Analysis of urban development on land cover changes of three cities of Gujarat state, India

Urbanization generally serves as a key navigator of the economic growth and development of the country. There is a need for fast and accurate urban planning to accommodate more and more people in the city area. Remote sensing technology has been used for planning the expansion and design of city are...

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Main Authors: Patel Alpesh M., Suthar Anil
Format: Article
Language:English
Published: University of Novi Sad, Department of Geography, Tourism and Hotel Management 2022-01-01
Series:Geographica Pannonica
Subjects:
Online Access:https://scindeks-clanci.ceon.rs/data/pdf/0354-8724/2022/0354-87242204356P.pdf
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author Patel Alpesh M.
Suthar Anil
author_facet Patel Alpesh M.
Suthar Anil
author_sort Patel Alpesh M.
collection DOAJ
description Urbanization generally serves as a key navigator of the economic growth and development of the country. There is a need for fast and accurate urban planning to accommodate more and more people in the city area. Remote sensing technology has been used for planning the expansion and design of city areas. A novel machine learning (ML) classifier formed by combining AdaBoost and extra trees algorithm have been investigated for change detection in the urban area of three cities in the Gujarat region of India. Using Indian Remote Sensing (IRS) Resourcesat-2 LISS IV satellite images, the performance of the object-based AdaBoosted extra trees classifier (ABETC) in terms of overall accuracy (OA) and kappa coefficient (KC) for urban area change detection was compared to benchmarked object-based algorithms. As the first step in object-based classification (OBC), the Shepherd segmentation algorithm was used to segment satellite images. For all three cities, the object-based ABETC demonstrated the highest efficiency when compared to conventional classifiers. The rise in the built-up area of Ahmedabad city has been noted by 87.39 sq km from the year 2011 to 2020 showing the urban development of the city. This increase in the built-up area of Ahmedabad was compensated by the depletion of 30.26 sq. km. vegetation area, and 57.13 sq. km. of open land class. The built-up area of Vadodara and Rajkot city has been enlarged by 17.24 sq km and 6.79 sq km respectively. The highest OA of 96.04% and KC of 0.94 has been noted for a satellite image of Vadodara city with a novel object based ABETC algorithm.
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spelling doaj.art-74a7d0cf3dbf475b894e309889fdecf52023-01-31T08:07:40ZengUniversity of Novi Sad, Department of Geography, Tourism and Hotel ManagementGeographica Pannonica0354-87241820-71382022-01-0126435637210.5937/gp26-394400354-87242204356PAnalysis of urban development on land cover changes of three cities of Gujarat state, IndiaPatel Alpesh M.0https://orcid.org/0000-0001-8846-4771Suthar Anil1https://orcid.org/0000-0003-3267-8636Gujarat Technological University, Ahmedabad, Vishwakarma Government Engineering College, Department of Electronics and Communication, Chandkheda, IndiaNew L. J. Institute of Engineering and Technology, Ahmedabad, IndiaUrbanization generally serves as a key navigator of the economic growth and development of the country. There is a need for fast and accurate urban planning to accommodate more and more people in the city area. Remote sensing technology has been used for planning the expansion and design of city areas. A novel machine learning (ML) classifier formed by combining AdaBoost and extra trees algorithm have been investigated for change detection in the urban area of three cities in the Gujarat region of India. Using Indian Remote Sensing (IRS) Resourcesat-2 LISS IV satellite images, the performance of the object-based AdaBoosted extra trees classifier (ABETC) in terms of overall accuracy (OA) and kappa coefficient (KC) for urban area change detection was compared to benchmarked object-based algorithms. As the first step in object-based classification (OBC), the Shepherd segmentation algorithm was used to segment satellite images. For all three cities, the object-based ABETC demonstrated the highest efficiency when compared to conventional classifiers. The rise in the built-up area of Ahmedabad city has been noted by 87.39 sq km from the year 2011 to 2020 showing the urban development of the city. This increase in the built-up area of Ahmedabad was compensated by the depletion of 30.26 sq. km. vegetation area, and 57.13 sq. km. of open land class. The built-up area of Vadodara and Rajkot city has been enlarged by 17.24 sq km and 6.79 sq km respectively. The highest OA of 96.04% and KC of 0.94 has been noted for a satellite image of Vadodara city with a novel object based ABETC algorithm.https://scindeks-clanci.ceon.rs/data/pdf/0354-8724/2022/0354-87242204356P.pdfurbanizationchange detectionobject based classificationmultispectral image
spellingShingle Patel Alpesh M.
Suthar Anil
Analysis of urban development on land cover changes of three cities of Gujarat state, India
Geographica Pannonica
urbanization
change detection
object based classification
multispectral image
title Analysis of urban development on land cover changes of three cities of Gujarat state, India
title_full Analysis of urban development on land cover changes of three cities of Gujarat state, India
title_fullStr Analysis of urban development on land cover changes of three cities of Gujarat state, India
title_full_unstemmed Analysis of urban development on land cover changes of three cities of Gujarat state, India
title_short Analysis of urban development on land cover changes of three cities of Gujarat state, India
title_sort analysis of urban development on land cover changes of three cities of gujarat state india
topic urbanization
change detection
object based classification
multispectral image
url https://scindeks-clanci.ceon.rs/data/pdf/0354-8724/2022/0354-87242204356P.pdf
work_keys_str_mv AT patelalpeshm analysisofurbandevelopmentonlandcoverchangesofthreecitiesofgujaratstateindia
AT sutharanil analysisofurbandevelopmentonlandcoverchangesofthreecitiesofgujaratstateindia