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.

Keywords

airborne laser scanning; lidar; vegetation; histogram matching; change detection; multi-temporal; forest

Published in

Remote Sensing Letters
2013, volume: 4, number: 2, pages: 190-199
Publisher: Taylor & Francis

SLU Authors

• Nyström, Mattias

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Holmgren, Johan

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Olsson, Håkan

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science

Publication identifier

• DOI: https://doi.org/10.1080/2150704X.2012.714087

Permanent link to this page (URI)

https://res.slu.se/id/publ/78553