An autonomous image based approach for detecting glacial lake outburst floods
The potential danger caused by glacier margin lakes and the related risk of glacier lake outburst floods (GLOF) increases constantly due to glaciers retreating in many parts of the world. Reasons for this development are on the one hand the new formation and enlargement of glacier margin lakes due to melt water. On the other hand, retreating and thinning glacier tongues lead to a decrease of the back pressure against the dammed glacier lakes.
The paper describes the design of a photogrammetric GLOF monitoring system, based on monoscopic image sequence analysis for automatic detection of water level changes. The presented approach for measuring the water line in an image sequence is based on directional edge detection in LoG-filtered image data. After that, the water level is determined by a transformation of image measurements into object space based on orientation parameters of the camera and a geo-referenced lake basin model. The model can for instance be determined by photogrammetric methods after a GLOF; it may also be determined portion-wise by analysing shore lines at various water levels. Camera orientation parameters are determined by a local GPS-supported photogrammetric network. Comparing the determined water level changes with reference data provided by a water gauge, the precision is estimated in the order of one decimetre.
A major challenge is the automatic detection of the water line in image sequences under varying light and visibility conditions. The paper will also discuss promising approaches such as multispectral images as well as a statistical analysis of grey value changes over short image sequences to eliminate disturbing reflections on the rough water surface.