Change detection of mountain birch using multi-temporal ALS point clouds

Abstract

The use of multi-temporal laser scanner data is potentially an efficient method for monitoring of vegetation changes, for example, at the alpine treeline. Methods for relative calibration of multi-temporal airborne laser scanning (ALS) data sets and detection of experimental changes of tree cover in the forest–tundra ecotone was tested in northern Sweden (68° 20′ N, 19° 01′ E). Trees were either partly or totally removed on 6 m radius sample plots to simulate two classes of biomass change. Histogram matching was successfully used to calibrate the laser metrics from the two data sets and sample plots were then classified into three change classes. The proportion of vegetation returns from the canopy was the most important explanatory variable, which provided an overall accuracy of 88%. The classification accuracy was clearly dependent on the density of the forest.

Acknowledgements

This study is part of the research programme Environmental Mapping and Monitoring with Airborne laser and digital images (EMMA), financed by the Swedish Environmental Protection Agency. The laser data sets were acquired through cooperation with the University of Lund and the new national elevation model project (NNH) at the Swedish National Land Survey. The Abisko Scientific Research Station is acknowledged for supporting the work with field data acquisition.