Visualization of Urban Growth Pattern in Chennai Using Geoinformatics and Spatial Metrics

Aithal, Bharath H.; Ramachandra, T. V.

doi:10.1007/s12524-015-0482-0

Cited by 85 publications

(23 citation statements)

References 36 publications

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“…The accuracy of the image classification was assessed using the kappa coefficient (KC) [35,36]. We compared the classified images with the ground truth data obtained from Google Earth image of 2016 and the landuse and landcover map of 2005 from the Indian Geo-platform of ISRO-Bhuvan (http://bhuvan.nrsc.gov.in) [16]. Subsequently, a confusion matrix and KC were calculated using eCognition Developer (8.7) for each of the classified images of the three years [48].…”

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confidence: 99%

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

Padmanaban

Bhowmik

Cabral

et al. 2017

Entropy

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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.

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

Padmanaban

Bhowmik

Cabral

et al. 2017

Entropy

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“…Chennai has witnessed a remarkable development of IT sectors, educational institutes, health care, and retail and manufacturing industries in recent decades [18].…”

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confidence: 99%

“…Chennai is also rich in natural resources [23]. As a result, the population has increased rapidly in the last two decades [18].…”

Section: Study Areamentioning

confidence: 99%

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

Padmanaban¹,

Cabral²,

Ak³

et al. 2017

Preprint

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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 Peer-reviewed version available at Entropy 2017, 19, , 163; doi:10.3390/e19040163 2 addressing the social-ecological consequences of urban sprawl and the protection of ecosystem services.

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“…Many traditional mathematical and statistical approaches are unsuitable for spatial studies such as urban studies for several reasons; for example, with the use of traditional mathematical and statistical approaches, summarising site information becomes difficult, and the effects of representation patterns in spatial data are different from those in statistical data [9]. Hence, mathematical and statistical methods have been integrated and developed with other modern techniques such as GIS and RS techniques to measure spatial dynamics and land-use changes [10]. These techniques can be used to better understand urban growth patterns in metropolitan areas provided that appropriate spatial data are made available [9].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Urban Growth Patterns using Spatio-temporal Data and Analysis

Bakhit¹,

Abdelkader²

2019

AJAS

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Modeling urban growth trends has become one of the critical issues in the last decades. This study aims to evaluate spatio-temporal urban growth trends using spatial modeling. For this purpose, four land-use maps were used to visualize historical urban growth trends in Seremban, Malaysia. Land Change Modeller (LCM) was used to evaluate the spatial trend of Land-use and land-cover (LULC) in Seremban. The results of the study confirm that urban areas in Seremban hugely increased from 1984 to 2010. The main reasons to increase urban areas are that economic and population growth in Malaysia in general and Seremban in particular. This study confirms that the LCM model is one of the effective spatial techniques that should be taken into account in urban planning studies.

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Visualization of Urban Growth Pattern in Chennai Using Geoinformatics and Spatial Metrics

Cited by 85 publications

References 36 publications

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

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

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

Assessment of Urban Growth Patterns using Spatio-temporal Data and Analysis

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