**1. Introduction**

Optical remote sensing provides us with a two dimensional representation of land-surface vegetation and its reflectance properties which can be indirectly related to biophysical parameters (e.g. NDVI, LAI, fAPAR, and vegetation cover fraction). However, in our interpretation of the world around us, we use a three-dimensional perspective. The addition of a vertical dimension allows us to gain information to help understand and interpret our surroundings by considering features in the context of their size, volume and spatial relation to each other.

In contrast to estimates of vegetation parameters which can be obtained from passive optical data, active lidar remote sensing offers a unique means of directly estimating biophysical parameters using physical interactions of the emitted laser pulse with the vegetation structure being illuminated. This enables the vertical profile of the vegetation canopy to be represented, not only permitting canopy height, metrics and cover to be calculated but also enabling these to be related to other biophysical parameters such as biomass.

This chapter provides an overview of this technology, giving examples of how lidar data have been applied for forest biomass assessment at different scales from the perspective of satellite, airborne and terrestrial platforms. The chapter concludes with a discussion of further applications of lidar data and a look to the future towards emerging lidar developments.

## **1.1 Context**

Aside from destructive sampling, traditional methods of calculating biomass for forest inventory, monitoring and management often rely on taking field measurements within sample plots, such as diameter at breast height (DBH) or Top/Lorey's height. This effort can be time, cost and labour intensive. Extrapolation of field measurements to larger areas relies on representative sampling of trees within a land-cover type and correct classification of land cover over large areas; both of which have inherent uncertainties.

Lidar remote sensing complements traditional field methods through data analysis which enables the extraction of vegetation parameters that are commonly measured in the field.

Lidar Remote Sensing for Biomass Assessment 5

surfaces within the footprint. This will vary with the nature of the surface; flat ice sheets producing abrupt returns with fast leading edge rises and multilayered, complex vegetation

Fig. 1. Representation of the interception of foliage, bark or ground surfaces by an emitted laser pulse. At each surface, some energy is reflected, transmitted (in the case of foliage) or

The location of every returned signal to a known coordinate system is achieved by precise kinematic positioning using differential GPS and orientation parameters by the Inertial Measurement Unit (IMU). The IMU captures orientation parameters of the instrument platform such as pitch, roll and yaw angles. Therefore, the GPS provides the coordinates of the laser source and the IMU indicates the direction of the pulse. With the ranging data accurately measured and time-tagged by the clock, the position of the returned signal can be

A waveform is the signal that is returned to the lidar sensor after having been scattered from surfaces that the laser pulse intercepts. Full waveform lidar systems record the entire returned signal within an elevation range window above a background energy noise threshold. An example of this from NASA's Geoscience Laser Altimeter System (GLAS; Section 2) is shown in Figure 2 (left). The scene shows a two-storey Douglas Fir canopy (*Pseudotsuga menziesii*) on a gentle slope of 4.9°. Typically, for vegetated surfaces on

absorbed.

calculated.

**1.3 Full waveform and discrete return systems** 

relatively flat ground, a bimodal waveform is produced.

creating broad returns (Harding *et al.*, 1998; Ni-Meister *et al.*, 2001).

Additionally, establishing allometric relationships between lidar and field measurements enables estimates to be extrapolated to stand, forest or national scales, which would not be feasible or very costly using field survey methods alone. Key aspects of biomass estimation from satellite, airborne and terrestrial lidar systems are outlined below.
