A. Calculating LST
Land surface temperature is how hot the “surface” of the Earth would feel to the touch in a particular location. From a satellite’s point of view, the “surface” is
whatever it sees when it looks through the atmosphere to the ground. It could be snow and ice, the grass on a lawn, the roof of a building, or the leaves in the
canopy of a forest. Thus, land surface temperature is not same as with air temperature that is included in the daily weather report. In order to get LST value
for 2 sets satellite imagery, the LST processing only used thermal band Band 61 and Band 62 for Image acquisition on 15 April 2000 and Band 6 for Image
acquisition on 16 May 2006.
According to Ghulam 2010, the steps to do the LST analysis are:
a. Converting DN to Radiance
The Landsat satellite imagery has Digital Number DN values range between 0 and 255.
The value of Lmin and Lmax is obtained from the header files. Open the .MET file using Word Pad or any text editor.
……….. 1
L is the radiance value for band i;
Lmin is the minimum spectral radiance the spectral radiance that is scaled to QCALMIN in wattsmeter squared ster m can be seen from the header
file;
Lmax is the maximum spectral radiance the spectral radiance that is scaled to QCALMAX in wattsmeter sq can be seen from the header file;
DN is the digital number;
Qcalmax = 255; and
Qcalmin = 1.
b. Converting Radiance to Brightness Temperature
The steps to do this process using Planck’s Radiance Function
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Where, C
1
=1.19104356×10
-16
W m
2
; C
2
=1.43876869×10
-2
m K In the absence of atmospheric effects, T of a ground object can be
theoretically determined by inverting the Planck’s function as follows:
This equation can be reformed as :
Let K
1
= C
1 5
, and K
2
= C
2
, and satellite measured radiant intensity B T = L , then above mentioned equation is collapsed into an equation
similar to the one used to calculate brightness temperature from Landsat TM image :
Therefore, K
1
and K
2
become a coefficient determined by effective wavelength of a satellite sensor. The value of K
1
and K
2
can be seen in Table 7. Table 7 ETM and TM thermal band calibration constants
………………….. 2
………………….. 3
………………... 4
…………………... 5
K
1
Wm
-2
sr
-1
µm
-1
K
2
Kelvin Image acquisition
666.09 1282.71
Image acquisition 607.76
1260.56 Source : Ghulam, 2010
B. Calculating NDWI
The Normalized Difference Water Index NDWI was developed to delineate open water in satellite imagery and enhance their presence in remotely-
sensed digital imagery McFeeters 1996. The NDWI makes use of reflected near- infrared radiation and visible green light to enhance the presence of such features
26
while eliminating the presence of soil and terrestrial vegetation features. The NDWI formula is :
………………….……... 6 where GREEN is a band that encompasses reflected green light such as band 2
and NIR represents reflected near infrared band such as band 4. The selection of these wavelengths was done to : 1 maximize the typical reflectance of water
features by using green light wavelengths 2 minimize the low reflectance of NIR by water features 3 take advantage of the high reflectance of NIR by vegetation
and soil features Mc Feeters 1996. As a result, water features are enhanced owing to having positive values and vegetation and soil are suppressed due to
have zero or negative values. Image processing software can easily be configured to delete negative values. This effectively eliminates the terrestrial vegetation and
soil information and retains the open water information for analysis. The range of NDWI is then from zero to one. Multiplying that value by a scale factor e.g., 255
enhances the resultant image for visual interpretation.
C. Supervised Classification
Supervised classification methods are based upon prior knowledge of the image, specifically the statistical nature of the spectral classes used to classify the
image Mather, 1986. As the preliminary step in supervised classification method, collecting training sample in image that will be classified is very
important task. In this research, the maximum likelihood method was used for the
classification process. This method is based on the priority of type coverage. Maximum likelihood method is one of effective methods and will give a good
image if the size and representative of the sample data used in the training area. Other terms of similar meaning are in situ data or collateral data, but both refer to
sample data gathered in order to establish a relationship between the sensor response and particular surface condition. It is commonly used to determine the
accuracy of categorized data obtained through classification.
27
28 Supervised spectral classification was used to automatically group pixels of
multispectral images into groups of predefined classes, based on the variation of reflectance among bands. The general image processing can be seen in Figure 8.
Figure 8 Image classification Lillesand and Kiefer, 2000
Figure 9 Swamps identification framework
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3.3.2 Suitability Analysis
The suitability assessment was conducted by considering the land system analysis. According to Hardjowigeno et al. 2001, the suitability criteria for
paddy crops are divided into highly suitable S1, moderately suitable S2, marginally suitable S3, and not suitable N. The factors that analyzed to classify
the suitability class are limited only the constricted factors for paddy field in swamps area namely peat depth, pH, slope and salinity. Table 8 describes the
suitability criteria of the constricted factors for paddy field in swamps area. Figure 10 shows the suitability analysis framework.
Figure 10 Suitability analysis framework
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Table 8 Suitability criteria for paddy field Factors S1
S2 S3
N Peat depth cm
50 100
100-150 150-200
pH 5.5-7.0 7.0-8.0
or 4.5-5.5
8.0-8.5 or 4.0-4.5
8.5 or 4.0
Slope 3
3-8 8-15
15-25 Salinity
mmhoscm 3.5
3.5-5.0 5.0-6.6
6.6-8.0 Source : Hardjowigeno et al., 2001
Overlaying for vector datasets is conducted to obtain an aggregate of layers that determines the suitability. In vector data, the map overlay operation is
done in pairs. For a more than two layers to be overlaid, it will be taken several steps. In this research includes four map layers. So, to make its process integrated
into single process, the each vector data were converted into raster data in ArcGIS 9.3. After that by using weighted overlay processing with the same weighting for
each suitability maps, the combination layer map was produced and can be treated as single map to the next processing.
3.3.3 Accuracy Assessment
Methods for automated land features mapping are concluded with an accuracy assessment of their result Congalton, 1991. The validation sampling
points is used on this account. Accuracy assessment is done to verify the analysis and classification result using “point sampling accuracy” approach according to
“confusion matrix”. The confusion matrix summarizes the relationship between two datasets that are classification map and reference information or alternative
model. The accuracy assessment for this research use formulation below:
a. Omission Error as known as Producer’s Accuracy
Takes into account the accuracy of individual classes; indicates the percentage of the time a particular land cover type on the ground was identified as that land cover
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type on the map. It expresses how well the map producer identified a land cover type on the map from the satellite imagery data.
………………….……... 7 O =
X
ii
X
+i
x 100
X
ii
= total number correct cells in a class
X
+i
= sum of cell values in the row
O = Omission
error
b. Commission Error as known as User’s Accuracy
Takes into account the accuracy of individual classes; indicates the percentage of the time a particular land cover type on the map is really that land cover type on the
ground. It expresses how well a person using the map will find that land type on the ground.
C = X
ii
X
+i
x 100
X
ii
= total number correct cells in a class
X
+i
= sum of cell values in the column
C = Commission
error
c. Overall Accuracy
Summarizes the total agreementdisagreement between the maps; only incorporates the major diagonal and excludes the omission and commission errors. It indicates how
well the map identifies all land cover types on the ground. ………………….……... 8
………………….……... 9
A = D N x 100
D =
total number correct cells as summed along the major diagonal N
= total number of cells in the error matrix.
A = Overall
accuracy
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IV. RESULT AND DISCUSSION
4.1 Swamps Identification
Kategori