The comparative vegetation cover assessment of the greater Bangalore using high resolution satellite imagery

Paper Details

Research Paper 01/08/2013
Views (395) Download (27)

The comparative vegetation cover assessment of the greater Bangalore using high resolution satellite imagery

Malini A. Shetty, Somashekar R. K.
J. Bio. Env. Sci.3( 8), 1-9, August 2013.
Certificate: JBES 2013 [Generate Certificate]


Bangalore is experiencing unprecedented urbanization in recent times due to concentrated developmental activity resulted in the increased population and consequent pressure on infrastructure and natural resources, which ultimately gives rise to plethora of serious challenges like climate change, green house effect and frequent flooding of low lying areas. Urban forests or urban vegetation is an integral part of this urban structure providing a lattice of green in an otherwise artificial landscape. “The value of an urban forest is equal to the net benefits that members of society obtain from it” (McPherson et al. 1997). In the present study vegetation distribution across 8 zones of Bangalore Metro area is assessed by NDVI and TNDVI transformed 2005 Quick Bird imagery. Both NDVI and TNDVI, a biophysical variables clearly unravel the pattern of vegetation distribution across different zones of Bangalore metro. Among the different zones high NDVI value was observed in Byatarayanapur followed by West. The zones in outskirts of the metro area once characterized by thick plantations and forest cover now shows phenomenal decrease in vegetation. The zones in central metro area once famous for parks, gardens and plenty of avenue trees mainly responsible for calling Bangalore as “garden city” is metamorphosized into concrete jungle. Urbanization is happening at a very fast rate and at the cost of agricultural land and plantation in the outskirts of metro, which is described as National Natural Resource Census (NRC) hot spot areas for further studies and monitoring. Urban sprawl is observed as 9% and around 177 km2 of agricultural land has been converted into built up area in the last 5 to 6 years. The Zone-wise assessment of vegetation distribution using high resolution satellite imagery can illustrate how urban vegetation cover and its associated benefits vary across the Bangalore Metro and this data can be used to compare urban vegetation cover estimates among zones.


Briggs DJ, Collins S, Elliott P, Fischer P, Kingham S, Lebret E, Pryl K, Van Reeuwijk H, Smallbone K, Van der Veen A. 1997. Mapping urban air pollution using GIS: a regression-based approach. International journal of Geographical Information Science Int J Geogr Inform Science 11, 699–718.

Brown JF, Loveland TR, Merchant JW, Reed BC, Ohlen DO. 1993. Using multisource data in global land-cover characterization: concepts, requirements, and methods. Photogrammetric Engineering and Remote Sensing 59. 977–987.

Che Lam K, Leung Ng S, Chi Hui W, Kin Chan P. 2005. Environmental quality of urban parks and open spaces in Hong Kong. Environmental Monitoring and Assessment 111, 55–73.

David J Nowak, Rowa A Rowntree, Gregory McPherson E. Susan M Sisinni, Esther R Kerkmann, Jack C Stevens 1996. Measuring and analyzing urban tree cover. Landscape and Urban Planning 36, 49-57.

Egbert SL, Park S, Price KP. 2002. Using conservation reserve program maps derived from satellite imagery to characterize landscape structure. Computers and Electronics in Agriculture Comput Electron Agric 37, 141-156.

Evans DL, Zhu Z, Winterberger K. 1993. Mapping forest distributions with AVHRR data. World Resource Review 5, 66–71.

Fung T. & Siu W. 2000. Environmental quality and its changes, an analysis using NDVI.. International Journal of Remote Sensing 215, 1011– 1024.

He C, Zhang Q, Li Y. 2005. Zoning grassland protection area using remote sensing and cellular automata modeling—a case study in Xilingol steppe grassland in northern China. J Arid Environ Journal of Arid Environments 63, 814-826.

Jain S, Kohli D, Rao R.M, Bijker W. 2011. Spatial metrics to analyse the impact of regional factors on pattern of urbanisation in Gurgaon, India. In: Journal of the Indian society of remote sensing = Photonirvachak 39(2), 203-212.

Jiang X, Wan L, Du Q, Hu BX. 2008. Estimation of NDVI images using geostatistical methods. Earth Science Frontiers 15(4), 71–80.

Lillesand TM and Kiefer RW. 2000. Remote Sensing and Image I nterpretation, 4th ed. Wiley & Sons.

Loveland TR, Merchant JW, Ohlen DO and Brown JF. 1991. Development of land-cover characteristics database for the conterminous U.S. Photogrammetric Engineering and Remote Sensing 57(11), 1453–1463.

Lyon JG, Yuan D, Lunetta RS and Elvidge CD. 1998. A change detection experiment using vegetation indices. American Society of Photogrammetry 6(2), 143-150.

Maik Netzband and carsten jurgens 2010. Urban and Suburban areas as a Research Topic for Remote Sensing. In: Tarek Rashid and carsten jurgens (eds). Remote sensing of Urban and suburban areas, Springer Publishers pp 1-13.

McPherson EG, Nowak DJ, Heisler G, Grimmond S, Souch C, Grant R and Rowntree R. 1997. Quantifying urban forest structure, function, and value: the Chicago urban forest climate project. Urban Ecosystems 1(1), 49–61.

Myeong S, Nowak D J and Duggin MJ. 2006. A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment 101, 277–282.

Nagendra H and Gopal D. 2010. Street trees in Bangalore: Density, diversity, composition and distribution. Urban Forestry and Urban Greening 10, 1016.

Peng L, Chen S, Liu Y and Wang J. 2008. Application of CITY green model in benefit assessment of Nanjing urban green space in carbon fixation and runoff reduction. Frontiers of Forestry in China 3 (2), 177–182.

Ramachandra TV and Uttam Kumar 2009. Geo informatics for urbanization and urban sprawl pattern analysis. In: Joshi P. K. et al (eds) Geoinformatics for Natural Resource Management, Nova Science Publishers, pp 425-474.

Ross SL and Christopher DE. 1999. Remote sensing change detection ; Environmental Monitoring Methods and Applications. Taylor and Francis Ltd., Gun Powder Square, London.

Sudha P and Ravindranath NH. 2000. A study of Bangalore urban forest. Land Scape and Urban Planning, 47, 47-63.

Townshend JRG, Justice CO and Skole D. 1994. The 1 km resolution global data set: needs of the International Geosphere Biosphere Programme. International Journal of Remote Sensing 15, 3417– 3441.

Tucker  Compton  J. 1979.  Red and photographic infrared linear combinations f or monitoring vegetation. Remote Sensing of Environment 8, 127-150.

Undi J Quackenbush, Paul F Hopkins and Gerald J KInn 2000. Developing Forestry products from High Resolution Digital Aerial Imagery. Photogrammatic Engineering & Remote sensing 66 (11), 1337-1446.

Walsh SJ, Moody A, Allen TR and Brown DG. 1997. Scale dependence of NDVI and its relationship to mountainous terrain. In D.A. Quattrochi and M.F. Goodchild(Eds.), Scale in remote sensing and GIS, FL:Lewis Publishers, Boca Raton, pp 27-55.

Wang J, Price KP and Rich PM. 2001. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. International Journal of Remote Sensing 22(18), 3827–3844.

Weng Q, Lu D and Schubring J. 2004. Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment 89, 467–483.

Wilson JS, Brothers TS and Marcano E. 2000. Remote sensing of spatial and temporal vegetation dynamics in Hispaniola: A comparison of Haiti and the Dominican Republic. Geocarto International 15(2), 5-17.

Wilson JS, Clay M,  Martin E, Stuckey D  and Vedder-Risch K. 2003. E valuating environmental influences of zoning in urban ecosystems with remote sensing. Remote Sensing of Environment 86 (3), 303–321.