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Entropy 2017, 19(4), 163; doi:10.3390/e19040163

Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

1
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal
2
Stockholm Resilience Centre, Stockholm University, Kraftriket 2B, SE-104 05 Stockholm, Sweden
3
Tomsk State University, Lenin Avenue 36, 634050 Tomsk, Russia
4
Institute for Geoinformatics (IFGI),Westfälische Wilhelms-Universität, Heisenbergstraße 2, 48149 Münster, Germany
5
School of Humanities And Social Sciences, Nanyang Technological University, 14 Nanyang Drive, Singapore 637332, Singapore
*
Authors to whom correspondence should be addressed.
Received: 4 January 2017 / Revised: 1 April 2017 / Accepted: 5 April 2017 / Published: 7 April 2017
(This article belongs to the Special Issue Entropy for Sustainable and Resilient Urban Future)
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Abstract

Urban sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, adversely affects the provision of ecosystem services. The quantification of US is thus crucial for effective urban planning and environmental management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive US triggered by the doubling of total population over the past three decades. However, the extent and level of US has not yet been quantified and a prediction for future extent of US is lacking. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10 km suburban buffer of Chennai. The level of US was then quantified using Renyi’s entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services. View Full-Text
Keywords: urban sprawl; random forest classification; spatial metrics; Renyi’s entropy; sustainability; land change modelling; remote sensing; urban growth model; Chennai urban sprawl; random forest classification; spatial metrics; Renyi’s entropy; sustainability; land change modelling; remote sensing; urban growth model; Chennai
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Padmanaban, R.; Bhowmik, A.K.; Cabral, P.; Zamyatin, A.; Almegdadi, O.; Wang, S. Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu. Entropy 2017, 19, 163.

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