9-11 Nov. 1999 
ugh details are closely 
to creating DTMs have 
rsities (Axelsson, 1999: 
height and volume have 
s using small-footprint 
a; Naesset, 1997b) and 
l.; 1999; Means et al., 
ll-footprint lidar have a 
plored. 
1 with image data offers 
feature recognition and 
yf information are so 
ctural information, such 
ical distribution, while 
mposition such as roof 
ps and proportions of 
rbs or grass. Progress 
r imagery to make 3-D 
oes (hazards 
areas 
iass, basal area, leaf 
^ts, vegetation 
r imagery 
  
models (Haala, 1999), including automated extraction of 3-D 
features such as buildings (Maas & Vosselman, 1999), 
highlights this potential. This capability will be enhanced 
with co-collection and automated coregistration of raster 
imagery as some companies are starting to do. 
There are several important opportunities for environmental 
assessment using lidar without evidence in the literature that 
they have been explored. 
Canopy fuels Wildland fuels are produced by vegetation in 
proportion to its productivity, which can be indexed for a 
given vegetation type by height and canopy cover, both of 
which are easily measured by lidar. Canopy fuels are 
especially hard to inventory and are an important driver of 
crown fires, the most destructive and hard to control and 
model (Rothermel, 1991). Lidar provides much information 
on canopies and offers significant promise for predicting 
distribution in 3-D space and density of fine fuels, however, 
developing this potential will be a challenge. 
Riparian zones Riparian vegetation is an important 
component of stream habitat because of its input of wood and 
leaves and the shade it provides, reducing heat stress on 
sensitive fish (Gregory et al, 1991) (Figure 1). These 
functions are correlated with distance from stream, vegetation 
height and canopy density, all of which can be assessed by 
lidar. 
Arboreal habitat Arboreal habitat is very important for 
many species, some of which are threatened or endangered 
(Forsman et al., 1984), yet it is the most difficult type of 
terrestrial wildlife habitat to assess. Lidar should be very 
helpful here because it can index the height, density and 
vertical distribution of a vegetation canopy. For this to be 
realized, we must develop the ability to integrate small- 
footprint lidar reflections into a vertical canopy profile, as has 
been successfully achieved in Costa Rica (Blair & Hofton, 
1999). 
Future cost and availability 
In the future costs of small-footprint lidar products should 
decline and types and accuracy of products should increase 
due to several factors. Processing into DTMs and models of 
features that now requires significant operator interpretation 
will become more automated. Competition in the industry 
will increase. Repetition rates of lidars will increase and 
associated computer costs will decrease, allowing data to be 
International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 3W14, La Jolla, CA, 9-11 Nov. 1999 
    
collected more efficiently and at the higher densities needed 
for some applications, and/or reducing the cost per unit area. 
Flexibility in footprint size and spacing and in flying height 
will increase. A great improvement for applications to dense 
forest stands will be commercial availability of a lidar with a 
full waveform return. This will blur the difference between 
large-footprint and small footprint lidars. 
The availability in the next few years of data from the satellite 
lidar, VCL (Dubayah et al., 1999), will provide every major 
landowner with large-footprint (25m) lidar data. Single tracks 
of contiguous footprints will be spaced 2 km apart over the 
earth’s land surface between 65 degrees North and South, and 
this data will be publicly available. The future is bright for 
this new technology; we will learn much about our world (and 
solar system) through data lidars provide. 
6 References 
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and future expectations. ISPRS J Photogram & Remote Sens 
54(2-3):64-67. 
Aldred, A. H. & Bonnor, G. M., 1985. Application of airborne 
lasers to forest surveys. Petawawa National Forestry Institute. 
Information Report PI-X-51, 62 p. 
Arp, H., Griesbach, J.C. & Burns, J. P., 1982. Mapping in 
tropical forests: A new approach using the laser APR. 
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Axelsson, P., 1999, Processing of laser scanner data - 
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Baltsavias, E. P., 1999. Airborne laser scanning: existing 
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Blair, J. B. & Coyle, D. B. 1996. Vegetation and topography 
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Blair, J. B., Coyle, D. B., Bufton, J. L. & Harding D. J. 1994. 
Optimization of an airborne laser altimeter for remote sensing