding changes in erosion compared to deposition. Quantile
regression analysis suggested that deposition increased gradually
with increased roughness, whereas erosion increased rapidly with
decreased roughness. Erosion is generally expected to increase with
decreased vegetation cover and density, though complexity in these
relationships exists at lower densities of vegetation because of the
spatial structure of turbulence and related potential for increased
erosivity adjacent to individual roughness elements (e.g., shrubs)
(Fryrear, 1985; Findlater et al., 1990; Lee, 1991a,b; Raupach et al.,
1993). Deposition, in contrast, generally occurs in shrublands when
sediment is trapped by vegetation and is greatest at intermediate
levels of vegetation porosity (Grant and Nickling, 1998; Raupach et al.,
2001; Lee et al., 2002; Okin et al., 2006). While we did not relate
surface change or surface roughness to vegetation porosity, we did
relate these variables to field measurements of vegetation basal cover,
a measure of vegetation abundance. Results implied that both surface
roughness and sediment deposition were greatest for low to
intermediate values of vegetation basal cover.
Breshears et al. (2009) suggested that aeolian transport is greatest
for intermediate levels of shrub cover in undisturbed but not
disturbed shrublands. Our plotted data suggested that the greatest
deposition corresponded with intermediate values of surface roughness (e.g., inverse surface roughness ∼ 15–30 m− 1 in Fig. 8), though
we did not specifically model a nonlinear relationship for the upper
quantiles of surface change. We also did not specifically model
separate relationships for undisturbed and disturbed surfaces. All of
the surfaces on which we detected deposition were likely subjected to
sediment transported from burned surfaces that were upwind as well
as background atmospheric deposition.
Relationships between surface roughness and surface change at
the among-playette/coppice scale were consistent with, though not as
strong as, at the landscape scale. The low correlation might be
resultant of several sources of measurement error, including the
average: vertical (∼5 cm) and horizontal (b1 m) errors in LiDAR
measurements, horizontal error (b1 m) in erosion bridge GPS
locations, and vertical error (∼1–2 mm) in erosion bridge surface
change measurements. We also acknowledge that erosion bridges are
designed to measure surface change irrespective of whether the
change was driven by wind or water transport. Some water-driven
transport certainly might have occurred in erosion bridge locations
during the study, however, based on our previous research (Sankey et
al., 2009a,b) and observations in the field sediment transport was
predominantly driven by wind in the burned environment that we
studied. In light of the findings of spatial patterns of aeolian transport
at finer-than-landscape scales, future research to examine relationships of aeolian transport with surface roughness from higher
resolution LiDAR (e.g., increased point density or ground-based
LiDAR systems) is needed.
6. Conclusion
LiDAR characterization of the roughness of burned and unburned
surfaces suggested that the reduction of vegetation by fire produced
surfaces that were relatively smooth compared to unburned surfaces
in shrub steppe at a landscape scale. Surface change because of
subsequent aeolian transport varied as a function of surface roughness
at the landscape scale. Burned surfaces that were smooth were
characterized by net erosion, and the adjacent unburned surfaces that
were rough were characterized by net deposition. Furthermore,
erosion appeared to vary at a greater rate as a function of surface
roughness compared to deposition amongst burned and unburned
surfaces. This study also found that predominant patterns of surface
change occurred at and within the scale of microtopography on
burned surfaces. These findings are important for understanding
feedbacks between soil processes and vegetation establishment, as
well as longer-term evolution of post-fire surfaces in shrublands of
cold desert environments such as the Snake River plain. Future
research that further elucidates the relationships of aeolian erosion
and deposition processes with surface roughness at microtopographic
to landscape scales and over longer time periods is required, as these
processes can drive the redistribution of sediment within and
between disturbed and undisturbed landscapes. Such processes
have important implications for landscape connectivity and cycling
of sediment, nutrients, and pollutants within and amongst geomorphic, ecological, and management boundaries.
Acknowledgments
This material is based on work supported in part by the U.S. Army
Research Laboratory and the U.S. Army Research Office under grant
number W911NF-07-1-0481. LiDAR data were provided through a
seed grant from the National Center for Airborne Laser Mapping
(NCALM) supported by NSF. Additional support was provided by an
Inland Northwest Research Alliance Ph.D. research fellowship. S.M.
Stoller Corporation provided logistical and technical support at the
Idaho National Laboratory. The authors specifically wish to acknowledge Dr. Roger Blew and Jeremy Shive of S.M. Stoller Corporation for
their support of this project. Amber Hoover and Eli Eversole assisted in
field measurements.
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