Preprint Article Version 1 NOT YET PEER-REVIEWED

Urban Sprawl Detection Using Remotely Sensed Data: A Case of Chennai, Tamilnadu

Version 1 : Received: 4 January 2017 / Approved: 5 January 2017 / Online: 5 January 2017 (09:20:29 CET)

How to cite: Padmanaban, R.; Cabral, P.; Bhowmik, A.; Zamyatin, A.; Almegdadi, O. Urban Sprawl Detection Using Remotely Sensed Data: A Case of Chennai, Tamilnadu. Preprints 2017, 2017010023 (doi: 10.20944/preprints201701.0023.v1). Padmanaban, R.; Cabral, P.; Bhowmik, A.; Zamyatin, A.; Almegdadi, O. Urban Sprawl Detection Using Remotely Sensed Data: A Case of Chennai, Tamilnadu. Preprints 2017, 2017010023 (doi: 10.20944/preprints201701.0023.v1).

Abstract

Urban sprawl propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, substantially alters ecosystem services. Hence, the quantification of urban sprawl is crucial for effective urban planning, and environmental and ecosystem 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 urban sprawl triggered by the doubling of total population over the past three decades. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed spatial metrics to quantify the extent of urban sprawl within a 10km suburban buffer of Chennai. The rate of urban sprawl was quantified using Renyi’s entropy, and the urban extent was predicted for 2027 using land-use and land-cover change modeling. A 70.35% increase in urban areas was observed for the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was ≥ 0.9, exhibiting a two-fold rate of urban sprawl. The spatial metrics values indicate that the existing urban areas of Chennai became denser and the suburban agricultural, forests and barren lands were transformed into fragmented urban settlements. The forecasted urban growth for 2027 predicts a conversion of 13670.33ha (16.57 % of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value of 1.7. Our findings are relevant for urban planning and environmental management in Chennai and provide quantitative measures for addressing the social-ecological consequences of urban sprawl and the protection of ecosystem services.

Subject Areas

Random forest classification; urban sprawl; spatial metrics; Renyi’s entropy; sustainability; land change modelling; remote sensing; urban growth model; Chennai

Readers' Comments and Ratings (1)

Importance: How significant is the paper to the field?
Outstanding/highlight paper
100%
Significant contribution
0%
Incremental contribution
0%
No contribution
0%
Soundness of evidence/arguments presented:
Conclusions well supported
100%
Most conclusions supported (minor revision needed)
0%
Incomplete evidence (major revision needed)
0%
Hypothesis, unsupported conclusions, or proof-of-principle
0%
Comment 1
Received: 5 January 2017
Commenter: Thirumarban
Commenter's Affiliation: staffordshire university
The commenter has declared there is no conflict of interests.
Comment: Outstanding/highlight paper
Conclusions well supported
+ Respond to this comment
Discuss and rate this article
Views 0
Downloads 0
Comments 1
Metrics 0
Discuss and rate this article

×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.