CSIRO Earth Observation Centre Report 2003/02CSIRO Atmospheric Research Report 2003/30

CSIRO Canopy Lidar Initiative:Mount Gambier Field Trial

Jenny Lovell, Glenn Newnham, Darius Culvenor,

David Jupp and Nicholas Coops

CSIRO Earth Observation CentreCSIRO Atmospheric Research

CSIRO Canopy Lidar Initiative: Mount Gambier Field Trial

A Report for ForestrySA on Preliminary Findings

and Future Directions.

July 2003

Jenny Lovell1, Glenn Newnham1, Darius Culvenor2, David Jupp1 and Nicholas Coops2

1Earth Observation Centre, CSIRO Atmospheric Research,

GPO Box 3023, Canberra, ACT 2601 Phone: 02 6246 5891

2CSIRO Forestry and Forest Products

Private Bag 10, Clayton South, Vic 3169 Phone: 03 9545 2346

Email: jenny.lovell@csiro.au

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1. Introduction The Commonwealth Scientific and Industrial Research Organisation (CSIRO) has an ongoing commitment to develop methods that maximise the benefits of lidar technology for forestry applications in Australia and world-wide. CSIRO Atmospheric Research (AR) and Forestry and Forest Products (FFP) have formed a collaborative team, under the banner of the CSIRO Canopy Lidar Initiative (CLI), to undertake algorithm development and define measurement protocols that best exploit current and future lidar technologies and implement new and advanced lidar technology for forest measurement. This initiative is being partly funded by the Forest and Wood Products Research and Development Corporation (FWPRDC) with the support of a number of industry partners, among whom ForestrySA is a major collaborator. As one of its strategies, the CLI is investigating the capacity of current airborne terrain lidar systems to provide data from which useful forest inventory parameters can be derived. Although the inherent limitations of these instruments (which are optimised for mapping terrain height) place constraints on the accuracy and cost-effectiveness of derived forestry information products, the focus of the CLI is to find the best level of processing that can be achieved with this currently available and very useful source of data. A more detailed discussion of the issues involved can be found in Lovell et al. (2003), which is part of a Canadian Journal of Remote Sensing Special Issue on lidar remote sensing for forestry. In the future, when airborne canopy lidar becomes available to the forestry industry, a range of other products and even more cost-effective outcomes are expected. The CLI also has a complementary focus to airborne lidar sensing: the development of an advanced ground based system ECHIDNA™, which can be used either in combination with airborne lidar to scale up detailed forest variables to large areas, or as a stand-alone instrument, providing a complete site-based forest inventory with both primary productivity and environmental data outputs. At this time, the instrumentation being used to help develop the ECHIDNA™ is in an experimental form and built from simple off-the-shelf components. This prototype is being used for preliminary testing of theoretical concepts on which the final instrument will be based and was deployed at Mt Gambier to help with that development. Airborne laser scanners (ALS) are well-proven in their acquisition of data for the definition of ground surface elevation (Digital Elevation Model, DEM). In addition, the data may be used to obtain measures relating to canopy height and foliage cover, including profiles of foliage amount at different heights, which can be used to assess crown form. More advanced processing techniques can be used to delineate and map tree crowns with a similar theoretical basis to that used in the analysis of remotely sensed spectral data. The use of allometric methods in forest inventory is based on the assumption that trees of a particular age and at sites of a given quality will tend to follow predictable overall growth in all their dimensions over time. It is therefore reasonable to expect that similar allometric relationships may exist between ALS-derived canopy properties and other inventory parameters and measures of site quality.

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The aim of the Mt Gambier field trial was to demonstrate some useful processing options offered from ALS data and to provide inputs for future algorithm development, instrument measurement protocol and instrument design. In support of this aim our specific objectives were to:

• estimate a selection of stand characteristics using a commercial airborne terrain lidar system;

• estimate a selection of stand characteristics using CSIRO’s prototype ground-based forest canopy lidar system;

• validate these estimates using data derived from traditional forest measurement techniques.

Section 2 provides background information on the sites visited and data acquisition. Section 3 presents results from the ALS data and indicates methods by which Site Quality Index (SQ) may potentially be derived from such data. Details of the preliminary ECHIDNA™ field trial are presented in Section 4. Section 5 summarizes the key results and suggests future steps towards enhancing the application of lidar technology to forest assessment through on-going collaboration.

2. Field Sites and Data Acquisition The Mt Gambier field trial involved jointly funded the collection of airborne lidar data commissioned by ForestrySA and AAM GeoScan using the AAM GeoScan Pty Ltd Airborne Laser Scanner (ALS) and ground-based lidar data collected using CSIRO’s Rangefinder Prototype ECHIDNA™ (RPE) instrument. The ALS instrument used is an Optech ALTM 3025 system with a scan angle of up to ±20 degrees, a measurement rate of 25,000 samples per second and a nominal beam width of approximately 0.25 m at the ground. The two ALS datasets provided to CSIRO by ForestrySA were recorded on 7 July 2002 with a nominal point spacing of 1.2 m. These datasets covered 2 km tiles at two sites, South Patchells and Springs Road, near Mt Gambier, the location of which is shown in Figure 1. The ALS data were provided in a format classified by AAM GeoScan Pty Ltd into first hit ground, first hit non-ground and last hit, as well as unclassified time sequential format to allow pairing of first and last hits.

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Figure 1: Location of the two ALS and RPE measurement sites relative to the township of Mount

Gambier

The South Patchells site is owned and operated by ForestrySA and consists of densely stocked, 9 year old Pinus radiata. ForestrySA uses specific plots within the site as calibration points, from which site quality is mapped as an indication of forest productivity. These test plots cover a range of site qualities from SQ1 (most productive) to SQ7 (least productive). RPE measurements were recorded at South Patchells on 14 August 2002 at the centre of three measurement plots, each representing a different site quality class. The sites and their location as measured by hand-held GPS are listed in Table 1.

Table 1: Plot locations and site quality indices at South Patchells

Forestry SA Plot Number

Site Quality Index

Easting (UTM 54S, WGS84)

Northing (UTM 54S, WGS84)

3 SQ I 487282 5839632 10 SQ III 487905 5839157 18 SQ VI 487838 5838900

The second site at Springs Road also owned and operated by ForestrySA and consists of extensively thinned 20 year old P. radiata. Within this site is a fertilizer trial that is a joint Co-Operative between the local Pine Growers and CSIRO FFP. A single plot within the Springs Road site (plot 9) was selected and RPE data recorded at the centre, south-east and south-west corners on 15 August 2002. The grid coordinates of the plot centre were recorded as 468720 E, 5820446 N.

Springs Road

South Patchells

Mount Gambier Patchells

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3. Airborne Lidar 3.1. Digital Elevation Model

A digital elevation model (DEM) was generated for a 1 km x 1 km area containing plots 3, 10 and 18 at South Patchells. The procedure was to accept the ground classification provided by AAM and grid these data at 2-m resolution (using Delauney triangulation). The area is relatively flat, with an elevation range of 67 to 72 metres. The DEM is shown in Figure 2 with heights ranging from dark blue to white through a spectrum of blue, red and yellow. A grey-scale version of the DEM is also shown, in which the shades have been stretched to highlight linear features. The separation of the linear features that appear as lighter shaded lines running north-south and east-west in the right hand image in Figure 2, is about 50 m and the difference in height is approximately 0.2-0.3 m. The highly regular nature of these lines suggests that some artefact of the data or processing methodology may be responsible. However, it is possible that these features result from the systematic heaping of post-harvest debris into ‘windrows’ as part of preparation for the next rotation plantings.

Figure 2: Digital Elevation Model for the South Patchells site derived from classified last return

ALS data. Units are metres. In the right-hand image, all heights above 69 m are shown as white.

The Springs Road DEM was computed by triangulation, using the same procedure as that used for South Patchells DEM. Figure 3 shows a colour-scale image and a shaded surface representation of the area. Note that the northing and easting axes have both been reversed so that the surface is viewed from the north looking uphill. The surface plot shows some low-level variance, most likely due to debris remaining from previous thinning operations. Fire-breaks/roads are also clearly visible.

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Figure 3: Digital Elevation model for Springs Road site derived from classified last return ALS.

North is at the bottom of the images.

The range in heights over the Springs Road area is 49 – 77 m. Plot 9 is situated on a north-west facing slope (right-hand side of surface image). The slope and aspect calculated from the DEM are 3º and 331º (map grid) respectively. Slopes and aspects (magnetic bearings) measured in the field are tabulated below (Table 2). Magnetic declination is approximately 9.7º.

Table 2: Measured slope and aspect at Springs Road Plot 9.

Aspect (magnetic) Slope (percent) Slope (angle) 350º 5.2% 3.0º 304º 7.4% 4.2º 262º 6.6% 3.8º

3.2. Predominant Height Predominant height (PDH) is a measure of stand height calculated as the mean height of the 75 tallest trees per hectare. In practice, a smaller number of trees within a smaller plot area are used to estimate PDH (e.g. 10 trees per 0.13 ha). PDH has been shown to have a functional relationship to plot size (e.g. Matern, 1976), so this must be specified as part of a complete definition of the variable, to ensure consistent usage is made in modelling and monitoring in any specific area of use. ALS data can be used to estimate predominant height based on any specified plot size and sampling strategy. In general, PDH is scaled to smaller plots by specifying that a plot of area A hectares requires the heights of the 75N A= tallest trees be averaged. To make this calculation from ALS data, PDH can be calculated as the average of the

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N highest hits that are separated by more than one crown size. For small plot sizes, the actual number per hectare will differ from 75 due to the need for an integer value of N. For example, the method we will employ here uses a plot size of 0.05 ha, within which four heights are averaged (i.e. 80 per hectare). Within-plot stratification may also be used to overcome small-area bias. The quadrant-based sampling strategy employed in the field by ForestrySA is an example where the plot is stratified to reduce the effect of spatial correlation in heterogeneous stands and it can also be used with ALS data. In this case, a 0.05 ha plot is defined (20 m x 25 m) and divided into quadrants. The PDH is calculated from the highest hit in each quadrant. This also results in a sampling of 80 per hectare. These are just two examples of many ways in which ALS data may be used to flexibly estimate PDH in a way consistent with its varying definitions among foresters.

3.2.1. South Patchells First-return lidar data were corrected to above ground height by subtracting the interpolated 2-m horizontal resolution DEM. The three field plots (20 m x 25 m) were then located and predominant heights calculated by the two methods described above. Comparison with the field plot measurements is shown in Table 3. The uncertainties quoted are the standard deviation of the heights used in each average.

Table 3: Predominant height comparison. Quoted uncertainties are the standard deviation of the heights used in the average.

Plot ID Field PDH (m)

Lidar quadrant PDH (m)

Lidar PDH (m)

Plot 3 19.2 ± 0.29 18.3 ± 0.62 18.6 ± 0.24 Plot 10 17.6 ± 0.42 16.0 ± 0.61 16.3 ± 0.10 Plot 18 13.5 ± 0.95 11.5 ± 1.1 11.8 ± 0.67

The quadrant-based lidar and field PDH values are in agreement within one standard deviation at plots 3 and 10 and within two standard deviations at plot 18. In all cases, the lidar methods under estimate that measured from the ground. This is expected due to the sampling nature of the ALS and the conical shape of the crowns. It is unlikely that the lidar beam would consistently hit the apex of each crown in its sampling and in these cases the strength of the return signal will typically be weak due to the small reflecting area and may not be recorded as a hit. The PDH values calculated when the position of the averaged heights is not spatially restricted are higher than those from the quadrant-based method and the variance of the averaged heights is less. In a completely uniform plot, the two methods would give the same result, but spatial variation in height will always lead to a smaller value by the quadrant-based method. Linear regression of the quadrant-based PDH values using the standard deviation of the quadrant heights as uncertainties in x and y gives: ( ) ( )4.28 3.60 0.82 0.21lidar fieldPDH PDH= ± + ± .

Due to the nature of the sampling and ranging technology used in terrain lidar, the observed bias in height estimates is unavoidable and its degree will depend on the tree type and density. It is possible bias corrections can be developed for specific situations and it is certain that intense sampling (shot density) can reduce it - and this

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could well be a topic to explore in the future. Due to the uncertainty in GPS position (probably �� m, but possibly up to 15 m) compared with the size of the plots, it is uncertain whether the field and lidar quadrants are coincident. This will be of most significance at plot 18 where the spatial variance in the local area was greatest and will contribute to the observed variation. Figure 4 shows the lidar predominant height calculated by both methods for 20 m x 25 m plots over a 1 km x 1 km area. A fire-break/road can be clearly seen on the left. The area to the right of this is all 9-10 year old P. radiata. To the left are older stands (yellow). A swampy area in the south, observed near plot 18, shows up as an area of lower predominant height (blue-black) near the bottom of the image. The spatial patterns are very similar between the two methods, with one notable difference being the clarity of the road between the two plantation areas. The quadrant-based method gives much lower PDH along the road. The width of the road is comparable to the size of the plots, so it is very likely that one or two quadrants of each plot contain no trees, therefore reducing the mean height for the plot. In the other method (4 per 0.05 ha), there is no spatial restriction on the location of the averaged heights (except that returns must be from different trees), so the mean height is dominated by the part of the plot that does contain trees.

Figure 4: Predominant Height for 9 year old P. radiata in 20 x 25 m plots calculated by the quadrant method (left) and the 4 per 0.05 ha method (right).

The distribution of PDH by the two methods over the1 km x 1 km area is shown as a histogram in Figure 5. The two distributions are very similar, but again demonstrate that the quadrant-based method (black line) leads to slightly lower PDH. Again the low PDH resulting from the spatial correlation of high and low points in the vicinity of the firebreaks is seen as a distinction between the two methods.

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Figure 5: Histogram of predominant heights at the South Patchells site calculated by the

quadrant method (black) and the 4 per 0.05 ha method (red).

3.2.2. Springs Road Field data for plot 9 provided by Barrie May (CSIRO, FFP) contain measured heights for nine trees within the 0.13 ha plot, corresponding to approximately 69 trees per hectare. Crown size was also measured at the time of this field trial for 12 trees (crown radii in the four cardinal directions), the average diameter being 4.6 m. Lidar-based estimates of predominant height in the 40 m x 40 m area centred on the Springs Road plot have been calculated using the standard definition of 75 trees per hectare (12 samples in the 0.16 ha plot). A calculation based on 69 trees per hectare over a 0.16 ha plot would also require averaging of 12 samples. A crown size of 4.6 m was enforced to ensure that multiple heights from the same tree were not included in the PDH calculation. For simplicity, the 40 m x 40 m plot area used in the lidar data analysis has been aligned with the cardinal directions, however, it should be noted that this does not correspond exactly with the true plot boundary. A comparison of the results is shown in Table 4. The lidar PDH underestimates the field-measured height (as expected), but there is agreement between the lidar and field estimates within two standard deviations.

Table 4: Predominant Height comparison at Springs Road Plot 9

N PDH (m) StdDev of heights in PDH calculation (m) 75 per ha (12 samples) 27.8 0.47 Field (9 per 0.13 ha) 29.1 0.69

For a 1 km x 0.4 km area containing plot 9, 40 m x 40 m plots (0.16 ha) have been used to calculate predominant height by the average of the 12 highest hits (as described above). This corresponds to a general definition of 75 highest trees per

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hectare. The standard deviation of the heights used in the PDH calculation was also calculated for each grid point. These results are shown in Figure 6. There are two distinct areas visible with lower PDH in the eastern part of the image. This is also demonstrated in the histogram plot of PDH. Within the taller group, there is also an increasing trend in PDH towards the north-west. This is associated with an area of lower elevation (see Figure 3) and is probably due to increased soil moisture resulting from drainage from the surrounding area. The standard deviation of averaged heights used in the PDH calculation shows less distinct spatial patterns, which may become more apparent at different spatial scales.

Figure 6: Predominant height (top) and standard deviation of averaged heights (middle) for

40 m x 40 m plots at the Springs Road site. The bottom panel shows histograms of the same data.

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3.3. Vegetation Cover The size and growth rate of different components of a forest (e.g. foliage, stems, trunks, roots) is usually highly correlated in a specific site and for a given mix of age classes and types of tree. In well-managed, single-age plantations, such relationships can be quite strong and are the basis for allometric methods. These methods use measured aspects of forests or trees to infer other parameters (e.g. timber volume inferred from basal area, or foliage amount from diameter at breast height (DBH)). The practical advantage of such methods is that a parameter that can be relatively easily measured can be used to estimate less accessible properties. Airborne sensors, with their vertical view, are more sensitive to canopy elements than to stems. Estimates of canopy cover (either in total, or as a profile) are directly available from ALS data. Again, due to the ranging and sampling information these lidars provide, the information has limits but it is still well worth extracting when terrain lidar is flown over forest areas. In particular, canopy cover is related to the foliage amount in individual trees in the same way that basal area is related to DBH. Therefore a correlation between DBH and foliage amount can lead to a similar relationship between basal area and canopy cover. Site quality indices are defined for a particular plantation type at a specific age. Therefore, the parameters measured in an assessment of site quality (e.g. basal area, PDH) are likely to be highly correlated, as with other aspects of the canopy such as foliage cover. It is probable then, that variables derived from ALS could be used for the indirect prediction of site quality. The following results provide an indication of relationships that may prove sufficient to map site quality.

3.3.1. Total Cover Total canopy cover is the fraction of area that is covered by any part of the canopy. Terrain lidar interacts with the canopy cover in a complex way and in the past the use of single-hit data has led to biased estimates and variable outcomes. The ALS flown at Mt Gambier provides first and last hit information that can improve the capacity of the system to map foliage cover and profiles. To use information from both first and last ALS returns in the cover calculations provided here, each lidar shot was allocated to one of three categories according to the classification (vegetation or ground) of the first and last returns from each shot;

1. veg-veg; 2. veg-ground, and 3. ground-ground.

An estimate of cover was made by weighting shots in category (1) with weight 1.0 and those in category (2) with weight 0.5 and summing them as a proportion of the total shots. In effect, when the first hit is foliage and the last is ground, it is assumed that half the beam hit the foliage and half hit the ground. It is possible to obtain ALS data with the intensity of the return beam included and this can improve the estimate.

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The cover measure we have provided is an estimate of the fractional vertically projected cover, because the foliage area is sensed from a vertical view. Foliage angle information derived from multi-angular scanning is necessary to convert these measures into actual foliage amounts due to the complex variations in the angles at which canopy elements occur. Figure 7 shows an image of cover for the South Patchells area in the same 20 m x 25 m plots as used for predominant height calculations. Figure 8 shows a similar plot for Springs Road, using 20 m x 20 m areas.

Figure 7: Fractional projected foliage cover in 20 m x 25 m plots for the South Patchells site.

Figure 8: Fractional projected foliage cover in 20 x 20 m plots for the Springs Road site.

It is interesting to compare the spatial patterns in the cover images and the PDH images. The fire-breaks between compartments are quite evident in cover, but are not revealed in the PDH images. This is reasonable, since cover is an average measure over the whole plot area, while PDH is an average height of discrete sample trees.

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A scatter graph of cover against height is a useful way to visualise some aspects of canopy structure. This is similar to a stand height curve in which PDH (or some other measure of height) is plotted against DBH or basal area, leading to the prediction of height when only DBH is known. In Figure 9, cover and predominant height are plotted for the two areas shown in Figure 7. Note that the plot size used at Springs Road in this case is 20 m x 20 m (smaller than that used for PDH in Section 3.2.2) in order for the results to be more comparable with the South Patchells results. The green points represent the South Patchells site and the red represent the Springs Road site. The points spatially coinciding with the field plots are indicated on the graph. The Springs Road site was largely uniform with a few outliers due to roads and firebreaks. The taller forest to the east of the South Patchells site forms a separate cluster of points, whereas the even-aged plantations display a roughly linear trend.

Figure 9: Projected Cover plotted against Predominant Height for the South Patchells (green)

and Springs Road (red) sites shown in Figure 7 and Figure 8. Data for the field plots are also shown as boxes enclosing the plot number (South Patchells) and a triangle (Springs Rd plot 9).

The location of field plots 3, 10 and 18 within Figure 9 indicate some functional relationship between cover, PDH and site quality. Definition of this relationship between these variables and SQ requires additional information on the assessed site quality of a wider range of sites covered by the ALS. If such a relationship can be established it provides an efficient method of mapping site quality.

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3.3.2. Cover Profile The vertical distribution of canopy components is a valuable input to light microclimate and rainfall interception models, that can be used to model forest growth. Such a profile also provides information on vegetation stratification and understorey. Lidar data can be used to probe the vertical distribution of canopy components by considering the penetration of all laser shots within a given area. Specifically, the laser power reflected from different levels within the canopy is compared to the total power incident on a given region and the probability of a laser pulse reaching a given height in the canopy computed. A model (such as the Weibull distribution used by Yang et al. (1999)) can be fitted to the foliage profile and the parameter values taken to be indicative of the spatial variation of the canopy structure. Terrain lidars such as the ALS provide a limited but useful sample of this returned power. The effectiveness depends on shot density and could be improved if more than first and last hits were recorded – such as multiple hits and returned intensity. Nevertheless, by effective processing, there is a great deal of useful information to be obtained from standard ALS data. Figure 10 shows an example of an apparent foliage profile and the fitted model. The left-hand panel is the estimated cumulative projected foliage area per unit area. The value at zero (i.e. the ground) is the estimated total projected foliage cover. The middle panel shows the derivative of the model, which is the relative foliage density at different heights through the canopy. The right-hand panel of Figure 10 is a comparison of modelled foliage profiles in the vicinity of field plots 3, 10 and 18. The contrast in foliage density and distribution between these sites (of differing SQ) is immediately apparent. The profile for plot 3 (SQ1) shows the majority of foliage in the upper part of the canopy while the balance is progressively lower at the two lower quality sites. Part of this effect may be due to poorer penetration of the ALS to lower parts of the very dense SQ stands.

Figure 10: Example cumulative foliage profile and model fit (left), model derivative (middle) and

comparison of modelled foliage profiles at the three test sites (right).

The spatial variation of the four Weibull model parameters fitted to data in 20 m x 20 m plots in the South Patchells area is shown in Figure 11. This is a convenient way of summarizing the spatial patterns of a complex vertical structure. The variety of patterns indicates that the parameters are each sensitive to different

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aspects of the canopy. A classification based on these parameters may prove to be a useful delineation of different stand characteristics.

Figure 11: Spatial representation of Weibull model parameters (a, b, c, d) fitted to vertical foliage

profiles in 20 m x 20 m plots. Low values of each parameter appear in black/blue while high values are yellow/white.

(a)

(b)

(c)

(d)

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The parameter a is a canopy scaling factor for the distribution, equivalent to planting more trees within a particular area, thus the firebreaks can be clearly seen between the dense plantation compartments. The parameter b is a within-crown scaling factor that is sensitive to the foliage density of each tree. The Weibull c is an asymmetry parameter related to the vertical distribution of foliage, not the total foliage area. High values of c result from a greater concentration of foliage toward the bottom of the canopy and vice versa. This is shown by the low values (blue areas) in the mature plantation in the north-east part of the image. The parameter d describes the height of the distribution, which represents a percentile canopy height i.e. a certain percentage of the foliage lies below this height. The actual percentage depends on crown shape but could be calibrated for a given forest type. The white parts of this image are areas where the model was not well constrained in which case the height parameter is quite unstable. Another approach to vegetation classification is to use both first and last return ALS data to define a set of heights representing each 10th percentile of height and use these as input to a classifier. Figure 12 (top panel) shows a colour composite of three of these levels for 20 m x 20 m plots. The red band is the 100th percentile height (i.e. all other points in the distribution are lower); green is the 40th and blue is the 10th percentile. There are clear spatial patterns that suggest delineation into structural classes. Two even-age areas were identified with planting dates in 1991 and 1992. An unsupervised (K-means) classifier was applied to the 10 height levels over each of these two areas to produce maps of seven classes. These are shown in the middle two panels of Figure 12. The relationship of these classes to SQ could be tested or alternatively, a sample of SQ data points could be used as a training set for a supervised classification to determine whether this interpretation of the ALS data is suitable to classify site quality. The bottom panel of Figure 12 is a surface of the median (50th percentile) level. This is the height at which half the lidar hits were above and half below. The colours shading part of the surface are the classes described above. Heights of median energy return derived from airborne canopy lidar have been found to correlate well with aboveground biomass (Drake et al., 2002). This measure is sensitive to changes in both the vertical arrangement of the canopy and the openness. Areas of high density drive the level up, due to lack of penetration to lower levels, while in more open areas, the level is reduced due to the large return from the ground. The surface derived here from ALS data is affected by the various biases due to the sampling and ranging nature of the instrument. The median height in the tall, more open stands appears very low. This is probably due to the insensitivity of the ALS to low-level foliage, while still recording a strong signal from the ground (which is a more reflective target).

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Figure 12: The top panel is a colour composite of the 100th, 40th and 10th percentile heights of

ALS first and last returns in 20 m x 20 m plots. The middle two panels show seven classes resulting from an unsupervised classification of the 10 percentile height levels. The bottom panel is a surface of the median height (50th percentile) coloured according to the classes.

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4. Ground-Based Lidar The ground based ECHIDNA™ is a patented instrument concept that is envisaged to provide a detailed description of forest structure using a hemispherical scan of lidar pulses about a fixed point. The information can then be used to calibrate lidar data from airborne instruments or conversely airborne instruments can be used to scale up the more detailed forest variables retrieved using the ECHIDNA™. At the current stage of the development, we have utilised instrumentation known collectively as the rangefinder prototype ECHIDNA™ (RPE). This is a research tool built from off-the-shelf components, designed for preliminary testing of the ECHIDNA™ concept and the development of next stage instrument specifications. However, it does provide useful information and was deployed at Mt Gambier in support of the airborne data. For the Mt Gambier trial, the RPE instrument was set up on a tripod one metre from the canopy floor, at the field plot locations specified in Section 2. The instrument sampled 100 points at equally spaced azimuth angles along the almucantar for each of 18 zenith angles, moving from a zenith angle of 75� to 8�. At each point the instrument records a range, zenith, azimuth and the number of repeat pulses required before a valid range was recorded. When no return laser pulse is detected after three repeats, a range of zero is returned, indicating a gap in the canopy (see Figure 13).

Figure 13: Hemispherical scans retrieved using the rangefinder prototype ECHIDNA™ (RPE).

Scans are plotted using a polar projection, where the zenith is at the centre and the horizon is at the edge of the circle. Each spot represents the foliage density at a single location, with white areas indicating canopy gaps and darker shades of green representing increasing levels of foliage density, estimated as the inverse of the number of laser pulses required to retrieve a valid range.

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The RPE collects a hemispherical dataset similar to a hemispherical photograph, with the additional advantage that every point measurement on the hemisphere has a corresponding measure of range. This allows limited vertical profiles of forest variables, such as the gap probability and cumulative foliage area index, to be estimated. However, the RPE represents an initial stopgap in the ECHIDNA™ development and brings with it a number of limitations imposed by the simple rangefinder on which it is based. The RPE has a relatively low sampling rate, due to the primitive scanning mechanism and the slow measurement speed of the rangefinder. Over a scanning period practical in field studies, this leads to a low density of measurements across the hemisphere. Given the high spatial variation in canopy architecture, this may restrict the instrument’s ability to properly characterise the vertical and angular variation in the canopy gap probability. Possibly the greatest limitation imposed by the RPE is the retrieval of only one range for each laser pulse. If the beam is completely unobstructed by the canopy, then the instrument correctly records a canopy gap for the beam area. However, if any part of the beam is obstructed by a canopy component large enough to return a signal above the instrument noise threshold, then the assumption must be made that the complete beam area has been intercepted. Since it is only the first return above the signal noise threshold that is recorded, the detection range of the instrument is also restricted to foliage in close proximity. This would understandably lead to an overestimated RPE derived foliage density profile that is biased toward the base of the canopy. This single pulse detection issue would be less significant if the beam divergence was very low. However, given a finite beam width, any canopy gaps less than the beam cross sectional area cannot be distinguished from the canopy components surrounding them. The beam divergence of the RPE is approximately 3 mr, leading to a cross sectional area greater than 0.07 m2 at a range of 100 m. The effect of this on the instrument bias is discussed in the following sections. Despite these limitations, the RPE has provided valuable information to supplement airborne lidar data and is being used, based on trials such as the Mt Gambier experiment, to assist in development of specifications for the recently commissioned ECHIDNA™ prototype called the ECHIDNA™ validation instrument or EVI.

4.1. Foliage Density The gap probability profile was computed for each zenith angle using 100 measurements around the almucantar. The projected foliage cover can be computed from this gap probability profile using a logarithmic relationship, taking into account the increasing canopy depth as the beam zenith angle increases. Since the projection of non-spherical foliage elements into the beam direction changes with zenith angle, there is significant dispersion in the projected foliage cover profiles computed directly from the RPE data. This dispersion provides information about the angular distribution of the foliage and can be modelled using a statistical foliage angle function (Campbell, 1986). The value of this function is primarily dependent on the

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mean inclination angle of the foliage (needles and branches), and its estimation allows the calculation of the foliage area index profile from the projected foliage cover. Theoretically, computing the more relevant foliage area density profile is then a simple matter of taking the derivative of the foliage area index with respect to height. However, limitations of the data can lead to spurious computed foliage area densities. This can be overcome by fitting a smooth function to the foliage area index, such as the Weibull distribution (referred to in Section 3.3.2) allowing the foliage area density to be computed analytically. Each RPE dataset was reasonably well modelled, with r 2 values no lower than 0.83. The computed Weibull functions (the foliage area index) and their derivatives (the foliage area density) are shown in Figure 14.

Figure 14: The fitted Weibull functions (______) describing the cumulative foliage area index

profile and their derivatives (------) showing the estimated foliage area density profile , as derived from the RPE hemispherical scans.

For the purpose of comparison, a multi-level series of hemispherical photographs was recorded at each RPE measurement site using a mast to elevate the camera into the canopy. This allowed a gap probability profile to be built up piecewise and the foliage area index profile to be estimated by fitting the Weibull function. The data points derived from the hemispherical photographs and the fitted Weibull functions are shown in Figure 15. Fitting the Weibull function to these data has the dual advantage of stabilizing the cumulative distribution and allowing extrapolation of the foliage profile to inaccessible heights where the canopy extends above height of the mast (15 m). However, for the Springs Road site the physical height of the canopy was much greater than the maximum height of the mast. This lead to variations in the parameter values for the three relatively homogeneous measurement points as illustrated by the estimates shown in Table 5.

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Figure 15: The fitted Weibull functions (______) describing the cumulative foliage area index

profile and their derivatives (------) showing the estimated foliage area density profile, as derived from gap probability measured using multi-level hemispherical photography ( + ).

The two sets of foliage area index profiles modelled using the Weibull function show an obvious bias in the RPE data. Although the ground level foliage area index values were similar, there is a distinct bias in the foliage density toward lower heights. This is due to the single return limitation combined with the beam size of the present RPE instrument. This also leads to deviation between some Weibull parameter values estimated using the RPE and hemispherical photography. Values for canopy height showed good agreement between the two methods and are listed in Table 5 along with predominant height measurements recorded using traditional field techniques.

Table 5: Canopy height estimated using the RPE and multi-level hemispherical photography. There is significant overestimation in the hemispherical photography estimates for Springs Road due to the limited height of the camera mast. Predominant height (PDH), was measured by Forestry SA and CSIRO FFP.

Site RPE (m)

Multi-level hemi-photo

(m)

PDH (m)

Patchells 03 14.05 15.89 19.02

Patchells 10 14.02 15.56 17.6

Patchells 18 8.07 14.06 13.48

Springs Centre 30.70 39.78 29.1*

Springs SE 27.13 49.83 29.1*

Springs SW 28.96 44.84 29.1*

* average of nine tree heights within Plot 9, data supplied by CSIRO.

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4.2. Stem Density Horizontal RPE scans were used to gather information relating to the number density and mean diameter of individual stems at South Patchells plot 18 (100 points around the almucantar) and the centre of plot 9 at Springs Road (180 points around the almucantar). Valid ranges were returned for all beam pulses in each scan. The distribution of return points in Figure 16 indicates a higher density of stems at the South Patchells site with a mean range of 4.7 m and a maximum range of 22.2 m. At Springs Road the mean range was 21.2 m and maximum 76.6 m.

Figure 16: Distribution of return points for horizontal scans at South Patchells plot 18 and the

centre of Springs Road plot 9.

Gap profiles as a function of range from the instrument can be derived from the data in a similar manner to that of the hemispherical scans. The negative logarithm of this gap profile (shown in Figure 17) can be used to estimate both the mean diameter at breast height D and stem number density �.

Figure 17: Negative logarithm of the gap profile derived from RPE horizontal scans at South

Patchells plot 18 and the centre of Springs Road plot 9 along with the fitted function that accounts for the divergence of the RPE beam with increasing range.

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Modelled �D values for each horizontal scan is given in Table 6 and compared with data from field records. These data show that the RPE overestimated �D in both cases. The present specifications of the RPE do not allow it to distinguish between tree trunks and other foliage elements. Consequently, the foliage at the base of the trunks at Patchells 18 would lead to the perception of an increased trunk diameter.

Table 6: Values for the product of the mean diameter at breast height and the stem number density (�D) computed from the horizontal RPE scans along with validation data computed from field records.

Plot �D��

(RPE) �D

(Field Records) Patchells 18 0.0282 0.0176 Springs Road 0.0178 0.0108

The more advanced specifications of the next generation ECHIDNA™ will rectify many of the limitations imposed by the current RPE, both in terms of distinguishing between hard targets (stems) and soft targets (branches and needles/leaves), and by providing the ability to compute the fraction of the initial pulse (intensity) returned from objects over all ranges. Scan patterns and speed will also be enhanced, allowing better coverage of complex spatial patterns.

5. Conclusions and Future Directions This report highlights potential information products achievable using current technology lidar instruments for forest inventory. The CSIRO Canopy Lidar Initiative is continuing to further refine the algorithms used and to develop the specifications for a more advanced ground based ECHIDNA™ instrument. The emphasis in processing ALS data is to minimise the effect of the inherent biases due to discrete range returns, spot size and spatial sampling and to implement efficient processing methods. These are intended to ensure the best use is made of lidar technology and to prepare for the opportunities that will be afforded when airborne lidars that are optimised to forest mapping (i.e. canopy lidars) become more readily available to the forestry industry. A number of observations can be made, based on the work completed. Potential indicators or surrogates of site quality seem to be available through variables that can be derived with reasonable accuracy from the ALS data. Further investigation may yield significant advances in this area, reducing the need for laborious and costly activities in site quality assessment that involve walking transects to extend intensive site information to larger areas. The geo-located digital output from the lidar-based techniques also has a significant efficiency advantage, providing an ideal format for direct GIS integration with information from intensive sites and with other spatial data.

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Tree heights derived from classified ALS data have produced predominant height measurements that are biased but comparable with field measurements, even within the dense (SQ1) plantation where the potential for accurate ground detection was low. Two forms of PDH calculation have been demonstrated: the quadrant-based method used by ForestrySA; and a method that does not restrict tree position. This demonstrates the flexibility of ALS data to determine PDH for any plot size or sampling strategy. Comparison results in a larger number of plots could be used to derive in specific bias corrections appropriate to the forests under management at Mt Gambier. It has been shown that the use of both first and last ALS returns allows beam penetration through the canopy to be assessed and measures of cover derived. While terrain lidar has limitations for this purpose it still has very valuable information content. Foliage cover profiles can be extracted and modelled. The model parameters provide a spatial summary of the canopy vertical structure and have potential for classification into structural classes that may prove to be of value for SQ classification. The data point locations of three different site qualities within a structure diagram (Figure 9) is indicative of a SQ dependency in the ALS-derived variables. Height (represented in Figure 9 by PDH) is an important measure in the classification of site quality. Identification of a greater number of sites could help lead to the development of a functional relationship between these variables that may provide an efficient indicator of site quality. There is further interest in the possibilities of classification of the data based on foliage profile information, either using model parameters (e.g. Figure 11) or height percentiles (Figure 12). The extra dimension of vertical arrangement of canopy structure may provide a richer data source for site quality classification than using the simpler relationship of cover versus PDH. The ground-based ECHIDNA™ system is in an early developmental stage. Nevertheless, the information products reported here illustrate some of the potential benefits that will come to fruition with the next generation instrument. The instrument provides a point based statistical characterisation of the canopy, with output variables related to stem and foliage densities that may be incorporated into forest growth and wood volume estimates. It also provides an opportunity for scaling up the point variables using the ALS dataset. Advanced products, such as wood volume estimates and the assimilation of lidar data into growth modelling, will require continued testing of the ECHIDNA™ technology and validation of new algorithms. However, delivery of such products is a realistic aim of future work in this area.

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Acknowledgements The CLI team would like to thank ForestrySA and AAM GeoScan for their ongoing interest in this project. In particular their contribution of the ALS datasets, the selection of field plots, the provision of inventory data and the many practical suggestions have been critical components of this work. The CLI would also like to thank staff at the Mt Gambier office of CSIRO Forestry and Forest Products for field assistance during the trail and contributions to the definition of industry focussed objectives for the project.

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