Dr Nichole Sheldrick<\/p>\n\n\n\n
Leicester University<\/p>\n\n\n\n
ens4@leicester.ac.uk<\/a><\/p>\n\n\n\n Dr Maria Carmela Gatto<\/p>\n\n\n\n Polish Academy of Sciences<\/p>\n\n\n\n maria.carmela.gatto67@gmail.com<\/a><\/p>\n\n\n\n <\/p>\n\n\n\n Script<\/strong><\/p>\n\n\n\n https:\/\/code.earthengine.google.com\/19471bd071c6448380e3cdf26b1ccdd9<\/a><\/p>\n\n\n\n Introduction<\/strong><\/p>\n\n\n\n There\u2019s been a lot of excitement on remote sensing twitter lately since the Dynamic World land cover product was released (Brown et al 2022). Straight away, it occurred to us that it could be used with our existing Google Earth Engine workflow for monitoring damage to archaeological sites at risk of destruction from land use change such as urban and agricultural expansion. The key thing about this product is that it is dynamic, while other ready-made land cover products have been constructed for set time periods. Dynamic World is derived from supervised classification using a Fully Convoluted Neural Network model of open-source Sentinel-2 satellite images. The output is divided into 9 classes describing the materials present on the Earth\u2019s surface. It can be incorporated into Google Earth Engine scripts and manipulated according to the user\u2019s workflow. Google Earth Engine is a programming interface that allows free access to a catalogue of remote sensing datasets as well as cloud-based high performance processing power.<\/p>\n\n\n\n Workflow<\/strong><\/p>\n\n\n\n Using Google Earth Engine, we incorporated the Dynamic World layer into Rayne\u2019s existing change detection code (see detailed methods in Rayne et al 2020). This was originally set up as part of the work of the Endangered Archaeology of the Middle East and North Africa Project (EAMENA)<\/a>, which since 2015 has been populating a vast database of archaeological sites. The code comprises a simple per-pixel change detection which compares two Sentinel-2 median composites, constructed by first filtering the image collections by date and cloud cover. The information from the change layer is then appended to user-defined buffers around each site. <\/p>\n\n\n\n One of the case studies we discussed in the 2020 paper was the region around Aswan in Egypt. The script found that there had been change within 100 m of 73 of 279 archaeological sites, with an overall accuracy of 85% (user\u2019s 61%, producer\u2019s 78%). The main limitation of EAMENA\u2019s change detection approach is that it does not identify change between different types of landcover. The Dynamic World dataset offers a way to do this, so we\u2019ve tested it on the same case study in Egypt.<\/p>\n\n\n\n Two composite date ranges were used for this analysis, March-May 2020 and 2022. For the per-pixel Sentinel-2 change detection, a threshold value of 0.2 was applied to the change layer. Other than the dates, the main difference between this script and the one published in 2020 is that the harmonized Sentinel-2 dataset was used (“COPERNICUS\/S2_HARMONIZED”) and a mode, rather than a mean, reducer was used on the output sites. Dynamic World composites of matching dates were also created and compared by creating a change layer, from which data was appended to the site buffers in the same way. The information of which landcover classes were involved was retained.<\/p>\n\n\n\n Results<\/strong><\/p>\n\n\n\n The Aswan area has been surveyed by the AKAP\/Borderscape project<\/a> since 2005. They have recorded hundreds of sites including settlements, scatters of stone tools, temples, cemeteries, and rock art. Unfortunately, the sites are now threatened by modern development activities including urban expansion.<\/p>\n\n\n\n Table 1: Error matrix of Sentinel-2 based change detection. Producer\u2019s accuracy was 59%, user\u2019s 100%, and overall it was 79%.<\/em><\/p>\n\n\n\n
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