A Proposed Methodology for Detecting the Urban Footprint in Egypt. Issue 1 (1st February 2022) - Mahmoud, N.; Samir, N.; Fathi, A.; Mohamed, W.
Abstract: The detection of informal built-up environments that sprawl on agricultural land is difficult to codify specially in particular with the increase in the population in the Arab Republic of Egypt. In 2017, a law was passed to remove building infractions in rural areas according to aerial photographs taken by the military and the building codes built after the 2019 Construction Violations Reconciliation Law (CVRL) were regulated, which mandates the demolition of any buildings built without a permit that are capped at 8.2 million units Since 2007. The use of remote sensing, which is a powerful technology that relies specifically on updated satellite images, is a key tool for detecting infringements built-up regions. The European Space Agency's Sentinel Synthetic Aperture Radar (SAR) constellation is a key component of this research because of its advantages that allow for improved spatial resolution and atmospheric independence, and it takes day and night images, making it useful for a wide range of ground cover detection applications., a technological advantage that surpasses Optical sensors for the Sentinel 2 and Landsat missions that rely on clear weather conditions. The study aims to evaluate the use of radar satellites compared to optical satellites to detect urban built-up areas and encroachments on agricultural areas. The study was conducted on an area of 64 square kilometres, in Damietta City, East Delta, Egypt. Supervised classification was also used to derive the overall accuracy of radar satellite images and compare them with optical satellite images, using Error Matrix tables on Scup + QGIS programs. The results of the study resulted in reaching a high accuracy of land cover classification from the process of combining the Sentinel-1A, 1B images in Maximum likelihood (ML) algorithms compared to Sentinel-2 images, reaching up to an overall accuracy (OA) of 80%, while the classification in Random Forest (RF) algorithms reached an overall accuracy (OA) of 87%. This paper recommends using the method of merging (SAR) Sentinel 1A, 1B images and applying it, as well as conducting periodic monitoring of urban expansions using remote sensing techniques, to make sustainable decisions for the future of urban expansion master plans in the region and to quickly explore informal built-up encroachments.