T 9 Land use
report for base No
ssRegion Area h
Percent able
area summary map
Cla a
age 1 Fresh
Water 15,300
2.11 2 Forest
37 irport
s 486
6
ted-Paddy Field 9 Cropland
1 724,700
100 ,100
5.12 3 Domestic
A 500
0.07 4 Settlement
5,900 0.81
5 Plantation 71,000
9.80 6 Swamps
,900 7.19
7 Irrigated-Paddy Field
400 0.06
8 Non irriga
8,900 1.23
98,700 3.62
T o t a l Source : Rupa Bumi Indonesia, 2003
4.1.1 LST Analysis
on on 16 May 2006 the range values are 3
o
C to 37
o
C and mean value is 25.4
o
C. Table 10
alue for each imagery
Image Acquisition LST Value
o
C
Table 10 shows the summary of LST value for each datasets from image processing. For image acquisition on 15 April 2000 the LST range values are 2
o
C to 35
o
C and mean value is 22.8
o
C. For image acquisiti
Summary of LST v
Min M
M ax
ean
15 April 2000 2
35 22.8
16 May 2006 3
37 25.4
Tem
cloud cover and haze, so that only small part o
perature at the cloud cover and haze
The minimum value around 2
o
C indicated to the presence of cloud cover and haze. For image acquisition on 15 April 2000 cloud covered almost of 5 of
whole area of interest. For image acquisition on 16 May 2006, this image is only a small part of the areas were affected by
f area had low temperature 3
o
C. Figure 12 shows the LST distribution of image acquisition on 15 April 200
which most of the areas having the LST distribution value range 20
o
C - 35
o
C.
34
For the next analysis, the value range 2
o
C - 12
o
C is omitted by making the class to the “NO DATA” class because the value is not the really land surface temperature
but affected by the cloud cover and haze. Figure 13 shows the LST distribution of image acquisition on 16 May 2006 which most of the areas having the LST
distribution value range 21
o
C - 37
o
C. For the next analysis, the value range 3
o
C - 12
o
C is also classified to the “NO DATA” class.
Figure 12 LST distribution of image acquisition on 15 April 2000
Figure 13 LST distribution of image acquisition on 16 May 2006
35
4.1.2 NDWI Analysis
Table 11 shows the minimum, maximum and mean of NDWI value for each class in training area for image acquisition on 15 April 2000. Water class has the
positive value range with the biggest minimum and maximum value; meanwhile the other classes have the range value from negative value until positive value.
Table 12 shows the minimum, maximum and mean of NDWI value for each class in training area for image acquisition on 16 May 2006.
According to the NDWI value, image acquisition on 16 May 2006 has the same characteristics with the image acquisition on 15 April 2000, whereas water
class having the positive value range and bigger value than others. It indicates that the water presence in the nature such as water body, ocean and inundation area
always having positive value for minimum and maximum NDWI. NDWI value indicates high correlation with moisture content of land cover. Bigger NDWI value
means bigger the moisture content of land cover than others. Table 11 Minimum, maximum and mean NDWI value for Image acquisition on
15 April 2000
ClassRegion Minimum Maximum Mean
Water 0.00 0.61
0.03 Cloud -0.15
0.38 0.18
Bare land -0.32
0.25 -0.06
Cloud shadow -0.14
0.36 0.10
Forest -0.35 0.16
-0.17 Inundation area
-0.34 0.52
0.01 Paddy field
-0.37 0.18
-0.22 Shrub -0.34
0.17 -0.20
Settlement -0.27 0.36
0.01
36
Table 12 Minimum, Maximum and Mean NDWI value for image acquisition on 16 May 2006
ClassRegion Minimum Maximum Mean
Water 0.06 0.38
0.25 Bare land
-0.54 0.00
-0.33 Settlement -0.54
0.01 -0.37
Mangrove -0.61 0.00
-0.53 Forest -0.65
-0.39 -0.51
Paddy field -0.64
-0.36 0.54
Shrub -0.66 0.00
-0.48 Inundation area
-0.61 0.29
0.15 Fish pond
-0.47 0.13
-0.07
According to the minimum and maximum NDWI value each classes of the training area, it is used as threshold value to delineate swamps and not swamps
areas.
Figure 14 and Figure 15 shows the NDWI distribution of image acquisition on 15 April 2000 and image acquisition on 16 May 2006.
Figure 14 NDWI distribution for image acquisition on 15 April 2000
37
Figure 15 NDWI distribution for image acquisition on 16 May 2006
4.1.3 Image Classification
