Automated Extraction of Urban Impervious Area from Spectral-Based Digital Image Processing Techniques
Urban area comprises a complex mix of diverse land cover types and materials; it is often difficult to separate these classes due to their heterogenic nature. Studying and monitoring urban areas and its environment are closely associated with the study of impervious surfaces, which are anthropogenic features through which water cannot infiltrate into the soil. In the present study, spectral indices were developed using spectral information from satellite remote sensing sensor. Several spectral indices like vegetation index, soil-adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI); water index, modified normalized difference water index (MNDWI); and urban indices, normalized difference built-up index (NDBI), built-up index (BUI) and index-based built-up index (IBI), were implemented in the study. The combination of various spectral indices can be used, and finally using NDBI, BUI and IBI, principal component analysis (PCA) was performed, the first component of which was classified through unsupervised classification through K-means algorithm to extract urban built-up impervious features. The methodology has the potential to identify and automatically extract urban impervious features from other land use-land cover classes and is established over the city of Kolkata (India). Maps showing effective classification of urban areas were developed. The approach is further successfully operated over a forested area in order to extract settlements within the forest patch that proves the transferability of the method and can be universally accepted.
KeywordsUrban Resourcesat LISS-III Spectral indices PCA K-means Classification
The author acknowledges Science and Engineering Research Board (SERB), Department of Science and Technology (DST), India, for providing funds under SERB National Post-Doctoral Fellowship scheme (File No.: PDF/2015/000043).
- Abdikan S, Sanli FB, Ustuner M, Calò F (2016) Land cover mapping using sentinel-1 SAR data. The international archives of the photogrammetry. Remote Sens Spat Inf Sci 41:757–761Google Scholar
- Bauer ME, Heiner, NJ, Doyle JK, Yuan F (2004) Impervious surface mapping and change monitoring using Landsat remote sensing. Paper presented at the ASPRS Annual Conference Proceedings, Denver, ColoradoGoogle Scholar
- Johnson BA, Iizuka K, Bragais MA, Endo I, Magcale-Macandog DB (2017) Employing crowdsourced geographic data and multi-temporal/multi-sensor satellite imagery to monitor land cover change: a case study in an urbanizing region of the Philippines. Comput Environ Urban Syst 64:184–193CrossRefGoogle Scholar
- Kawamura M, Jayamana S, Tsujiko Y (1996) Relation between social and environmental conditions in Colombo Sri Lanka and the urban index estimated by satellite remote sensing data. Int Arch Photogramm Remote Sens 31:321–326Google Scholar
- Lee J, Lee SS, Chi KH (2010) Development of an urban classification method using a built-up index. Paper presented at the Selected Topics in Power Systems and Remote Sensing, Sixth WSEAS International Conference on Remote Sensing, Iwate Prefectural University, JapanGoogle Scholar
- Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. Paper presented at the Proceedings of the Third ERTS Symposium, Washington, DCGoogle Scholar
- Shao Z, Fu H, Fu P, Yin L (2016) Mapping urban impervious surface by fusing optical and SAR data at the decision level. Remote Sens 8:1–21Google Scholar
- Sinha S, Sharma LK (2013) Investigations on potential relationship between biomass and surface temperature using thermal remote sensing over tropical deciduous forests. Res Rev J Space Sci Technol 2(3):13–18Google Scholar
- Sinha S, Sharma LK, Nathawat MS (2013) Integrated geospatial techniques for land-use/land-cover and forest mapping of deciduous Munger forests (India). Univers J Environ Res Technol 3:190–198Google Scholar
- Sinha S, Pandey PC, Sharma LK, Nathawat MS, Kumar P, Kanga S (2014) Remote estimation of land surface temperature for different LULC features of a moist deciduous tropical forest region. In: Srivastava PK, Mukherjee S, Gupta M, Islam T (eds) Remote sensing applications in environmental research. Springer International Publishing, Cham, pp 57–68. https://doi.org/10.1007/978-3-319-05906-8_4 CrossRefGoogle Scholar
- Sinha S, Santra A, Mitra SS (2018) Automated extraction of built-up areas within forests using remote sensing. In: Santra A, Yadav NK (eds) Proceedings of national conference on advancement in civil engineering practice and research. Excel India Publishers, New Delhi, pp 96–99Google Scholar
- Xu H (2002) Spatial expansion of urban/town in Fuqing and its driving force analysis. Remote Sens Technol Appl 17:86–92Google Scholar