Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana

The current trends of land use dynamics have revealed a significant transformation of settlement spaces. In the Wa Municipality of Ghana, the changes in land use and land cover are inspired by a plethora of driving forces. In this study, we assessed the geo-physical drivers of settlement expansion u...

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Autores principales:	Mawuli Asempah, Wahib Sahwan, Brigitta Schütt
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	savannah vegetation random forest classifier regression analyses receiver operating characteristics (ROC) urbanisation Agriculture S
Acceso en línea:	https://doaj.org/article/5d7619844d14420e9e977d2a3e29e065
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id	oai:doaj.org-article:5d7619844d14420e9e977d2a3e29e065
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spelling	oai:doaj.org-article:5d7619844d14420e9e977d2a3e29e0652021-11-25T18:09:59ZAssessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana10.3390/land101112512073-445Xhttps://doaj.org/article/5d7619844d14420e9e977d2a3e29e0652021-11-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1251https://doaj.org/toc/2073-445XThe current trends of land use dynamics have revealed a significant transformation of settlement spaces. In the Wa Municipality of Ghana, the changes in land use and land cover are inspired by a plethora of driving forces. In this study, we assessed the geo-physical drivers of settlement expansion under land use dynamics in the Wa Municipality of Ghana. The study employed geospatial and remote sensing tools to map and analyse the spatio-temporal dynamics of the landscape, using Landsat satellite imageries: thematic mapper (TM), enhanced thematic mapper (ETM) and operational land imager (OLI) from 1990 to 2020. The study employed a binomial logistic regression model to statistically assess the geo-physical drivers of settlement expansion. Random forest (RF)–supervised classification based on spatio-temporal analyses generated relatively higher classification accuracies, with overall accuracy ranging from 89.33% to 93.3%. Urban expansion for the last three decades was prominent, as the period from 1990 to 2001 gained 11.44 km<sup>2</sup> landmass of settlement, while there was 11.30 km<sup>2</sup> gained from 2001 to 2010, and 29.44 km<sup>2</sup> gained from 2010 to 2020. Out of the independent variables assessed, the distance to existing settlements, distance to river, and distance to primary, tertiary and unclassified roads were responsible for urban expansion.Mawuli AsempahWahib SahwanBrigitta SchüttMDPI AGarticlesavannah vegetationrandom forest classifierregression analysesreceiver operating characteristics (ROC)urbanisationAgricultureSENLand, Vol 10, Iss 1251, p 1251 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	savannah vegetation random forest classifier regression analyses receiver operating characteristics (ROC) urbanisation Agriculture S
spellingShingle	savannah vegetation random forest classifier regression analyses receiver operating characteristics (ROC) urbanisation Agriculture S Mawuli Asempah Wahib Sahwan Brigitta Schütt Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana
description	The current trends of land use dynamics have revealed a significant transformation of settlement spaces. In the Wa Municipality of Ghana, the changes in land use and land cover are inspired by a plethora of driving forces. In this study, we assessed the geo-physical drivers of settlement expansion under land use dynamics in the Wa Municipality of Ghana. The study employed geospatial and remote sensing tools to map and analyse the spatio-temporal dynamics of the landscape, using Landsat satellite imageries: thematic mapper (TM), enhanced thematic mapper (ETM) and operational land imager (OLI) from 1990 to 2020. The study employed a binomial logistic regression model to statistically assess the geo-physical drivers of settlement expansion. Random forest (RF)–supervised classification based on spatio-temporal analyses generated relatively higher classification accuracies, with overall accuracy ranging from 89.33% to 93.3%. Urban expansion for the last three decades was prominent, as the period from 1990 to 2001 gained 11.44 km<sup>2</sup> landmass of settlement, while there was 11.30 km<sup>2</sup> gained from 2001 to 2010, and 29.44 km<sup>2</sup> gained from 2010 to 2020. Out of the independent variables assessed, the distance to existing settlements, distance to river, and distance to primary, tertiary and unclassified roads were responsible for urban expansion.
format	article
author	Mawuli Asempah Wahib Sahwan Brigitta Schütt
author_facet	Mawuli Asempah Wahib Sahwan Brigitta Schütt
author_sort	Mawuli Asempah
title	Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana
title_short	Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana
title_full	Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana
title_fullStr	Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana
title_full_unstemmed	Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana
title_sort	assessment of land cover dynamics and drivers of urban expansion using geospatial and logistic regression approach in wa municipality, ghana
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5d7619844d14420e9e977d2a3e29e065
work_keys_str_mv	AT mawuliasempah assessmentoflandcoverdynamicsanddriversofurbanexpansionusinggeospatialandlogisticregressionapproachinwamunicipalityghana AT wahibsahwan assessmentoflandcoverdynamicsanddriversofurbanexpansionusinggeospatialandlogisticregressionapproachinwamunicipalityghana AT brigittaschutt assessmentoflandcoverdynamicsanddriversofurbanexpansionusinggeospatialandlogisticregressionapproachinwamunicipalityghana
_version_	1718411561129738240

Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana

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