**5. Further applications of lidar remote sensing**

#### **5.1 Forest growth models**

Forest growth models such as the Ecosystem Dynamics model, ED, (Hurtt *et al.*, 2004) and the Tree and Stand Simulator, TASS, (Goudie & Stearns-Smith, 2007) enable forest growth scenarios to be predicted. Remote sensing analysis can be used as a valuable tool to provide observational inputs to models and in order to produce detailed inventories for long-term scenario modelling.

Airborne lidar stand level analysis can be used to produce statistically-derived model inputs. This approach is being undertaken as part of the NASA Carbon Monitoring System (NASA, 2010) using an interpolated surface 80th percentile canopy height model as an input to the ED model (e.g. Hurtt *et al.*, 2004). This method can potentially be applied across large areas and could be achieved with relatively low density lidar data such as might be acquired for regional or national campaigns.

Alternatively, Suárez, 2010, used a tree list generated from individual tree delineation as baseline inventory data from which to predict future scenarios and demonstrate processes at work within stands using the distance-dependent model TASS. These processes include competition, establishment of a dominance hierarchy and recovery from catastrophic events such as wind damage or thinning. This means that the biological principles behind such models adapt them to local conditions, unlike empirical models and suggesting wider application may be possible.

The temporal dimension provided by the TASS simulations provides a valuable insight into the long-term effects of each stand intervention or natural disturbance. Not only growth increments, but timber products can also be predicted with this method. In addition, management practices can be balanced by the constraints introduced by the future risk of wind damage. The scale of analysis and the possibility of creating future scenarios contribute to a substantial reduction in the level of uncertainty associated with forest management.

Lidar Remote Sensing for Biomass Assessment 19

reflectance ratio is calculated and this profile allows the signals within the waveform from ground and vegetation to be differentiated. This would offer a valuable response to the challenging situation of combined vegetation and ground signals within large footprint

Once issues of eye sensitivity at optical wavelengths and energy requirements are fully addressed, multispectral lidar concepts could offer the opportunity for enhanced vegetation

The emerging technology of photon counting lidar offers the potential for low energy expenditure and potential high altitude operation allowing extended laser lifetime and large area coverage. This newest type of lidar technology is currently generally operated at green wavelengths (532 nm), in some airborne systems due to a greater efficiency of the detector and, in the case of NASA's ICESat II, as a result of technical readiness. Low laser energy output ensures eye safety of these instruments despite operating at a visible wavelength. A high pulse repetition rate and photon detection probability produces a high point density even whilst flying at greater altitudes whilst a narrow pulse duration (~1ns) allows photons

One significant factor is that photons returned from the emitted pulse cannot be distinguished from ambient noise. Acquiring data at night or dusk would minimise the difficulties of noise posed by solar background illumination, and sensor specifications such

Initial analysis within NASA's Carbon Monitoring System initiative (NASA, 2010) using the 3D Mapper single photon scanning lidar developed by Sigma Space Corporation, USA, suggests that promising results may be obtained from small footprint photon counting sensors for the generation of vegetation products. The greater point density of the point cloud which is produced, in excess of that which is typically collected by discrete return airborne lidar data, aims to improve the characterisation of vegetation canopies and offers the opportunity for established analysis techniques to be applied to this new technology. The Slope Imagining Multi-polarisation Photon-counting Lidar (SIMPL) is an example of an airborne small footprint photon-counting profiling lidar which operates at both 1064nm and 532 nm wavelengths (Dabney *et al.*, 2010). A single pulse is emitted which is split into four beams, each with four channels for green and NIR wavelengths, each of which at parallel and perpendicular polarisations. The two polarisations respectively identify photons which have been reflected from a single surface or which have undergone multiple scattering. The four beams are distanced approximately 5 metres apart, producing four profile 'slices' through the canopy. The laser repetition rate of 11.4kHz and an aircraft speed of

as the use of a small detector instantaneous field of view would also assist this.

100m/second may be expected to produce 5-15 detected pulses per square metre.

Using SIMPL, Harding *et al.*, in press 2011, have explored the influence of lidar wavelength on the ability to determine standard waveform metrics which may be employed to predict biomass. By aggregating detected photons over a distance along the transect, the authors calculated a cumulative height distribution (such as that used for waveform or discrete return analysis). Height of median energy (HOME) and canopy cover metrics were compared and little difference was found between the two wavelengths, suggesting that lidars using 532nm could produce comparable biomass estimates to those obtained by

waveforms on sloped surfaces.

analysis using lidar systems.

current 1064nm systems.

**6.2 Photon counting lidar systems** 

to be located with greater vertical precision.

Lidar data therefore provide a useful contribution as a baseline input position from which future scenarios can be determined. Subsequent lidar campaigns or observations of landcover disturbance from optical data (Huang *et al.*, 2010) could furthermore allow model predictions to be validated or calibrated to closer match observed growth trends.
