EVI Time Series Jan 2008
– Dec 2010
EVI Time Series Geographic Projection
Layer Stacking Re-Projected to
Geographic Projection
EVI with 69 Layers Administrative
boundary AOI
Subseting EVI of AOI
Unsupervised classification
Classified Image of EVI
RICE PRODUCTION ESTIMATION AND CROP CALENDAR
Rice field from RBI Map Rice Productivity from BPS
Temporal Analysis Supervised classification
Regression Analysis
RICE SPATIAL DISTRIBUTION AND AREA ESTIMATION
Figure 3-2. Flowchart of the research
3.3.2 Processing MODIS Data
In order to process MODIS data, there are several remote sensing technique was applied to get the imagery become useable. Vegetation indices were used to
enhance the features of the objects of interest in the study. As the blue band is sensitive to atmospheric conditions, it is used to adjust the reflectance in the red
band as a function of the reflectance in EVI. Cloud covers become a problem where a research using MODIS data
conducted in tropical area like Indonesia. The best way to remove cloud cover and get a clear image data is to make composite of imagery with different dates for the
same location. In this research, the daily MODIS EVI data were composited for each 16 days and resulting of 69 EVI image temporal data ranging from January
2008 to December 2010 with interval of 16 days. The next technique to do is to change the 16-day of EVI MODIS image projection from sinusoidal projection
into geographic projection in order to get the common projection that can be overlaid with other data, and the results were used to do next process. All of the
69 EVI MODIS satellite data ranging from January 2008 to December 2010 then were stacked to create an image with 69 bands that will make it able to be
analyzed as multi temporal image of 16 days interval of EVI data. Next of the process is to subset to 69 bands EVI image with administrative boundary of
Karawang, Subang, and Indramayu Regency and resulting multi temporal EVI image of research area. Once the image was stacked and subsets in to research
area, then the image need to be classified to identify the land cover of the research area. In this research the classification process were done in two steps, first is
using unsupervised classification method then continued with supervised classification based on temporal behavior of land cover.
The iterative self-organized unsupervised clustering algorithm ISODATA of the ERDAS imagines software was used to derive spectral classes from 69 EVI
image data layers. The ISODATA procedure is iterative in that it repeatedly performs an entire classification outputting a thematic raster layer and
recalculates statistics. Self-organizing refers to the way in which it locates clusters with minimum user input. The ISODATA clustering method uses spectral
distance, as in the sequential method, but iteratively classifies the pixels, redefines
