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Spectral Indices Based Change Detection in an Urban Area Using Landsat Data

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

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Abstract

This paper proposes a technique to detect the change in some dominantly available classes in an urban area such as vegetation, built-up, and water bodies. Landsat Thematic Mapper (TM) and Landsat 8 imageries have been selected for a particular area of NCR (National Capital Region), New Delhi, India. In this study, three spectral indices have been used to characterize three foremost urban land-use classes, i.e., normalized difference built-up index (NDBI) to characterize built-up area, modified normalized difference water index (MNDWI) to signify open water and modified soil-adjusted vegetation index (MSAVI2) to symbolize green vegetation. Subsequently, for reducing the dimensionality of Landsat data, a new FCC has been generated using above mentioned indices, which consist of three thematic-oriented bands in place of the seven Landsat bands. Hence, a substantial reduction is accomplished in correlation and redundancy among raw satellite data, and consequently reduces the spectral misperception of the three land-use classes. Thus, uniqueness has been gained in the spectral signature values of the three dominant land-use classes existing in an urban area. Further, the benefits of using MSAVI2 as compared with NDVI and MNDWI as compared to NDWI for the highly urbanized area have been emphasized in this research work. Through a supervised classification, the three classes have been identified on the imageries and the change between the image pairs has been found. The overall accuracy (OA) of change detection is 92.6 %. Therefore, the study shows that this technique is effective and reliable for detection of change.

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Acknowledgments

The author would like to thank ‘Housing and urban development corporation’ (HUDCO, New Delhi) to support this research through ‘Rajiv Gandhi HUDCO fellowship,’ given to the institutes of National repute in India.

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Authors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Abhishek Bhatt & S. K. Ghosh

  2. Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India

    Anil Kumar

Authors
  1. Abhishek Bhatt
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  2. S. K. Ghosh
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  3. Anil Kumar
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Corresponding author

Correspondence to Abhishek Bhatt .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Dept of Mathematics, South Asian University New Delhi, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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Abhishek Bhatt, Ghosh, S.K., Anil Kumar (2016). Spectral Indices Based Change Detection in an Urban Area Using Landsat Data. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_39

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

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