Skip to main content
SpringerLink
Log in

A Study of Urban-Landscape Characteristics of Bhopal City (India) in a Geo-Spatial Environment

  • Chapter
  • First Online:
Making Cities Resilient

Part of the book series: The Urban Book Series ((UBS))

Abstract

Bhopal was shortlisted as an aspirant in the smart-cities challenge by the Ministry of Urban Development, Government of India. The Indian government’s Smart Cities model is an innovative sustainable urban-development solution that uses information and communication technologies and other means to improve quality of life, efficiency of urban operation and services, and competitiveness while ensuring that it meets the needs of present and future generations with respect to economic, social, and environmental aspects. To provide a set of strategic and operational research methodologies and systems solutions that cater to the needs of the Bhopal developing sectors, current trends of urbanization with their impact on the health of the city must be studied. This chapter aims to quantify the spatio-temporal patterns of urban expansion and their relationships with land-surface temperature (LST) as a prime indicator of city health in Bhopal. The process was studied using LST and the urban land–cover pattern derived from Landsat TM/ETM satellite data for two decades (1995–2015). In this study, the four major land-cover classes mapped include (i) built-up areas, (ii) water, (iii) vegetation, and (iv) others. Three spectral indices were used to characterize three foremost urban land-use classes: (1) a normalized difference built-up index (NDBI) to characterize built-up areas; (2) a modified normalized difference water index (MNDWI ) to signify open water; and (3) a soil-adjusted vegetation index (SAVI) to symbolize green vegetation. Land-use and land-cover (LULC) maps prepared using the NDBI, MNDWI , and SAVI had, respectively, an overall accuracy of 90, 88, and 86% and kappa coefficient of 0.8726, 0.8455, and 0.8212 for 1989, 2006, and 2010. These changes, when attributed in increasing surface temperature in the study region, show a positive correlation between LST and NDBI, a negative correlation between LST and SAVI , and a perfectly negative correlation between NDBI and MNDWI .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aires F, Prigent C, Rossow WB, Rothstein M (2001) A new neural network approach including first-guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature and emissivities over land from satellite microwave observations. J Geophys Res 106:14887–14907

    Article  Google Scholar 

  • Amiri R, Weng Q, Alimohammadi A, Alavipanah SK (2009) Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sens Environ 113:2606–2617

    Article  Google Scholar 

  • Archer RW (1994) Urban land consolidation for metropolitan Jakarta expansion, 1990–2010. Habitat Intern 18(4):37-52

    Article  Google Scholar 

  • Artis DA, Carnahan WH (1982) Survey of emissivity variability in Thermography of urban areas. Remote Sens Environ 12(4):313–329

    Article  Google Scholar 

  • Becker F, Li ZL (1990) Temperature-independent spectral indices in thermal infrared bands. Remote Sens Environ 32(1):17–33

    Article  Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote sens Environ 62(3):241–252

    Article  Google Scholar 

  • Chen S, Zeng S, Xie C (2000) Remote sensing and GIS for urban growth analysis in China. Photogram Eng Remote Sens 66:593–598

    Google Scholar 

  • Chen XL, Zhao HM, Li PX, Yin ZY (2006) Remote sensing image—based analysis of the relationship between urban heat Island and land use/cover changes. Remote Sens Environ 104:133–146

    Article  Google Scholar 

  • Dai C, Tang L, Jiang P, Lin J (1996) Satellite remote sensing for monitoring urban expansion and environmental remediation. In: Population dynamics and management of urban environment, the 47th IAF congress, Beijing, China, pp 9–16

    Google Scholar 

  • Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl Geogr 29(3):390–401

    Article  Google Scholar 

  • Dontree S (2010) Relation of land surface temperature (LST) and land use/land cover (LULC) from remotely sensed data in Chiang Mai—Lamphun basin. In: SEAGA conference 2010 Hanoi, Vietnam. Available from: http://seaga.xtreemhost.com/seaga2010/CS5D_Dontree.pdf. Accessed 15 Sept 2012

  • Dousset B, Gourmelon F (2003) Satellite multi-sensor data analysis of urban surface temperatures and land cover. ISPRS J. Photogram Remote Sens 58(1–2):43–54

    Article  Google Scholar 

  • Fan F, Wang Y, Wang Z (2008) Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environ Monit Assess 137:127–147

    Article  Google Scholar 

  • Friedl MA, McIver DK, Hodges JC, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sens Environ 83(1-2):287–302

    Article  Google Scholar 

  • Gallo KP, Tarpley JD, McNab AL, Karl TR (1995) Assessment of urban heat islands: a satellite perspective. Atmos Res 37:37–43

    Article  Google Scholar 

  • Hathout S (2002) The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. J Environ Manag 66:229–238

    Article  Google Scholar 

  • Herold M, Goldstein NC, Clarke KC (2003) The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86:286–302

    Article  Google Scholar 

  • Huete AR (1988) A soil adjusted vegetation index (SAVI). Remote Sens Environ 25:295–309

    Article  Google Scholar 

  • Huete AR, Jackson RD (1987) Suitability of spectral indices for evaluating vegetation characteristics on arid environments. Remote Sens Environ 23:213–232

    Article  Google Scholar 

  • Jasuf SK, Wong NH, Hagen E, Anggoro R, Hong Y (2007) The influence of land use on the urban heat island in Singapore. Habitat Int 31:232–242

    Article  Google Scholar 

  • Jensen JR, Cowen DC (1999) Remote sensing of urban/suburban infrastructure and socio-economic attributes. Photogram Eng Remote Sens 65(5):611–622

    Google Scholar 

  • Jiang J, Tian G (2010) Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environ Sci 2:571–575

    Article  Google Scholar 

  • Kaufman Y, Tanre D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote Sens 30:261–270

    Article  Google Scholar 

  • Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, George PS (2001) The causes of land-use and land-cover change: moving beyond the myths. Glob Environ Change 11(4):261–269

    Article  Google Scholar 

  • Landsat Project Science Office (2002) Landsat 7 science data user’s handbook. Goddard Space Flight Center, Available from http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_toc.html

  • Lombardo MA (1985) Ilha de Calor Nas Metrópoles: O Exemplo de São Paulo (in Portuguese), 1st edn. Hucitec, São Paulo, Brazil, p 244

    Google Scholar 

  • Mcfeeters SK (1996) The use of normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432

    Article  Google Scholar 

  • Mohamed M, Plante R (2002) Remote sensing and geographic information systems (GIS) for developing countries. In: Geoscience and remote sensing symposium 2002. IGARSS’02. 2002 IEEE international vol 4, pp 2285–2287

    Google Scholar 

  • Owen TW, Carlson TN, Gillies RR (1998) An assessment of satellite remotely sensed land cover parameters in quantitatively describing the climatic effect of urbanization. Int J Remote Sens 19:1663–1681

    Article  Google Scholar 

  • Prata AJ, Caselles V, Coll C, Sobrino JA, Ottle C (1995) Thermal remote sensing of land surface temperature from satellites: Current status and future prospects. Remote Sens Rev 12(3–4):175–224

    Article  Google Scholar 

  • Qin Z, Karnieli A, Berliner P (2001) A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int J Remote Sens 22:3719–3746

    Article  Google Scholar 

  • Rao PK (1972) Remote sensing of urban “heat islands” from an environmental satellite. Bull Am Meteor Soc 53:647–648

    Google Scholar 

  • Ridd MK (1995) Exploring a VIS (vegetation-impervious surface soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int J Remote Sens 16(12):2165–2185

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation system in the great plains with ERTS. Third earth resources technology Satellite-1 symposium, Greenbelt: NASA SP-351, pp 3010–3017

    Google Scholar 

  • Serra P, Pons X, Sauri D (2008) Land-cover and land-use change in a mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Appl Geogr 28:189–209

    Article  Google Scholar 

  • Sharma R, Ghosh A, Joshi PK (2012) Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990–2009). Environ Monit Assess 273–287. https://doi.org/10.1007/s10661-012-2792-9

    Article  Google Scholar 

  • Sobrino JA, Raissouni N, Li ZL (2001) A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing Environ 75(2):256–266

    Article  Google Scholar 

  • Streutker DR (2002) A remote sensing study of the urban heat island of Houston, Texas. Int J Remote Sens 23(13):2595–2608

    Article  Google Scholar 

  • Sun D, Pinker RT (2003) Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES-8). J Geophys Res 108:4326–4241

    Google Scholar 

  • Tan MH, Li XB, Lu CH (2003) An analysis of driving forces of urban land expansion in china. Econ Geogr 5:012

    Google Scholar 

  • Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384

    Article  Google Scholar 

  • Weng Q, Yang S (2004) Managing the adverse thermal effects of urban development in a densely populated Chinese city. J Environ Manag 70(2):145–156

    Article  Google Scholar 

  • Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sens Environ 89(4):467–483

    Article  Google Scholar 

  • Weng Q, Lu D, Liang B (2006) Urban surface biophysical descriptors and land surface temperature variations. Photogram Eng Remote Sens 72(11):1275–1286

    Article  Google Scholar 

  • Xiao H, Weng Q (2007) The impact of land use and land cover changes on land surface temperature in a karst area of China. J Environ Manag 85:245–257

    Article  Google Scholar 

  • Xiao J, Shen Y, Ge J, Tateishi R, Tang C, Liang Y, Huang Z (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape Urban Plann 75(1):69–80

    Article  Google Scholar 

  • Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033

    Article  Google Scholar 

  • Yeh AGO, Li X (1997) An integrated remote sensing-GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China. Int Plann Stud 2:193–210

    Article  Google Scholar 

  • Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24(3):583–659

    Article  Google Scholar 

  • Zhou W, Huang G, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape Urban Plann 102(1):54–63

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Geomatics Section, Indian Institute of Technology, Roorkee, Uttrakhand, India

    Anuj Tiwari

  2. Department of Geography, Shivaji College, University of Delhi, New Delhi, India

    Prabuddh Kumar Mishra

Authors
  1. Anuj Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prabuddh Kumar Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prabuddh Kumar Mishra .

Editor information

Editors and Affiliations

  1. Department of Geography, Shaheed Bhagat Singh College, University of Delhi, New Delhi, Delhi, India

    Vishwa Raj Sharma

  2. Department of Geography, Shaheed Bhagat Singh College, University of Delhi, New Delhi, Delhi, India

    Chandrakanta

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Tiwari, A., Mishra, P.K. (2019). A Study of Urban-Landscape Characteristics of Bhopal City (India) in a Geo-Spatial Environment. In: Sharma, V., Chandrakanta (eds) Making Cities Resilient. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-319-94932-1_15

Download citation

Publish with us

Policies and ethics

Search

Navigation