Towards Achieving Sustainability of Coastal Environments: Urban Growth Analysis and Prediction of Lagos, State Nigeria

IF 0.3 Q4 REMOTE SENSING
T. Idowu, R. Waswa, K. Lasisi, Kenneth Mubea, M. Nyadawa, J. Kiema
{"title":"Towards Achieving Sustainability of Coastal Environments: Urban Growth Analysis and Prediction of Lagos, State Nigeria","authors":"T. Idowu, R. Waswa, K. Lasisi, Kenneth Mubea, M. Nyadawa, J. Kiema","doi":"10.20944/preprints202007.0560.v1","DOIUrl":null,"url":null,"abstract":"The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"South African Journal of Geomatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20944/preprints202007.0560.v1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 2

Abstract

The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.
实现沿海环境的可持续性:尼日利亚拉各斯城市增长分析与预测
预计未来30年最广泛的城市增长将发生在发展中国家。尼日利亚的拉各斯——非洲人口第二多的大城市——就是一个典型的例子。为了实现更具可持续性和弹性的城市,有必要对主要城市的城市增长模式进行建模并分析其影响。本文采用多层感知器(multilayer Perceptron, MLP)神经网络对拉各斯州的城市增长进行过渡建模,利用马尔可夫链分析对变化进行预测,模型准确率达到81.8%。结合kappa相关统计,利用ArcGIS对模型结果进行了创新性的可视化验证。结果表明,到2031年,建成区将成为研究区空间最广泛的土地利用资源类别,其覆盖率将从1986年的9%上升至34.1%。2016年至2031年间,光秃秃地区的覆盖率预计将增加53%。相反,2016年至2031年期间,预计林地和湿地的损失分别为24.9%和68.3%。鉴于可持续发展目标的第11个目标(重点是实现可持续城市和社区)、非洲联盟《2063年议程》的目标,以及根据观察到的城市增长趋势,该研究建议优先考虑垂直扩张,而不是研究区域目前的水平城市增长趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
82
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信