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Research article2017Peer reviewedOpen access

A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory

Nilsson, Mats; Nordkvist, Karin; Jonzen, Jonas; Lindgren, Nils; Axensten, Peder; Wallerman, Jorgen; Egberth, Mikael; Larsson, Svante; Nilsson, Liselott; Eriksson, Johan; Olsson, Hakan


The National Mapping Agency in Sweden has conducted an airborne laser scanning (ALS) campaign covering almost the entire country for the purpose of creating a new national Digital Elevation Model (DEM). The ALS data were collected between 2009 and 2015 using Leica, Optech, Riegi, and Trimble scanners and have a point density of 0.5-1.0 pulses/m(2). A high resolution national raster database (12.5 m x 12.5 m cell size) with forest variables was produced by combining the ALS data with field data from the Swedish National Forest Inventory (NFI). Approximately 11500 NFI plots (10 meter radius) located on productive forest land, inventoried between 2009 and 2013, were used to create linear regression models relating selected forest variables, or transformations of the variables, to metrics derived from the ALS data. The resulting stand level relative RMSEs for predictions of stem volume, basal area, basal-area weighted mean tree height, and basal-area weighted mean stem diameter were in the ranges of 17.2-22.0%, 13.9-18.2%, 5.4-9.5%, and 8.7-13.1%, respectively. It was concluded that the predictions had an accuracy that were at least as good as data typically used in forest management planning. Above ground tree biomass was also included in the national raster database but not validated on a stand -level. An important part of the project was to make the raster database available to private forest owners, forest associations, forest companies, authorities, researchers, and the general public. Thus, all predicted forest variables can be viewed and downloaded free of charge at the Swedish Forest Agency's homepage (http://www. (C) 2016 Elsevier Inc. All rights reserved.


Nationwide forest database; National forest inventory; Airborne laser scanning

Published in

Remote Sensing of Environment
2017, Volume: 194, pages: 447-454