Analysis of urban growth using Landsat TM/ETM data and GIS—a case study of Hyderabad, India

Wakode, Hemant Balwant; Baier, Klaus; Jha, Ramakar; Azzam, Raffig

doi:10.1007/s12517-013-0843-3

Cited by 62 publications

(33 citation statements)

References 14 publications

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“…As with most city-region scale urban expansion studies (see e.g. Herold et al 2002;Wakode et al 2014), the images had a spatial resolution of 30 m. The satellite images were first geometrically referenced to the WGS 1984 UTM Zone 30 N coordinate system. The 2001 image had 20 % cloud cover while the 1986 and 2014 images had cloud cover \10 %.…”

Section: Landsat Satellite Data Acquisition and Preparationmentioning

confidence: 99%

“…Wakode et al 2014;Xu et al 2007). Among the commonly used satellite sensors is the Thematic Mapper (TM) on board the Landsat series satellite platforms, providing quality land cover data at spatial and temporal resolution required for land-use change studies.…”

Section: Introductionmentioning

confidence: 99%

“…Notwithstanding, combined with Geographical Information Systems (GIS), Landsat satellites have provided multi-spectral and multi-temporal data that have been used extensively for land cover mapping, environmental modelling and land use modelling (e.g. Schneider 2012; Masek et al 2000;Wakode et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the spatio-temporal patterns of settlement growth in a metropolitan region of Ghana

2016

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Retrospective understanding of the magnitude and pace of urban expansion is necessary for effective growth management in metropolitan regions. The objective of this paper is to quantify the spatialtemporal patterns of urban expansion in the Greater Kumasi Sub-Region (GKSR)-a functional region comprising eight administrative districts in Ghana, West Africa. The analysis is based on Landsat remote sensing images from 1986, 2001 and 2014 which were classified using supervised maximum likelihood algorithm in ERDAS IMAGINE. We computed three complementary growth indexes namely; Average Annual Urban Expansion Rate, Urban Expansion Intensity Index (UEII) and Urban Expansion Differentiation Index to estimate the amount and intensity of expansion over the 28-year period. Overall, urban expansion in the GKSR has been occurring at an average annual rate of 5.6 %. Consequently, the subregion's built-up land increased by 313 km 2 from 88 km 2 in 1986 to 400 km 2 in 2014. The analysis further show that about 72 % of the total built-up land increase occurred in the last 13 years alone, with UEII value of 0.605 indicating a moderate intensity of urban expansion. Moreover, the metropolitan-core of the sub-region, being the focal point of urban development and the historical origins of expansion, accounted for more than half of the total built-up land increase over the 28-year period. Over the last decade and half however, urban expansion has spilled into the neighbouring peripheral districts, with the highest intensity and fastest rate of expansion occurring in districts located north and north east of the subregional core. We recommend a comprehensive regional growth management strategy grounded in effective strategic partnerships among the respective administrative districts to curb unsustainable urban expansion.

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Section: Landsat Satellite Data Acquisition and Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying the spatio-temporal patterns of settlement growth in a metropolitan region of Ghana

2016

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“…India continues to lose its agricultural land to: a) urban conversion (Fazal, 2001;Wakode et al, 2013), b) reduction in suitability for cultivation (Lal, 2000;Prokop and Poreba, 2012;Xiao and Ximing, 2011), and c) agricultural land abandonment (Dhanmanjiri, 2011). The country currently faces the dilemma of needing to increase agricultural productivity while highly productive agricultural lands are being converted to urban uses (Brahmanand et al, 2013;Chadchan and Shankar, 2012;Fazal, 2001;Kalamkar, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Data from yearbooks lack detailed spatially explicit information and often remote sensing estimates lack detailed temporal information (Boucher and Seto, 2009;Kaufmann and Seto, 2001). Studies have utilized multi-temporal remote sensing images to investigate urban growth and have reported significant loss of agricultural lands in different Indian cities including Vadodara (Sandhya Kiran and Joshi, 2013), Saharanpur (Fazal, 2001), Hyderabad (Rahman et al, 2011;Wakode et al, 2013), and Aligarh (Farooq and Ahmad, 2008). Most studies using multi-temporal remote sensing images analyze land-use change over a defined period of five years (Farooq and Ahmad, 2008;Wakode et al, 2013), ten years (Fazal, 2001;Wakode et al, 2013), or other time periods, depending upon the availability of images.…”

Section: Introductionmentioning

confidence: 99%