|. 9-77 Nov. 1999
form Processing
mplementations of these
ractive MacArthur-Horn),
. Lefsky, computes stand
, and has served as the
he processing algorithms.
n the latest version of a
lemented in the Interactive
t is available at
.V (X-windows Lidar
outines developed by M.
edict transmittance profiles
ce below).
VEFORM LIDAR
AENTS
d lidar measurements of
consisting of twelve field
nents in eastern deciduous
lina, USA. He found good
| and lidar measurements,
ments to underestimate the
jleasurements of maximum
es of the canopy height
hod (MacArthur and Horn,
ccurate as those obtained
netric principle. Means et
| (1129595) and excellent
height and field estimates
dominant and co-dominant
ric principle. Subsequent
published) indicates high
stimates of maximum stand
onships between field and
nd canopy height are not
he total adjusted power of
otal power of the canopy
reement between field and
/o), and, with the exception
found a relationship near
ites of cover. Means et al.,
between lidar and field
sligible difference between
1S=0.08).
International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3W14, La Jolla, CA, 9-11 Nov. 1999
Transmittance
Parker et al., (In Prep) have examined the relationship between
field and lidar measurements of the vertical distribution of
transmittance in both eastern deciduous and western coniferous
forests. Although the lidar measurements estimate the
transmittance of direct illumination at the nadir angle of the
sensors, and the field measurements estimate the transmittance
of both direct and diffuse illumination at the sun azimuth
angle, the two measurements closely track each other, both in
terms of the total vertical distribution of transmittance, and
several key derived statistics (Fig. 2).
80
Tower
[Wind River
2
c
Heightm
Fractional Transmittance
Fig. 2. Comparison of field (solid) and lidar (dashed) estimates
of transmittance as a function of height in an eastern
(Tower) and western (Wind River) forest. Parker et al (In
Prep)
Canopy Height Profiles
Validation of the ability of SLICER to estimate field
measurements of the canopy height profile (CHP) are presented
in Lefsky (1997) and Harding et al., (Submitted). Despite the
inherent difficulties in comparing the upward looking field
estimates and the downward looking lidar estimates of the
CHP, good agreement between the two estimates was found for
four stands of differing age (Fig. 3). They also found that
SLICER estimates of the CHP fell within the range of
variability observed when different subsets of the field CHP
data were compared. Lefsky (1997) applied a smoothing
algorithm to both field and lidar estimates of the CHP, and
found no statistically significant differences between the field
and lidar estimates of the CHP.
Corn Contees [Towers Belt-
40 ood
= 30
=
B
20
10
Er ceu gro oc caa cmt cap
mnc >S ER =s mR 5S =: 5
Fig. 3. Comparison of lidar (line) and field (bars) estimates of
the canopy height profile. Harding et al. (Submitted)
5. APPLICATIONS OF WAVEFORM LIDAR
MEASUREMENTS
Three published studies document the utility of SLICER for
prediction of forest stand structure. Lefsky (1997) and Lefsky et
al., (1999a) used data from SLICER to predict aboveground
biomass and basal area, using indices derived from the canopy
height profile, in eastern deciduous forests. In Lefsky et al.,
(1999a), a number of height related indices were evaluated for
the prediction of stand basal area and biomass, and the
quadratic mean canopy height was found to be the best overall
predictor. The quadratic mean canopy height is the mean height
of the canopy height profile, with each element of the profile
weighted by its squared height. 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 CHP, and applied directly
to the lidar estimates of the CHP, resulting in unbiased
estimates of forest structure. Means et al., (1999) applied
similar methods to 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 estimates.
The third published study (Lefsky et al., 1999b) is the first to
take advantage of SLICER’ ability to measure the three-
dimensional distribution of canopy structure in a direct fashion
(Figure 4). Five-by-five blocks of waveforms (corresponding
to a 50 x 50 m field plot) were processed using the novel
canopy volume profile algorithm. Following the procedures
above, each waveform was transformed into an estimate of the
canopy height profile (CHP), the relative distribution of the
canopy as a function of height. A threshold value was then used
to classify each element of the CHP into either “filled” or
“empty” volume, depending on the presence or absence (in the
waveform) of returned energy. A second step classifies the
filled elements of the matrix into an “euphotic” zone, which
contains all filled elements of the profile that are within the
uppermost 65 % of canopy closure, and an “oligophotic” zone,
consisting of the balance of the filled elements of the profile.
These two classifications were then combined to form three
classes; empty volume beneath the canopy- (i.e., closed gap
space), filled volume within the euphotic zone, and filled
volume within the oligophotic zone.
These same classes are then computed for each of the twenty
five SLICER waveforms in the 5 by 5 array. The waveforms
were then compared, and a fourth class was added, “open” gap
volume is defined as the empty space between the top of each
of the waveforms and the maximum height in the array. At this
point, the total volume of each of the four classes of canopy
structure can be tabulated for each 5 by 5 array of waveforms.
To determine the ability of SLICER measured canopy structure
indices to predict aboveground biomass and Leaf Area Index
(LAI), stepwise multiple regressions were performed using as
independent variables the total volume of each of the four
