Mapping Multiple Horticulture Crops using Object Oriented Classification Techniques

摘要

The fundamental requirement for proper planning in the Indian horticultural sector is the availability of reliable statistical database in terms of area and production at different spatial hierarchies (tehsil, district, state). Remote sensing and Geo-ICT tools offer a simple, fast, efficient and cost-effective method of not just updating the horticulture crop inventory but also integrating the database, thus making it conducive for easy retrieval, analysis and decision-making. Medium and high resolution remote sensing data like LISS-IV and PAN prove to be effective in inventorying crops like mango, citrus and oil palm. Object oriented techniques work best in identifying and mapping fruit orchards as against per pixel classifiers, which are more useful for field crops. This is because the information needed for image analysis and classification is represented in meaningful image objects and their mutual relations. This study aims at mapping multiple crops, viz. mango and oil palm in Krishna district of Andhra Pradesh. Multi-resolution segmentation has been done after assigning scale parameter and weightages to various parameters like shape, compactness, color, smoothness and NDVI. Subsequently, the potential mango and oil palm areas have been delineated based on texture and shape/geometry information obtained from high resolution PAN data. Field validation of the crop map indicated 89% agreement with field data. Hence multiple high resolution datasets have the potential to map the spatial distribution of mango and oil palm plantations at district and sub-district level. Object oriented classification techniques use the form, texture and spectral information in a sequential manner to delineate multiple horticulture crops.