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Title
Remote sensing for resources development and environmental management
Author
Damen, M. C. J.

erations
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CPU time
(min.)
5
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3
15
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Symposium on Remote Sensing for Resources Development and Environmental Management / Enschede /August 1986
Methods of contour-line processing of photographs
for automated forest mapping
R.I.Elman
All-Union ’Lesprojekt’ Association, Moscow, USSR
:eded should be the
according to the
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ABSTRACT: Automated extraction of forest boundaries on aerial and space photographs is con
sidered» The methods depend on the contour line clearness on photos and differ in the auto
mation degree. The contour extraction is followed by restoration aimed at elimination of de
fects in contour lines which appeared during the input» The next stage is the random conto
ur networks processing» It is meant for the transformation by contour lines from the pattern
into the vector format in order to cut the network into elementary unraraified ’threads’, to
code threads, etc» The method is realized in the form of the program package for the disp
lay processor and is used in the forest mapping automation.
find the optimal
S data for forest
following conclusions
own that the past
cover type classifi-
all wavelength bands
, one can find that
ns more bands,
ring techniques
curacy because both
pening the images to
) Principal component
n improving the
lassification because
ast enhancement for
e also not good enough
ation because the
e difference in
s with different
tio images. (5) Six
bands are unable to
The mixed bands
s their appreciation
ommittee, Council of
for providing the
so due to the Center
sarch, National
cessing services.
1.A. Mead 1983.
)n accuracy using
statistical tech
iring and Remote
L983, pp.1671-1678.
Computer-aided
rational system to
isat MSS data, LARS
» University, West
L-231.
Accuracy of forest
Compatible tape,
itional symposium on
PP.1155-1163.
; species mapping
sing of Environment
1 CONTOUR LINES EXTRACTION
In the forest mapping automation with the
use of Information obtained from aerial and
space photos one has to do with operations
on contour networks formation making up the
contour basis of the complied map» These
operations may be quite different by the
characteristics, labour-consuming nature and
complicacy depending on the primary method
of contour lines obtaining»
In the world practice the semi-automatic
Input of the graphical information with the
help of digitizers Is widely used» Such in
put Is effective when the contour network is
not too complex and has a lot of rectilinear
segments» The preference is given to it also
In case of superimposing several heterogene
ous contour networks, the automatic separa
tion of which is difficult» However this me
thod is slow and axhaustlng as it requires
a lot of manual work»
A higher degree of automation and accelera
tion of the process of contour lines input
can be achieved by using television or opti
cal-mechanical input devices» However in this
case means of input and extraction of conto
urs on photographs depend on the kind of
units to be delineated» For example, units
having a clear, high contrast image on aeri
al and space photos are successfully deline
ated automatically, with the help of simple
threshold procedures or filters described in
detail in literature (Prett 1982) (Roberts,
Sobel, Kirsch et al»)» Such units in silvi
cultural tasks are: forest boundaries, fresh
burns, cutovers, etc» But still the major
part of the necessary contour lines is left
unextracted» That is why this method is used
rarely, in particular model tasks»
A more complicated but more effective me
thod of the extraction of contour lines se
parating different natural units from each
other is based on the image segmentation
with a further contouring of the extracted
segments» In our work (Elman, Bakhtinova,
Potapov, Sviridova & Bersneva 1984; Elman &
Pamorozski 1986) colour and partially textu
re features were used for image segmentati
on» For example, in (Elman, Bakhtinova, Po
tapov, Sviridova & Bersneva 1984) the seg
mentation of space scanner photos obtained
with the ’Fragment* equipment (Selivanov,
Gektin, Panfilov A Fokin 1981) was carried
out» In experimental work video information
written on the magnetic tape, in three spec
tral bands (0»6-0»7, 0»7-0»8 and 0»8-l»l mic
rometres) was used»
According to the representation of land
categories, economic significance, and the
r ossibility of the test plot control the fol-
owing set of classes was determined: A -
pine stands, B - birch stands, C - aspen sta
nds, D - water surfaces, E - land non-covered
with forest (arable land)» The test plots of
all classes were chosen by the forest stands
plans at scale 1:25000 with further field
investigations» The plots were selected in
side homogeneous compartments with the area
of 20-30 ha» Minimal plots of the fixed area
of about 5 ha were taken as control plots
by which the experiment results assessment
was made (Table 1)»
Table 1» The results of desses extraction
by colour features»
K
Machine
solutions
Q
N
P
A B
C
D
E
number
7.
A
63
63
63
100
B
50
13
63
50
79
C
14
33
07
54
33
61
D
54
54
54
100
E
92
07
99
92
93
Mean
estimate
~rrr
292
~S7~
Notes: K - actual classes
Q - number of omissions
N - total plots
P - identified correctly
The table analysis shows that classes of pine
stands and water surfaces are Identified with
the best reliability, and birch and aspen
stands are mostly confused» There are omis
sions in the arable land class 'which is in-