Change detection of mountain birch using multi-temporal ALS point clouds. ARTICLE
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Date
2012-04-27
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Remote Sensing. Remote Sensing Letters
Abstract
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.
Description
The effect of ongoing climate change on sub-arctic and alpine forests has led to increased interest in monitoring potential changes in the forest-tundra ecotone. In addition to climate change, insect damage, browsing pressure by herbivores as well as anthropogenic impacts will contribute to changes in the sub-arctic forest-tundra ecotone. These changes are difficult to monitor with manual methods because of the complex mosaic pattern of the ecotone.
Airborne laser scanning (ALS) can efficiently be used to estimate tree height, biomass, and canopy closure in the forest-tundra ecotone (Nyström et al. 2012). In the future, series of ALS data will become available collected with various resolutions, scanning systems, system parameters, etc. Therefore, research is needed to find methods for efficient calibration and change detection of multi-temporal data.
Keywords
LIDAR, Vegetation, Change detection, Histogram matching