7/ 4000
900
(7
96
“ny Ot
rocessing.
measured
et spruce
| canvas.
lying the
alibration
»pectralon
intensities
cho at the
". As the
with the
scanner
R(4)) is
itting was
ed from a
e and the
dividually
deviation)
kscattered
r than 2%
and better
ffected by
| expected
istrument.
1umber of
ance, the
sision due
ium range
orm echo
nsity. The
12-meter
lity point
range and
s.
asured in
JAR. The
ym lack of
while the
1e LIDAR
a passive
-tungsten-
Fig. 3. The Norway spruce.
Spectrometer
0.37
MNNYL 3341
9
N
a
T
©
N
^, "Bpactometor
e
Lx
C1
Backscattered Reflectance
e
=
0 1 À. L 1 J
500 600 700 800 900 1000
Wavelength / nm
Fig. 4. Passive spectrometer measurement (solid line) and
hyperspectral LiDAR (dashed line) of same areas of
the tree are shown.
Spectra of the passive spectrometer and LiDAR measurements
of selected regions of interest are presented in Fig. 4. A clear
distinction between the tree trunk and the top can be observed
in the shape of the spectra. The LiDAR and passive
spectrometer spectral shapes are clearly similar. In case of the
tree top, the LiDAR observes less light than the passive
measurement in near-infrared. This difference is caused by
multiple scattering in a medium with a low optical density and a
high single scattering albedo. In an active LIDAR measurement,
only a small spot on the target is illuminated and observed. A
significant part of the pulse energy is lost outside the sensor
field of view, if multiple scattering plays a major role in
reflectance and the scattering mean free path is long in the
medium. This is not experienced in passive measurement as the
same amount of light is scattered both in and out of the sensor
field of view. The backscattered reflectance from Spectralon is
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
not significantly affected by this effect, as Spectralon has a high
single scattering albedo but only a short mean free path. As the
LiDAR backscattered reflectance is calibrated with that of the
Spectralon panel, the backscattered reflectance values are
decreased for bright and low optical density targets such as
needles.
The backscattered reflectance values produced by the LiDAR
do not strictly follow the definition of reflectance factor for
three reasons: First, due to hot spot effect (Hapke, 1993), the
99% Spectralon is not a Lambertian surface in backscattering
direction causing systematic error in the reflectance values.
Second, the illuminated surface area of the target is not constant
(as in the definition of reflectance factor) and this results in
uncertainty in the returned intensity. Third, part of the
transmitted light is lost outside the sensor field of view due to
multiple scattering, as described above. Despite these
limitations, the backscattered reflectance is a practical quantity
providing intensity readings independent of measurement
distance. For most applications, the backscattered reflectance
spectra can be exploited similarly to traditional reflectance
factors (e.g., in the computation and comparison of spectral
indices), but caution should be used when accurate absolute
values are needed.
Different vegetation indices can be obtained from the measured
dataset. For this study we selected Normalized Difference
Vegetation Index (NDVI) (Tucker, 1979), water concentration
index (Penuelas et al, 1993) and Modified Chlorophyll
Absorption Ratio Index (MCARII) (Haboudane et al., 2004). In
Fig. 5, these indices have been applied to the measured dataset
of the spruce.
Fig. 5. Different spectral indices are calculated for 5 cm voxels
and the full point cloud is colored according to the
result.
