TÉCNICAS E INSTRUMENTOS DE RECOLECCIÓN DE DATOS

Multi-scale predominant height assessment

This section tests the hypothesis that the scale of assessment (i.e. the defined area of a plot or “stand”) influences the reported stand height. If the assessment area was too small, it may not represent the wider stand. If it was too large then it may include tree heights from other stands with different structure. It was assumed that a “stand” was structurally relatively homogeneous, though it was recognised that in some situations determining homogeneity can be difficult. As described previously, predominant height was calculated as the mean of 10 m cells with a canopy height greater than or equal to 2 m, within a defined area, where the maximum LiDAR return per 10 m cell was used. Because the NE Victorian LiDAR data were collected with a wider swath than Injune, this analysis concentrated on the NE Victorian LiDAR in the first instance. This method was applied at the following scales (Figure 32):

• field plot scale (30 x 30m - 0.09ha) (solid yellow) • transect area scale (100 x 100m - 1ha) (red outline)

• LiDAR swath overlap scale (~400 x 400m ~ 20ha) (grey outline),

Apparent “holes” in the lattice indicate that no LiDAR returns higher than 2 m were observed for that cell.

Figure 32: Predominant stand height assessment using LiDAR 10m cells at different spatial extents at CFMF plot 212 (Ovens river).

An example of the raw LiDAR data in profile, for NE Victorian plot 212, has the data sliced to the full width of the swath, and was approximately 100 m deep (Figure 33). The different scales of assessment, namely field plot, transect area, and swath extent, were indicated. This plot illustrates potential issues with plot height representativeness, as the location may not reflect the wider stand. This issue may be more pronounced in highly variable forest environments such as riparian zones within floodplains (Figure 33). In this example, it can be seen that the field plot was located within a cluster of taller trees, but the wider stand appears to

have tree canopy that was generally shorter. Note that in this illustration the trees appear to have quite narrow crowns. This was an artefact of the display, as the X distance (x-axis) scale was 4 times greater than the tree height scale (y-axis), so it was not indicative of the actual crown shape (see also the plot hemispherical photos in Appendix B).

Figure 33: Illustration of CFMF plot p212 with plot, transect and stand scales of assessment. The LiDAR point data slice was approx. 100m deep.

To test whether the actual location of the field plot has an affect on the predominant height result, additional analyses were undertaken. Here field and transect ‘plots’ were randomly located within the LiDAR swath area, and a mean value derived from the respective samples. This was primarily to test if a single field or transect plot was adequately sampling the wider stand (as defined by the LiDAR swath). The analysis method used the 10 x 10 m cell lattice which covers the whole LiDAR swath at the plot location (approx 400 x 400 m or 20 ha). A start cell was randomly selected from within the swath area, and from this start cell a transect area was derived (i.e. 100 cells in a 10 x 10 array, with the start cell located in the lower left corner). At the centre of the transect area, a field plot area (9 cells in 3 x 3 array) was subsequently extracted. The predominant height was calculated for both assessment scales. This process was then repeated 30 times throughout the LiDAR swath area and for each of the plots across the landscape.

For the Injune multi-scale predominant height calculation, the analyses where undertaken using square and rectangular assessment areas respectively, due to the secondary sampling unit layout (Figure 35). The first iteration of assessment begins with the centre secondary sampling unit (# 15), from which the predominant height was calculated based on all 10m cells. The second iteration then adds the 4 adjacent border secondary sampling units (5, 14, 16, and 25) to the selection set, and predominant height calculated. The third iteration selects the 3 x 3 matrix of secondary sampling units centred on secondary sampling unit 15. Subsequent iterations then include additional secondary sampling unit columns of three secondary sampling units on each side (6 in total included per iteration) moving outwards, until the 7th _{iteration calculates the predominant height using all 30 secondary sampling units. Note}

the outer row/column (for 2 sides) of 10 m cells with a height of 0 m was initially generated to capture any additional LiDAR swath returns caused by adverse aircraft movement. For this analysis, these cells were not used as these 10 m cells were outside the primary sampling unit area.

Table 16: Multi-scale predominant height assessment buffer selection areas for NE Victorian plot locations.

iteration Buffer circle area (ha) (radius (m))

Mean number of 10m cells assessed (>2m height) 1 0.1 (18) 9 2 0.5 (40) 48 3 1.0 (56) 93 4 2.0 (80) 186 5 5.0 (126) 461 6 10 (178) 913 7 15 (219) 1324 8 20 (256) 1666

Figure 34: Multi-scale assessment of predominant height for NE Victorian sites, with example for plot 212 shown.

Figure 35: Example of predominant height assessment at a range of scales for Injune (PSU 142 shown, with 10m cells within 30 larger 0.25ha SSU’s).

Multi-scale canopy cover assessment

In a similar process to that used with predominant height, the foliage-branch cover estimates were extended to the swath width using LiDAR at a known range of scales. This was due to the limitation of not precisely knowing the view extent of the hemispherical photography versus the LiDAR. The utility of LiDAR as a multi-scale dataset to compare to a range of other sensors was initially trialled on plot 220 in NE Victoria. At this location the trial of woody non- woody assessment was undertaken for the LiDAR swath area (522 ha), and used LiDAR, SPOT5, and an early version of the Australian Greenhouse Office (AGO) Landsat TM derived woody non-woody layer data as part of the CFMF pilot project. The hemispherical photo value was for the field plot area, to assess if the plot location and this data source were representative of the wider stand.

Subsequently the LiDAR analyses were extended to investigate how foliage-branch cover estimate changed with an increasing circular assessment area, for all plot locations in NE Victoria, and using the full extent of the LiDAR data (approx 400 m x 400 m). The analysis utilised vector rings of increasing radius up to 220 m, to approximate the full swath extent, and which were intersected with the LiDAR point data. Foliage-branch cover was then calculated as the proportion of LiDAR returns > 2 m height above ground, with respect to all returns. Assessment rings were generated in 10 m radius increments, with the addition of 5 m and 15 m rings to increase the spatial resolution of the assessment at finer scales.

For Injune, various sized rectangles were used instead of circles due to the rectangular shape of the primary sampling unit (Figure 35). The Y distance incrementing by 10 m, with 5 and 15 m distance measurements also included to increase the spatial resolution of the assessment at finer scales. The Y distance reached a maximum distance of 150 m, to assess the whole primary sampling unit. It was observed that the X distance of the assessment rectangles were equal to the Y distance multiplied by 3.333.

Figure 36: Illustration of the multiple scales of FBC circular assessment for NE Victorian plots. LiDAR (2m+) for plot 212 has red/orange colour as highest returns (~ 40 m), with lowest non-

ground dark blue. Background image was Landsat ETM.

LiDAR and API crown cover comparison

Historically, estimates of cover derived from API have been the primary source of information for NFI reporting. However the categorical nature of the estimates can cause difficulties in translating and calibration with other sources or types of forest cover data. The mid-point of the API cover class was chosen as the variable to correlate with a LiDAR derived crown cover estimate, as it provides an indication of the relative accuracy of the cover interpretation. The limitation of this method was that it likely introduces bias as it was derived from a categorical classification, and especially in classes with a large range. The mean and range of LiDAR derived crown cover was calculated using 25 m cells within the API polygon. For this analysis the API polygon extent could range from being smaller than the LiDAR primary sampling unit, to being much larger (Figure 11). As a result, the comparative analysis

included an assessment of the coverage of LiDAR crown cover cells within the API polygon, to investigate if less LiDAR coverage within the polygon also led to a less accurate comparison of cover. The classes chosen for this were arbitrary, but selected such that there was useful class distinction for making assessments about the results, given the difficulties in comparing categorical interpreted data with empirically based crown delineations.

Crown Separation method test

The current nationally consistent crown cover estimation method as developed by Walker et al.,(1988) and Penridge and Walker (1988), was presented in the field survey handbook for soils and vegetation (McDonald et al., 1998). One of the stated limitations of the method was having accurate and realistic crown cover maps with which to calibrate and test the range of assumptions that were inherent in the method (Penridge and Walker, 1988). The development of LiDAR derived crown delineations allows some of the methodological assumptions to be examined in more detail, for both similar and different environments to that used in Penridge and Walker (1988). However, it was beyond the scope of the thesis to undertake a full assessment for all field plots.

An initial comparison was done using two field plots at the Injune field site. This study area was chosen as it has a wider range of consistently collected cover data. One plot (p142-13) was selected that closely matches the environment used to develop the crown separation zig-zag method, namely grazed poplar box (Eucalyptus populnea) woodland in central Queensland. The second plot (p81-16) was selected to test the robustness of the crown separation method in a more challenging forest (mature Angophora-Callitris woodland with dense understorey of regenerating Acacia, Lysicarpus, and Callitris), whilst still being in the same general landscape (the two plots were 25 km apart). The primary assumption behind the comparison used in this thesis was that the LiDAR tree crown delineations were an accurate representation of the tree crowns, and as would be observed in the field. The crown separation comparison test uses a combination of field data and LiDAR crown delineation. The field stems points were used to determine the zig-zag transect path, and the LiDAR crown delineations were used to calculate

the actual lengths of crown and inter-crown gap along the transect, as per the McDonald et al., (1998) method description.