Composición del 15M

Figure 6.6 shows the relationship between the estimated PAI derived from TLS and the direct measurements of LAI, for Tree 01. The values are shown by vertical strata (L1-L4) for the two datasets (λ1545nm and NDI) and for the three scanning positions (South, North-East, North-West). The total tree PAI values are also shown in each graph by the two methods used for calculation; using a single cylinder (closed circles) and summing the four layers (open circles).

For the four strata, both datasets showed a similar pattern. An overestimation of LAI from TLS was observed for the bottom three layers (L1, L2, L3). This was due to the inclusion of woody material in the calculations. The highest vertical layer (L4) shows a significant underestimation of LAI using TLS. Despite the methods that were in place to acount for occlusion, these results suggest that the top of the tree canopy was not sampled sufficiently to allow an accurate estimation of LAI.

Figure 6.6 shows that summing the layers generates a PAI closer to the measured value, due to the fact that this method deals with some of the effects of occlusion. However, due to the underestimation of layer 4 seen in all of the datasets, an accurate tree PAI estimation has not been produced.

Figure 6.6. Relationship between estimated PAI from TLS and measured LAI for the four vertical layers (L1-L4) and total tree for Tree 01 for datasets 1545nm (left) and NDI (right).

Solid squares indicate single cylinder for the layer/tree and empty squares indicate summing of layers. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 E st im at ed P A I Measured LAI L3 L1 L4 Tree L2 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 E st im at ed P A I Measured LAI L3 L1 L4 Tree L2 1545nm NDI

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Figure 6.7 shows the estimated LAI values for Tree 01 and by tree layer (L1-L4) and for the three scanning positions (South, North-East, North-West). Two thresholding methods have been used to determine the returns resulting from leaves, based on the ρ1545nm and the NDI. Compared with Figure 6.6, using the leaf component of the point cloud and accounting for the amount of material in the footprint has generated estimates of LAI of a similar magnitude to the direct measureements, for layers 1 to 3. The underestimation of LAI for the toppmost layer, and the tree scale estimates is still evident. However, from removing the ‘woody’ returns and accounting for partial hits, the variance between estimates from the different scanning positions has decreased.

Figure 6.7. Relationship between estimated LAI from TLS and measured LAI for the four vertical layers and total tree for Tree 01.

Although the results outlined above suggest that this approach has potential for calculating LAI for a single tree from dual-wavelength TLS, it is clear that further research is required into this experimental approach, particularly in the areas acknowledged in Table 6.2. This chapter also highlights the challenges that exist when attempting to validate the

performance of a leaf-wood classification for a real tree (as opposed to a virtual tree construction). In this research, an attempt to validate the ‘leaf’ component has been made via the parameter of LAI. As there is no standard method for estimating LAI for individual trees, uncertainties will be introduced from both the leaf-wood separation procedure and the LAI calculations, and the two sources of error cannot easily be uncoupled.

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 E st im at ed L A I Measured LAI L4 Tree L3 L2 L1 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 E st im at ed L A I Measured LAI L4 Tree L3 L2 L1 1545nm NDI

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Table 6.2. Discussion of factors relating to the approach that require further attention.

Factor Discussion

Field protocol To ensure an accurate validation of the TLS-derived LAI with the direct

LAI, further consideration should be given to the assignment of height strata. For instance, during the direct measurements, the tree was divided into height strata based on the measured position along the trunk.

Therefore, the leaves were assigned to a height strata bases on the height position of their branch. In contrast, for the tree TLS point cloud, each return in the point cloud was assigned to a height strata based on the z- value that it was located (e.g. Figure 6.5). This is most important for the upper layers where the majority of leaf material is present. A possible solution would be to combine the upper two layers. This may improve accuracy as L3 is currently over-estimated, and L4 underestimated (Figure 6.6 and 6.7).

Beer-Lambert Law This approach is based on a number of assumptions, as outlined in Table

6.1. Further investigation is required to ensure that the modified Beers- Lambert Law approach is valid for the scenario present in this chapter.

Validation The results in Figure 6.7 suggest that TLS-derived LAI has a higher

accuracy at low LAI. However, in order to characterise the relationship (and therefore assess the accuracy) of the measured and estimated parameters, a more extensive dataset may be required which includes a full range of LAI values. Once the reflectance calibration has been improved for the remaining two trees for which validation data is available, a larger dataset will be available.

Occlusion The LAI for the upper layer of the canopy is currently underestimated

(Figure 6.6 and 6.7). This may be partly due to differences into assigning the leaf returns to height strata (as discussed above), but further

consideration should be given to occlusion in the tree canopy, to ensure that this significant factor is accounted for in the calculations.

Clumping The Clumping Index is used as a correcting factor in the calculations to

convert effective LAI to true LAI, and its value has a substantive impact on the results. Correcting for clumping is a complex problem in TLS and investigation of alternative methods to derive the Clumping Index could be sought and compared to those generated here.

Leaf-wood separation

Future consideration should also be given to improving the leaf-wood separation for Tree 01 which may improve the LAI calculations. This includes improving the reflectance calibration and further investigation into defining the spectral-based threshold applied to perform the classification into the two components.

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6.5

Conclusion

In order to fulfil the aim of this chapter and contribute to research Objective 2, a method for estimating LAI on the tree scale using dual-wavelength TLS has been developed. This was carried out by considering the tree point cloud as a set of 3D points encased in a cylindrical volume. PAI and LAI were estimated based on the principles outlined in the Beer-Lambert Law. The approach outlined above also attempted to account for

observations discussed in previous chapters:

 Effect of occlusion (Chapter 2, Chapter 5) – splitting the cylinder into multiple vertical layers allowed for the beams to be identified that should pass through the target layer but were blocked by lower vegetation.

 Returns not included in the NDI point clouds (Chapter 5) – the returns were identified which were present in either wavelength point cloud but not matched during the NDI process.

 Overestimation of LAI due to inclusion of woody material (Chapter 2) – returns classified as wood were removed from the point cloud so that true LAI could be computed. Furthermore, the beams blocked by woody material were accounted for during the LAI calculations as a further measure for correcting for occlusion.  Overestimation of LAI due to the inclusion of partial hits (Chapter 2) - Using a

foliage only point clouds allows the apparent reflectance for each return to be scaled by the reflectance of a full hit on a leaf, therefore accounting for the amount of material in the footprint.

The novelty in the approach described in this chapter lays in the utilisation of single scan, large single tree, leaf-only TLS point clouds, but more work is required to ensure the validity and application of approach. The following chapter examines the separation of woody and leafy material on plot scale and generation of plot PAI and LAI, for the five test plots surveyed at Delamere Forest.

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