United States [47]. Several algorithms have been developed for filtering out the ground returns from LIDAR data, although much of this research and development work has been carried out in the commercial sector and is considered proprietary. Even after the ground reflections have been filtered out from the all-return data set, there is variability in the derived terrain model due to the choice of the gridding algorithm. Bater and Coops [6] evaluated the error in the LIDAR-derived terrain models associated with various interpolation algorithms including linear, quintic, natural neighbor, regularized spline, spline with tension, a finite difference approach, and an inverse distance weighted interpolation algorithm at spatial resolutions of 0.5, 1.0, and 1.5 meters, and found that 0.5 meter terrain models were the most accurate, and the natural neighbor algorithm provided the best results for interpolation, although the differences in accuracy between the algorithms were minor.

Given the highly irregular and ill-defined nature of a forest canopy surface, the characteristics of LIDAR-derived canopy surface models are highly dependent upon type of filtering and interpolation algorithms employed as well as the input parameters of these algorithms. The most common approach to generating LIDAR canopy surface models is to extract the highest LIDAR return within a given grid cell area and then employ an interpolation algorithm, such as kriging, linear, or inverse distance weighting (IDW), to generate a regular grid [44]. Often, additional processing is required to remove anomalous elevations within the surface and produce an accurate representation of the true canopy surface [8].

10.3.2 Individual Tree-Level Measurement Using Lidar

Airborne LIDAR can be used to acquire highly accurate measurements of individual tree height (Fig. 10.5). In a test carried out in western Washington, Andersen et al. [1] investigated the influence of beam divergence setting (i.e. laser footprint size), species type (pine vs. fir), and digital terrain model error on the accuracy of height measurements. This study found that tree height measurements obtained from narrow-beam (0.33 m), high-density (6 points/m2) LIDAR were more accurate (mean error ± SD = −0.73 ± 0.43 m) than those obtained from wide-beam (0.8 m) LIDAR (−1.12 ± 0.56 m). This was likely due to the fact that with wide-beam LIDAR the energy is spread out over a large area, which decreases the strength of the returns from a tree top and lessens the likelihood that they exceed the noise threshold [35]. In addition, this study found that tree height measurements on Ponderosa pine were more accurate (−0.43 ± 0.13 m) than those obtained for Douglasfir (−1.05 ± 0.41 m), largely because the size of the Douglas-fir leader is a smaller target than the top of a Ponderosa pine tree. These results were consistent with the accuracies for LIDAR-based tree height measurements reported in other studies in various forest types [14, 28, 48].

Fig. 10.5 Lidar-based individual tree height measurement, upper Tanana valley of interior Alaska, USA. Units are meters

10.3.2.1 Automated Individual Tree Measurement Using Lidar

Because LIDAR represents direct, and automatically georeferenced, digital measurements of 3D forest canopy structure, it is considerably easier to automate the individual tree detection and measurement process with LIDAR than is the case with digital photogrammetry. In fact, over the last ten years, a considerable amount of attention has been devoted to analysis of airborne LIDAR at the individual tree level. In general, these approaches tend to operate upon the high-density LIDAR canopy height model that is formed from gridding the LIDAR returns from the top of the canopy surface and subtracting the elevation of the underlying LIDAR terrain model. A variety of computer vision algorithms have been proposed for isolating the features within this canopy height model that correspond to individual tree crowns, including spectral analysis using wavelets [12], morphological watershed segmentation [20, 49], valley-following [41], and level-set analysis [21]. Of these techniques, morphological watershed segmentation is probably the most robust and widely used. This algorithm is based on the immersion process, as described in [53]. In this process, the canopy height model is inverted, and then starting at the local minima, water is poured in that fills up various catchment basins (watersheds). At each point where water from two different catchment basins merge, a dam is built. The result of the process is a complete tessellation of the image defined by the locations of the dams surrounding every watershed [53]. In a forestry context, these individual watersheds often correspond to individual tree crowns.

The morphological watershed segmentation technique can be very effective in situations where the tree crowns are distinct morphological features, even if the trees are closely spaced in a closed canopy. However, the technique is not as effective in stands where crowns are intermixed (e.g. deciduous stands). Figure 10.6

Fig. 10.6 Lidar-based individual tree crown segmentation, upper Tanana valley of interior Alaska, USA. Black circles indicate position and size of crown segments. Surface is color-coded by canopy height (blue: low canopy height, red: high canopy height). Inset shows area surround around a field plot, and green circles indicated field-measured trees

shows the result of a watershed-based individual tree crown segmentation algorithm applied to high-density airborne LIDAR collected over a boreal forest area in interior Alaska. As is evident from the inset in this graphic, which shows a comparison of the field-measured tree crowns to the watershed-based tree crowns (estimated locations and crown widths indicated by the black circles) within a 1/30th ha plot area, the segmentation algorithm successfully identified several of the larger crowns within the plot, but does not successfully delineate the smaller tree crowns that are not resolved in the 1-meter resolution LIDAR canopy height model. This algorithm also tends to over-segment in complex stands, since there is often morphological complexity even within a single tree crown

Once the forest area is segmented into individual tree crowns, the raw LIDAR can be extracted for each segment and used to obtain more detailed information on the tree. For example, the highest LIDAR return within the segment provides an estimate of the tree top [1]. In leaf-off conditions, the intensity values of the raw LIDAR returns within a crown segment can be used to classify the segment into conifer or deciduous species class. For example, Kim et al. [22] used a linear discriminant function to classify various species of trees in the Pacific Northwest of the United States using mean intensity of LIDAR returns in the upper portion of the crown as the primary metric and reported a classification rate of 83.4 % for separating coniferous and deciduous trees using leaf-off LIDAR data, and 73.1 % using leaf-on LIDAR data.

10.3.2.2Comparison of Lidar-Based and Photo-Based Individual Tree Measurements

Individual tree measurements, acquired using high-density LIDAR and large-scale aerial photography, were compared to field-based measurements acquired on an inventory plot established in the upper Tanana valley of interior Alaska (Fig. 10.7). The aerial photography was acquired using a low-cost, non-metric digital single

Fig. 10.7 Comparison of photogrammetric and LIDAR individual tree height measurement techniques, upper Tanana valley of interior Alaska, USA. Center graphic shows 1/30th ha circular plot (dashed line), black circles indicate locations and estimated crown sizes from automated segmentation of LIDAR canopy height model, green circles indicate location of field-measured trees within plot, and blue dots indicate locations of individual tree crowns observed in aerial photo stereo model. Upper left inset graphic shows the plot area in stereo (red-blue glasses are needed for stereo viewing) and the black cross is positioned to measure the top of a selected tree in the plot. The upper right inset graphic shows this same tree top measured in the LIDAR point cloud (color coded by height). The field-measured height of this white spruce tree is 22.25 meters, the LIDAR-measured height is 22.03 m, and the photogrammetrically-measured height is 23.8 m. The error in the LIDAR measurement is likely due to the LIDAR pulses missing the top of the tree crown [1], while the error in the photogrammetric measurement is likely a combination of the errors in the coarse terrain model and difficulty in identifying the true elevation of the tree top when viewed in stereo

lens reflex (SLR) camera mounted on a Cessna 185 aircraft flying at approximately 1000 meters above ground level (AGL). It should also be noted that this low-cost system did not have image motion compensation. In order to remove one source of error in the comparisons, the ground control points for the exterior orientation of the non-metric imagery were acquired from the airborne LIDAR, using the FUSION interactive LIDAR measurement environment [11, 30]. The photo-based tree height measurements were acquired by the following process: (1) photogrammetrically measuring the elevations of several points on bare ground distributed throughout the area, (2) using these points to generate a terrain model, (3) photogrammetrically measuring tree top elevation for all visible trees in the area, and (4) estimating tree heights as the difference between the tree top elevation and the elevation of the underlying terrain model. Lidar-based tree height measurements were generated similarly by subtracting the elevation of the LIDAR-based terrain elevation from the elevation of the highest LIDAR return within an individual tree crown segment.