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1.	Bohlin, Jonas, et al. (author) Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory 2017 In: Silva Fennica. - : Finnish Society of Forest Science. - 0037-5330 .- 2242-4075. ; 51 Journal article (peer-reviewed)abstract Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid-and south Sweden. Regression models were developed and applied to 12.5 m x 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image-and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

Bohlin, Jonas, et al. (author)
Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory
2017
In: Silva Fennica. - : Finnish Society of Forest Science. - 0037-5330 .- 2242-4075. ; 51
Journal article (peer-reviewed)abstract
- Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid-and south Sweden. Regression models were developed and applied to 12.5 m x 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image-and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

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