238
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008
The signal was de-noised, taking care to preserve the energy
ratio between the bands, assuming that any noise bias does not
distort the ratio. The ratio of visible to near infra-red was
smoothed with Gaussians of decreasing width so that the
smallest Gaussian to leave a single crossing point of the second
differential (corresponding to a maximum gradient) was used.
Using the smallest possible Gaussian ensured the best
localisation of the edge. To avoid the large gradients of the
signal start and end only a window between the first and last
crossing points of the third differential (after smoothing) was
used. The position of the maximum gradient was taken as the
start, the last return above noise as the end and the average
taken as the centre of the ground return respectively. Figure 8
shows the mean error in derived ground position for one
hundred simulated waveforms with 5% noise added over forests
with different canopy covers on a 30° slope. Error bars show the
standard deviations.
-25
!
-30 |
-35 ' * *- 1
0 10 20 30 40 50 60 70 80 80 100
Canopy covar(S)
Figure 8. Ground position error against canopy cover
The method performs well provided there is a reasonable
ground return. For very dense canopies the ground return is less
than the noise threshold and so would be hard to see, even
without topography.
If it can be assumed that the tallest trees are equally spaced
across the laser footprint then the tree height will be the
distance between the first signal return and first ground return.
The forest is unlikely to be so homogeneous over a 30m
footprint. The measure of canopy height need not be the tree
top. Lefsky et al (1999) showed the possibilities of using
parameters such as the median canopy height to estimate
biomass and other biophysical parameters through allometrics.
This can be calculated provided the energy returned from the
canopy and ground can be separated. The ground start and end
could be used to further constrain the Gaussian fitting then the
median of the canopy calculated. How this relates to biomass
will depend upon the forest’s horizontal and vertical
heterogeneity.
Conclusions
This paper has briefly outlined work in progress on the
development of algorithms for measuring tree height from
above canopy waveform lidar. The standard method of Gaussian
decomposition has been implemented, using realistic
simulations to precisely quantify the technique’s error (mean
overestimate of 5.7m for <75% cover; refining of the method is
needed).
A method for determining ground position of signals blurred by
topography or understory with dual waveband lidar has been
introduced and shown to work reliably for all but the densest
canopy covers and the errors quantified (mean RMSE of 3.2m
for <65% cover). Relatively few simulations were available
(100 over sloping and 225 over flat forests); more are being
processed with a wider range of densities, slopes and tree
species for use in further investigations. These will explore the
affect of noise, thresholds, range resolution, canopy cover and
pulse length on derived tree height error.
A reliable method for determining a measure of canopy height
over slopes is needed. The median canopy position may be the
easiest to derive. Its sensitivity to heterogeneity and noise will
be investigated.
The premature estimation of tree top suggests that the threshold
used was not high enough. This could be increased to the mean
noise value plus four or more times the standard deviation, but
having this fixed would increase the chances of thresholding out
weak ground returns. An adaptive threshold based upon
estimates of maximum likely tree heights or signal shape (the
tree top is unlikely to be isolated from the rest of the tree) may
give a better solution and should be investigated.
Acknowledgments
This work was funded by the NCEOI (Natural environment
research council Centre for Earth Observation and
Instrumentation) for project SA-014-DJ-2007 (Hyperspectral
Imaging LiDAR) and the Hollow waveguide technology for
C0 2 and canopy measurements (A-scope) project through the
Centre for Terrestrial Carbon Dynamics (CTCD).and the
EPSRC, studentship no. GR/9054658
References
Blair JB, Rabine DL, Hofton MA. 1999. The laser vegetation
imaging sensor: a medium-altitude digitisation-only, airborne
laser altimeter for mapping vegetation and topography. ISPRS
journal of photogrammetry and remote sensing, 54, pp. 115-
122.
Disney, M., Lewis, P., Saich, P., 2006. 3-D modelling of forest
canopy structure for remote sensing simulations in the optical
and microwave domains. Remote Sensing of Environment, 100,
pp. 114-132.
Harding DJ, Carabajal CC, 2005. ICESat waveform
measurements of within-footprint topographic relief and
vegetation vertical structure. Geophysical research letters, 32,
L21S10.
Hofton MA, Minster JB, Blair JB. 2000. Decomposition of laser
altimeter waveforms. IEEE transactions on geoscience and
remote sensing, 38, pp. 1989-1996.
Hofton MA, Rocchio LE, Blair JB, Dubayah R, 2002.
Validation of vegetation canopy lidar sub-canopy topography
measurements for a dense tropical forest. Journal of
geodynamics, 34 pp. 491-502.
Hosgood G, Jacquemond S, Andreoli G, Verdebout J, Pegrini
G, Schmuck G, 1995. Leaf optical properties experiment 93
(LOPEX 93). Ispra, Italy; European commission, Joint Research
Centre institute of remote sensing applications.