Abstract

In this study, mapping of tree growth was performed using data from the two nation-wide acquisitions of airborne laser scanning in Sweden. Following the successful first national acquisition performed in 2009 - 2015, a new, repeated, scanning is now launched and ongoing. The first scanning provided new, accurate (in accuracy as well as in spatial resolution) data about the forest and quickly found wide-spread use in the forest industry. It outperformed previous methods and provided a new standard of data capture for forest management planning. The addition of a second scanning provide information also about changes, where forest tree growth is of high interest in the industry. This study presents the first results from large-scale assessment of growth for basal area-weighted mean tree height (H) and mean stem volume (V), using the bi-temporal scannings and sample-plot data from the National Forest Inventory. Growth was most accurately assessed by the direct change metrics of the scannings, although the accuracies were moderate. The accuracy of forecasts, i.e. only utilizing the predicted forest state at the first scanning, were similar for H but inferior for V, though.

Keywords

Forestry; growth; airborne laser scanning; time-series

Published in

eISBN: 978-1-7281-6374-1
DOI: 10.1109/IGARSS39084.2020.9323955

Conference

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), SEP 26-OCT 02, 2020, ELECTR NETWORK

SLU Authors

• Wallerman, Jörgen

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

• Nyström, Kenneth

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

• Nilsson, Mats

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

• Axensten, Peder

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

• Egberth, Mikael

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

• Jonzen, Jonas

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

• Sandström, Emma

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

• Fransson, 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

Remote Sensing
Forest Science


Publication identifier

DOI: https://doi.org/10.1109/IGARSS39084.2020.9323955

Permanent link to this page (URI)

https://res.slu.se/id/publ/113360