Examining the Effect of Ancillary and Derived Geographical Data on Improvement of Per-Pixel Classification Accuracy of D
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RESEARCH ARTICLE
Examining the Effect of Ancillary and Derived Geographical Data on Improvement of Per-Pixel Classification Accuracy of Different Landscapes Uttam Kumar1,5 • Anindita Dasgupta1 • Chiranjit Mukhopadhyay2 T. V. Ramachandra1,3,4
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Received: 10 August 2016 / Accepted: 8 June 2017 Ó Indian Society of Remote Sensing 2017
Abstract Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. Multi-resolution remote sensing (RS) data coupled with additional ancillary topographical layers (both remotely acquired or derived from ground measurements) with appropriate classification strategies would be more effective in capturing LC dynamics and changes associated with the natural resources. Ancillary information would make the decision boundaries between the LC classes more widely separable, enabling classification with higher accuracy compared to conventional methods of RS data classification. In this work, we ascertain the possibility of improvement in classification accuracy of RS data with the addition of ancillary and derived geographical layers such as vegetation indices, temperature, digital elevation model, aspect, slope and texture, implemented in three different terrains of varying topography—urbanised landscape (Greater & T. V. Ramachandra [email protected]; http://ces.iisc.ernet.in/energy 1
Energy & Wetlands Research Group [CES TE15], Centre for Ecological Sciences, Indian Institute of Science, Third Floor, E Wing, New Bioscience Building [Near D Gate], Bangalore, Karnataka 560012, India
2
Department of Management Studies, Indian Institute of Science, Bangalore, Karnataka 560 012, India
3
Centre for Sustainable Technologies (Astra), Indian Institute of Science, Bangalore, Karnataka 560 012, India
4
Centre for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP), Indian Institute of Science, Bangalore, Karnataka 560 012, India
5
NASA Ames Research Center, Moffett Field, Mountain View, CA 94035, USA
Bangalore), forested landscape (Western Ghats) and rugged terrain (Western Himalaya). The study showed that use of additional spatial ancillary and derived information significantly improved the classification accuracy compared to the classification of only original spectral bands. The analysis revealed that in a highly urbanised area with less vegetation cover and contrasting features, inclusion of elevation and texture increased the overall accuracy of IKONOS data classification to 88.72% (3.5% improvement), and inclusion of temperature, NDVI, EVI, elevation, slope, aspect, Panchromatic band along with texture measures, significantly increased the overall accuracy of Landsat ETM? data classification to 83.15% (7.6% improvement). In a forested landscape with moderate elevation, temperature was useful in improving the overall accuracy by 6.7 to 88.26%, and in a rugged terrain with temperate climate, temperature, EVI, elevation, slope, aspect and Panchromatic band significantly improved the
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