Improving Forest Inventory:
Integrating Single Tree Sampling With Remote Sensing Technology
C.J. Goulding1, M. Fritzsche1, D.S. Culvenor2
1
Scion, New Zealand Forest Research Institute Limited, Private Bag 3020, 49 Sala Street,
Rotorua 3046, New Zealand. tel +64 7 343 5899 [email protected],
[email protected]
2
CSIRO, Private Bag 10, Clayton South, Victoria 3169, Australia [email protected]
Abstract
Individual tree sampling is an alternative method to plot-based inventory for forest assessment
particularly in small, irregularly shaped areas. The biggest obstacle to adoption by industry is
obtaining an accurate total tree count with an unbiased method of selecting and measuring sample
trees. If this could be achieved then per hectare sampling variation across the stand could be
avoided and sampling need only be concerned with between tree variability. In order to develop a
cost-effective procedure, Scion and CSIRO are testing semi-automated estimates of tree counts
from remotely sensed imagery combined with methods of ground-based sample tree selection and
measurement. TIMBRS is a user-friendly, tree identification and crown delineation program that
analyses high spatial resolution digital imagery in a ‘top–down’, spatial clustering approach using
local spectral maxima and minima to delineate crown centres and boundaries. Measuring
individual trees on the ground requires that the sample trees be selected randomly in an unbiased
manner without incurring excessive walking time and cost. Boundary trees must be appropriately
represented. A very narrow transect laid out in a “zig-zag” or a “structured” walk achieves this
with minimal bias while reducing non-productive time, almost eliminating difficult walking
through thick vegetation where the stand abuts open land. Validation of the project concept has
been completed, with good estimates of stocking. Double sampling, where every tree encountered
in the walk is measured for dbh and a proportion cruised to estimate merchantable log-grade mix,
improved sampling efficiency and demonstrated that the concept was viable.
Introduction
The intensively managed plantations of New Zealand and Australia are measured using in-place
inventory, several times throughout a stand’s rotation. The Radiata pine stands yield multiple log
products with a wide range of grades and processing destinations. Information from merchantable
volume inventory obtained at mid-rotation and pre-harvest is used to optimise the value to be
recovered from a stand. The current operational system assesses stem-wood qualities by
“cruising” individual trees measured in sample plots accessed from the ground. The per hectare
values are then multiplied by estimates of net stocked area to derive totals, (Deadman and
Goulding, 1979; Gordon, Lawrence and Pont, 1995; Gordon, Wakelin and Threadgill, 2006). A
typical company would inventory area’s averaging 40 to 50 hectares or less, with a plot density of
one to every one or two hectares.
If an accurate total tree count could be obtained from remotely sensed imagery, individual tree
sampling could provide an alternative method to plot-based inventory, avoiding per hectare
IUFRO Division 4: Extending Forest Inventory and Monitoring over space and time
sampling variation across the stand. Sampling need only be concerned with between tree
variability, reducing field costs. In small stands with irregular boundaries, such as farm woodlots,
the need for an accurate estimate of net stocked area is avoided.
Image Processing for Tree Identification
TIMBRS is a user-friendly, semi-automated implementation of the TIDA tree identification and
delineation algorithm, originally developed for Eucalyptus forests (Culvenor, 2002, Culvenor et
al., 1998). The spatial resolution of imagery required for accurate tree counts depends on the type
of forest and its age class. Satellite imagery from the QuickBird high spatial resolution sensor
was available for the study area. The imagery has approximately 2.4 m spatial resolution in its
multi-spectral bands (blue, green, red and near-infrared) and 0.6 m spatial resolution in a single
panchromatic band. Prior to importing the images into the software, the multi-spectral bands were
spatially sharpened to an effective spatial resolution of 0.6 m using the panchromatic band.
Within a stand defined by a polygon delineated on the image, TIMBRS used local spectral
maxima as indicators of the likely location of a tree crown.
Three small stands were first assessed using TIMBRS and then the actual total stocking in each
of the stands was obtained by physically counting all the stems in the field. Fully automated tree
location and counting without any user interaction may be highly desirable, but this cannot yet be
achieved with any degree of confidence. Seen from above, Radiata pine crowns are “unruly”,
sometimes with multiple leaders, irregularly shaped crowns and broken tops, as well as the
problems of determining the crowns of suppressed trees. In this study, all semi-automated image
processing was carried out by a forestry scientist with no previous experience who, once familiar
with the software, could produce an acceptable result within an hour, see Figure 1 and 2 and
Table 1.
Figure 1. QuickBird image of Stand.
Figure 2. Acceptable tree count.
IUFRO Division 4: Extending Forest Inventory and Monitoring over space and time
Site
Stand
Age
Area
Field
Tree count
TIMBRS Comparison
Tree count
1
106/2
27
7.5ha
1865
1846
99%
2
849/1
30
4.6ha
1691
1691
100%
3
893/1
32
4.4ha
974
1025
105%
Table 1. Accuracy of tree counting by image processing
Individual Tree Sampling
Selecting individual trees in an efficient
manner such that they constitute a valid
random sample is not simple in practice. The
aim was to develop an auditable field
method that was practical, reduced walking
time and could be used by commercial field
crews. After some experimentation, it was
decided to traverse the stand in a set of
narrow transect paths, measuring each tree
encountered for dbh and additionally
cruising alternate sample trees for stem
characteristics in order to estimate log
product yield. Trees encountered were
approximately 10 to 12 m apart, see Figure
3. The three trial stands had been measured
prior to the project using existing practices
by regular inventory contractors to provide a
control. Circular plots of 0.02 to 0.04 ha had
been laid out on systematic grids. The stands
varied in area from 4 to 7.5 ha, with low to
high hindrances due to understorey and steep
terrain. In two of the stands, a boundary was
ill-defined in the GIS and the net stocked
area was poorly estimated.
Individual tree sampling was trialled in each
stand, aiming to measure approximately 100
trees for dbh, half of which were also cruised
for merchantable volumes as a double
sampling option.
Figure 3.
lines.
Individual trees and sample-
IUFRO Division 4: Extending Forest Inventory and Monitoring over space and time
Sampling Efficiency
Figure 4 shows the change in the confidence intervals expressed as a percentage of the mean
merchantable volume (total recoverable volume, TRV) per hectare for varying numbers of
sample trees measured in plots or separately as individual stems. For equivalent confidence
intervals, single tree sampling required far fewer stems to be cruised than sampling using
bounded plots. The same trends were apparent for the estimates of volumes for each of the log
grades.
Figure 4. The Change in Precision of Total Recoverable Volume (TRV) with Sample Size:
Plot- and Individual Stem- sample based.
Conclusions
Single tree sampling could be more efficient in estimating merchantable volume than
conventional bounded plots, given an accurate tree count from remote sensing. The image
processing software does not require specialist operators, but does require some knowledge of
the forests, as would be expected of a resource forester. Field costs are directly related to the
amount of walking through the stand, the time to layout a circular plot and the number of trees to
be cruised for stem qualities. All three cost components were significantly reduced using
individual tree sampling. The map of individual tree locations is useful in its own right. A key
requirement of the field method is that each tree be selected in an unbiased manner with
approximate equal probability and with minimal correlation between adjacent sample trees. An
alternative sampling design that reduced the delays caused by dense vegetation hindrance on the
stand border is to lay out the transect paths in a “zig-zag” pattern, or to use a pre-planned
IUFRO Division 4: Extending Forest Inventory and Monitoring over space and time
“structured walk” where the field-crew began and ended the sample lines at the same, convenient
point (see MacLaren and Goulding, 1993). Further work is being carried out to confirm that total
costs including imagery and processing are less than those incurred by current inventory
practice.
Literature Cited
Culvenor, D.S., 2002 . TIDA: An Algorithm for the Delineation of Tree Crowns in High Spatial
Resolution Remotely Sensed Imagery, Comput. Geosci. 28 (2002), pp. 33–44.
Culvenor, D.S., Coops, N.C., Preston, R. and Tolhurst, K. 1998. A spatial clustering approach to
automated crown delineation. In, Hill, D.A. and Leckie, D.G. eds. “Automated interpretation
of high spatial resolution digital imagery for forestry”. Victoria, British Columbia, pp 67-80.
Deadman, M.W. and Goulding, C.J., 1979: A Method for the Assessment of Recoverable
Volume by Log-types. New Zealand Journal of Forestry Science 9(1): 225-39.
Gordon, A.D., Lawrence, M.E. and Pont, D. 1995. Assessing the Potential Log Yield of Stands
Prior to Harvesting. In Proceedings of the Institute of Foresters of Australia 16th Biennial
conference "Applications of New Technologies in Forestry." Ballarat, Victoria. 18-21 April
1995.
Gordon, A.D., Wakelin, S.J., Threadgill, J.A., 2006: Using Measured and Modelled Wood
Quality Information To Optimise Harvest Scheduling And Log Allocation Decisions. New
Zealand Journal of Forestry Science 36(2/3): 198–215 (2006).
MacLaren, P. and Goulding C.J. 1993: The structured walk - a practical inventory system. New
Zealand Journal of Forestry 37(4):20-23.
IUFRO Division 4: Extending Forest Inventory and Monitoring over space and time