HIERARCHICAL CLASSIFICATION FOR ASSESSMENT OF HORTICULTURAL CROPS IN MIXED CROPPING PATTERN USING UAV-BORNE MULTI-SPECTRAL SENSOR

Abstract

Assessment of horticultural crops under mixed cropping system has been a challenge, both for horticulturists and also to the remote sensing communities. But the recent developments in wide range of sensors onboard Unmanned Aerial Vehicles (UAVs) has opened up new possibilities in identification, mapping and monitoring of horticultural crops. This paper presents the results made from a pilot exercise on horticultural crop discrimination using Parrot Sequoia multi-spectral sensor onboard a UAV. This exercise was carried out in Nongkhrah village, Ri-Bhoi district of Meghalaya state located in the north eastern part of India having mixed horticultural crops. A two level hierarchical classification system was followed for identification and delineation of the major horticultural crops in the village. Parrot Sequoia multi-spectral sensor having four bands has been found to be effective in discrimination of horticultural crops based on variation in spectral response of six horticultural crops viz., pineapple, banana, orange, papaya, ginger and turmeric using three commonly used indices viz., Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI). NDVI and GNDVI showed nearly similar spectral response, whereas separability among the horticultural crops significantly improved with the use of NDRE. The first level of classification involving the five broad land cover classes has resulted an overall accuracy of about 91%, whereas the second level of classification for delineating the five selected horticultural crops has provided an overall accuracy of 79.8%.