Comparing field measurements and annotations as training data for UAV-based detection of standing dead trees in coniferous forest

Abstract

ABSTRACT Deadwood provides a habitat for a large number of species and acts as a significant carbon storage. Thus, deadwood mapping allows planning various actions related to biodiversity conservation and forest management. Airborne laser scanning (ALS) provides an efficient means for monitoring large forested areas and is thus a potential method for deadwood mapping. Machine learning (ML) can be used for automating deadwood detection from ALS data. However, ML requires training data, collecting of which is laborious using field measurements. This study inspected the feasibility of training data manually annotated from aerial images for the mapping of individual standing dead trees. The standing dead trees were mapped from an ALS dataset collected with an unmanned aerial vehicle (UAV). The study was carried out by performing ML-based standing dead tree detection using annotated training data and field-measured training data, and comparing the performances of the models trained on these two datasets. The study found that using annotated training data improves the performance of standing dead tree detection due to its higher availability compared to field-measured data. For large trees (height > 14 m), the precision, recall, Cohen’s kappa score, and Matthews correlation coefficient achieved by the best classifier trained on annotated data were 0.23, 0.48, 0.17, and 0.20, respectively. In comparison, the corresponding metrics for the best classifier trained on field-measured data were 0.14, 0.52, 0.04, and 0.13. Annotated standing dead tree data is not a representative sample of the true standing dead tree population, as small trees and trees without crowns (snags) can often not be identified from aerial images. However, the study found that identifying such trees is challenging even when using field-measured training data, and thus using annotated training data does not bias results. In general, the results of the study showed that ALS-based standing dead tree detection should focus on the detection of large trees.