was carried out iteratively first entering the variable that explains the most variance in the data, until no more variables could be eliminated. Selected
variables that yielded a negative coefficient value though significant were eliminated since they seemed to suggest that negative crop area is existent. The
selected variables were then used to run the multiple linear regressions for the different crop areas. Only significant predictors of crop area were included in the
multiple linear regression models. The results of the multiple linear regressions were coefficients that had to be spatially distributed in the form of value maps.
4
RESULT AND DISCUSSION
4.1 Temporal MODIS Processing
The 16 days interval of MODIS EVI composite ranging from January 2008 to December 2010 are compiled and stacked using image processing software and
resulting one image consisting of 69 layers. To focus more on the study area, the resulting image is clipped by the area of interest which is the three regencies in
West Java: Karawang, Subang, and Indramayu see figure 4.1. Unsupervised classifications were carried out to generate a map with a pre-defined number of
classes. Unsupervised indicates that no additional data were used or expert’s guidance applied, to influence the classification approach.
Figure 4-1. MODIS Scene clipped to research location
Regarding the resultant graphs from the divergence separability, 45 classes were chosen from the high average and low minimum separability. The result
shows that based on the image spectral characteristic, land cover in the research area were best differentiated using 45 classes of land cover classification.
Figure 4-2. Classification divergence statistic
4.2 Data Extraction
45 classes are selected based on the divergence separability which can explain the patterns of EVI behavior from 2008 to 2010 with the interval of
sixteen days see figure 4-2. For further analysis, a process of averaging the data annually is being done, as well as the grouping the classes with similar pattern
see figure 4-3. The crop statistics of research location were attained in tabular format which consists of the number of yield in hectare for each one of the
regency. The analogue crop area data reported in hectares was entered into Microsoft Excel used in the data processing for this study as an agricultural
parameter to distribute crop area.
Figure 4-3. Result from unsupervised classification of 45 class EVI
The supervised classification was done by grouping temporal pattern that have similar behavior of phenology. When the first classification using ISODATA
clustering carried out, the process was based on image spectral characteristic differentiation. Meanwhile on the second classification process, it was done based
on temporal behavior phenology differentiation of the 45 classes in order to get smaller class. Out of 45 classes, 19 classes were derived from supervised
classification process which able to differentiate land cover based on spectral characteristic and temporal characteristic.
The grouping process of EVI classes needs to be done carefully because there are vegetations that have similar pattern throughout a year. Not all classes
can be grouped with other, some classes has its own temporal characteristic or phenology so that cannot be grouped with other and stand alone as one group. The
result from the classification can be seen in Table 4-1 and figure 4-4 which shows the grouping result of EVI classes and later will be used for identifying rice spatial
distribution.
Table 4-1. Derived groups of EVI grouping based on similarity pattern
No Group
EVI Classes No
Group EVI Classes
1 A
Class 1, 4 11
K Class 21
2 B
Class 2 12
L Class 24
3 C
Class 3 13
M Class 25, 26, 27
4 D
Class 5 14
N Class 28, 29, 35
5 E
Class 6 15
O Class 30, 31, 32, 34
6 F
Class 7, 8, 9 16
P Class 33
7 G
Class 10, 11, 12 17
Q Class 36, 39, 42
8 H
Class 13, 14, 15 18
R Class 37, 38, 40, 41, 44
9 I
Class 16, 18, 22, 23 19
S Class 43, 45
10 J
Class 17, 19, 20
Figure 4-4. 19 class of EVI supervised grouping
