RESEARCH ARTICLE Examining the Effect of Ancillary and Derived Geographical Data on Improvement of Per-Pixel Classification Accuracy of Different Landscapes Uttam Kumar 1,5 • Anindita Dasgupta 1 • Chiranjit Mukhopadhyay 2 • T. V. Ramachandra 1,3,4 Received: 10 August 2016 / Accepted: 8 June 2017 Ó Indian Society of Remote Sensing 2017 Abstract Effective conservation and management of nat- ural 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 resour- ces. 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 vegeta- tion indices, temperature, digital elevation model, aspect, slope and texture, implemented in three different terrains of varying topography—urbanised landscape (Greater Bangalore), forested landscape (Western Ghats) and rug- ged terrain (Western Himalaya). The study showed that use of additional spatial ancillary and derived information significantly improved the classification accuracy com- pared 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% improve- ment), and inclusion of temperature, NDVI, EVI, elevation, slope, aspect, Panchromatic band along with texture mea- sures, significantly increased the overall accuracy of Landsat ETM? data classification to 83.15% (7.6% improvement). In a forested landscape with moderate ele- vation, 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 classification accuracy to 89.97% (10.84% improvement) compared to the classification of only original spectral bands, suggesting selection of appropriate ancillary data depending on the terrain. Keywords Land cover Á Classification Á Accuracy Á Ancillary layers Á DEM Á Vegetation indices Á Texture Introduction Classification of remote sensing (RS) data accurately is a requirement for many applications such as global change research (Nemani et al. 2011), geological research (Smith et al. 1985), wetlands mapping (Ramachandra and Kumar 2008), crop estimation (Pacheco and McNairn 2010), vegetation classification (Hostert et al. 2003), forest & T. V. Ramachandra cestvr@ces.iisc.ernet.in; 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 123 J Indian Soc Remote Sens DOI 10.1007/s12524-017-0698-2