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It has already been stated that satellite measurements are needed to collect regular, consistent data (section 3.2). For lidar this means emitting a laser pulse downwards from above the canopy. An example of a full waveform return from above a forest is shown in figure 10. From this it can be seen that certain structural parameters can be directly measured, the most obvious being tree height (Dubayah and Drake 2000), which is not directly measurable by any other remote sensing method. Other variables can be measured more directly than with other methods, such as canopy cover, which needs only an estimate of ratio of canopy to ground reflectance to scale the ratio of energy returns from these elements; a far simpler method than the radiative transfer models needed for passive optical measurements. Figure 11 gives a list of biophysical parameters and how they relate to lidar measurements.

The first step for the physically based measurement of all biophysical parameters from lidar is the separation of canopy from ground returns. This gives the relative energies returned from canopy and ground, tree height, the shape of the canopy return (related to foliage distribution)

Figure 10: Lidar waveform over forest with features marked

and the shape of the ground return (related to ground slope). Without this separation, parameters must be related to lidar metrics by site specific relationships (for example Lefsky et al. (2007)), removing the benefit of direct measurement. This separation requires the identification of the start and end of the canopy and ground returns.

For any forest on flat ground (as shown in figure 10) this needs returns from both the tree top and ground. This is obviously impossible with first return systems, where only tree tops will be measured unless there are large gaps in the canopy. Even for discrete return systems, all of the signal may come from the canopy in dense forests (Næsset and Økland 2002). Much processing is needed to get an idea of the ground with discrete lidars, extrapolating between adjacent footprints (Clark et al. 2004) and even then errors are in the order of 2m. Returns from the canopy “blind” the lidar to later targets and this is the main limitation of discrete sensors, we have no idea what is not being measured. For tree height, as long as some signal reaches the ground this is not an issue and the technique has been used successfully (Innes and Koch 1998, Omasa et al. 2003, Patenaude

et al. 2004, Donoghue and Watt 2006) but for parameters that depend on the relative energies

(canopy cover and foliage profile) accurate inversions are not possible with discrete return systems. Full waveform suffers no such problem, returns from all surfaces being recorded, although binned into discrete range intervals. Energy is conserved, allowing inversion of far more parameters.

Attempts have been made to use a canopy top map from first return lidar to get tree height by subtracting an external digital elevation model (DEM) (Boudreau et al. 2008). This gave usable results but global DEMs are not available with sufficient accuracy over forests to make this a practical solution (Rosette et al. 2007) and even the proponents of this method cite the need for a specific biomass measuring mission (Boudreau et al. 2008).

Another issue with discrete return is that the exact way a recording is triggered is not always known and so how a range relates to a target is uncertain. These triggering mechanisms are proprietary and not generally released by lidar manufacturers (Lefsky et al. 2002) so it is not clear whether the range is to the point at which the signal first rises above a threshold, the maximum intensity after the threshold or a more complex algorithm. What is fairly certain is that some signal will be lost due to the thresholding (Baltsavias 1999) and so the range to tree tops will be

overestimated, leading to an underestimate in tree height. As waveform lidars record all reflected signal this truncation is avoided and steps can be taken to extract the true tree tops (more on this in chapter 4.3).

For these reasons all authors agree that full waveform lidar is preferable for measuring vegetation and discrete return should only be used as a stop-gap until waveform datasets are more widely available. This is gradually happening with a number of waveform lidars commercially available (Wagner et al. 2006), in some cases replacing discrete lidars for every day use (such as the Riegl VZ-400 terrestrial scanner). Table 1 shows a list of waveform lidars used for measuring vegetation with their characteristics and primary references.

One key consideration when using lidar for forestry is footprint size and coverage. In order to measure tree height accurately there need to be returns from the tree top and so the area of constant coverage needs to be big enough to ensure this. Figure 12 illustrates a set of small widely spaced footprints missing tree tops and so underestimating tree height. It is generally accepted that to be sure of measuring the top the area of coverage needs to be around the size of a crown, between a 10m and 30m diameter footprint (Zimble et al. 2003, Hyde et al. 2005). This can be achieved with either a single footprint (Hyde et al. 2005) or by aggregating arrays of smaller footprints (Reitberger et al. 2008).

Figure 12: Small footprint missing tree tops leading to underestimate of stand characteristics, taken from Zimble et al. (2003)

Instrument Pulse width Altitude Swath Footprint Spacing Repetition Digitiser Wavelength Reference

ICESat 11.9ns 600km NA 52m-90m 1km 40Hz 1ns 1064nm (Harding and Carabajal 2005)

1981 lidar 450m NA 10m 2.5ns (Nelson et al. 1984)

SLICER up to 8km 56m 10m 20Hz 0.7ns (Harding et al. 1994)

LVIS 16.9ns up to 8km 1km 10-30m 10m 400Hz 2ns 1064nm (Hofton et al. 2000)

ALTM 3100 13.6ns < 2.5km 0.3m-0.8m <100kHz 1ns (Wagner et al. 2006)

LMS-Q560 6.8ns 1.5km 0.5m <100kHz 1ns (Wagner et al. 2006)

TopEye II 6.8ns < 1km 1m <50kHz 1ns (Wagner et al. 2006)

Table 1: Table of lidar systems with pulse lengths

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analysis. Some have tried to identify individual tree crowns using image analysis techniques. This would be a great advantage to biomass estimates, where stem density is as important as tree height (Patenaude et al. 2004). One study coarsened the vertical range of a first return surface map (the same technique could be applied to the first return of a waveform lidar) until local maxima were some expected separation (Friedlander and Koch 2000). This required a lot of supervision to guide the process but demonstrates that crowns could be found. A more recent effort used wavelet analysis (Kaiser 1994) to find crowns of known shape but unknown size from a crown surface map (Falkowski et al. 2006). This worked very well for regular shaped trees such as conifers, requiring little supervision once the tree shape had been decided, but may struggle with deciduous species with their less regular crowns (as would any method) (Omasa et al. 2007).

The footprint size will affect the amount of energy that can be emitted whilst being eye-safe. The broader the footprint the lower the intensity will be for a given energy and so more can be used (Nelson et al. 1984). This is a great bonus for waveform lidar, where returns are spread over many bins, each requiring sufficient energy for detection. Many authors believe that waveform lidars need large footprints (Næsset and Økland 2002); certainly all current discrete return systems are small footprint. Some of the newer commercial waveform lidars have small footprints (Wagner

et al. 2006), perhaps aided by improved detector efficiencies, but these are airborne instruments.

The energy requirements for spaceborne systems are much greater and so it seems that despite the advantages of small footprints, spaceborne lidar will remain large footprint for the foreseeable future (Lefsky et al. 2002, Dubayah et al. 2008). In addition the scanning needed to get constant coverage may not be possible at the speed of a satellite (Omasa et al. 2003).

As this thesis is concerned with global measurements, ideally regularly and consistently, the rest of the above canopy sections of this thesis will focus on large footprint lidar.