干旱区生态环境知识资源中心(Arid): Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt

Arid

DOI	10.3390/rs13224498
	Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
	Mostafa, Eman; Li, Xuxiang; Sadek, Mohammed; Dossou, Jacqueline Fifame
通讯作者	Li, XX (corresponding author), Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Inst Global Environm Change, Dept Earth & Environm Sci, Xian 710049, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:22
英文摘要	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-2 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.
英文关键词	Time-series Landsat images urban sprawl Gharbia governorate Remote Sensing (RS) Support Vector Machines (SVM) logistic regression Markov Chain (MC) Fuzzy TOPSIS
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000725830600001
WOS关键词	LAND-USE CHANGE ; MODEL SELECTION ; MARKOV-CHAIN ; CLASSIFICATION ; INTEGRATION ; ALLOCATION ; REGRESSION ; CROPLAND ; IMPACTS ; IMAGERY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373730
作者单位	[Mostafa, Eman; Li, Xuxiang; Sadek, Mohammed; Dossou, Jacqueline Fifame] Xi An Jiao Tong Univ, Sch Human Settlements & Civil Engn, Inst Global Environm Change, Dept Earth & Environm Sci, Xian 710049, Peoples R China; [Mostafa, Eman; Sadek, Mohammed] Benha Univ, Shoubra Fac Engn, Surveying Engn Dept, Cairo 11672, Egypt
推荐引用方式 GB/T 7714	Mostafa, Eman,Li, Xuxiang,Sadek, Mohammed,et al. Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt[J],2021,13(22).
APA	Mostafa, Eman,Li, Xuxiang,Sadek, Mohammed,&Dossou, Jacqueline Fifame.(2021).Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt.REMOTE SENSING,13(22).
MLA	Mostafa, Eman,et al."Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt".REMOTE SENSING 13.22(2021).