Remote sensing has the ability to generate the geometric arrangement of spatial data close to the true state of the earth's surface in large numbers and fast. This situation requires a management system and appropriate data handling and efficient so that spatial information from remote sensing images obtained can be useful for a broad interest.
Remote sensing is never separated from the Geographic Information System (GIS). The spatial data of remote sensing is one of the basic data used in GIS analysis. In the development of GIS data is also useful in the processing of remote sensing data (Barus and Wiradisastra, 2000). GIS is very good in the process of data management, both spatial data attributes and data. Integration of spatial data and attribute data in a computerized system that is referenced geographic advantages of GIS.
Remote sensing data is a reflection of the results of data objects of different wavelengths that were captured by a sensor and convert it into numerical data and can be viewed in graphical form or image (imaginery) (Purwadhi, 2001). While the utilization of remote sensing data performed as it is available in large quantities, can show a very wide strip, is available for areas difficult to reach, available for a fast, and can show an object that does not appear in the form of a recognizable object (Sutanto , 1989). One example is the application of remote sensing data to see the vegetation index and estimate the amount of absorption Carbon Dioxide (CO2) by plants. NDVI (Normalized Difference Vegetation Index) is a method frequently used to exploit spectral data of vegetation index (Spectral Vegetation Index (SVI)) from remote sensing. Spectral vegetation indices from remote sensing data are formed because of differences in wave reflection from the leaves of living plants with other objects on the surface of the earth on the green wavelength (visible) and near infrared (invisible) (Horning, 2004)
The ability of an image (imaginery) capture and display any information from the earth's surface depends on the spatial resolution, temporal resolution, radiometric resolution and spectral resolution (Purwadhi, 2001). Each image type has a type different resolutions both spatial resolution, temporal resolution, radiometric resolution and spectral resolution, resulting in the ability of an image capture and display information is also different. This objec also occur on the ability of image capture and display the information in the vegetation index.
Pictured above is an example of an image where the image shown is the distribution of vegetation is supported by the view from IKONOS imagery. and underneath the picture is the color distribution of vegetation index that is processed through GIS program2.
Processing of remote sensing data by using GIS is expected to provide information quickly and precisely so that it can be used as soon as possible for purposes of analysis and data manipulation.
