tool
ional
ch is
Cap
were
its et
water
d TC
over
PC
le 2)
lated
ness,
Nave
ance
nted
Allen
tude.
e the
gine
nded
ure 2
yeen
ctor
S
yunts
odel
In order to determine changed and non-changed regions
threshold value was determined using the remote sensing
analyst's expert knowledge of the study area. Therefore, the
histogram of change magnitude image is analyzed to detect
change or no change threshold. Furthermore, Normalized
Vegetation Index (NDVI) images of SPOT 4 and SPOT 5 were
used for threshold determination.
4. RESULTS
In this study mCVA method was applied to different dated
SPOT 4 and SPOT 5 MS data set by using derived TC
coefficients. By using the derived coefficients brightness,
greenness and wetness images were produced. The model
produced several image maps that characterize land cover
change in the Terkos Water Basin, istanbul. Three TC
‘difference’ images; one change vector magnitude image, three
vector direction images; and one final landscape dynamic image
based on the derived brightness, greenness, and wetness were
produced.
Magnitude image is given in Figure 3 categorized as changed,
and no changed area. White colors showed the changed area
and black colors showed the non-changed areas. Gray tones can
be described as minimum, lower, medium and higher changes.
Legend
j Champ
Ho Change
Figure 3. Change vector magnitude representing the intensity of
change between a pixel’s brightness, greenness, and wetness in
2003 and 2007.
This image represent the intensity of the change, which
increases in the sand, agricultural areas, open spaces with
little/or no vegetation and a part of vegetated areas. The largest
increase in change vector magnitude is registered in agricultural
fields because of the shape differences and heterogeneous
product type in the area. The presence of growing crops and
growing young trees at the time of SPOT 5 image acquisition
resulted in extreme land cover changes in the area. The area that
shows little change is urban areas and some of the agricultural
fields. This area is under conservation by the low and
urbanization is forbidden in the conservation zones. Because of
this reason, urbanization is very limited in the area.
Magnitude image classified as changed and non-changed area
based on threshold value. This value is determined by using
statistic of the magnitude image and analyst knowledge about
the study region. NDVI image helped for decision of the
threshold value. The resultant image depicted in Figure 4. As a
result of this process the area of changed and unchanged was
found 3853 ha and 24884.84, respectively.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
DB Change Area
No-Chainged Area
Figure 4. Changed and no-changed area
Direction images were produced based on given equations in
Figure 2. Direction images for greenness/brightness,
wetness/brightness and wetness/greenness were given in Figure
S.
Direction green hrighr.
Birection vet bright
Dirschon wet green
Figure 5. Direction images produced by mCVA
Longitude vector direction (direction green/bright) image shows
a large brightness increase in the vegetated areas. Barren lands
show increase in brightness. Conversely, most of the semi
natural and mixed forest communities register a vector direction
change towards the greenness axis. The colatitude vector
direction (direction wet/green) image shows very small vector
changes in the greenness-wetness plane. We can see very small
changes in the open spaces with little or no vegetation. Also, it
is possible to see some changes in the sand dune of the study
area. The colatitude vector direction is the vector direction
component that shows larger variation of vector displacements.
The results were quite similar from the longitude and colatitude
vector direction image. Accounting for vector changes in the
wetness-brightness plane, colatitude was able to enhance more
bright features in the vegetated and agricultural lands. The most
important disagreement of colatitude with the other two vector
direction components is at the areas occupied with young
generation trees in the north site of the lake (sandy area).
At the last stage, three direction images were stacked and
unchanged areas were masked from the direction images before
ISODATA unsupervised classification. As a result of the
classification six categories were produced such as high
greenness, high brightness, high wetness, medium greenness,
medium brightness and medium wetness. The most dynamic
landscapes were considered in this study to avoid
overestimating because of small changes. Total area of change
for each category obtained by classifying direction and
magnitude images is displayed in Figure 6 and Table 3.
