Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery

1. In a landscape consisting primarily of intensive forestry interspersed
with some protected areas, multifunctional forestry with retention trees
can play a crucial role in nature conservation. Accurate mapping of
retention trees is important for guiding landscape-level conservation and
forest management and improving landscape connectivity. Sizeable dead and
living retention trees play a particularly important ecological role but
even their large-scale inventory is often intensive through field work
and/or inaccurate. We aimed to detect and classify retention trees using
the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5
pulses/m2) in conjunction with unrectified color-infrared (CIR) aerial
imagery. 2. Applying photogrammetric principles, we added spectral
information from the CIR imagery to the ALS-derived point cloud. For a
training dataset of 160 retention trees from 19 stands and a
geographically separate validation dataset of 79 trees from 8 stands, we
segmented trees via individual tree detection (ITD), removed most trees
belonging to the regenerating vegetation layer, and classified trees into
living conifers, living broadleaves, and dead trees by linear discriminant
analysis. 3. The detection rate via ITD differed considerably for dead and
living trees, with 41.7% of all dead and 83.8% of all living trees being
detected with relatively low commission error rates. Dead trees with
smaller diameters and heights were more likely missed, while grouping
caused living tree omission. For classification into living conifers,
living broadleaves, and dead trees, an overall accuracy of 67.3% was
achieved in training and 71.2% in validation data only ALS-derived
metrics. When adding spectral metrics, the overall accuracies were 79.6%
and 61.0% for training and validation, respectively. 4. Our findings imply
that wall-to-wall large-scale high density ALS data can be used to detect
retention trees rather accurately – even larger dead trees – and that
metrics derived solely from ALS data can accurately classify detected
retention trees into living conifers, living broadleaves, and dead trees.
Considering the ecological value of retention trees, our results are
promising and indicate that ALS data of the studied pulse density are a
cost-effective option for large area mapping of retention trees in
countries with such data available.