干旱区生态环境知识资源中心(Arid): Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques

Arid

DOI	10.3390/rs12030584
	Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques
	Blasco, Jose Manuel Delgado 1,2; Cian, Fabio 3,4; Hanssen, Ramon E.1; Verstraeten, Gert 2
通讯作者	Blasco, Jose Manuel Delgado
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2020
卷号	12 期号:3
英文摘要	Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of the urban transformation, these rapid changes need to be mapped with high precision and frequency. Urban detection in rural areas in optical remote sensing is problematic when urban structures are built using the same materials as their surroundings. To overcome this limitation, we propose a multi-temporal classification approach based on satellite data fusion and artificial neural networks. We applied the proposed methodology to data of the Egyptian regions of El-Minya and part of Asyut governorates collected from 1998 until 2015. The produced multi-temporal land cover maps capture the evolution of the area and improve the urban detection of the European Space Agency (ESA) Climate Change Initiative Sentinel-2 Prototype Land Cover 20 m map of Africa and the Global Human Settlements Layer from the Joint Research Center (JRC). The extension of urban and agricultural areas increased over 65 km(2) and 200 km(2), respectively, during the entire period, with an accelerated increase analysed during the last period (2010-2015). Finally, we identified the trends in urban population density as well as the relationship between farmed and built-up land.
英文关键词	multi-temporal land cover mapping machine learning satellite data fusion urban growth land reclamation landscape dynamics Egypt Google Earth Engine AI4EO
类型	Article
语种	英语
国家	Netherlands ; Belgium ; Italy ; USA
开放获取类型	Green Published, gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000515393800243
WOS关键词	URBAN-GROWTH ; SPATIAL STRUCTURE ; CITIES ; DESERT ; URBANIZATION ; SETTLEMENTS ; CHALLENGES ; AGREEMENT ; SPRAWL ; SPACE
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/2XILL650/315425
作者单位	1.Delft Univ Technol, Dept Geosci & Remote Sensing, NL-2628 CN Delft, Netherlands; 2.Univ Leuven, Dept Earth & Environm Sci, Div Geog & Tourism, KU Leuven, B-3001 Leuven, Belgium; 3.Ca Foscari Univ Venice, Dept Econ, I-30121 Venice, Italy; 4.World Bank Grp, Washington, DC 20433 USA
推荐引用方式 GB/T 7714	Blasco, Jose Manuel Delgado,Cian, Fabio,Hanssen, Ramon E.,et al. Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques[J],2020,12(3).
APA	Blasco, Jose Manuel Delgado,Cian, Fabio,Hanssen, Ramon E.,&Verstraeten, Gert.(2020).Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques.REMOTE SENSING,12(3).
MLA	Blasco, Jose Manuel Delgado,et al."Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques".REMOTE SENSING 12.3(2020).