Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data

  • Authors:

  • Milap Punia;P. K. Joshi;M. C. Porwal

  • Affiliations:

  • Centre for the Study of Regional Development, Jawaharlal Nehru University, New Delhi 110067, India;Department of Natural Resources, TERI University, New Delhi 110070, India;Forestry and Ecology Division, Indian Institute of Remote Sensing, Dehradun 248001, India

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

In this study we explored the potential of multi-temporal IRS P6 (Resourcesat) Advanced Wide Field Sensor (AWiFS) data for mapping of LULC for Delhi, India. The study presents the result of a decision tree classification of seasonal composite data (three seasons). The study has identified 13 classes with description of cropping pattern namely, double crops, kharif, rabi and zaid from 56m spatial resolution AWiFS data. Delhi has a diverse range of land use predominantly mosaic of built-up. More than half of the area is urban settlement. Results indicate that the temporal data set with a good definition of training sites can result in good overall accuracy (=91.81) as well as individual classification accuracies (producers accuracy =76.92 and users accuracy =60). It is evident that AWiFS data can be used to provide timely and detailed LULC maps with limited ancillary data. The AWiFS derived maps could be very useful as input to biogeochemical models that require timely estimation of LULC patterns.