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A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory
- Source :
- Remote Sensing of Environment. 194:447-454
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- 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, Riegl, and Trimble scanners and have a point density of 0.5–1.0 pulses/m2. A high resolution national raster database (12.5 m × 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.skogsstyrelsen.se/skogligagrunddata ).
- Subjects :
- Forest inventory
010504 meteorology & atmospheric sciences
Laser scanning
Forest management
0211 other engineering and technologies
Soil Science
Geology
02 engineering and technology
computer.file_format
01 natural sciences
Basal area
Geography
Linear regression
Computers in Earth Sciences
Raster graphics
Digital elevation model
computer
Weighted arithmetic mean
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Remote sensing
Subjects
Details
- ISSN :
- 00344257
- Volume :
- 194
- Database :
- OpenAIRE
- Journal :
- Remote Sensing of Environment
- Accession number :
- edsair.doi...........afd760bb0e8237a9f10b45fd398c9a57
- Full Text :
- https://doi.org/10.1016/j.rse.2016.10.022