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XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
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3.12 Tree Species and Density Estimation using Image
It is important to estimate tree species and density for forest
resource estimation. For example, in the beginning of tree
planting in the artificial forest, single species such as Japanese
cedar or Japanese cypress is planted. But after a long time,
surrounding broad-leaved forest intrudes into the sub-
compartment. In this case, the single species described in the
forest registration is not correct any more. Similar problem
exists for the tree density. For some sub-compartments under
frequent investigation, the forest density information is kept
updated. But due to the increase of tree un-thinning area in
these days, tree density is not accurate at all and also becomes
not uniform inside the sub-compartments.
Therefore, we make experiments to examine the possibility of
estimating tree species and density from images. At first, we
conduct a field survey in the natural forest in Tatera Mountain
in Tsushima, and then we investigate the possibility of tree
species classification based on 3D shape and colour distribution
of tree crown using aerial photograph in this area. Figure 6
shows that tree species is able to be classified based on R and B
value of the images.
Next, we compare the number of trees acquired by
photographic interpretation with that from object-based image
analysis. The result shows that there is strong correlation
between these two numbers obtained from different methods.
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Figure 6. Result of Species Classification by Colour
32 GIS system
As described in chapter 3.1, it is proved that estimation of tree
height, density and species has reached certain accuracy level
for practical use. But next problem is what kind of system
should be provided for the practical work like the work in the
tree farm. In many cases, the GIS systems owned by forest
owner's cooperative and so on are equipped with various
general functions but users usually only use the simple function
like map viewing.
Then, we divide the whole system into two stages, firstly to
generate basic data, and then to estimate resources by using the
basic data. In the first stage, that is, the stage to generate DSM
and DTM from aerial photograph, it is better to conduct aerial
photography and data processing in a large scale considering
the cost. So, not individual forest owner’s cooperative, but
prefecture or federation of forest owner’s cooperative
association or service vender should carry out this stage for all
the concerned forest area. On the other hand, resource
management including the lumber volume estimation in certain
sub-compartment is useful for various planning trials of tree
thinning in helping the individual forest owner’s cooperative to
carry out a massive work plan.
Therefore, in the proposed forest management GIS, we provide
a simple interface targeting estimation of forest resource
harvested from a sub-compartment for the end users (Figure 7).
The input of the system is the compartment data (shape format),
that include tree species, forest age, site class, area, and so on,
forest base map as a background image, ortho-photo generated
from aerial photographs, DSM and DTM generated by stereo
processing. These input data is managed in layer level and also
displayed as layers on the display similar to common GIS
system.
‘Database
# «Compartment data
# “Forest road, Loading area;
& Forest base map f
% -DSM
*DTM
i. "Orthophoto
m NS
Merc *
» H
P
ia M
x
Tree Density Estimation Forest Road Setting
Figure 7. Forest Resource Management GIS
Output
s *tree volume
Harvest *tree density
E: Estimation -number of timber
*volume of timber remnants
*cost
*income
With the proposed forest resource management GIS system, it
is possible to carry out estimation of tree density and then forest
resource in each sub-compartment. Operation procedures are
described as follows.
3.2.1 Tree Density Estimation
The processing unit of this system is sub-compartment. At first,
user selects the target sub-compartment and click “density”
button. Then, the window of tree density estimation appears,
shown as the left figure of Figure 8. In this window, the
selected sub-compartment is wholly displayed. Then user sets
the plot region inside the sub-compartment. The area of plot
region can be selected from 5 candidates, 0.04ha, 0.05ha, 0.1ha,
0.2ha and 0.25ha, similar to the conventional method of tree
density estimation on photographic surveying. For example,
when 0.1ha is selected, rectangle that corresponds to 0.1ha is
automatically displayed on the screen, obtained on the basis of
ground resolution of orthophoto. User sets the location of the
plot region in the target sub-compartment by dragging the
rectangle by mouse on the screen. Then magnified view is