the 1064 nm laser wavelength is very low, typically in the range
0.01 to 0.1 (e.g., Williams, 1991)
Canopy Height Profile Algorithm
In order to derive canopy height profiles from the raw SLICER
waveform distributions we adapted the MacArthur and Horn
(1969) transformation method. Two assumptions inherent to the
transformation (Aber, 1979) also apply to the transformation of
SLICER waveforms. The horizontal distribution of canopy
components within a layer is assumed to be random with
respect to layers above and below. In other words a Poisson
distribution is assumed with no horizontal clumping of canopy
components. Also, the leaf inclination distribution is assumed
to be constant as a function of height so that the projected leaf
area in the direction of observation (up-looking from the
ground or down-looking for SLICER) is related in a constant
way to total leaf area. Several additional assumptions specific
to the SLICER waveforms must also be made. As a
replacement for the proportion of clear-sky to plant interception
sightings, defining gap fraction viewed upward from the
ground, the proportion of ground return to canopy return signal
strength is used. However, in order for this proportion to
represent downward-viewed gap fraction the ground return
signal strength is modified in order to account for any
difference in the average reflectance at 0° phase angle (i.e.,
direct backscatter) of the ground and canopy at the laser
wavelength. In most circumstances this ratio between ground
and canopy reflectance is not known at the scale of the laser
footprints and a value must be assumed. Application of the
method to SLICER waveforms also assumes that the reflectance
of the canopy components is constant as a function of height.
Whereas for the ground sightings each canopy intercept counts
equally in the resulting distribution, for SLICER an equivalent
surface area contributes greater return signal as reflectance
increases. This assumption inherently implies that the ratio of
woody to leafy surface area and the woody and leafy
reflectances are constant as a function of height. Finally, it is
assumed that multiple scattering, causing lengthened photon
travel paths, does not contribute significantly to delayed signal
in the waveform, because either the amount of multiply-
scattered photons received in the backscatter direction is small
as compared to singly-scattered photons or the magnitude of
any resulting delay is small. Implications for each of these
assumptions are considered in Harding et al., (Submitted).
The effect of occlusion (the decrease in return energy that
occurs with increasing depth in the canopy, and which is due to
the previous interception of the laser energy) on the NCPD is
corrected by weighting this distribution by -1 x In(1- closure)
(MacArthur and Horn, 1969; Aber, 1979), transforming the
result to a cumulative distribution of canopy area projected in
the direction of the laser beam (Fig. 1c). The cumulative
distribution is normalized and converted to an incremental
height distribution, yielding the Canopy Height Profile (CHP),
which depicts the fraction of projected canopy area per
measurement interval (Fig. 1d). The heights of the CHP
intervals are referenced to the absolute backscatter energy and
no comparison of energy between laser shots is made.
International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3W14, La Jolla, CA, 9-11 Nov. 1999
Software Implementations of Waveform Processing
Algorithms
There are two main software implementations of these
processing algorithms. IMH (Interactive MacArthur-Horn),
developed by D. Harding and M. Lefsky, computes stand
height and canopy height profiles, and has served as the
software for most applications of the processing algorithms.
The IMH functions are included in the latest version of a
SLICER data browser and editor implemented in the Interactive
Data Language (IDL) that is available at
http://denali.gsfc.nasa.gov/lapf. XLV (X-windows Lidar
Viewer) is an extension of those routines developed by M.
Lefsky, and includes the ability to predict transmittance profiles
and canopy volume measurements (see below).
4. VALIDATION OF WAVEFORM LIDAR
MEASUREMENTS
Height
Lefsky (1997) examined field and lidar measurements of
maximum stand height for a dataset consisting of twelve field
plots with coincident lidar measurements in eastern deciduous
forests of Maryland and North Carolina, USA. He found good
correlation (r°= 78%) between field and lidar measurements,
but a tendency for the lidar measurements to underestimate the
height of the tallest stands. Field measurements of maximum
height were derived from estimates of the canopy height
profile, using the optical quadrat method (MacArthur and Horn,
1969) and were therefore not as accurate as those obtained
using methods based on the trigonometric principle. Means et
al. (1999) found high correlation (29596) and excellent
agreement between lidar estimates of height and field estimates
of "canopy height" (mean height of dominant and co-dominant
trees) obtained using the trigonometric principle. Subsequent
analysis of that dataset (Lefsky, Unpublished) indicates high
correlation between lidar and field estimates of maximum stand
height (17-9490), and that the relationships between field and
lidar estimated maximum height and canopy height are not
significantly different from identity.
Cover
Plant cover has been estimated as the total adjusted power of
the ground return, divided by the total power of the canopy
return. Lefsky (1997) found good agreement between field and
lidar measurements of cover (R°=65%), and, with the exception
of two clearly outlining points, found a relationship near
identity for the lidar and field estimates of cover. Means et al.,
(1999) found excellent agreement between lidar and field
estimates of cover (R94), with negligible difference between
field and lidar estimates of cover (RMS=0.08).
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