**6.1 Waveform simulation and multispectral lidar**

Previous work has demonstrated the value of lidar modelling of vegetation for discrete return lidar (Disney *et al.*, 2010) and full waveform systems (Ni-Meister *et al.*, 2001; North *et al.*, 2010; Sun & Ranson, 2000). Using a simulation approach, the sensitivity of lidar data to surface structural and optical properties can be explored to improve our understanding and interpretation of the estimation of lidar-derived biophysical parameters.

Recent discussions have turned to the prospects of multispectral lidar sensors for vegetation analysis (cArbomap, 2011; Woodhouse *et al.*, in press 2011). This project is led by Dr Iain Woodhouse at the University of Edinburgh and the concept has been considered in a simulation study by Morsdorf *et al.*, 2009. The authors demonstrated the opportunity of detecting seasonal and vertical change in normalised difference vegetation index (NDVI) which would allow canopy and ground signals to be distinguished. The variability of chlorophyll content during the growing season was also detected thereby indicating the amount of photosynthetically active biomass.

Additionally, Hancock, 2010 has demonstrated the potential offered by dual wavelength lidar using wavelengths selected either side of the electromagnetic spectrum red edge. A

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

Vegetation plays a significant role in global climate, water, energy and biogeochemical cycles, particularly concerning carbon, with approximately one quarter of atmospheric carbon dioxide fixed annually as gross primary production. To accurately model this and other land surface processes in General Circulation Models (GCM), properties such as radiation absorption, plant physiology, surface characteristics and climatology are required. These models require multi-

Computer-generated models of the biosphere provide a valuable means to improve understanding of the immensely complex interactions between interdependent systems affecting the Earth. By their very nature, models function as generalisations of reality and a series of component models replicating the interplay of systems often provide input to

Dynamic Vegetation Models are particularly valuable in enabling prediction of the carbon balance under changing ecosystem structure and composition brought about by climatic changes. Vegetation is often represented within each grid cell as generalised Plant Functional Types and climate-driven habitat changes are used to model vegetation succession and plant lifecycle. Where vegetation height is currently considered as static over time, models could benefit from future global lidar observations of vegetation height (e.g. Lefsky, 2010, Los *et al.*, 2011) or biomass, particularly if signal sensitivity permits growth over the sensor lifetime to be observed. Furthermore, the use of lidar could inform validation of LAI, fractional canopy cover or NDVI products (Los *et al.,* 2008, 2011) which

Previous work has demonstrated the value of lidar modelling of vegetation for discrete return lidar (Disney *et al.*, 2010) and full waveform systems (Ni-Meister *et al.*, 2001; North *et al.*, 2010; Sun & Ranson, 2000). Using a simulation approach, the sensitivity of lidar data to surface structural and optical properties can be explored to improve our understanding and

Recent discussions have turned to the prospects of multispectral lidar sensors for vegetation analysis (cArbomap, 2011; Woodhouse *et al.*, in press 2011). This project is led by Dr Iain Woodhouse at the University of Edinburgh and the concept has been considered in a simulation study by Morsdorf *et al.*, 2009. The authors demonstrated the opportunity of detecting seasonal and vertical change in normalised difference vegetation index (NDVI) which would allow canopy and ground signals to be distinguished. The variability of chlorophyll content during the growing season was also detected thereby indicating the

Additionally, Hancock, 2010 has demonstrated the potential offered by dual wavelength lidar using wavelengths selected either side of the electromagnetic spectrum red edge. A

predictions to be validated or calibrated to closer match observed growth trends.

temporal global datasets that can only be obtained from remotely sensed sources.

are produced using indirect relationships with optical reflectance properties.

interpretation of the estimation of lidar-derived biophysical parameters.

**5.2 Prospects for global modelling** 

complex broader-themed Biosphere models.

**6. Emerging technologies** 

**6.1 Waveform simulation and multispectral lidar** 

amount of photosynthetically active biomass.

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 waveforms on sloped surfaces.

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

#### **6.2 Photon counting lidar systems**

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 to be located with greater vertical precision.

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

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 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 current 1064nm systems.

Lidar Remote Sensing for Biomass Assessment 21

As emerging technologies such as photon counting or multispectral lidar sensors come into operation, the capacity for wider coverage and increasingly accurate lidar-derived

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Bucksch, A. and Fleck, S. (2011). Automated Detection of Branch Dimensions in Woody

Dabney, P., Harding, D., Abshire, J., Huss, T., Jodor, G., Machan, R., Marzouk, J., Rush,

Dielmo (2011). Online data server for airborne lidar. Available online at:

Disney, M.I., Kalogirou, V., Lewis, P., Prieto-Blanco, A., Hancock, S. and Pfeifer, M. (2010).

cArbomap (2011). cArbomap; Multispectral lidar. Available online at: carbomap.com.

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applications for biomass assessment will further expand.

**8. References** 

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NASA's forthcoming ICESat II mission is due for launch in early 2016 (GSFC, 2011). In contrast to ICESat I, its successor will carry a medium footprint, photon-counting profiling lidar operating at 532nm wavelength. This instrument is named ATLAS, the Advanced Topographic Laser Altimeter System. The current planned configuration is for a single emitted pulse which is split into six beams, arranged as three adjacent pairs. Each pair will have a stronger and a weaker beam (100μJ and 25μJ respectively) which aims to address issues of detector sensitivity when alternating between bright and dark surfaces such as ice and water. A distance of 3.3km is anticipated between each pair and members of the pair will be separated by 90m. The high repetition rate of 10kHz from an altitude of ~496km will produce overlapping footprints of 10m diameter which will be distanced at 0.7m intervals. 1-3 photons are anticipated to be detected per footprint and, although the spatial location of photons within the footprint will be unknown, the aggregation of returns along the ground tracks will allow a vertical profile to be created. Although, like with its predecessor, the primary objective of ICESat II is not the retrieval of vegetation, one of its science objectives is measuring vegetation height as a basis for estimating large-scale biomass and biomass change (GSFC, 2011). This new technology will offer a new perspective of the world and open opportunities for different approaches to global vegetation analysis.
