Full text: Technical Commission VII (B7)

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Fig. 3. The Norway spruce. 
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Backscattered Reflectance 
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 
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 

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