B.J.Devereux, G.S.Amable, T.Cockerell
Unit for Landscape Modelling, University of Cambridge,
Sir William Hardy Building, Tennis Court Road,
Cambridge, CB2 1QB
Email: bjd1@cam.ac.uk
The last quarter of a century has seen major progress in vegetation analysis as a consequence of advances in passive infrared sensors capable of mapping pattern and quantity in the form of NDVI. An inherent limitation of these sensors, however, is their inability to penetrate dense canopies and as a consequence they can only provide information about the canopy top layer. Indices of vegetation quantity are thus likely to be biased and mapping can yield few insights into the
3d structure of forest canopies. This in turn means that remote sensing has so far made only a very small contribution to understanding ecological processes – in particular those related to energy availability and utilisation, at various levels within the canopy. Airborne LiDAR on the other hand, offers the potential to extract 3d structural information about canopies that can complement optical data and yield insights into such processes. This paper presents a model for characterising the 3d structure of forest canopies based on classification of
LiDAR pulses. It describes the implementation of the model for three contrasting woodland types which have been surveyed with an Optech ALTM 3033 laser scanner at the Woodwalton Fen SSSI. The implications for within canopy energy budgets are considered.
Although the development of LiDAR (Light Detection And Ranging) systems as remote sensing systems dates back to several decades ago, particularly those developed for bathymetry, routine and widespread use of LiDARs are only beginning to me made. This has been helped by the development of commercial turnkey airborne LiDAR systems since the mid-1990s. LiDAR measurements are the most detailed and perhaps the most accurate measurements of height and, by implication, the 3-dimentional properties of features of the natural and cultural landscape.
Compared with other forms of direct measurements, detailed and accurate LiDAR measurements can be made over spatially extensive areas very quickly and with high degree of efficiency, saving practitioners and analysts who regularly need to collect such data a substantial amount of time and money. Thus LiDARs have found application in many fields, including terrain modelling, floodplain and flood risk mapping, city modelling, power transmission cable monitoring and forestry, where it competes and complements effectively with traditional methods. Unsurprisingly, much effort has gone into comparing LiDARs with other techniques including radar interferometry, photogrammetry, and various direct measurements involving the use of field instruments and techniques.

Airborne laser scanning and its application in forestry and vegetation canopy studies are providing scientists new insights into the vertical structure of vegetation and the energy dynamics in such environments. Existing remote sensing techniques applied in vegetation studies have relied on spectral information and derived indices. A key weakness of these techniques is that they only measure the horizontal distribution of spectral response and are therefore less effective in providing a comprehensive description of the vegetation canopies and associated ecological processed. Such spectral indices of vegetation tend to contain some information about vegetation state with very little information by way of the volume and quantity of vegetation.
LiDARs have the potential to change this. The earliest applications of LiDARs in forestry and vegetation studies were made in the early 1980s (see for example, Arp et al 1982; Nelson et al 1984; Aldred and Bonner 1985, and Shreier et al 1985). Since then many studies have shown that traditional forest and canopy parameters such as tree height, basal area, volume and other forest stand characteristics can be extracted from LiDAR measurements. Examples include,
Nilsson (1996), Nelson (1997), Magnussen and Boudewyn (1998), Means et al (1999), Dubayah and Drake (2000), Means et al (2000), Naesset (2002), Persson et al (2002) and Lim et al (2003).
It can be noted from some of these studies that comparisons of airborne LiDAR measurements with traditional forest measurement techniques have shown that LiDARs tend to under-estimate tree heights. This is a fundamental characteristic of LiDAR, which reflects the different nature of the pattern of interaction between the laser beams and the surface of vegetation, an important difference which has implications for the use of LiDAR height measurements.
For vegetation studies, two types of LiDAR systems can be distinguished. The continuous waveform or profiling LiDAR systems developed primarily for spaceborne experiments measure the full return waveform at very high vertical resolutions over a largefootprint area, ranging from 10 – 100m in diameter. Examples of such systems include the
Vegetation Canopy Laser or VCL (Dubayah et al.
1997), the Scanning Lidar Imager of Canopies by Echo Recovery or SLICER system (Blair et al.
1994), Laser Vegetation Imaging Sensor OR
LVIS (Blair et al.
1999) and the Geoscience Laser Altimeter System (or GLAS) instrument on board the Ice, Cloud, and Land Elevation Satellite or ICESat (Zwally, et al, 2002). A number of experiments and 3-dimensional canopy modelling exercises have been conducted using data from these systems. For example, large footprint, full waveform systems have been used for examining the vertical structure of forest canopies (e.g. Lefsky et al.
1999; Harding et al . 2001), vertical patterns of photosynthetically active radiation (e.g. Parker et al.
2001), and above ground biomass (Means et al.
1999).
The second type of LiDAR systems are the discrete return systems, which provide single or multiple discrete range measurements using pulse laser, with small footprints (between 0.1 and 1m in diameter) and at very high densities of measurement. These systems are the most commonly available and are deployed from airborne platforms. They provide detailed and highly accurate height measurements of up to five returns per laser beam. The accuracy and accessibility of these systems have made them very popular for forestry studies, particularly in
North American and Scandinavian countries. They have been used primarily for forest survey and mensuration (e.g. Naesset, 2002; Persson et al.
2002). This use is expected to grow in other parts of the world. Although height measurements made with these LiDAR systems can and are being used directly in existing models for forestry applications there is need to extend the use of these detailed and high-density measurements further into models and applications which can be linked with other remotely sensed data. Attempts have been made (for example by Means et al
2000) to simulate waveform-type returns from high-density, small footprint LiDAR. Blair and
Hofton (1999) have demonstrated that by integrating small footprint returns across an area equivalent in size to a large footprint it was possible to elucidate the vertical structure of complex rainforest canopies in Costa Rica. Riano et al . (2003) also suggested this possibility in the

context of forest mapping for fire models.
In this paper we present a model, which describes the 3-dimetional structure of the surface of vegetation canopies as measured by an airborne LiDAR system. We present the descriptive statistical model results for three types of deciduous woodland canopies. We show how these types of models can be used to characterise and differentiate each type of canopy directly from the point clouds. These models are comparable to waveform-type vegetation models. We also discuss possible applications of the models in providing indications of the patterns of surface and within canopy energy interactions. The results presented here are part of an on-going research on the modelling of vegetation canopies and the extraction of environmental parameters from airborne terrain mapping laser systems.
The site used for this study is Woodwalton Fen, a SSSI (or Site of Special Scientific Interest) about 12km south of Peterborough in East Anglia. Extending over an area of about 3km by 1km,
Woodwalton Fen covers a virtually flat terrain and is composed mainly of semi-natural woodland and grassland habitats and reed beds. A dense network of drainage channels divides the site into a patchwork of these wetland habitats. It has a rich variety of fauna and flora and boasts of some 47 red data book species and two very rare plants. The success of the conservation and management of this and other nearby reserves has led to the conception of the
Great Fen Project, which will extend the nature reserve northwards from Woodwalton Fen over several hectares of farmland to join with Holme Fen, thereby creating over 3000 hectares of rich fenland habitat (English Nature, 2003). For this study, the site offers a unique collection of vegetation canopies: a variety of deciduous woodlands species in various communities, and woodland canopies ranging widely in structural complexity. The northern part of the site is dominated by dense sallow woodland whilst stands of silver birch, hawthorns and mixed woodland are predominant in the south. Altogether the woodlands and sallow carr cover about
100 ha of the site.
For the analysis described below three types of woodland canopies were used, namely: sallow, birch and mixed woodland canopies. Sallow, which is mainly grey willow ( Salix cinerea ), takes advantage of the damp conditions to develop into dense thickets typically reaching 5 – 8 meters above the ground. The top layer of these canopies is often dense (Figure 1a) with little penetration of light through to the lower layers of the canopy. The stands of birch, which are dominated by silver birch ( Betula pendula ), typically reach heights of over 20 metres. In terms of canopy density and openness the birch stands are the direct opposite of the sallow. Thus there is sufficient light penetration and the possible development of lower storey vegetation within these stands. The mixed woodland stands consist of a mixture of birch ( Betula spp .), hawthorn
( Crataegus spp .) and alder ( Alnus spp .). They are the tallest and structurally the most complex of the canopies found at this site (Figure 1b), with the top layer of the canopy dominated by birch whilst the alder appears in isolated clumps.

(a) (b)
Figure 1: Aerial photo subsets of two of the woodland canopies showing the different structural properties. Sallow
(a) have dense and uniform canopy whilst mixed woodland (b) has a more varied structure with multi-storey layers within the canopy. The boxes show the dimensions of the 100x100m sample plots used in analysis (see section 2.3).
The LiDAR data were collected with Cambridge University's Airborne Laser Terrain Mapper (or
ALTM) system, model 3033 (by Optech Inc, Canada), on board a Piper Chieftain Navajo PA31 aircraft. Over a twelve-month period in 2002/03 several LiDAR surveys were conducted over the site as part of an ongoing monitoring programme for the Fen, aimed at exploring the value of
LiDAR for understanding seasonal vegetation dynamics. The results presented in this paper were based on data collected on 8th October 2002, under leaf-on conditions. The survey was planned and flown at 1000m above terrain. Utilizing the full +/- 20° scan angle and maintaining a 30-
35% overlap between strips, two overlapping flight strips were enough to cover the areas of interest at the site. Given the 33,333Hz pulse repetition rate of the ALTM 3033 system, a scanning frequency of 28Hz produced laser point measurement density of approximately 1 point per square meter at the normal survey flying speed of the PA31 aircraft.
The raw survey data were processed by the Processing Facility at the Cambridge
University Unit for Landscape Modelling, using Applanix’s POSPAC and Optech’s REALM software suites, a combination of GPS/INS post-processing and laser point computation software systems. The integrity and accuracy of the laser measurements were maintained by means of a rigorous process of continuous system calibration and site-specify accuracy tests. The calibration process involves flying over different kinds of calibration targets, which were independently measured in detail, using a total station. The LiDAR measurements were compared with the ground measurements to determine the ranging accuracy of the LiDAR and to derive calibration coefficients for removing any biases in the 3-dimensional pointing accuracy as well as look angle offsets. Overall system accuracy of better than +/- 15cm has been achieved. However, comparisons of repeat surveys have shown that relative accuracies are in the order of 5-7cm. The main product of the initial processing is the LiDAR point cloud data comprising of coordinate pairs for each first and last pulse elevation value together with the corresponding intensity values. The coordinates were computed in Universal Transverse Mercator (or UTM) coordinate system and the WGS84 spheroid/datum.
We begin the description of the small footprint LiDAR canopy model by considering the nature of the interaction between laser beams from the LiDAR and the vegetation canopy. Starting with

a single tree, suppose a measurement system pointing vertically downward (such as an airborne
LiDAR) is used to make several height measurements of this tree from directly overhead so that only the topmost part or the surface of the crown can be measured. If n randomly spaced height measurements ( h
1
, h
2
,..., h n
) were made off the 3-dimensional surface of the tree crown and n is sufficiently large, then the frequency distribution function of the n measurements, F(h) , will provide a descriptive model of the 3-d structure and shape of the surface of the tree crown. This is illustrated in Figure 2. Such a model also provides the basis for determining the height of the base of the crown ( h base
), the tree height or height of the top of the crown ( h max
), and hence the depth of the crown ( h max
- h base
).
Figure 2. A single tree (left) and frequency distribution model (right) of the height measurements off the crown surface of the tree.
Vegetation canopies are composed of a number of trees growing together often utilising as much of the space available as possible. Therefore in practice this single-tree model is less common.
Depending on the spacing between trees a group of trees with touching or merged crowns will have a different shape, and frequency distribution models of such trees will be different from those of single isolated trees, whether or not they are the same species. A canopy (rather than single-tree) distribution model however will provide a better description of the canopy, provided that n is sufficiently large and is from a spatially extensive domain, large enough to represent the collection of trees in the canopy.
Thus, if sampling density and spatial extent of the n height measurements are adequate, then the vegetation canopy surface can be modelled from the frequency distribution of the heights. We stress that this is not a model of the complete 3D structure of the canopy; it only describes the canopy surface. In the rest of this paper we test the usefulness of this model using graphical representations of a-posteriori observations based on first pulse LiDAR height measurements of the three canopies described above. To assess the model for pattern consistency and structural similarity we compare models created from different samples plots of the same type of canopies. We further compare the models for different canopies. Vegetation canopies often have gaps, either between or within the tree crowns. Therefore surface height measurements may be obtained from parts other than the topmost surface of the tree crowns.
Consequently, in areas where there are gaps, height measurements may be made from beneath the surface of the canopy, such as from stems and branches or from lower storey vegetation, including the under-storey, or perhaps from the ground. Therefore the form and structure of the models are expected to be more complex than the single-tree representation shown in Figure 2.
To extend the model further, we combine distribution models created from first and last pulse observations. By so doing we introduce canopy density or openness, revealing the pattern

of light interactions with and through the canopy. In areas where the canopy is open enough to allow the last pulses to penetrate to levels beneath the surface of the canopy further information about lower storey vegetation can be obtained, this also being a measure of the canopy openness.
The LiDAR point cloud data were imported into GIS software and overlaid on an aerial photo-mosaic of the site. An existing vegetation cover type map of the site was used to identify areas of the three types of canopies. For each canopy type at least four sample plots measuring about 100m by 100m were selected and LiDAR point cloud data were extracted for each sample plot for subsequent analysis. We believe that plots of this size provide a more than adequate representation of the canopies studied, in terms of the spatial extent and the number of point observations. Since the site is on a virtually flat terrain, there was no need subtract terrain height elevation values, thus the computed height values used were heights above the WGS84 datum rather than heights above ground level.
Figure 3 shows the frequency distributions of the first pulses for four sample plots of sallow. The histograms in the diagram were produced using 50cm bins. There are a few differences between the plots, reflecting the natural differences in conditions within the different plots. The height of the tallest trees in the canopies at the four sites, h max
, ranges from the 55 to 57.8m, plot 1 having the lowest maximum height and plot 2 the highest. Sample plots 2 and 4 contain a few height values from beneath the canopy, that is, from within the under-storey level. This indicates that there are more gaps in these plots than in the other two plots. These differences may have important ecological implications.
Figure 3: Frequency distribution models of first pulse observations of sallow canopies taken from four different sample plots.
Apart from these differences, the distinctly similar shape of the distributions is characteristic of the sallow canopies. This shows that although these sample plots were hundreds of metres apart, the form and structure of the canopy surface is striking similar. For all four plots

the crown part of the models approximate a Gaussian distribution, which corresponds with symmetrical S-shaped cumulative distributions. The bulk of the canopy surface is between 49 and 53m, that is 4.5 – 7.5m above the ground (the ground level is approximately 45.5m above ellipsoid). Also the height of the base of the crown is at the 48.5 - 49m level. Thus all four sites have the very similar crown depth.
Comparing this characteristic model for sallow with the models for birch and mixed woodland stands (Figure 4) shows the main structural differences between the canopies. There are major differences in the shape of the distributions as well as in the heights of the main components of the canopies. As shown, the model for birch stands is characterised by tall and considerably open canopies. The main tree crown parts of the canopy range between 58.5 and
68m. But there is considerable penetration of the laser to lower levels of the canopy with a significant proportion of the measurements coming from the ground and under-storey layer, that is about 0- 2m above the ground. This indicates that there are more gaps in the birch stands. The mixed woodlands are characterised by multi-storey canopy structures. There are two distinct storeys, one storey at between 52 and 57.5m and the other at between 57.5 and 69m. These distinct peaks in the model can be associated with the different types of tree species, and associated differences in vertical structures found in these mixed woodland stands. In the upper layer are birch and alder trees and the lower layers are hawthorn and saplings competing for the light at those levels. Again, as with the birch canopies, there are gaps in the canopy allowing a significant (but relatively lower) proportion of the laser measurements from the ground and under-story levels. A summary of the model parameters shows that sallow, mixed woodland and birch canopies have respectively 0.7%, 5.1% and 14.3% of the first pulse measurements from the ground and under-storey levels. This provides a measure of the amount of gaps in the respective canopies.
Figure 4. Canopy surface models of the three canopies of birch, sallow and mixed woodland. The right hand column shows vertical profile plots of the first pulse (black dots) and last pulse elevations (grey dots) against easting coordinates.

The vertical profile diagrams shown in Figure 4 portray another view of the vertical structure of the canopy surface. The vertical layers in the canopy, particularly for sallow, are clearly visible. The two storeys in the mixed woodland can also be seen. The amount of gaps in these canopies are shown by the number of first pulses (black dots) along or near the ground level, providing further confirmation for the observation concerning the gaps in these canopies.
The profile diagrams in Figure 4 also show the last pulse measurements (grey dots). Compared with the first pulse profiles these portray a different property of the canopies. Whereas the first pulse model shows the structure of the surface of the canopy and presence or absence of gaps, the last pulse observations show the degree of penetration possible beneath the top surface. They show the location of vegetative materials within the 3-dimensional column of the canopies.
Figure 5 combines and compares the first and last pulse frequency distribution models. (The model for a canopy of hawthorns is included for comparison.) The frequencies have been normalised so that within- and between-chart comparisons can be made of the relative frequencies.
Figure 5: Combined first pulse and last pulse models reveal further structural properties of the canopies as well as information about canopy openness.
For sallow, about 20% of the last pulses reach the ground and under-storey levels. This is much higher for mixed woodland and birch canopies at about 28% and 30% respectively. If one assumes that the ground and under-storey levels are about 2m thick, then these proportions are respectively 22%, 35% and 46%. Comparing these with the first pulse gap proportions (in section 3.1) shows that there is a higher degree of light penetration to lower storeys of the mixed woodland and birch canopies. A high proportion of the last returns were from or near the surface of the sallow canopies. This proportion is much less for mixed woodland and birch canopies. For

these, a significant proportion of the last returns were from within the vertical column of the canopies, that is, somewhere between the tree crowns and the under-storey layer. This reflects the degree of openness of the canopies and can be quantified using the ratio of the proportion of the first and last returns at each level in the canopy. These results confirm that sallow is the densest of the three canopies (but is slightly less dense than the canopy of hawthorn shown) and birch is the most open.
The combination of gaps (as derived from the first pulse models) and canopy openness
(from last pulse models) provides a simple and direct way of characterising the light interaction
(and degree of penetration) with the vegetation canopies described above. These have ecological implications, particularly with respect to the plant communities that one would expect to find in the under-storey layer of these canopies. For example, one would expect to find higher illumination levels at or near the ground level, and therefore more lower-storey or ground-level vegetation development and rich floral compositions in the birch stands than in the mixed woodlands. The sallow stands, which have much less illumination reaching the under-storey layer, would have much less green vegetation. Dry leaves and dead branches and twigs may dominate these, and consequently a different type of sub-canopy ecosystem is to be found.
Small footprints LiDAR data over an appropriately sized area provides detailed characterisation of vertical structure of even the most complex, semi-natural vegetation canopies. In this paper we have shown how the first pulse heights measurements, which records the interaction with the outer surface of the canopy, can be used in a frequency distribution model to characterise the canopy surface morphology. Using measurements obtained over three semi-natural woodland canopies in Woodwalton Fen we have shown that these canopy surface models can be used to characterise and describe the canopies. Canopy gaps and the degree of laser penetration of the canopy can also be mapped using combinations of first and last pulse measurements. The surface models have similar characteristics as those generated from large footprint waveform LiDAR systems.
Birch canopies are the most open and have more gaps than mixed woodland canopies, which also exhibit multi-layer characteristics, reflecting the properties of the different tree species in these canopies. Sallow have considerably dense canopies with very little by way of gaps. This ability to identify gaps, areas of open and areas of dense canopy is clearly of considerable significance for understanding light penetration to the under-storey and the impact it has on composition and vigour. In addition to characterising vertical structure, the cumulative height distributions from the histograms also provided very clear discrimination between the vegetation classes involved. This suggests that the integration of this information with spectral information derived from other sensors will bring significant progress towards detailed and more informative mapping and description of semi-natural vegetation communities.
Future work will extend this analysis and implement this canopy modelling approach in wide area canopy mapping and will incorporate this information with spectral data in more detailed vegetation classification and characterisation.
This project was funded by the Sir Isaac Newton Trust for which the authors are grateful. We would also like to acknowledge the support of Lin Kay and Peter Purcell at the NERC Airborne
Remote Sensing Facility for their support. Thanks also to Jim Gammie of English Nature for supporting the acquisition of survey data over Woodwalton Fen. Support from Gill Renshaw and

Robin Fuller of the ULM, Cambridge are also acknowledged.
ALDRED, A.H. and BONNER, G.M., Applications of Airborne Lasers to Forest Surveys ,
Information Report PI-X-51, Canadian Forest Service, Agriculture Canada, Chalk River,
Ontario, Canada, 62p.
ARP, H., GRIESACH, J-C. and BURNS, J.P., 1982, Mapping in tropical forests: A new approach using the laser APR. Photogrammetric Engineering and Remote Sensing , 48 (1),
91-100.
BLAIR, J.B., COYLE, D.B., BUFTON, J.L. and, HARDING, D.J., 1994, Optimization of an airborne laser altimeter for remote sensing of vegetation and tree canopies. Proc.
International Geoscience and Remote Sensing Symposium, IGARSS’94, Pasadena CA, 8-
12 August, 939-941.
BLAIR, J.B., RABINE, D.L. and HOFTON, M.A., 1997, The Laser Vegetation Imaging Sensor
(LVIS): a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography. ISPRS Journal of Photogrammetry and Remote Sensing , 54 ,
115-22.
BLAIR, J.B. and HOFTON, M.A., 1999, Modelling laser altimeter return waveforms over complex vegetation using high resolution elevation data. Geophysical Research Letters ,
26 (16), 2509-12.
DUBAYAH, R.O. and DRAKE, J.B., 2000, Lidar remote sensing of forestry. Journal of
Forestry, 98, 44-46.
DUBAYAH, R.O. BLAIR, J.B., BUFTON, J.L., CLARK, D.B., JAJA, J., KNOX, R.,
LUTHCKE, S.B., PRINCE, S. and WEISHAMPEL, J., 1997, The Vegetation Canopy
LiDAR Mission. Proc. American Society for Photogrammetry and Remote Sensing ,
Washington DC, 5-7 December, 100-12.
ENGLISH NATURE, 2003, The Great Fen Project. http://www.greatfen.org.uk/Great Fen
Project Information.htm
, 11 March 2003.
HARDING D., LEFSKY, M., PARKER, G. and BLAIR, J., 2001, Laser altimeter canopy height profiles: Methods and validation for closed-canopy, broadleaf forests. Remote Sensing of
Environment, 76 , 283-97.
LEFSKY, M., COHEN, W., ACKER, S., PARKER, G., SPIES, T. and HARDING, D., 1999,
Lidar remote sensing of the canopy structure and biophysical properties of Douglas-Fir
Western Hemlock forests. Remote Sensing of Environment , 70 , 339-61.
LEFSKY, M., COHEN, W., PARKER, G. and HARDING, J., 2002, LiDAR remote sensing for ecosystem studies. Bioscience , 52 (1), 19-30.
LIM, K., TREITZ, P., WULDER, M., ST-ONGE, B. and FLOOD, M., 2003, LiDAR remote sensing of forest structure. Progress in Physical Geography , 27 (1), 88-106.
MAGNUSSEN, S. and BOUDEWYN, P., 1998, Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Canadian Journal of Remote
Sensing , 28 , 1016-31.
MEANS, J., ACKER, S., HARDING, D., BLAIR, J., LEFSKY, M., COHEN, W., HARMON,
M. and MCKEE, W., 1999, Use of large footprint scanning airborne Lidar to estimate forest stand characteristics in the western cascades of Oregon. Remote Sensing of
Environment , 67 , 298-308.
MEANS, J., ACKER, S., FITT, B., RENSLOW, M., EMERSON, L. and HENDRIX, C., 2000,
Predicting forest stand characteristics with airborne scanning LiDAR. Photogrammetric
Engineering and Remote Sensing , 66 (11), 1367-71.
NAESSET, E., 2002, Predicting forest stand characteristics with airborne scanning laser using a practical two stage procedure and field data. Remote Sensing of Environment , 80 , 88-99.

NELSON, R., KRABILL, W. and MACLEAN, G., 1984, Determining forest canopy characteristics using airborne laser data. Remote Sensing of Environment , 15 , 201-12.
NELSON, R., 1997, Modelling forest canopy heights: the effects of canopy shape. Remote
Sensing of Environment , 60 , 327-34.
NILSON, M., 1996, Estimation of tree heights and stand volume using an airborne lidar system.
Remote Sensing of Environment , 56 , 1-7.
PARKER, G., LEFSKY, M., and HARDING, D., 2001, Light transmittance in forest canopies determined using airborne laser altimetry and in-canopy quantum measurements. Remote
Sensing of Environment , 76 , 298-309.
PERSSON, A., HOLMGREN, J. and SODERMAN, U., 2002, Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering and
Remote Sensing , 68 (9), 925-32.
RIANO, D., MEIER, E., ALLGOWER, B., CHUVIECO, E. and USTIN, S., 2003, Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behaviour modelling. Remote Sensing of Environment , 86 , 177-86.
SCHEIER, H., LOUGHEED, J., TUCKER, C. and LECKIE, D., 1985, Automated measurements of terrain reflection and height variations using an airborne infrared laser system. International Journal of Remote Sensing , 6 (1), 101-13.
ZWALLY, H. J., (2002), Ice, Cloud,and land Elevation Satellite, http://icesat.gsfc.nasa.gov/index.html
, 12 June 2004.