Methods in Ecology and Evolution (Jul 2023)
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
Abstract
Abstract 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 colour‐infrared (CIR) aerial imagery. 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 eight 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. 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 using only ALS‐derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation respectively. 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.
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