Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt

Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and state-of-the-...

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Autores principales:	Eman Mostafa, Xuxiang Li, Mohammed Sadek, Jacqueline Fifame Dossou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Time-series Landsat images urban sprawl Gharbia governorate Remote Sensing (RS) Support Vector Machines (SVM) logistic regression Science Q
Acceso en línea:	https://doaj.org/article/49c8f9c66d86411aa76f21fc1490ddc2
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spelling	oai:doaj.org-article:49c8f9c66d86411aa76f21fc1490ddc22021-11-25T18:53:41ZMonitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt10.3390/rs132244982072-4292https://doaj.org/article/49c8f9c66d86411aa76f21fc1490ddc22021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4498https://doaj.org/toc/2072-4292Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and state-of-the-art remote sensing products and methodologies to address the issue of random urban expansion, which negatively impacts environmental sustainability. The study aims (1) to investigate the land-use/land-cover (LULC) changes over the past 27 years, and to simulate the future LULC dynamics over Gharbia; and (2) to produce an Urbanization Risk Map in order for the decision-makers to be informed of the districts with priority for sustainable planning. Time-series Landsat images were utilized to analyze the historical LULC change between 1991 and 2018, and to predict the LULC change by 2033 and 2048 based on a logistic regression–Markov chain model. The results show that there is a rapid urbanization trend corresponding to a diminution of the agricultural land. The agricultural sector represented 91.2% of the total land area in 1991, which was reduced to 83.7% in 2018. The built-up area exhibited a similar (but reversed) pattern. The results further reveal that the observed LULC dynamics will continue in a like manner in the future, confirming a remarkable urban sprawl over the agricultural land from 2018 to 2048. The cultivated land changes have a strong negative correlation with the built-up cover changes (the R<sup>2</sup> were 0.73 in 1991–2003, and 0.99 in 2003–2018, respectively). Based on the Fuzzy TOPSIS technique, Mahalla Kubra and Tanta are the districts which were most susceptible to the undesirable environmental and socioeconomic impacts of the persistent urbanization. Such an unplanned loss of the fertile agricultural lands of the Nile Delta could negatively influence the production of premium agricultural crops for the local market and export. This study is substantial for the understanding of future trends of LULC changes, and for the proposal of alternative policies to reduce urban sprawl on fertile agricultural lands.Eman MostafaXuxiang LiMohammed SadekJacqueline Fifame DossouMDPI AGarticleTime-series Landsat imagesurban sprawlGharbia governorateRemote Sensing (RS)Support Vector Machines (SVM)logistic regressionScienceQENRemote Sensing, Vol 13, Iss 4498, p 4498 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Time-series Landsat images urban sprawl Gharbia governorate Remote Sensing (RS) Support Vector Machines (SVM) logistic regression Science Q
spellingShingle	Time-series Landsat images urban sprawl Gharbia governorate Remote Sensing (RS) Support Vector Machines (SVM) logistic regression Science Q Eman Mostafa Xuxiang Li Mohammed Sadek Jacqueline Fifame Dossou Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
description	Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and state-of-the-art remote sensing products and methodologies to address the issue of random urban expansion, which negatively impacts environmental sustainability. The study aims (1) to investigate the land-use/land-cover (LULC) changes over the past 27 years, and to simulate the future LULC dynamics over Gharbia; and (2) to produce an Urbanization Risk Map in order for the decision-makers to be informed of the districts with priority for sustainable planning. Time-series Landsat images were utilized to analyze the historical LULC change between 1991 and 2018, and to predict the LULC change by 2033 and 2048 based on a logistic regression–Markov chain model. The results show that there is a rapid urbanization trend corresponding to a diminution of the agricultural land. The agricultural sector represented 91.2% of the total land area in 1991, which was reduced to 83.7% in 2018. The built-up area exhibited a similar (but reversed) pattern. The results further reveal that the observed LULC dynamics will continue in a like manner in the future, confirming a remarkable urban sprawl over the agricultural land from 2018 to 2048. The cultivated land changes have a strong negative correlation with the built-up cover changes (the R<sup>2</sup> were 0.73 in 1991–2003, and 0.99 in 2003–2018, respectively). Based on the Fuzzy TOPSIS technique, Mahalla Kubra and Tanta are the districts which were most susceptible to the undesirable environmental and socioeconomic impacts of the persistent urbanization. Such an unplanned loss of the fertile agricultural lands of the Nile Delta could negatively influence the production of premium agricultural crops for the local market and export. This study is substantial for the understanding of future trends of LULC changes, and for the proposal of alternative policies to reduce urban sprawl on fertile agricultural lands.
format	article
author	Eman Mostafa Xuxiang Li Mohammed Sadek Jacqueline Fifame Dossou
author_facet	Eman Mostafa Xuxiang Li Mohammed Sadek Jacqueline Fifame Dossou
author_sort	Eman Mostafa
title	Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
title_short	Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
title_full	Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
title_fullStr	Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
title_full_unstemmed	Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
title_sort	monitoring and forecasting of urban expansion using machine learning-based techniques and remotely sensed data: a case study of gharbia governorate, egypt
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/49c8f9c66d86411aa76f21fc1490ddc2
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_version_	1718410584185110528

Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt

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