Egyptian Journal of Remote Sensing and Space Sciences (Feb 2022)

Detecting the environmental risk on the archaeological sites using satellite imagery in Basilicata Region, Italy

  • Abdelaziz Elfadaly,
  • K. Abutaleb,
  • Doaa M. Naguib,
  • Rosa Lasaponara

Journal volume & issue
Vol. 25, no. 1
pp. 181 – 193

Abstract

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Floods, along with coastal erosion, are among the most dangerous events that threaten heritage sites worldwide. Space agencies (e.g. NASA and ESA) create new platforms and applications that can help for providing the time in studying the changes occurring in the land use/land cover besides flood events such as the Google Earth Engine platform. This study aims to use the most up-to-date data, methods, and platforms in studying the changes in the land cover and its impacts on the archaeological sites of Metaponto and Policoro areas in Basilicata Region, southern Italy. In this study, the Sentinel-1 (C-band) and Landsat time-series data are used in detecting the effects of the weather and land cover changes on the heritage sites in Metaponto and Policoro regions on Ionian coastal, Italy. While, Landsat (MSS, TM, and ETM) data are downloaded from the USGS site, the Sentienl1 data are imported and analysed using the Google Earth Engine platform. As well, the Digital Shoreline Analysis System (DSAS) statically software through the End Point Rate (EPR) and the Linear Regression Rate (LRR) models are used for estimating shoreline rate of change over the long-time period. Also, the historical topographic maps returning to 1941 are used to detect the ancient land cover of the study area. As well, the ArcMap 10.5 software is used in analysing the topographic maps and Optical Landsat data. The results of this study showed that the flooding events during 2014, 2016, 2018, 2019, and the total period between 2014 and 2021 respectively covered areas by km2 based on the total space of the non-water area about 8.3, 1.7, 0.5, 0.6, and 39.17. Such results of this study can help decrease the risk around the heritage areas by detecting the most affected areas across the shoreline changes, and the previous flood events.

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