Modelling urban sprawl using remotely sensed data

Detalhes bibliográficos
Autor(a) principal: Padmanaban, Rajchandar
Data de Publicação: 2017
Outros Autores: Bhowmik, Avit K., Cabral, Pedro, Zamyatin, Alexander, Almegdadi, Oraib, Wang, Shuangao
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://doi.org/10.3390/e19040163
Resumo: Padmanaban, R., Bhowmik, A. K., Cabral, P., Zamyatin, A., Almegdadi, O., & Wang, S. (2017). Modelling urban sprawl using remotely sensed data: A case study of Chennai city, Tamilnadu. Entropy, 19(4), 1-14. [163]. https://doi.org/10.3390/e19040163
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spelling Modelling urban sprawl using remotely sensed dataA case study of Chennai city, TamilnaduChennaiLand change modellingRandom forest classificationRemote sensingRenyi's entropySpatial metricsSustainabilityUrban growth modelUrban sprawlPhysics and Astronomy(all)SDG 15 - Life on LandPadmanaban, R., Bhowmik, A. K., Cabral, P., Zamyatin, A., Almegdadi, O., & Wang, S. (2017). Modelling urban sprawl using remotely sensed data: A case study of Chennai city, Tamilnadu. Entropy, 19(4), 1-14. [163]. https://doi.org/10.3390/e19040163Urban 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNPadmanaban, RajchandarBhowmik, Avit K.Cabral, PedroZamyatin, AlexanderAlmegdadi, OraibWang, Shuangao2017-12-28T23:10:17Z2017-04-072017-04-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/e19040163eng1099-4300PURE: 3260902http://www.scopus.com/inward/record.url?scp=85024484929&partnerID=8YFLogxKhttps://doi.org/10.3390/e19040163info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-10T15:41:30ZPortal AgregadorONG
dc.title.none.fl_str_mv Modelling urban sprawl using remotely sensed data
A case study of Chennai city, Tamilnadu
title Modelling urban sprawl using remotely sensed data
spellingShingle Modelling urban sprawl using remotely sensed data
Padmanaban, Rajchandar
Chennai
Land change modelling
Random forest classification
Remote sensing
Renyi's entropy
Spatial metrics
Sustainability
Urban growth model
Urban sprawl
Physics and Astronomy(all)
SDG 15 - Life on Land
title_short Modelling urban sprawl using remotely sensed data
title_full Modelling urban sprawl using remotely sensed data
title_fullStr Modelling urban sprawl using remotely sensed data
title_full_unstemmed Modelling urban sprawl using remotely sensed data
title_sort Modelling urban sprawl using remotely sensed data
author Padmanaban, Rajchandar
author_facet Padmanaban, Rajchandar
Bhowmik, Avit K.
Cabral, Pedro
Zamyatin, Alexander
Almegdadi, Oraib
Wang, Shuangao
author_role author
author2 Bhowmik, Avit K.
Cabral, Pedro
Zamyatin, Alexander
Almegdadi, Oraib
Wang, Shuangao
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Padmanaban, Rajchandar
Bhowmik, Avit K.
Cabral, Pedro
Zamyatin, Alexander
Almegdadi, Oraib
Wang, Shuangao
dc.subject.por.fl_str_mv Chennai
Land change modelling
Random forest classification
Remote sensing
Renyi's entropy
Spatial metrics
Sustainability
Urban growth model
Urban sprawl
Physics and Astronomy(all)
SDG 15 - Life on Land
topic Chennai
Land change modelling
Random forest classification
Remote sensing
Renyi's entropy
Spatial metrics
Sustainability
Urban growth model
Urban sprawl
Physics and Astronomy(all)
SDG 15 - Life on Land
description Padmanaban, R., Bhowmik, A. K., Cabral, P., Zamyatin, A., Almegdadi, O., & Wang, S. (2017). Modelling urban sprawl using remotely sensed data: A case study of Chennai city, Tamilnadu. Entropy, 19(4), 1-14. [163]. https://doi.org/10.3390/e19040163
publishDate 2017
dc.date.none.fl_str_mv 2017-12-28T23:10:17Z
2017-04-07
2017-04-07T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.3390/e19040163
url https://doi.org/10.3390/e19040163
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1099-4300
PURE: 3260902
http://www.scopus.com/inward/record.url?scp=85024484929&partnerID=8YFLogxK
https://doi.org/10.3390/e19040163
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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