NDVI Derived Sugarcane Area Identification and Crop Condition Assessment

Remote sensing offers an efficient and reliable means of collecting the information required, in order to map crop type and acreage. Besides providing a synoptic view, remote sensing can provide structure information about the health of the vegetation. The spectral reflectance of a field will vary with respect to changes in the phenology (growth), stage type, and crop health, and thus can be measured and monitored by multispectral sensors. In the present study IRS LISS II digital data and NDVI (Normalized difference vegetation index) has been used to identify the sugarcane area and its condition assessment. Ratio image are often useful for discriminating subtle differences in spectral variation in a scene that is masked by brightness variations. For identifying the area and condition of dense crop like sugarcane, NDVI image is often useful because of its canopy cover, crop biomass and vigour. The accuracy achieved was 85.25% for NDVI image of IRS LISS II digital data.