DOI: 10.5061/dryad.fqz612jw3
title: Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery

description: 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.
description: The field data was collected as followed: Two field inventories
 were performed to gather data for a training dataset (collected in May
 2021) and a validation dataset (collected in October 2021) for which no
 permits were required. To include a sufficient number of standing dead
 trees in both datasets, we mostly inventoried stands that had at least one
 standing dead tree. We define retention trees as trees that stuck out at
 least three meters from the regenerating vegetation layer. We included
 stands that underwent final harvesting at any time in the past and the
 regenerating vegetation layer thus ranged from 0 to 13 m (recent to old
 final felling). All standing retention trees above 9 cm
 in DBH were inventoried. We selected 9 cm DBH as a cut-off point because
 it is reasonable to assume that thinner dead trees are likely missed
 frequently by the laser pulses. For suitable retention trees, we recorded
 tree species, conditions (dead or living), positions, heights, diameters,
 and only for dead trees, stem and crown conditions. Tree species that were
 recorded were Norway spruce, Scots pine, silver birch, downy birch,
 European aspen, rowan, goat willow, and grey alder. Note, however, that we
 grouped broadleaves and conifers to obtain good samples sizes for the
 classification. Positions were recorded with a Trimble Geo 7x Global
 Navigation Satellite Systems handheld and a Trimble Tornado antenna
 (Trimble, California, US) at an accuracy of &lt; 1 m for horizontal
 coordinates. With the help of publicly available CIR orthophotos provided
 by the National Land Survey of Finland, the tree positions were verified
 where visible and corrected if required. Tree heights were measured with a
 Haglöf Vertex IV and the accompanying Transponder T3 (Haglöf, Långsele,
 Sweden). Diameters were measured in two distinct ways. If a tree was
 intact, DBH was measured at 1.3 m height above ground. If a tree was
 broken, diameter was estimated at the middle height of the remaining
 standing tree column. For dead trees only, we recorded if the stem was
 intact or broken (either naturally or artificially), and if the stem was
 intact, whether the crown was entirely present, partially present, or
 absent. In total, we collected field data on 160 retention trees from 19
 stands for the training dataset and on 79 retention trees from 8 stands
 for the validation dataset. The two remotely sensed
 datasets [airborne laser scanning (ALS) data with an average pulse density
 of 5 pulses/m2 and unrectified color-infrared aerial (CIR) imagery] that
 we used were collected by third parties. Note that we cannot share the raw
 ALS data and unrectified CIR images on Dryad for legal reasons. We
 processed these data by applying photogrammetric principles and thus added
 spectral information (red, green, and near infrared bands) from the
 unrectified multispectral imagery to the ALS-derived point cloud. This was
 done through collinearity equations with knowledge of camera locations and
 orientations at the time of exposure of each image and internal camera
 parameters. The per channel digital number value for each pixel was
 averaged from all the images where a 3D point was observed. The result of
 this process was a point cloud that included both XYZ-coordinates and
 spectral information for each return. We have obtained permission to share
 these ALS data with already attached spectral values of all stands
 included in our analyses. A final processing step that we have already
 applied to these .laz files are height normalization, setting heights of
 points that were negative to zero, and removing outliers (high height
 values).
description: R (version 3.5.3 but later versions such as 4.2.1 also worked)
 and LAStools (research/commercial license) are required. Additionally, a
 GIS like QGIS is useful for visualisation (starting from QGIS version 3.26
 there is also good support for .las/.laz files).