Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment

Airborne laser scanning (ALS) is currently one of the most promising remote sensing techniques for quantitative retrieval of forest parameters. While ALS has reached an operational status for mapping of boreal forests, its large area application over mountainous environments is lacking behind. This is because alpine forests often have high horizontal and vertical structural diversity and are situated in steep terrain. Also, ALS data acquisition and processing is more demanding over mountainous areas than over relatively flat regions. In this study we have used state-of-the-art ALS technology and software packages to map canopy heights and to estimate tree heights for a 128 km(2) region in the western part of the Austrian Alps. Spruce and fir are the dominant tree species. Rather than employing data and methods tuned for a particular task and for a small study area, we solely use data and methods which already serve other operational applications. Thus, it is ensured that the results obtained in this study are of practical relevance. For the validation of the ALS derived canopy heights we have used 22000 ground control points and field-measured forest inventory data from 103 sample plots, which are operationally used by the local forest administration. The validation of the digital terrain model (DTM) with the ground control points shows that over non-forested terrain DTM errors increase from 10 cm for relatively flat terrain (local slope< 10 degrees) to over 50 cm for local slopes greater than about 60 degrees. The validations of the ALS derived single-tree heights and Lorey's mean heights show good correlations using both, three dimensional first pulse points (R-2 = 0.73-0.84) and a grid-based canopy height model (R-2 = 0.68-0.87). Overall, the results demonstrate that airborne laser scanning has now reached the maturity to be used for mapping canopy heights of complex alpine forests throughout large areas. (C) 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V All rights reserved.