Glacial lake changes from cloud processing of optical satellite images

Glacial lakes are an important component of terrestrial meltwater storage and respond to climate change and glacier retreat. Although there is evidence of rapid worldwide growth of glacial lakes, changes in frequency and magnitude of glacier lake outburst floods (GLOFs) under climatic changes are not yet understood. This thesis proposes a set of methods for regional scale glacial lake mapping and GLOF detection using a large time-series of optical satellite images and the cloud processing tool Google Earth Engine. The methods are presented for various temporal scales, from 2-week Landsat revisit period to annual resolution. The proposed method shows, how constructing an annual composite of pixel values such as minimum or maximum values can help to overcome traditional problems associated with water mapping from optical satellite data like clouds, terrain and cloud shadows. The method only involves two band ratios of multispectral satellite images for annual-resolution glacial lake mapping. The thesis also presents how the proposed method can be used to produce a complete regional-scale glacial lake inventory, using the Greater Caucasus as example