January 2002 / Vol. 52 No. 1 • BioScience 19
Articles
Remote sensing has facilitated extraordinary
advances in the modeling,mapping,and understanding
of ecosystems.Typical applications of remote sensing involve
either images from passive optical systems, such as aerial
photography and Landsat Thematic Mapper (Goward and
Williams 1997), or to a lesser degree, active radar sensors
such as RADARSAT (Waring et al.1995).These types of sensors
have proven to be satisfactory for many ecological applications,
such as mapping land cover into broad classes
and, in some biomes, estimating aboveground biomass and
leaf area index (LAI).Moreover,they enable researchers to analyze
the spatial pattern of these images.
However,conventional sensors have significant limitations
for ecological applications. The sensitivity and accuracy of
these devices have repeatedly been shown to fall with increasing
aboveground biomass and leaf area index (Waring
et al. 1995, Carlson and Ripley 1997, Turner et al. 1999).
They are also limited in their ability to represent spatial patterns:
They produce only two-dimensional (x and y) images,
which cannot fully represent the three-dimensional structure
of,for instance,an old-growth forest canopy.Yet ecologists have
long understood that the presence of specific organisms,and
the overall richness of wildlife communities,can be highly dependent
on the three-dimensional spatial pattern of vegetation
(MacArthur and MacArthur 1961), especially in systems
where biomass accumulation is significant (Hansen
and Rotella 2000). Individual bird species, in particular, are
often associated with specific three-dimensional features in
forests (Carey et al.1991).In addition,other functional aspects
of forests,such as productivity,may be related to forest canopy
structure.
Laser altimetry,or lidar (light detection and ranging),is an
alternative remote sensing technology that promises to both
increase the accuracy of biophysical measurements and extend
spatial analysis into the third (z) dimension. Lidar sensors
directly measure the three-dimensional distribution of
plant canopies as well as subcanopy topography,thus providing
high-resolution topographic maps and highly accurate estimates
of vegetation height,cover,and canopy structure.In addition,
lidar has been shown to accurately estimate LAI and
aboveground biomass even in those high-biomass ecosystems
where passive optical and active radar sensors typically fail to
do so.
Lidar sensors
The basic measurement made by a lidar device is the distance
between the sensor and a target surface, obtained by determining
the elapsed time between the emission of a shortduration
laser pulse and the arrival of the reflection of that
pulse (the return signal) at the sensor’s receiver. Multiplying
this time interval by the speed of light results in a measurement
of the round-trip distance traveled, and dividing that
figure by two yields the distance between the sensor and the
target (Bachman 1979). When the vertical distance between
a sensor contained in a level-flying aircraft and the Earth’s surMichael
A. Lefsky (e-mail: lefsky@fsl.orst.edu) is a research assistant
professor in the Forest Science Department, Oregon State University,
and codirector of the Laboratory for Applications of Remote
Sensing in Ecology in Corvallis, OR 97331. Warren B. Cohen is a research
forester with the USDA Forest Service and director of the Laboratory
for Applications of Remote Sensing in Ecology, USDA Forest
Service, Forestry Sciences Laboratory, Pacific Northwest Research
Station, Corvallis, OR 97331. Geoffrey G. Parker is a forest ecologist
with the Smithsonian Environmental Research Center, Edgewater,
MD 21037-0028. David J. Harding is a geoscientist in the geodynamics
branch of the Laboratory for Terrestrial Physics at NASA’s
Goddard Space Flight Center, Greenbelt, MD 20771. © 2002
American Institute of Biological Sciences.
Lidar Remote Sensing for
Ecosystem Studies
MICHAEL A. LEFSKY, WARREN B. COHEN, GEOFFREY G. PARKER, AND DAVID J. HARDING
LIDAR, AN EMERGING REMOTE SENSING
TECHNOLOGY THAT DIRECTLY MEASURES
THE THREE-DIMENSIONAL DISTRIBUTION
OF PLANT CANOPIES, CAN ACCURATELY
ESTIMATE VEGETATION STRUCTURAL
ATTRIBUTES AND SHOULD BE OF
PARTICULAR INTEREST TO FOREST, LANDSCAPE,
AND GLOBAL ECOLOGISTS
20 BioScience • January 2002 / Vol. 52 No. 1
Articles
face is repeatedly measured along a transect, the result is an
outline of both the ground surface and any vegetation obscuring
it.Even in areas with high vegetation cover,where most
measurements will be returned from plant canopies, some
measurements will be returned from the underlying ground
surface, resulting in a highly accurate map of canopy height.
Key differences among lidar sensors are related to the
laser’s wavelength,power,pulse duration and repetition rate,
beam size and divergence angle,the specifics of the scanning
mechanism (if any), and the information recorded for each
reflected pulse.Lasers for terrestrial applications generally have
wavelengths in the range of 900–1064 nanometers, where
vegetation reflectance is high.In the visible wavelengths,vegetation
absorbance is high and only a small amount of energy
would be returned to the sensor. One drawback of working
in this range of wavelengths is absorption by clouds, which
impedes the use of these devices during overcast conditions.
Bathymetric lidar systems (used to measure elevations under
shallow water bodies) make use of wavelengths near 532 nm
for better penetration of water. Early lidar sensors were profiling
systems, recording observations along a
single narrow transect.Later systems operate in
a scanning mode, in which the orientation of
the laser illumination and receiver field of view
is directed from side to side by a rotating mirror,
or mirrors, so that as the plane (or other
platform) moves forward, the sampled points
fall across a wide band or swath, which can be
gridded into an image.
The power of the laser and size of the receiver
aperture determine the maximum flying
height, which limits the width of the swath
that can be collected in one pass (Wehr and
Lohr 1999). The intensity or power of the return
signal depends on several factors: the total
power of the transmitted pulse,the fraction
of the laser pulse that is intercepted by a surface,
the reflectance of the intercepted surface
at the laser’s wavelength,and the fraction of reflected
illumination that travels in the direction
of the sensor. The laser pulse returned after
intercepting a morphologically complex surface,
such as a vegetation canopy, will be a
complex combination of energy returned from
surfaces at numerous distances, the distant
surfaces represented later in the reflected signal.The
type of information collected from this
return signal distinguishes two broad categories
of sensors. Discrete-return lidar devices
measure either one (single-return systems) or
a small number (multiple-return systems) of
heights by identifying, in the return signal,
major peaks that represent discrete objects in
the path of the laser illumination. The distance
corresponding to the time elapsed before
the leading edge of the peak(s), and sometimes
the power of each peak, are typical values recorded by
this type of system (Wehr and Lohr 1999). Waveformrecording
devices record the time-varying intensity of the returned
energy from each laser pulse,providing a record of the
height distribution of the surfaces illuminated by the laser
pulse (Harding et al.1994,2001,Dubayah et al.2000).By analogy
to chromotography, the discrete-return systems identify,while
receiving the return signal,the retention times and
heights of major peaks; the waveform-recording systems
capture the entire signal trace for later processing. Conceptual
differences between the two major categories of lidar sensors
are illustrated in Figure 1.
Both discrete-return and waveform sampling sensors are
typically used in combination with instruments for locating
the source of the return signal in three dimensions.These include
Global Positioning System (GPS) receivers to obtain the
position of the platform, Inertial Navigation Systems (INS)
to measure the attitude (roll, pitch, and yaw) of the lidar
sensor,and angle encoders for the orientation of the scanning
mirror(s).Combining this information with accurate time refFigure
1. Illustration of the conceptual differences between waveformrecording
and discrete-return lidar devices. At the left is the intersection of
the laser illumination area, or footprint, with a portion of a simplified tree
crown. In the center of the figure is a hypothetical return signal (the lidar
waveform) that would be collected by a waveform-recording sensor over the
same area. To the right of the waveform, the heights recorded by three varieties
of discrete-return lidar sensors are indicated. First-return lidar devices
record only the position of the first object in the path of the laser illumination,
whereas last-return lidar devices record the height of the last object in the
path of illumination and are especially useful for topographic mapping.
Multiple-return lidar, a recent advance, records the height of a small number
(generally five or fewer) of objects in the path of illumination.
erencing of each source of data yields the absolute position
of the reflecting surface, or surfaces, for each laser pulse.
There are advantages to both discrete-return and waveformrecording
lidar sensors.For example,discrete-return systems
feature high spatial resolution,made possible by the small diameter
of their footprint and the high repetition rates of
these systems (as high as 33,000 points per second),which together
can yield dense distributions of sampled points.Thus,
discrete-return systems are preferred for detailed mapping of
ground (Flood and Gutelis 1997) and canopy surface topography,as
in Figure 2.An additional advantage made possible
by this high spatial resolution is the ability to aggregate the
data over areas and scales specified during data analysis,so that
specific locations on the ground, such as a particular forest
inventory plot or even a single tree crown, can be characterized.
Finally, discrete-return systems are readily and widely
available,with ongoing and rapid development,especially for
surveying and photogrammetric applications (Flood and
Gutelis 1997).The primary users of these systems are surveyors
serving public and private clients,and natural resource managers
seeking a cheaper source of high-resolution topographic
maps and digital terrain models (DTMs). A potential drawback
is that proprietary data-processing algorithms and established
sensor configurations designed
for commercial use may not
coincide with scientific objectives.
A detailed technical review of the
various sensors can be found in
Wehr and Lohr (1999). Baltsavias
(1999) reviews a directory of sensors
and lidar remote sensing firms.
The advantages of waveformrecording
lidar include an enhanced
ability to characterize
canopy structure,the ability to concisely
describe canopy information
over increasingly large areas, and
the availability of global data sets
(the extent of their coverage varies,
however).Examples of waveformrecording
laser altimeters include
MKII (Aldred and Bonnor 1985)
and a similar system described in
Nilsson (1996),as well as a series of
airborne devices developed at
NASA’s Goddard Space Flight Center,starting
with a profiling sensor
described by Bufton and colleagues
(1991) and including SLICER
(Scanning Lidar Imager of
Canopies by Echo Recovery; Blair
et al. 1994, Harding et al. 1994,
2001), SLA (Shuttle Laser Altimeter;
Garvin et al.1998),LVIS (Laser
Vegetation Imaging Sensor; Blair et
al. 1999), and VCL (Vegetation
Canopy Lidar; Dubayah et al. 1997) satellite. One advantage
of these waveform-recording lidar systems is that they record
the entire time-varying power of the return signal from all illuminated
surfaces and are therefore capable of collecting more
information on canopy structure than all but the most spatially
dense collections of small-footprint lidar (Figure 3).In
addition,waveform-recording lidar integrates canopy structure
information over a relatively large footprint and is capable
of storing that information efficiently, from the perspective
of both data storage and data analysis.Finally,only waveformrecording
lidar will, in the near future, be collected globally
from space.
Spaceborne waveform-recording lidar techniques have
been successfully demonstrated by the Shuttle Laser Altimeter
missions (Garvin et al. 1998), which were intended to
collect topographic data and to test hardware and algorithm
approaches from orbit. These data were collected along a
single track,using footprints of approximately 100 meters in
diameter,which limits their utility for the measurement of vegetation
canopy structure,especially in high-slope areas (Harding
et al. 2001). The Ice, Cloud, and Land Elevation Satellite
(ICESat) mission, scheduled for launch in December 2001,
will carry the Geoscience Laser Altimeter System, which will
January 2002 / Vol. 52 No. 1 • BioScience 21
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Figure 2. Canopy surface topography of a subsection of the Wind River Canopy Crane
Research Facility in Washington State. The data have been gridded; individual lidar
samples would be represented by individual points in three-dimensional space.
22 BioScience • January 2002 / Vol. 52 No. 1
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make measurements along a single track with 70-m diameter
footprints, which approaches the size needed to characterize
vegetation in low- and moderate-slope areas.
The Vegetation Canopy Lidar mission, scheduled to be
launched around 2003, is the first satellite specifically designed
with the problem of vegetation inventory in mind.VCL
is a waveform-recording system,expected to inventory,using
25-m diameter footprints, canopy height and structure over
approximately 5% of the Earth’s land surface between ±68°
latitude during its 18-month mission (Dubayah et al. 1997).
Associated with the VCL mission is the Lidar Vegetation
Imaging System (LVIS),an airborne,wide-swath mapping system
developed at NASA’s Goddard Space Flight Center that
is being used to validateVCL’s capabilities.Although LVIS can
collect waveforms with 25-m diameter footprints contiguously
across swaths over 2 kilometers in width,VCL is a sampling
device. It will make waveform measurements along a group
of three transects,spaced every 2 km perpendicular to the randomly
placed ground track of the satellite, resulting in a
“web”of samples covering the Earth’s surface.These samples
will not provide images of canopy structure, but they could
be combined with images from other sensors (such as Enhanced
Thematic Mapper+ data from Landsat 7) using a
number of strategies,withVCL data augmenting or even replacing
the roles usually played by field-collected data (Lefsky
et al. 1999c, Dubayah et al. 2000).
Although we present them as distinct types,discrete-return
and waveform-recording lidar are closely related.The correspondence
between data from each is illustrated in Figure 4,
using data collected with a first-return lidar at the Wind
River Canopy Crane Research Facility. Section a (left) illustrates
the three-dimensional distribution of first-return data
from within a 25-m footprint centered on a tree approximately
50 m tall.Section b (right) illustrates the distribution of these
points as a function of height. Blair and Hofton (1999)
demonstrated that this vertical distribution of the singlereturn
data is closely related to the waveforms recorded by
waveform-recording devices when certain conditions are
met—the most important being a high density of samples collected
using a very small footprint (on the order of 25 cm) so
Figure 3. Measurements of canopy structure made using NASA’s SLICER (Scanning Lidar Imager of Canopies by Echo Recovery)
device. Panel a shows ground topography and the vertical distribution of canopy material along a 4-km transect in the
H. J. Andrews Experimental Forest, Oregon. Each column is the width of one laser pulse waveform. Panels b, c, and d show
close-ups of the canopies of three 550-m transects in young, mature, and old-growth Douglas fir–western hemlock forest
stands, with their ground elevations adjusted to a uniform level.
that elevation data can be collected from within very small gaps
in the canopy structure.To completely simulate a lidar waveform,the
vertical distribution of the discrete return would have
to be corrected for the spatial and temporal distribution of
energy within the lidar pulse and receiver response, as described
in Blair and Hofton (1999).
Applications of lidar remote sensing
Only a few areas of application for lidar remote sensing have
been rigorously evaluated.Numerous other applications are
generally considered feasible, but they have not yet been explored;
developments in lidar remote sensing are occurring
so rapidly that it is difficult to predict which applications will
be dominant in 5 years.Currently,applications of lidar remote
sensing in ecology fall into three general categories: remote
sensing of ground topography,measurement
of the three-dimensional
structure and function of vegetation
canopies, and prediction of forest
stand structure attributes (such as
aboveground biomass).
Topographic applications.
Mapping of topographic features is
the largest and fastest growing area of
application for lidar remote sensing,
because of its use in commercial land
surveys (Flood and Gutelis 1997).
Ecologists are also interested in topography
(and bathymetry),which often
has a strong influence on the structure,
composition, and function of
ecological systems. Traditional survey
and photogrammetric techniques
for determining ground elevations
are limited in several ways. The primary
disadvantages of traditional
surveying are its substantial time and
labor requirements and associated
costs.Photogrammetric methods for
determining elevations from aerial
photographs or images collected by
other sensors are an established alternative
to field surveys (Baltsavius
1999). However, they are inaccurate
in forested areas,where the ground is
not visible, and in areas of low relief
and texture, such as wetland areas
and coastal dune systems. In these
cases,airborne laser altimetry can be
an accurate and cost-effective alter-
native.
Topographic applications most often
use discrete-return data. When
ranging information from the lidar is
combined with position and pointing
information, the result is a series of xyz data points, or
“triplets,”describing the location of the observed surfaces in
three-dimensional space.With adequate quality control, the
accuracy of these points can achieve 50-cm root mean square
error (RMSE) in the horizontal planes and 20-cm RMSE in
the vertical.However,the elevations recorded in these triplets
will be associated with myriad features,including the ground,
human-made objects,clouds,vegetation,or anything else in
the path of the laser pulse. To extract a topographic surface
from these points, a series of filters must be applied to eliminate
points not on the ground surface. Numerous methods
exist for this process, but generally they combine highly automated
processes with some manual correction (Kilian et al.
1996,Kraus and Pfeifer 1998).Most commercial data suppliers
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Figure 4. Illustration of the potential for creating synthetic lidar waveforms from
discrete-return lidar data. Section a shows the three-dimensional distribution of
discrete-return lidar data from within a 25-m footprint. Section b shows the vertical
distribution of these returns.
a b
use proprietary routines they are often reluctant to describe
in detail, a potential problem for scientists.
Examples of topographic applications of lidar include
mapping of polar ice sheets for mass balance investigations
(Krabill et al.1999),mapping of wetlands and shallow water
(Irish and Lillycrop 1999), and high-resolution mapping of
topography under forest for geomorphic investigations and
hydrologic modeling (Harding and Berghoff 2000). The
mapping of dynamic features such as beaches and dunes
(Krabill et al.2000) is one application for which lidar is proving
to be particularly well suited. The ability of airborne lidar
to create surveys of the coastal environmental is being
demonstrated by the ALACE (Airborne Lidar Assessment of
Coastal Erosion) project, a joint program of the National
Oceanic and Atmospheric Administration’s Coastal Services
Center, the US Geological Survey’s Center for Coastal Geology,and
the National Aeronautics and Space Administration
(Krabill et al. 2000). Using the Airborne Topographic Mapper
(ATM) developed at NASA’s Wallops Flight Facility Observational
Sciences Branch, detailed topographical maps
are being created for areas along the Atlantic, Pacific, Great
Lakes,and Gulf of Mexico coastlines.Through periodic resurveying
of the same areas, precise measurements and images
of coastal change are produced (Figure 5).The resulting data
products are designed for accurate and cost-effective mapping
of coastal erosion and could easily be applied to gain further
understanding of the links between, for instance, geomorphologic
and vegetation dynamics in coastal dune ecosystems.
Measuring vegetation canopy structure and function.
In general, the single most important step in lidar
mapping of topography involves the deletion of data points
returned from vegetation and, in urban areas, buildings.
However,for most ecological applications,it is the returns from
the vegetation canopy that will be of primary interest.Canopy
structure—“the organization in space and time,including the
position,extent,quantity,type,and connectivity,of the aboveground
components of vegetation”(Parker 1995,p.74)—contains
a substantial amount of information about the state of
development of plant communities (Lefsky et al. 1999a,
1999b) and therefore about canopy function (Monsi and
Saeki 1953, Horn 1971, Hollinger 1989, Brown and Parker
1994) and vegetation-related habitat conditions for wildlife
(Hansen and Rotella 2000). The simplest canopy structure
measurements are of canopy height and cover (Figures 6a,6b).
Altimetric canopy heights have been compared,with varying
accuracy and strength of correlation,to maximum and mean
tree height in temperate (Maclean and Krabill 1986), tropical
(Nelson et al.1997,Drake et al.forthcoming),and boreal
(Naesset 1997a,Magnussen and Boudewyn 1998,Magnussen
et al.1999) forests.In addition,Ritchie and colleagues (1995)
found excellent agreement between lidar measurements of
height in both temperate deciduous forests and desert scrub.
The latter finding is particularly important,as it indicates that
vegetation height measurements can be made accurately
even on vegetation of short stature (~1 m),at least in low-slope
environments.
There are two general problems in determining vegetation
height using lidar data.Determining the exact elevation of the
ground surface poses difficulties for both discrete-return and
waveform-recording lidar.In complex canopies,elevations returned
from what appears to be the ground level in fact may
be from the understory,if the understory is dense enough to
substantially occlude the ground surface.In addition,each type
of lidar system presents difficulties in detecting the uppermost
portion of the plant canopy.With discrete-return lidar, very
24 BioScience • January 2002 / Vol. 52 No. 1
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Figure 5. Lidar measurements of the effect of Hurricane Bonnie on the topography of Topsail Island, North Carolina. Panel a
maps the overall change for this section of the island. Panel b shows pre- and posthurricane topography for a single profile.
Figure courtesy of the ALACE (Airborne LIDAR Assessment of Coastal Erosion) project.
high footprint densities are required to ensure that the highest
portion of individual tree crowns is sampled.With waveform
sampling devices, a large footprint is illuminated, increasing
the probability that treetops will be illuminated by
the laser. However, the top portion of the crown may not be
of sufficient area to register as a significant return signal and
therefore may not be detected.In either case,the height of the
canopy may be underestimated.
Estimates of canopy cover have been made using both
discrete-return and waveform-recording lidar sensors.These
estimates are made using the fraction of the lidar measurements
that are considered to have been returned from the
ground surface (Nelson et al. 1984, Ritchie et al. 1992, 1995,
1996, Weltz et al. 1994, Lefsky 1997), where the measurements
are the number of discrete returns, or the integrated
power of a waveform.In some cases,a scaling factor is needed
to correct for the relative reflectance of ground and canopy
surfaces at the wavelength of the laser (Lefsky 1997,Means et
al.1999).As with the measurement of canopy height,the definition
of the ground surface is a critical aspect of cover determination.If
the number (or power) of the measurements
assigned to the ground return is overestimated—that is,if the
elevation of the ground surface is overestimated—cover will
be underestimated, and vice versa.
Although the height and cover of the canopy surface are
useful canopy structure descriptions,there are more detailed
measurements that can better describe canopy function and
structure. The height distribution of outer canopy surfaces
(Figure 6c),which quantifies such important features as light
gaps (Watt 1947, Canham et al. 1990, Spies et al. 1990), has
been manually mapped in several studies (Leonard and Federer
1973,Ford 1976,Miller and Lin 1985).These maps were
laboriously made, using devices such as plumb bobs and
telescoping rods; with lidar,the process is greatly accelerated
(Nelson et al. 1984, Lefsky et al. 1999b). The vertical distribution
of all material within the canopy (not just the outer
canopy surfaces) may be inferred,using the foliage-height profile
technique (MacArthur and Horn 1969,Aber 1979) recently
adapted for use with waveform-recording lidar as the canopyheight
profile (Figure 6d; Lefsky 1997, Harding et al. 2001).
Calculation of these height profiles relies on assumptions
about the rate of occlusion of canopy surfaces that are not applicable
to all forests; however,they have been shown to yield
a good approximation in closed-canopy, temperate deciduous
forests (Aber 1979, Fukushima et al. 1998, Harding et al.
2001).
Lidar data have been used to predict the fractional transmittance
of light as a function of height (Figure 6e),based on
a series of assumptions relating the penetration of the laser
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Figure 6. Conceptual comparison of three canopy description methods. Panels a and b are a canopy profile diagram prepared
by Spies et al. (1990). Panel c is a canopy surface hypsograph, showing the vertical distribution of the upper canopy surface.
Panel d is a canopy height profile, showing the relative vertical distribution of foliage and woody surfaces. Panel e shows the
vertical profile of transmittance. Panel f is a canopy volume profile, showing the vertical distribution of four classes of canopy
structure. Figure adapted from Lefsky et al. (1999b), with permission from Elsevier Science.
26 BioScience • January 2002 / Vol. 52 No. 1
Articles
light into the canopy to the penetration of natural light into
the canopy.Although both the wavelength and orientation of
typical laser illumination differ from that of natural illumination,
a recent study (Parker et al. 2001) indicates that lidar
can accurately estimate the rate of photosynthetically active
radiation (PAR) absorption and define the location and depth
of the zone where the maximum rate of PAR absorption occurs
(Parker 1997).
Lidar has also been used to predict the aerodynamic properties
of plant canopies and landscapes. In modeling airflow
over a forest canopy,the aerodynamic roughness length is the
height at which the wind speed becomes zero. Menenti and
Ritchie (1994) used a profiling laser altimeter to predict aerodynamic
roughness length of complex landscapes containing
a mixture of grassland,shrub,and woodland areas,and found
good agreement with field estimates.
The techniques described so far use lidar data to make
measurements of canopy structure that had been made with
technologically simpler and more time-consuming methods.Lidar’s
ability to rapidly measure the three-dimensional
structure of canopies should stimulate the development of new
systems of canopy description. One such system, the canopy
volume method (CVM), is the first to take advantage of the
ability of a waveform-recording sensor (SLICER) to directly
measure the three-dimensional distribution of canopy structure.Using
lidar data,Lefsky and colleagues (1999b) were able
to treat the forest canopy as a matrix of voxels (threedimensional
pixels; Figure 7),each of which could be defined
as containing canopy or not,and either in the brightly or dimly
sunlit portion of the canopy.This information was then used
to describe the quantitative and qualitative differences in
canopy structure between four age classes (Figure 8).This approach
led to a better understanding of the structure of the
old-growth forest canopy,new visualizations of the multiplecanopy
aspect of old-growth development, and improved
estimates of forest stand structure.
Prediction of forest stand structure. Lidar data also
have been used to predict biophysical characteristics of plant
communities, most notably forests (Dubayah and Drake
2000).Although the following studies may not by themselves
constitute ecological research,they lay the groundwork for future
studies that use these relationships to map biophysical
variables over large extents (using data from sensors such as
LVIS and VCL), making possible a new class of
large-scale ecological research.
Prediction of forest stand structure using discrete
return lidar had its start in the work of
Maclean and Krabill (1986),who adapted a photogrammetric
technique—the canopy profile
cross-sectional area—to the interpretation of lidar
data. The canopy profile cross-sectional area
is the total area between the ground and the upper
canopy surface along a transect.When species
composition was taken into account,the authors
were able to explain 92% of the variation in grossmerchantable
timber volume (the volume of the
main stem of trees, excluding the stump and top
but including defective and decayed wood) in
stands dominated by oaks (Quercus spp.),loblolly
pine (Pinus taeda), or mixtures of the two types.
Similar methods have proved effective in a variety
of forest communities.Nelson et al.(1988) successfully
predicted the volume and biomass of
southern pine (Pinus taeda,P. elliotti,P. echinata,
and P. palustris) forests using several estimates
of canopy height and cover from discrete-return
lidar, explaining between 53% and 65% of variance
in field measurements of these variables.
Later work by Nelson et al.(1997) in tropical wet
forests at the La Selva Biological Station obtained
similar results for prediction of basal area,volume,
and biomass.They also developed a canopy structure
model that led to greater understanding of the
optimal spatial configuration of field sampling for
comparison with profiling lidar data. Naesset
(1997b) explained 45%–89% of variance in stand
volume in stands of Norway spruce (Picea abies)
Figure 7. Conceptual basis for the canopy volume method. The cells of the
matrix are 10 m in diameter and 1 m tall; they correspond to a 1-m vertical
bin within a single waveform. Each waveform is processed to remove
background noise (a), and a threshold value is used to classify each element
of the waveform into either “filled” or “empty” volume. The cumulative
top-down distribution of the waveform (b) is used to classify filled elements
of the matrix into a euphotic zone, which returns the majority of energy
back to the sensor, and an oligophotic zone, consisting of the balance of the
profile. These two classifications are then combined to form three canopy
structure classes: empty volume within the canopy (i.e., closed gap space),
the euphotic zone, and the oligophotic zone. “Open gap” volume is then defined
as the empty space between the top of each of the waveforms and the
maximum height in the array. Figure adapted from Lefsky et al. (1999b),
with permission from Elsevier Science.
and Scots pine (Pinus sylvestris),using measurements of maximum
and mean canopy height and cover.
Five published studies document the utility of waveformrecording
lidar in predicting forest stand structure. Nilsson
(1996) adapted a bathymetric lidar system for use in forest inventory,and
successfully predicted timber volume for stands
of even-aged Scots pine (P. sylvestris). He used the height
and the total power of each waveform as independent variables,
and explained 78% of variance. Lefsky and colleagues
(1999a) used data from SLICER to predict aboveground biomass
and basal area in eastern deciduous forests using indices
derived from the canopy height profile.Of particular note,they
found that relationships between height indices and forest
structure attributes (basal area and aboveground biomass)
could be generated using field estimates of the canopy height
profiles, and applied directly to the lidar-estimated profiles,
resulting in unbiased estimates of forest structure. Means
and colleagues (1999) applied similar methods to evaluate 26
plots in forests of Douglas-fir and western hemlock at the H.
J.Andrews Experimental Forest. They found that very accurate
estimates of basal area,aboveground biomass,and foliage
biomass could be made using lidar height and cover esti-
mates.
A fourth study (Lefsky et al. 1999b) used statistics derived
from the CVM to predict numerous forest structure attributes,including
several not previously predicted from lidar remote
sensing.Stepwise multiple regressions were performed
to predict ground-based measures of stand structure from
both conventional canopy structure indices (mean and maximum
canopy surface height,canopy cover,etc.) and CVM indices
such as filled canopy volume, open and closed gap volume,
and a canopy diversity index—the average number of
CVM classes per unit height.Scatterplots of predicted versus
observed stand structure attributes are presented in Figure 9.
Examination of the scatterplots indicates that the predicted
values of aboveground biomass and LAI show no asymptotic
tendency,even at extremely large values (1200 Mg per ha–1
of
aboveground biomass).In addition,the three-dimensional aspects
of the canopy lidar were able to accurately estimate indices
related to more complex aspects of the diameter distribution,
such as the standard deviation of diameter at breast
height (DBH) and the number of stems greater than 100 cm
DBH.
The fifth published study (Drake et al.forthcoming) extends
the application of waveform-recording lidar to a tropical wet
forest in Costa Rica, where, using the LVIS sensor, data were
collected near the La Selva Biological Station. Using a set of
indices describing the vertical distribution of the raw waveforms
and the fraction of total power associated with the
ground returns, they were able to predict field-measured
quadratic mean stem diameter, basal area, and aboveground
biomass, explaining up to 93%, 72%, and 93% of variance,
respectively.The resulting map (Figure 10),which depicts the
landscape scale patterns in aboveground biomass with unprecedented
detail and accuracy,should be useful in itself and
should also inform other studies in the area.
Conclusions
Lidar remote sensing only recently has become available as a
research tool,and it has yet to become widely available.Nevertheless,it
has already been shown to be an extremely accurate
tool for measuring topography, vegetation height, and
January 2002 / Vol. 52 No. 1 • BioScience 27
Articles
Figure 8. Canopy volume profile diagrams for representative young, mature, and old-growth Douglas-fir and western hemlock
forest plots. These diagrams indicate, for each 1-meter vertical interval, the percentage of each plot’s 25 waveforms that
belong to each of the four canopy structure classes. Young stands are characterized by short stature, a uniform upper canopy
surface, and an absence of empty space within the canopy. Mature stands are taller, characterized by a uniform upper canopy
surface but with a large volume of empty space within the canopy. Old-growth stands are distinguished from mature stands
by their uneven canopy surface and the broad vertical distribution of each of the four canopy structure classes. Whereas
stands from earlier stages in stand development have canopy structure classes in distinct vertical layers, in the old-growth
stands each canopy structure class occurs throughout the height range of the stands. This trait has been cited as a key physical
feature distinguishing old-growth forests from the simpler canopies of young and mature stands (Spies and Franklin 1991).
Figure adapted from Lefsky et al. (1999b), with permission from Elsevier Science.
cover,as well as more complex attributes of canopy structure
and function. In addition, the basic canopy structure measurements
made with lidar sensors have been shown to provide
highly accurate and nonasymptotic estimates of important
forest stand structure indices,such as leaf area index
and aboveground biomass.Because the basic measurements
made by lidar sensors are directly related to vegetation structure
and function,we expect that these findings will continue
to be corroborated in a variety of biomes,
with similar results.
The availability of lidar data will increase
with the launch of several spaceborne lidar
missions and the broader use of airborne
sensors for topographic mapping. As data
availability grows, a variety of applications
will become feasible.It is likely that lidar will
be useful in detecting habitat features associated
with particular species,including those
that are rare or endangered. For instance,
the large open-grown trees and associated
old-growth habitat that serve as nesting habitat
for marbled murrelets (Hamer and Nelson
1995) should be readily identifiable from
lidar data. Indices of structural complexity
also may be able to identify areas of probable
high biodiversity, which could then be
used to assist projects such as the national
Gap Analysis Program (GAP; Scott and Jennings
1998).
Another likely application of lidar data is
the identification of forest areas with accumulations
of fuels that make them particularly
susceptible to large,especially damaging
28 BioScience • January 2002 / Vol. 52 No. 1
Articles
Figure 9. Scatterplots of predicted and observed stand attributes from 22 plots at the H. J. Andrews Experimental Forest.
Figure adapted from Lefsky et al. (1999b), with permission from Elsevier Science.
Figure 10. Map of aboveground biomass (AGBM) predicted from LVIS data
over La Selva Biological Station, adapted from Drake et al. (forthcoming), with
permission from Elsevier Science and Jason Drake.
fires (Agee 1993).Lidar’s ability to discriminate the spatial pattern
as well as the total volume of materials within a forest
canopy would be especially useful for identifying,at the least,
classes of forest structure that are associated with varying fire
behavior. For instance, lidar should enable the detection of
“ladder” fuels, which provide a pathway for ground-level
fires to reach the upper canopy and cause more damaging
crown fires. In addition, the ability to identify the size and
depth of canopy gaps should allow estimation of the quantity
of large woody fuels associated with the creation of those
gaps.
More generally,lidar remote sensing shows great potential
for integration with ecological research precisely because it directly
measures the physical attributes of vegetation canopy
structure that are highly correlated with the basic plant community
measurements of interest to ecologists.Until recently,
detailed measurement and modeling of canopies have largely
been the province of specialists.By reducing the time and effort
associated with measuring canopy structure,lidar can foster
the wider incorporation of a canopy science perspective
into ecological research and put vegetation canopy structure
squarely at the center of efforts to measure and model
global carbon dynamics.
Acknowledgments
This work was supported by a grant from the Terrestrial
Ecology Program of NASA to Drs. Cohen and Lefsky. Development
of the SLICER instrument was supported by
NASA’s Solid Earth Science Program and the Goddard Director’s
Discretionary Fund. SLICER data sets available for
public distribution are documented at http://core2.gsfc.
nasa.gov/lapf.Acquisition of the SLICER data used here was
supported by a Terrestrial Ecology Program grant to Dr.
Harding. The SLICER work around SERC was also supported
by the Smithsonian Environmental Sciences Program
and NASA University Programs (grant numbers NAG
5-3017 to G. G. P. and NAS 5-3112 to M. A. L.). Additional
work was conducted at and supported by the Wind River
Canopy Crane Research Facility,a cooperative scientific venture
of the University of Washington, the US Forest Service
Pacific Northwest Research Station,and the Gifford Pinchot
National Forest.Special thanks to Dr.Ralph Dubayah and an
anonymous reviewer for their detailed reviews of an earlier
version of this article.
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