Change detection of mountain birch using multi-temporal ALS point clouds
Permanent URI for this collection
Use of multi-temporal laser scanner data is potentially a very efficient method
for monitoring of vegetation changes, for example at the alpine tree line. In this
study, methods for relative calibration of multi-temporal 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 six meter 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.
News
Mattias Nyström, Change detection of mountain birch using multi-temporal ALS point clouds