Next Article in Journal
A New Individual Tree Crown Delineation Method for High Resolution Multispectral Imagery
Previous Article in Journal
Enhanced Delaunay Triangulation Sea Ice Tracking Algorithm with Combining Feature Tracking and Pattern Matching
Previous Article in Special Issue
Land Cover Classification of Complex Agroecosystems in the Non-Protected Highlands of the Galapagos Islands
Open AccessArticle

Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques

1
Department of Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands
2
Department of Earth and Environmental Sciences, Leuven, Division of Geography and Tourism, KU Leuven—University of Leuven, B-3001 Leuven, Belgium
3
Department of Economics, Ca’ Foscari University Venice, 30121 Venice, Italy
4
The World Bank Group Washington, DC 20433, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 584; https://doi.org/10.3390/rs12030584 (registering DOI)
Received: 14 January 2020 / Revised: 3 February 2020 / Accepted: 8 February 2020 / Published: 10 February 2020
(This article belongs to the Special Issue Remote Sensing of Human-Environment Interactions)
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 km2 and 200 km2, 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. View Full-Text
Keywords: multi-temporal land cover mapping; machine learning; satellite data fusion; urban growth; land reclamation; landscape dynamics; Egypt; Google Earth Engine; AI4EO multi-temporal land cover mapping; machine learning; satellite data fusion; urban growth; land reclamation; landscape dynamics; Egypt; Google Earth Engine; AI4EO
Show Figures

Graphical abstract

MDPI and ACS Style

Delgado Blasco, J.M.; Cian, F.; Hanssen, R.F.; Verstraeten, G. Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques. Remote Sens. 2020, 12, 584.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop