Detecting the presence of standing dead trees using airborne laser scanning and optical data

ABSTRACT Deadwood is an important indicator of biodiversity in forest ecosystems. Identifying areas with large density of standing dead trees through field inventory is challenging, and remotely sensed data can provide a more systematic approach. In this study, we used metrics derived from airborne laser scanning (ALS) data (7.1 points m−2) and vegetation indices from optical images (HySpex sensor VNIR-1800: 0.3 m, SWIR-384: 0.7 m) to predict the presence of standing dead trees over a 15.9 km2 managed forest in Southern Norway. The dead basal area (DBA) of 40 sample plots was computed and used to classify the plots into presence/absence of standing dead trees. An area-based approach (ABA) using logistic regression was initially tested, but due to limited ground reference information, no statistically significant models could be formulated. A tree-based approach (TBA) was used to overcome this limitation. It identified trees on the ALS point cloud with a local maxima function and used a vegetation index to determine if the trees were dead. Between 18% and 42% of the predicted area with standing dead trees intersected a field recorded validation dataset. The TBA provided a good alternative to area-based regression models in the context of few standing dead trees.