Surface classification based on multi-temporal airborne LiDAR intensity data in high mountain environments
A case study from Hintereisferner, Austria
The use of airborne LiDAR (Light detection and ranging), also called airborne laser scanning (ALS), has evolved into a standard technique for acquiring topographic information in regional scale. Beside the geometric attributes (x,y,z), the backscattered signal, often termed intensity or amplitude, is contained in the resulting ALS point cloud. Several studies showed the great potential of using ALS intensity data as ALS input for calculating surface or object parameters, for example in forestry, glaciology, geomorphology and for for hydrological applications. Due to impacts of climate change in high mountain environments and because of economic and ecological dependencies (e.g. water supply, hydro power and winter tourism), the interest and in monitoring processes in these regions has strongly gained in importance during the last years. However, high-resolution, continuous data coverage and surface information acquisition in high mountain environment is very difficult to achieve. As previous studies indicated the use of laser scanning devices can be an effective monitoring instrument. In this study a threshold based classification method based on multi-temporal intensity raster data was developed to determine the main surface types (ice and water, snow, rock and raster vegetation) of a high mountain region. Before working with intensity data, it has to be corrected for known influences, such as spherical loss, topographic effects and others. Hence an intensity correction model and effects an a normalization are applied to the data. The classification is done for 7 data sets of the period between the years 2001 and 2008. Furthermore, the suitability of ALS intensity for surface classification in high mountain environments is investigated. The study area is located at the Hintereisferner in the upper Ötztal/Tyrol at in Austria. The classification results show that particularly ice and water bodies can be classified in an accurate manner from intensity raster data, while other surface classes are less accurately detected due to various factors, such as surface reflectance variability influencing the backscattered energy.
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Document Type: Research Article
Publication date: April 1, 2011
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