**3.2 Applications for biomass estimation**

### **3.2.1 Stand level analysis**

Stand-level inventory from airborne lidar follows a method originally devised by Næsset, 1997a, b. These two studies are particularly relevant because both have encouraged further work based on the notion that lidar data can be used in large-scale forest inventories, provided that georeferenced data from field plots could be used in a first phase to develop empirical relationships between lidar metrics such as percentiles of relative height above ground and the main parameters for forest management. Such relationships are used to

The use of airborne lidar data in forestry was originally focused on the construction of two cartographic products: Digital Terrain Models (DTMs) and Digital Surface Models (DSMs), which are used to describe the underlying terrain and top of forest surfaces respectively. These products are used to generate canopy height models (CHM) that subsequently provide accurate estimates of important forest parameters such as canopy heights, stand volume, and the vertical structure of the forest canopy. The estimation of canopy heights is performed by the subtraction of bare ground values (DTM) from the canopy layer (DSM). An accurate estimation of a CHM relies heavily on a good approximation of the ground

Fig. 5. Vegetation distribution and structure shown by classification of an airborne lidar point cloud. Data illustrated were provided courtesy of Forest Research, UK. Location:

environmental protection. Section 3.2 below considers both approaches.

More recently, increasingly sophisticated means of analysis are being applied for the estimation of important parameters at both stand (number of trees, volume, basal area, top height, percentage of canopy cover and crown layers) and individual tree level (individual tree heights, stem diameters and crown metrics). The potential areas of application span from timber production to biological diversity, carbon sequestration or general

Stand-level inventory from airborne lidar follows a method originally devised by Næsset, 1997a, b. These two studies are particularly relevant because both have encouraged further work based on the notion that lidar data can be used in large-scale forest inventories, provided that georeferenced data from field plots could be used in a first phase to develop empirical relationships between lidar metrics such as percentiles of relative height above ground and the main parameters for forest management. Such relationships are used to

surface underneath.

Scottish Highlands, UK.

**3.2.1 Stand level analysis** 

**3.2 Applications for biomass estimation** 

estimate, in a second phase, forest stand parameters for all the test plots in the study area, known as two–stage procedure for stand inventory (Næsset, 2002).

In this way, the stand-level approach provides a useful estimation of key stand parameters such as top height, canopy cover, tree density, basal area and volume. The established relationships allow forest parameters such as biomass to be directly inferred from the use of lidar metrics and for this assessment to be implemented across large forest areas where there is lidar coverage. Lidar systems provide point-wise anisotropic sampling unlike the full area coverage common in optical systems. As a result, laser data are interpolated in order to convert the same coverage to a continuous surface working-image and allow the distribution of forest parameters to be mapped.

This method does of course have some limitations. It is heavily dependent on abundant field data collection to train empirical relationships between field and lidar data that often are not easily transferable to other study areas. Different relationships may be present for morphologically similar species (Norway spruce, Sitka spruce or larch) and additionally, stand structure is a significant determinant factor. The effects derived from the spatial distribution of gaps, their size and the spatial distribution of standing trees (whether perfectly aligned, growing in a natural stand or thinned at different intensities) on the vertical interception of laser hits are not parameterised in this approach.

However, the real value of this method is demonstrated in its application in regional studies, where the combination of lidar and field measurements can optimise traditional surveys, particularly as part of large area forest inventories (Hollaus *et al.*, 2009).

#### **3.2.2 Individual tree based inventories**

Variability in characteristics is a feature of natural systems. Even in systems designed to be as uniform as possible, such as planted forests in monocultures, growing differences are inevitable. Understanding the factors controlling variability is important in developing our knowledge of how ecosystems operate and behave such as the way that trees grow and accumulate biomass in response to their environment and close interaction with their neighbours (e.g. competition for space, light and nutrients). Identifying and locating trees using airborne lidar data permits the spatial variability of biomass distribution within a forest to be considered.

Canopy delineation algorithms have been developed by several authors and most of these detect single trees using an interpolated canopy height model (section 3.1) by the detection of local maxima for tree location and watershed or pouring algorithms (as well as derivations of these algorithms) for the delineation of single tree crowns (e.g. Popescu *et al.*, 2003). However, latterly, approaches are becoming increasingly based on raw data using clustering or blob detection methods (e.g. Morsdorf *et al.*, 2004).

Figure 6 illustrates the delineation of individual tree crowns shown outlined by blue polygons and overlaid on a lidar canopy height model. Lighter grey shades indicate taller heights which are used to identify tree tops to infer height of individual trees.

Using known allometric relationships between tree height and crown width (derived from crown area) with stem diameter at breast height, volume can be calculated on an individual tree basis by approximating tree stems as a cone or by using more sophisticated taper functions or a crop form parameter which defines mean taper characteristics in a stand (Edwards & Christie, 1981; Matthews & Mackie, 2006). Individual tree volume can then be converted to biomass (by accounting for specific density) allowing the spatial distribution and variability of biomass to be mapped.

Lidar Remote Sensing for Biomass Assessment 15

In operational terms, current usage of airborne lidar data is generally confined to statistical analysis at stand/forest scales (Næsset, 1997a, b). Although the method has undergone minor adaptations by several authors, this approach has become a standard practice for large-scale inventories in some European countries (Finnish\_Forest\_Association, 2007;

Airborne lidar point clouds can be understood and interpreted intuitively and vegetation can be placed in the context of terrain, access routes and neighbouring competitors. The ability to remove the vegetated surface to reveal the terrain beneath provides valuable

Regression equations for biomass estimation may be site specific, however good relationships have been demonstrated using broad class distinctions such as broadleaves, conifers and mixed stands which may be more readily available using optical data or land cover type maps. This enables the spatial distribution of biophysical parameters to be represented over a forest scale which would be impossible using traditional field

The cost of airborne coverage may prohibit repeat lidar campaigns and so at best, such detailed information is likely to be available infrequently to monitor growth. However, looking to the future, several European countries have undertaken campaigns for partial or complete nation-wide coverage of airborne lidar data including The Netherlands (Duong *et al.*, 2009), Norway, Austria, Switzerland (Swisstopo, 2011) and Finland (Finnish\_Forest\_Association, 2007). A similar commitment of lidar acquisition is in progress in Spain on a region-by-region basis where the value-added benefit of such a resource has

Whilst generally of mid-low point density by necessity of cost and not necessarily intended specifically for vegetation applications, nevertheless, such previously-unobtainable national vertical profile datasets offer potential applications for a multitude of applications including mapping and reducing uncertainty of biomass distribution. Furthermore, as outlined above, airborne lidar data are already playing a significant role in large-scale forest inventory

The small and portable terrestrial lidar systems can be mounted on a static tripod or transported on a moving vehicle and therefore can be easily taken into the field. GPS measurements also allow these scans to be geolocated. In contrast to the viewing perspective from above provided by satellite and airborne sensors, terrestrial lidar provides a clear view

This measurement of a relatively small area within viewing distance of the scanner can be considered to replicate field plot measurements, however additionally provides an understanding of context which would not be possible from field data. The upward looking approach often leads to difficulty in detecting tree tops, however representation of tree stems, ground surface roughness and understorey vegetation offer a level of detail which

This approach causes only the side of stems facing the scanner to be detected in any one scan and also obscures the view of trees which are behind those closer to the instrument. Therefore

information for management purposes and topographic assessment.

been recognised for economic and social invigoration (Dielmo, 2011).

of the tree stem, understorey and ground surface (Figure 7).

cannot be retrieved using airborne instruments.

**3.3 Summary** 

measurements.

efforts.

**4. Terrestrial laser scanning** 

**4.1 Characteristics** 

Hollaus *et al.*, 2009; Næsset, 2004).

Fig. 6. An example of tree crown delineation with airborne lidar data using object oriented analysis in Definiens Developer (Source: Suárez, 2010).

Results show that lidar can detect most of the crowns of dominant and co-dominant trees in mature stands dominated by coniferous species, but finds difficulties for suppressed or subdominant trees, young stands, groups of trees growing in close proximity and for deciduous species. This will have an effect on biomass estimation when aggregating individual tree estimates to a stand level.

Small individuals will contribute relatively less to the overall stand volume than larger trees. However in many situations they may form a significant contribution to total biomass which should be accounted for. Authors such Maltamo *et al.*, 2004 have shown it to be possible to predict the height of small trees not detected by lidar using Weibull distributions.

The detection and estimation of individual tree heights for deciduous species is more difficult because of their more spherical crown shape compared to conifers (meaning that the tree top and crown boundaries can be located with less certainty using a CHM) and the higher probability for the occurrence of more than one apex in each tree. Moreover, crown delineation in deciduous stands becomes more difficult because the crowns of neighbouring trees often overlap (this is a common situation in conifer stands too).

The single tree approach allows individual tree counts, crown volume calculations, canopy closure or single tree height estimates. These are important inputs in order to derive estimates of diameter at breast height (DBH) distributions, volume or biomass. Published studies show that good results can be achieved with 65% to 90% of correct tree counts within conifer stands while for broadleaf trees the results are less accurate and an underestimation of volume is often reported.

Using canopy delineation from airborne lidar data, parameters such as biomass can be determined and spatially located in relation to their surroundings. This also allows the inhibiting effects on biomass accumulation introduced by larger trees over more disadvantaged neighbours to be observed.
