# Mapping Recent Shoreline Changes Spanning the Lateral Collapse of Anak Krakatau Volcano, Indonesia
## 3 Materials and Methods  
Despite a combined revisiting time of the Sentinel-2 constellation of between 2 and 5 days, the analysis has been conducted on only 45 of the 177 images available for the period 15 May 2018-1 November 2019 (Table A1). This is because only these images provide a clear unobscured view of the whole island without the presence of significant cloud cover or plumes. As mentioned above, this has meant that there is a ca. 2 month gap in the data sequence around the time of the lateral collapse and the immediately post-collapse intense explosive activity (16 November 2018 to 23 January 2019). The shoreline detection algorithm based on the 45 useable Sentinel-2 images, whose workflow is summarized in Figure 3, comprises three stages--image pre-processing and land-sea segmentation  
Figure 2: Spatial resolution versus wavelength for Sentinel-2 along the visible (VIS), Near-Infrared (NIR) and Short-Wave-Infrared (SWIR) spectra. The two labeled white boxes indicate the spectral bands with specific applications for atmospheric correction phenomena that need to be allowed for when selecting Sentinel-2 data for use in coastline mapping. Modified from [[http://esamultimedia.esa.int/docs/EarthObservation/Sentinel-2_ESA_Bulletin161.pdf](http://esamultimedia.esa.int/docs/EarthObservation/Sentinel-2_ESA_Bulletin161.pdf)]([http://esamultimedia.esa.int/docs/EarthObservation/Sentinel-2_ESA_Bulletin161.pdf](http://esamultimedia.esa.int/docs/EarthObservation/Sentinel-2_ESA_Bulletin161.pdf)). developed in GEE and the edge detection executed in QGIS. This workflow is applied individually to each of the Sentinel-2 images. The 45 Sentinel-2 images used as input to the workflow comprised Level-1C image products. These are composed of 100 \(\times\) 100 km\({}^{2}\) tiles that are ortho-images in cartographic geometry according to the UTM/WGS84 projection, with radiometric corrections applied to convert each pixel value from the digital number (DN) to Top of Atmosphere (TOA) reflectance. The TOA images are suited to time series analysis as they provide a standardized comparison between images acquired on different dates or by different sensors. Level-1C products are resampled with a ground sampling distance (i.e., pixel size) of 10, 20 and 60 m, depending on the native resolution of the different spectral bands (see Figure 2). To avoid potential interference on the spectral identification of the land-sea interface, the presence of any partial cloud cover is detected and masked in the Level-1C products by using thresholds for band 10 and 11 in the short-wave infrared (SWIR) (Figure 3). The cloud mask enables cloud-free and cloudy pixels to be identified, with the mask indicating the cloud type (i.e., dense clouds or cirrus clouds). Dense clouds are characterized by a high reflectance in the blue spectral region (Sentinel-2, band 2) and also the SWIR, differently from snow, whereas the low reflectance in band 2 and high reflectance in band 10 is used to identify cirrus clouds. Initially, the cloud mask is generated at a spatial resolution of 60 m, before being resampled at spatial resolutions of 10 m and 20 m to mask each corresponding spectral band. The re-sampling is not a geometric transformation but a radiometric interpolation (Figure 4a). The subsequent objective thresholding-based classification of a spectral index involves utilizing the spectral information contained within each pixel to differentiate between classes (e.g., different cover types). Here, the Normalized Difference Water Index (NDWI) is used to as a basis to discriminate between the land and sea based on their spectral characteristics.