Background Rice Crop Spatial Distribution And Production Estimation Using Modis Evi (Case Study Of Karawang, Subang, And Indramayu Regency)
over a period of time. The NDVI derived land cover can be one of the imperative inputs for species distribution modeling. The research showed that in order to
reduce the problem of co linearity in species distribution predictor variables, NDVI derived land cover can be used as a proxy for precipitation and digital
elevation model DEM derived parameter in Portugal. Finally, NDVI derived land cover data has good overall accuracy 85 which it makes suitable for use
as an input for many natural resource applications, such as herpetofauna studies as well as various environmental monitoring and planning applications.
One of the researches that try to predict rice production is Satellite Assessment of Rice in Indonesia SARI Project that was initiated in 1997 by
BPPT and Ministry of Agriculture in Indonesia. The project was trying to develop a monitoring system of rice in Indonesia using satellite imagery with optical and
SAR sensors. A rice-mapping method using SAR sensor has been established that is based on the temporal change of the backscatter - the so-called
˝change index˝ - at the field scale. The value of this change index depends on the points during the
growing cycle at which the SAR data are acquired, i.e. whether the change is over the growing season when the backscatter is increasing, or between harvesting and
the beginning of the next cycle when the backscatter is decreasing. In order to apply this method, these changes need to be accurately quantified. This is made
difficult because of speckle, a well-known effect in SAR imagery that gives a noisy aspect to images and introduces errors in the measurements of the
backscatter. A rice-field mapping algorithm has therefore been developed that enables one to reduce speckle, derive a suitable change index, and finally separate
rice and non rice areas. For the optical sensor, the project uses 1 km by 1 km NOAA imagery and using its NDVI to detect rice growth at coarse scale because
of the ability of NOAA satellite to scan earth surface twice a day. The temporal NDVI were able to shows rice crop growth stage over time.
In 2005, Domiri from LAPAN also did a research related to rice detection using satellite imagery. Domiri’s research objective is to observe and detect the
age of rice using MODIS satellite and produce rice growth equation model that can estimate rice age in vegetative phase and rice age in generative phase. The use
of MODIS satellite imagery gives the advantages of higher spatial resolution 250-500m compared to NOAA satellite imagery.
This research tries to enhance the previous research related to rice crop identification and monitoring. The use of time series MODIS EVI in this research
gives more detailed information in term of spatial resolution and the ability to describe the rice planting rotation in the research area. Spatial resolution of
MODIS imagery used in this research is 250m by 250m pixel size which is higher than NOAA spatial resolution of 1km by 1km pixel size and gives the ability for
MODIS imagery to identify land cover better than NOAA imagery. The use of EVI rather than NDVI in this research also to improve the result in identifying
vegetation because EVI has an improved sensitivity in high biomass regions and an improved vegetation monitoring characteristic.
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METHODOLOGY