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Extracting Structural Vegetation Components From
Small-Footprint Waveform Lidar for Biomass
Estimation in Savanna Ecosystems
Joseph McGlinchy, Member, IEEE, Jan A. N. van Aardt, Barend Erasmus, Gregory P. Asner, Member, IEEE,
Renaud Mathieu, Konrad Wessels, David Knapp, Ty Kennedy-Bowdoin, Harvey Rhody, Member, IEEE,
John P. Kerekes, Senior Member, IEEE, Emmett J. Ientilucci, Member, IEEE, Jiaying Wu, Member, IEEE,
Diane Sarrazin, and Kerry Cawse-Nicholson
Abstract—Measurement of vegetation biomass accumulation is
critical for ecosystem assessment and monitoring, but doing so typically involves extensive field data collection that yields relatively
crude structural outputs, e.g., plot- or site-level metrics. This study
assessed the utility of airborne light detection and ranging (lidar)
waveform features to explain structural and biomass variation in a
savanna ecosystem across a land-use gradient. The ability of aboveground waveform lidar features to model field-based woody and
herbaceous biomass measurements was evaluated statistically by
regression models using forward variable selection. Waveform features explained 76% of the variation in woody biomass in a regulated communal land use area (RMSE = 29.0 kg). The waveform
features were also correlated to herbaceous measurements in the
same land-use area, with increased correlations at higher biomass
levels. These results indicate that small-footprint waveform lidar
data potentially can be used as a single modality to describe heterogeneous woody cover in a savanna environment; however, further research is warranted during the full growing season to fully
evaluate its performance.
Index Terms—Biomass estimation, feature extraction, lidar,
waveform.
Manuscript received February 07, 2013; revised May 21, 2013; accepted June
25, 2013. Date of publication August 15, 2013; date of current version February 03, 2014. This work was funded through the Masters of Science program
in Imaging Science at the Chester F. Carlson Center for Imaging Science at
Rochester Institute of Technology, NY, USA.
J. McGlinchy is with the Environmental Systems Research Institute, Redlands, CA 92373 USA (corresponding author, e-mail: [email protected]).
J. A. N. van Aardt, H. Rhody, J. P. Kerekes, E. J. Ientilucci, and
K. Cawse-Nicholson are with the Chester F. Carlson Center for Imaging
Science, Rochester Institute of Technology, Rochester, NY 14623 USA
(e-mail: [email protected]; [email protected]; [email protected];
[email protected]; [email protected]).
G. P. Asner, D. Knapp, and T. Kennedy-Bowdoin are with the Carnegie Institution for Science, Stanford University, Stanford, CA 80309 USA (e-mail:
[email protected]; [email protected]; [email protected]).
B. Erasmus is with the School of Animal, Plant and Environmental Science,
University of the Witwatersrand, Johannesburg, South Africa (e-mail: Barend.
[email protected]).
R. Mathieu is with the Council for Scientific and Industrial Research, Pretoria,
South Africa (e-mail: [email protected]).
K. Wessels is with the Council for Scientific and Industrial Research, Pretoria,
South Africa, and also with the Centre for Geoinformation Science, Department
Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria,
South Africa (e-mail: [email protected]).
J. Wu is with Apple, Inc., Cupertino, CA 95014 USA (e-mail:
wjy@[email protected]).
D. Sarazzin is with the Department of National Defence Canada, Ottawa,
Ontario, Canada (e-mail: [email protected]).
Digital Object Identifier 10.1109/JSTARS.2013.2274761
T
I. INTRODUCTION
HE NEED forsynoptic descriptionsofecosystem structure,
specifically biomass or carbon estimation, has received
much attention (e.g., [1]–[3]). Such efforts have implications for
the management of carbon stocks [4], maintenance of structural
biodiversity [2], and support for sustainable resource extraction
[3], among other ecological applications. However, scalable
assessment of savanna ecosystem structure, specifically the
structure coupled to disparate vegetation components, e.g., trees
vs. bush vs. grass structures, remains elusive. Typical inventory
efforts rely on local-scale labor-intensive field surveys, which are
constrained by their inability to draw regional inferences; an example includes transects covering only 1 ha [5], [6]. Researchers
have circumvented this challenge for savanna ecosystems by
using a novel assessment alternative, discrete return light detection and ranging (lidar) remote sensing [1], [2]. The technology
for remote sensing of vegetation using lidar has seen considerable
advancement with the advent of full waveform digitizing sensors.
The intensity of the returning laser pulse is recorded as a function
of the time between laser emittance, interaction with a target
surface, and detection by the sensor. Waveform lidar sensors have
the advantage of recording the backscattered energy at a very high
sampling rate to derive information on waveform shape, typically
on the order of nanoseconds. The combination of high temporal
resolution detection and full backscatter digitization enables the
extraction of structural information that is embedded within the
waveform [7].
Various studies have shown that signal metrics, calculated
from large footprint lidar waveforms (footprint diameters on
the order of tens of meters), can be used to assess vegetation
structure in forested environments. Lefsky et al. [8] have shown
that waveform lidar measurements were able to explain 70%
of the variance in stand basal area and 80% of the variance in
aboveground biomass in a deciduous forest environment. In
another study, Lefsky [1] also found high correlations between
waveform-derived canopy heights and aboveground biomass
in boreal, coniferous, and deciduous forested environments
, 0.65, 0.87, respectively). Boudreau [9] developed
(
a generic airborne lidar-based biomass equation
from small-footprint lidar-derived mean and quadratic mean
canopy heights, while Neuenschwander [10] demonstrated that
features such as the canopy, ground, and the height of median
energy from small-footprint lidar waveforms can be used to
classify various land cover types with up to 85% accuracy.
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Fig. 1. (a) Map of South Africa and study area for this work. Our study focused on the protected Kruger National Park and the communal rangelands of Bushbuckridge (see localization of Land Uses 2, 7, and 8 in central map), along with site- (50 50 m, 36 plots) and plot-level field sampling strategy. Examples of
herbaceous biomass plot samples for Land Use 2, 7, and 8 are shown in (b), (c), and (d), respectively.
Drake [11] showed that measures such as tree height, crown
volume, and biomass could be accurately predicted and modeled from the waveform height of median energy and upper
quantile energy measurements, resulting in good correlation between waveform-derived features and estimated above-ground
biomass ( up to 0.94) for large footprint systems.
However, certain challenges remain in terms of land cover
assessment through biomass estimation, specifically: (i) most
previous work has dealt with large-footprint systems in forested
environments, with results in the measured field data typically
being an order of magnitude smaller in actual ground area than
the laser footprint and (ii) a detailed breakdown of woody,
herbaceous, and bare ground structural components along the
laser trajectory, similar to the “end member” concept in an
imaging spectroscopy context, is still lacking [12], [13]. This
latter aspect has bearing on our ability to map land cover types
in the structural (3D) domain, as opposed to the traditional
spectral approaches. For example, woody and herbaceous
vegetation often is highly intermixed in savanna ecosystems,
making them difficult to separate in terms of carbon and system
functional dynamics [14].
The objective of this study was thus to establish a method by
which to extract structural components, e.g., woody and herbaceous, from small-footprint lidar waveforms as they relate to
field biomass measurements.
II. METHODOLOGY
A. Study Area and System Specifications
The area under study encompasses land within and surrounding the Kruger National Park (KNP) in South Africa,
bounded by (
” S;
” E) and (
” S;
” E) (see Fig. 1(a)) and spanning an east-west land-use
gradient. This gradient is defined by sampling in the Kruger
National Park (state-owned conservation), Sabie Sands game
reserve (private conservation), and Bushbuckridge (communal rangelands; high rural population density) areas. The
topography is gently undulating with a slowly decreasing
terrain height toward the East, with an average elevation of
approximately 450 m. Vegetation communities are influenced
largely by geomorphological and pedological processes at the
landscape level. Dominant geology includes granite and gneiss
with local intrusions of gabbro. Vegetation has a discontinuous
overstory of woody plants, mostly in the 2–5 m height category,
and an herbaceous layer dominated by C4 grasses [15]. The
vegetation communities are classified as granite lowveld or
gabbro grassy bushveld [16]. This study area enabled an assessment of the various waveform lidar features and their ability
to describe structural parameters across a range of vegetation
structures and land uses.
The Carnegie Airborne Observatory (CAO) Alpha system
was used for acquiring the lidar data. CAO-Alpha consisted of
an integrated imaging spectrometer (hyperspectral sensor) and
a small-footprint scanning-waveform lidar system, which also
collects discrete return data. These systems are geometrically
well integrated by using a common global positioning system/
inertial measurement unit (GPS/IMU) data stream, boresight
alignment among sensor field-of-views, and co-mounting of instrumentation on a single, integrated platform. The waveform
lidar system operated at 1064 nm wavelength with full waveform digitization at one nanosecond temporal/vertical resolu-
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Fig. 2. Clockwise from top left: Tree height distributions for sites in land-use 2, 7, and 8 These graphs illustrate the range and variability in tree height across the
study area.
tion; this effectively implies signal digitization for every 0.15 m
for single, non-return direction of travel. The laser beam divergence was 0.56 mrad, corresponding to a 0.56 m ground footprint at an operational flying height of 1000 m above ground
[17].
Field data were collected in association with the airborne
data collection campaign during April-May 2008; this is the late
autumn/early winter season, with many trees showing signs of
leaf loss. This was highly variable, though, with certain species
retaining more of their leaves at this time of year. The field
data were collected from seven sites, distributed across two
land use (LU) types: a protected area within KNP (LU2, four
sites) and communal rangelands (LU7, three sites and LU8,
two sites) located within the Bushbuckridge area (Fig. 1(a)).
Each site consists of approximately 36 plot-level measurements
of herbaceous biomass, tree height and diameter, species, and
a qualitative assessment of cover (vegetation health, crusting,
bare soil, herbaceous, and woody cover) [18]. The sites are
50 50 m in size and were laid out with 10 m spacing between
plots, resulting in a grid-like pattern (see Fig. 1(a)). A Trimble
(Trimble® Recon® Handheld with aerial backpack) or Leica
(GS20 Professional Data Mapper with handheld aerial) differential GPS was used to collect accurate geographic coordinates
for each plot, which were differentially corrected to sub-meter
accuracy using the Nelspruit trigonometric base station one
second data (http://www.trignet.co.za/). The woody biomass
was sampled with variable-radius plots defined by the average
between-tree distance at each site, i.e., larger diameter was
used when average distance between trees increased; plot
radii ranged between 4–5 m. Histograms of the tree heights
measured in each land use are shown in Fig. 2. The herbaceous
biomass was sampled within a 0.25 m quadrant at the plot
center. Examples of the herbaceous biomass sampled in each
land use are shown in Figs. 1(b)–1(d). Summary statistics of
the woody and herbaceous biomass sampled in this study are
provided in Table I.
B. Feature Extraction
For a waveform to accurately represent the scattering crosssection, the incoming waveform requires physics-based preprocessing: (i) deconvolution with the outgoing pulse and the lidar
system response; (ii) tying ground interactions to the digital elevation model (DEM), i.e., for analysis of waveform interaction
in terms of height-above-ground; and (iii) correction of interactions for off-nadir effects. The reader is referred to a detailed
description of this processing chain by Wu et al. [19].
Our goal was to extract features related to the structural
components in the lidar waveforms based on our knowledge
of woody and herbaceous components present in the savanna
environment (Fig. 3). As the light pulse propagates through the
canopy to the ground, earlier interactions, i.e., those further
from the ground, are more correlated to the woody vegetation
(e.g., trees) and as the pulse approaches ground level, the
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Fig. 3. Potential structural components in a lidar waveform. (left) an example of pulse interaction with potential canopy, low-lying structure, and ground. (right)
lidar pulse interaction with various structural components embedded within the waveform. Picture is from the study area.
TABLE I
SUMMARY STATISTICS OF HERBACEOUS AND WOODY BIOMASS
SAMPLED IN THIS STUDY
process of removing the bare ground signal component was
accomplished by defining a bare ground reference waveform
and subtracting it from the waveform of interest after aligning
the bare-ground points. The bare ground reference waveform
was computed from samples of dirt roads identified on the hyperspectral images from the concurrent data collection, via the
assumption that such roads represent hard, diffuse scatterers.
The subtraction was done in such a manner so as to minimize
the amount of information removed from the waveform. This
was done to account for intensity variations between ground
peaks due to varying levels of energy attenuation, e.g., in the
case of a waveform with multiple aboveground interactions
(reduced ground return intensity) compared to a single bare
ground return. The amplitude of the ground reference waveform
therefore was matched to each waveform before performing
the subtraction by minimizing , defined as
(1)
interactions are more indicative of shrubby or herbaceous
vegetation [19].
1) Ground Removal: The CAO system’s relatively broad
16 ns outgoing pulse width allowed for a consistent ground
level laser interaction on a per-pulse basis. Extracting this bare
ground interaction from every waveform was hypothesized
to leave only aboveground interactions remaining in each
lidar waveform, including finer vegetation structure associated
with the ground, such as grass/herbaceous components. This
where
is the waveform under consideration,
is
the bare ground (road) reference waveform,
is the
ground reference with the amplitude matched to
, and
is the window over which the subtraction is being performed
(full-width-half-maximum (FWHM) of
). This process
is illustrated in Fig. 4 for cases of low to high signal biomass
present in the waveform. Although this removal of the per-pulse
ground interaction could potentially remove information related
to grassy content, we assume that the approach described above
would limit such omissions to removal of data related to the
bare ground components of the lidar return. This assumption
is based on the use of a hard target, diffuse scattering signal,
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Fig. 4. Clockwise from top left: an illustration of the ground removal algorithm as applied to waveforms of low to medium to high biomass plots. The solid, bold
signal represents the waveform after ground removal. The dashed line shows the portion of the signal that was removed by the algorithm. This resultant waveform
is then analyzed for above-ground interactions.
based on a ground return sample from gravel roads in the study
area.
Three structural metrics were extracted based on the amount
of ground information removed from the waveform: (i) the
ground ratio (roadRatio, e.g., the value of discussed above),
(ii) the fraction of the waveform removed by the subtraction
(ratioOut), and (iii) the amount of the ground pulse remaining
after the ground removal (leftover), defined as the area of
the ground pulse after ground removal divided by the area of
the original ground pulse. These metrics enabled a detailed
characterization of the ground-level laser interactions.
2) Aboveground Features: Waveform features extracted in
this study were similar to those identified by Neuenschwander
et al. [10] and the United States Geological Survey (USGS) [20]
and included the height of median energy (HOME), total integrated energy, canopy energy, ground energy, and canopy ratio
(CRR). HOME is defined as the height at which the integrated
energy of the waveform is equal to half of the energy of the
waveform above the ground return. The total energy of the lidar
signal, , is defined in Equation 2 (discretized version of [21])
as
(2)
where
is the received backscattered signal,
is the total
number of time bins, and the summation sign is used because
of the discretized nature of the signal. Canopy ratio (CRR) is
defined as the integral of the portion of the waveform return
reflected from above ground divided by the integral of the entire
waveform return [20]. An adaptation of the CRR metric (aCRR)
was also introduced to focus on the upper canopy elements. This
was achieved by considering the integral of the waveform up to
and including the first major laser–target interaction, rather than
the entire aboveground return, similar to CRRmin, as defined
by Muss et al. [22]. The first major interaction was defined as
the first valley following the first peak of a multiple-interaction
waveform.
A novel metric, related to the integrated energy of the signal,
was derived by measuring the amount of time it takes for ,
the lidar signal for a single interaction, to increase from 10%
to 90% of its total integrated energy. This measure was defined
as duration and is indicative of the type of structural component present in the waveform, since a waveform containing multiple interactions will theoretically take longer to bridge the time
from 10% to 90% of the total interaction. This metric is sensitive to the height above ground at which the laser-surface interaction occurs, and as such is hypothesized to be sensitive to
larger biomass, as is found in taller trees.
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C. Biomass Modeling
The metrics extracted from the waveforms were used as inputs to a forward selection regression model in Statistical Analysis Software (SAS, v9.2) to identify which features best describe the field-measured biomass. This approach successively
introduced variables into the model and retained them only if the
statistical performance of the model was improved [25], [26].
For a woody biomass estimate, plots that contained no woody
biomass or only small trees
were not included
in the modeling analysis.
The total number of structural variables extracted for each
plot was 18. The significance level for variable entry into the
model was set to
. As more variables enter the model,
the goodness of fit
was penalized via the adjusted
fit
metric [27]. The general equation of the regression model is
defined by
(4)
Fig. 5. First and second derivative summary statistics calculated per waveform
across a site in Land Use 8. Lighter areas represent larger values.
One other metric used to describe canopy presence was calculated as the volume of canopy present in each plot. This was
calculated as
(3)
where is the area defined by the spatial resolution of each
waveform (e.g.,
, is the footprint of the laser pulse) and
is defined as the height above ground of the first laser–canopy
interaction.
3) Derivative Features: Previous work has identified stressrelated spectral features for vegetation assessment using hyperspectral derivative analysis, especially for near-infrared spectral regions [23]. We therefore hypothesized that derivatives of
laser–target interactions in a vegetation environment are related
to how the scattering surface reflects light at the laser wavelength (1064 nm). Summary statistics of the lidar waveform’s
first and second derivative were computed as additional metrics
related to the aboveground structure. These included the mean,
median, mode, standard deviation, and range of the first and
second derivative signals. Since the signals were not normalized for intensity, the derivative signals were normalized by the
peak value to represent a percentage of the maximum change in
slope on a per-waveform basis. Derivatives are also shape descriptors, and as such should be independent of amplitude [24].
Fig. 5 shows image representations of these derivative statistics
for a site in Land Use 8; it is immediately clear that these measures are visually correlated to above-ground structure.
It should be noted that these metrics are by no means exhaustive, but arguably present a robust set, as defined by previous
studies and inclusive of novel approaches, for describing the
woody and herbaceous biomass in a savanna ecosystem. The
range of values for each feature extracted from each plot-level
waveform are summarized relative to the minimum and maximum lidar-estimated volume measurements in Table II.
where is the model coefficient (intercept),
is the variable
that has entered the model, is the variable coefficient, is the
number of variables that enter the model, and denotes one of
the field measurements to be modeled. The dependent, biomass
variables that were included in the modeling procedure included
the plot-level woody kilogram measurement (kg) and the plotcenter herbaceous measurement (g).
Windows around the plot center GPS points were constructed
to extract the appropriate waveforms for each plot commensurate with the plot sampling strategy. This was done to account
for GPS errors that might be present in the plot center herbaceous measurements, since these measurements were taken at
the waveform lidar footprint level. This ensured that the location where herbaceous measurements were made, actually
contained features from the appropriate waveforms. Land usespecific sizes therefore were chosen individually for each site,
thus ensuring that each window matched the specified field data
measurements.
The lidar waveforms within each window were aggregated
to construct a plot level waveform, from which the features discussed above were extracted. It should be noted that the process
of creating plot-level waveforms involved taking an arithmetic
average of adjacent waveforms. This (i) effectively applied a
linear averaging to potential nonlinear interactions and (ii) potentially smoothed interactions that occur at different locations
within the composite window, resulting in a spatial averaging of
the waveform lidar data.
III. RESULTS AND DISCUSSION
A. Woody Biomass Results
Due to the variable nature of each land use, different features
were required to explain woody biomass. In this case, the waveform features were able to explain 76%
of the variation in woody biomass estimates for LU8, but performed poorly in LU2 and LU7, as seen in Table III.
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VALUES
OF
TABLE II
EXTRACTED FEATURES CORRESPONDING TO MINIMUM AND MAXIMUM LIDAR-ESTIMATED PLOT VOLUME MEASUREMENTS (BOLD).
INDICATES THE GLOBAL MINIMUM AND MAXIMUM VALUE FOR THIS FEATURE WAS DIFFERENT
The model-predicted woody biomass values were plotted
against the field measured woody biomass for LU8, as shown
in Fig. 6. Analysis of the residuals for these two models showed
a relatively even distribution around the zero-line, although
residual values increased with larger biomass values. The major
drivers of woody biomass are the physical height of a tree and
its stem diameter-at-breast height (DBH) [28]. The allometric
equations for the tree species in this study area describe woody
biomass as a function of DBH only [29], and since lidar data are
inherently height- and cover-based measurements, this could
have contributed to poor residual distributions [30], along
with typical increased residual variance (heteroscedasticity) at
higher biomass levels [4].
The LU8 specific model was able to explain 76% of the variance in woody biomass measurements made at the plot level.
This model was a function of the 10–90% energy duration of
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TABLE III
. DESCRIPTION: LUX,
WOODY BIOMASS MODELING RESULTS
WHERE ‘X’ RELATES TO LAND USE 2, 7 OR 8, ALONG WITH
FEATURES SELECTED FOR EACH MODEL. SUBSCRIPT
NUMBERS DENOTE FIRST OR SECOND DERIVATIVE
TABLE IV
HERBACEOUS BIOMASS MODELING RESULTS FOR LU8 AT INCREASING LOWER
LIMITS AND DECREASING SAMPLE SIZES. THE SUBSCRIPT NUMBER ON THE
VARIABLE DENOTES SECOND DERIVATIVE
Fig. 6. Predicted vs. observed woody biomass values in LU8 (
,
). using land-use-specific models. Solid line is 1:1 line.
the waveform and the range of values in the waveform’s first
derivative. The presence of more distinct upper canopy elements
in LU8 was evident in the variable selection for the LU8 woody
biomass model.
These results reflect the physical environment of the different
land uses. The rangelands in LU8 are well-kept and regulated by
the local village, so the vegetation density higher above ground
is more prominent than in the other land uses. The poor modeling results in LU2 and LU7 can be partially explained by the
uniformity in lower-level woody cover. Tree cover above 5 meters within KNP was the lowest of the entire study area [3], [31],
and the majority of the tree cover in LU7 fell within the height
distribution of LU2 (see Fig. 2) . The LU2 sites also were in the
protected KNP area, where the pattern and density of the woody
biomass in this land use is driven more by wildlife utilization
than human interaction and preservation. LU7 and LU8, on the
other hand, are comprised of communal rangelands and fields.
The rangelands in LU7 are mostly dominated by shrub cover,
whereas tree cover is more dominant in the cultivated fields [3].
LU7 is heavily utilized and unregulated with fewer large trees,
but consists of a more uniform and dense shrubbery vegetation
layer, hence the woody components are physically more variable and dominant at lower heights. The lack of model predictive ability for this land use area was attributed to these factors.
In summary, we found that land use specific models are most
appropriate for modeling woody biomass variation in the savanna ecosystem represented in the study area, given the features that were extracted from the waveforms. Table III summarizes the relative performance for each model and the land
use generalization to which they were best applied.
B. Herbacious Biomass Results
Land uses 2 and 7 were determined to be unsuitable for this
study due to the senescent state of the vegetation in these regions (representative photos shown in Fig. 1(b)–(d). Land use
8 still contained a large amount of green biomass at the time
of the study, and waveform features accounted for a significant
amount of herbaceous biomass variation in this site. In particular, selected waveform features accounted especially well for
variation in higher herbaceous biomass values in this study area.
Table II shows the effect of increasing the lower limit of the
herbaceous biomass used for modeling purposes. The reader is
reminded that in removing the ground response from the waveform signals, small biomass components such as grass may be
removed as well, possibly resulting in poor correlation for lower
biomass levels.
We ascribed this improvement in modeling results to the following: As the lower herbaceous biomass limit was increased,
more physical structure was present and the CRR and aCRR
variables entered the regression models. If the waveform signal
contains only one interaction other than the ground interaction,
these two metrics are equal to each other. However, aCRR is the
more explanatory feature when the waveform contains multiple
interactions above ground, regardless of the height at which they
occur.
The herbaceous biomass modeling was expected to be
challenging for a number of reasons: (i) the outgoing pulse
width was set to 16 ns, which along with the vertical resolution of the sensor (1 ns = 0.15 m), led to merging of ground
and herbaceous features and limited detection of the multiple
scattering within the herbaceous elements, (ii) the majority
of the metrics extracted from the waveforms, aside from the
ground removal metrics, were directly related to the canopy
elements (e.g., CRR, aCRR, duration, and VOL), even though
the same features were available to the woody and herbaceous
modeling, and (iii) the low and narrow range (26-60 grams,
many with equivalent biomass measurements) of available
measured herbaceous biomass values had a significant impact
on the modeling results, effectively disabling a full range
model fit [4], (iv) the herbaceous backscatter of the waveform
is embedded in the ground pulse, i.e., since the bare ground
component was removed from the ground pulse, information
related to the herbaceous content may also have been removed,
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and (v) all field and remotely sensed data were collected in
the fall season. During this time much of the herbaceous cover
in the study area was in a senescent state, which does not
produce a strong return in the near-infrared wavelength at
which the lidar sensor operates (1064 nm) [32]. The limiting
factors that determined how well the herbaceous biomass can
be estimated in this environment therefore included not only
the amount of biomass measured within the plot, but also the
phenological stage of the vegetation. The results nonetheless
were encouraging and hinted at the potential of this approach
to model herbaceous biomass using small-footprint waveform
lidar signals.
IV. CONCLUSION
This study assessed the utility of small-footprint waveform
lidar data to model woody and herbaceous vegetation structural
variation across a land use gradient in a South African savanna landscape. This was accomplished by creating plot-level
composite waveforms linked to field measurements, thereby
extracting waveform signal features corresponding to areas of
aboveground structure, and incorporating these features into
regression modeling in order to explain the variance in field
biomass measurements. This incorporated shape and non-range
specific features in biomass models of vegetation, and was
able to use signal shape in generating waveform features to assess vegetation structure. Model performance was evaluated in
terms of the model fit parameters ( , adjusted for multiple variable entry, and RMSE) for explaining the variance in biomass
measurements within the individual land uses. Model fit values,
i.e., the amount of biomass variance explained, were as high
as
in the case of the individual regulated communal
area of land use 8 (LU8), attributed to the larger range of woody
biomass measurements and regulated fuel-wood harvesting in
this area. In the conservation land use area (LU2) and unregulated communal area (LU7), woody biomass measurements
were less accurately modeled. Similarly, the selected waveform
features were only able to properly explain the herbaceous
variation in LU8, but consideration had to be given to how
sensitive the modeling was to the observed range of biomass
values and the phenological state of the vegetation.
Through this effort, we have drawn the following specific
conclusions:
• The features extracted from the waveform lidar data were
sensitive to the varying vegetation structure within each
land use, as dictated by the different approaches to land
utilization on a per-land use basis. This was the case even
though the majority of the woody measurements fell into a
narrow biomass range.
• The presence of derivative features in these models
showed that these shape metrics are helpful in describing
the woody variation, without being directly related to a
physical aboveground entity, such as canopy height, cover,
etc.
• The herbaceous modeling exhibited increased performance for LU8 when the plot observations contained
larger biomass values; this was attributed to increased
backscatter from physical herbaceous structure.
We concluded that small-footprint waveform lidar has significant potential for quantifying complex above ground structure,
but further research is warranted in terms of both processing
and feature extraction. For example, the features extracted in
this study are not exhaustive. It would be interesting to examine
the ability to discern fine scale herbaceous measurements from
waveform lidar, whether this is accomplished by collecting data
with a narrower pulse width, during the peak growing season, or
both. However, the confirmation of our ability to extract woody
and herbaceous estimates, even if ideally for areas with more
significant biomass in the latter case, hints at the potential signal
content in waveform lidar signals over savanna vegetation.
ACKNOWLEDGMENT
The authors would like to thank Dr. Izak Smit (Kruger
National Park) and Dr. Jolene Fisher (University of the Witwatersrand) for inputs during waveform lidar feature extraction
and biomass estimation discussions. The airborne campaign
was supported by the Andrew Mellon Foundation. The Carnegie
Airborne Observatory is made possible by the W. M. Keck
Foundation, Gordon and Betty Moore Foundation, John D. and
Catherine T. MacArthur Foundation, Grantham Foundation,
and William Hearst III.
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Joseph McGlinchy received the M.S. degree in imaging science from the
Rochester Institute of Technology, Rochester, NY, USA, in 2010, and the
B.S. degree in electrical engineering from the University of Akron, Akron,
OH, USA, in 2008. During his time at Rochester Institute of Technology, his
research focused on feature extraction and algorithm development to facilitate land cover component determination and woody and herbaceous biomass
modeling in South African savanna ecosystems using airborne waveform
LiDAR and hyperspectral imagery.
He is a currently a member of the 3-D and Imagery team at Environmental
Systems Research Institute, Inc., and is interested in point cloud modeling
and processing, feature extraction from remotely sensed 2-D and 3-D data,
and 2-D and 3-D signal processing.
Jan A. N. van Aardt received the B.Sc. and Hons. degrees in forestry from
the University of Stellenbosch, Western Cape, South Africa, in 1996 and 1998,
respectively, and the M.S. and Ph.D. degrees in forestry and remote sensing
from the Virginia Polytechnic Institute and State University, Blacksburg, VA,
USA, in 2000 and 2004, respectively.
He is currently an Associate Professor within the Chester F. Carlson Center
for Imaging Science, Rochester Institute of Technology (RIT), Rochester, NY,
USA, and a member of the Digital Imaging and Remote Sensing group within
the Center. This group focuses on a complete system approach to remote
sensing applications based on system integration, processing workflow, and
applied algorithm development. Before joining the faculty at RIT, he worked in
the academic (Katholieke Universiteit Leuven, Leuven, Belgium) and private
(Council for Scientific and Industrial Research, South Africa) sectors. His
research interests include the application of imaging spectroscopy and light
detection and ranging for spectral-structural characterization of natural systems
(remote sensing of natural resources).
Barend Erasmus is interested in the effects of global change on various aspects of biodiversity, and how these impacts translate to system level responses
at different scales of observation. He prefers to collaborate across traditional discipline boundaries, and use multispectral and hypertemporal satellite imagery,
aerial photography and airborne lidar to investigate long term changes in savanna woody vegetation structure to understand land cover dynamics across
boundaries. The contrast between national parks and surrounding rural communities provide an interesting natural experiment to highlight systems-level
behaviors.
Gregory P. Asner is a faculty member of the Department of Global Ecology,
Carnegie Institution for Science and the Department of Environmental Earth
System Science, Stanford University. He explores interactions between species,
environment, land use and climate change, with ongoing research and capacity
building work focused on deforestation and forest degradation, biological diversity at large geographic scales, and terrestrial biogeochemistry. Dr. Asner
develops new scientific approaches and technologies for investigation and conservation assessment of large geographic regions, including their carbon emissions, three-dimensional habitat, chemical functioning, and biological diversity.
He leads the CLASlite forest monitoring project, Spectranomics biodiversity
project, and the Carnegie Airborne Observatory.
Renaud Mathieu received the Dipl.Ing. degree in agricultural sciences from
the École Supérieure d’Agriculture de Purpan, Toulouse, France, in 1990, the
M.Sc. degree in applied remote sensing from Cranfield University, Cranfield,
U.K., in 1991, and the Ph.D. degree in geographic information sciences from
Université Paris-Est Marne-la-Vallée, Paris, France.
He is currently a Principal Researcher with the Council for Scientific and
Industrial Research, Pretoria, South Africa, and leads the Earth Observation
Research Group in Natural Resources and Environment. His research interests
focus on the application of remote sensing technologies to soil and water conservation, natural resource and wildlife management, environment, and agriculture.
490
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 7, NO. 2, FEBRUARY 2014
Konrad Wessels is currently Chief Scientist and Research Group Leader for
the Remote Sensing Research Unit of the CSIR-Meraka Institute. He is an extraordinary senior lecturer at University of Pretoria, Centre for Geoinformation
Science. He was awarded a NASA Earth System Sciences fellowship and completed his Ph.D. in geography at the University of Maryland, specializing in
remote sensing of land degradation. He spent his post-doc at NASA Goddard
Space Flight Centre.
David Knapp received the B.S. degree from The Pennsylvania State University
in 1987 and the M.S. degree from the University of Illinois in 1991, both in
geography with a specialization in remote sensing and GIS.
He worked as a scientist for Hughes ITSS, contracted to NASA Goddard
Space Flight Center where he participated in various remote sensing research
projects including the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) and the Boreal Ecosystem-Atmosphere Study (BOREAS). He is
currently an image processing specialist and develops software for processing
satellite and aircraft imagery at the Carnegie Institution for Science Department
of Global Ecology.
Ty Kennedy-Bowdoin studies ecosystem dynamics using LiDAR at the
Carnegie Institution for Science. He was the first Carnegie staff member to
be fully dedicated to the newly formed Carnegie Airborne Observatory in
2007 and has been a key member of the team responsible for the development
of three ground-breaking sensor systems incorporating co-aligned LiDAR
and spectrometers to study carbon stocks, biodiversity, gap dynamics, and
deforestation in Central and South America, Hawaii, and Southern Africa.
Harvey Rhody received the B.S.E.E. from the University of Wisconsin,
Madison, WI, USA, the M.S.E.E. from the University of Cincinnati, Cincinnati, OH, USA, and the Ph.D. in electrical engineering from Syracuse
University, Syracuse, NY, USA.
He is a Professor of imaging science in the Chester F. Carlson Center for
Imaging Science at Rochester Institute of Technology. His research interests
include imaging systems, remote sensing, imaging algorithms and image processing and he has conducted numerous projects designed in those areas. His
current focus is in the area of 3D imaging, where he is developing courses and
leading research projects in 3D sensing. He has spent nearly four decades at
RIT as a faculty member and administrator, previously serving as head of the
Department of Electrical Engineering and president of the RIT Research Corporation.
John P. Kerekes (S’81–M’89–SM’00) received the B.S., M.S., and Ph.D.
degrees in electrical engineering from Purdue University, West Lafayette, IN,
USA, in 1983, 1986, and 1989.
From 1983 to 1984, he was a Member of the Technical Staff with the Space
and Communications Group, Hughes Aircraft Co., El Segundo, CA, where he
performed circuit design for communications satellites. From 1986 to 1989, he
was a Graduate Research Assistant, working with both the School of Electrical
Engineering and the Laboratory for Applications of Remote Sensing at Purdue
University. From 1989 to 2004, he was a Technical Staff Member with the Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA. From
2004 to 2013 he was an Associate Professor in the Chester F. Carlson Center
for Imaging Science, Rochester Institute of Technology, Rochester, NY, where
he is now Professor. His research interests include the modeling and analysis
of remote sensing system performance in pattern recognition and geophysical
parameter retrieval applications.
Dr. Kerekes is a member of Tau Beta Phi, Eta Kappa Nu, the American Geophysical Union, and the American Society for Photogrammetry and Remote
Sensing. He is a Senior Member of the Optical Society of America and SPIE.
From 1995 to 2004, he served as the Chair of the Boston Section Chapter of the
IEEE Geoscience and Remote Sensing Society (GRSS), and from 2007 to 2010
he served as the founding Chair of the Western New York Chapter of GRSS.
He was a Co-General Chair of IGARSS 2008 held in Boston, MA. Since 2010
he has been a member of the GRSS Administrative Committee (AdCom) and is
currently serving as the Vice-President of Technical Activities of the GRSS.
Emmett J. Ientilucci received the B.S., M.S., and Ph.D. degrees in imaging
science from the Rochester Institute of Technology, Rochester, NY, USA, in
1996, 1999, and 2005, respectively.
He is currently a Research Professor with the Digital Imaging and Remote
Sensing Laboratory, Chester F. Carlson Center for Imaging Science, Rochester
Institute of Technology. Prior to his faculty position, he was a Postdoctoral Research Fellow for the Intelligence Community. His research interests include
physics-based signature modeling, hyperspectral image analysis, radiometry,
hybrid detection methods, and atmospheric and radiative transfer modeling and
scattering from small particles as it relates to bio-aerosols. He has taught courses
and laboratories in radiometry, remote sensing, geometrical optics, measurement and analysis, photo science, dimensional metrology, and computer techniques for technicians, as well as served as Referee on eleven scientific journals.
He is currently the vice chair for the Western New York Geoscience and Remote
Sensing Society (GRSS) and has a manuscript prepared for his text book entitled Radiometry and Radiation Propagation. Dr. Ientilucci is also a member of
the Society for Photographic Instrumentation Engineers.
Jiaying Wu (M’09) received the B.S. degree in optical information science
from the University of Shanghai for Science and Technology, Shanghai, China,
in 2006 and the Ph.D. degree in Imaging Science from Rochester Institute of
Technology, Rochester, NY, USA, in 2012.
He is currently working at Apple Inc., Cupertino, CA, USA, as a color
imaging scientist. His doctoral research involves studies of waveform LiDAR
signal and 3D image processing in Digital Imaging and Remote Sensing
(DIRS) group at RIT. From 2007 to 2008, he was a research assistant with
the Real-Time Vision and Image Processing Laboratory, RIT. During this
time, his research focused on the development of medial augmented reality
algorithm based on 2-D/3-D image fusion to facilitate image-guided surgery.
In 2008 summer, he was a research intern with Sharp Research Laboratories of
America, Camas, WA, USA. The research topic involved LCD display color
modeling, and his work was published in SPIE Electronic Imaging in 2009. He
also spent a summer as an Image System Engineer with Microsoft Corporation,
Redmond, WA, USA, in 2010, where he worked on video compression and
quality assessment algorithms. His research interests include digital image
processing, remote sensing, computer vision, and color science.
Diane Sarrazin received the B.Sc. degree in physics and space science from
the Royal Military College of Canada in 2005.
She is an officer in the Canadian Forces currently working at Defence
Research and Development Canada, Quebec. She worked as Deputy to the
Senior Aircraft Maintenance Engineering Officer at 438 Tactical Helicopter
Squadron from 2006 to 2008. She received her M.Sc. in Imaging Science with
Remote Sensing application from Rochester Institute of Technology in 2010.
She worked as Imaging Officer for the Canadian Open Skies office from 2010
to 2012 and published an article in the Canadian Journal of Remote Sensing
entitled “Fusing small-footprint waveform LiDAR and hyperspectral data
for canopy level species classification and herbaceous biomass modeling in
savanna ecosystems” in 2011.
Kerry Cawse-Nicholson received the Ph.D. degree in computational and applied mathematics from the University of the Witwatersrand, South Africa, in
2012, obtained while working at the Remote Sensing Research Unit, Meraka
Institute, Council for Scientific and Industrial Research (CSIR), South Africa.
She is currently pursuing a post-doc at Rochester Institute of Technology, USA
in the field of waveform LiDAR. Her current research interests include image
processing, hyperspectral imagery and LiDAR.