Remote sensing techniques for habitat condition mapping: deadwood monitoring using airborne laser scanning data

Deadwood is a vital part of the habitat for many threatened species of animals, plants and fungi. Thus, presence of deadwood is an important indicator for the probability that a given site harbors threatened species. Nowadays, field work is the most common method for monitoring dead trees. However, it is time consuming, costly and labor-intensive. Therefore, there is a need for an automatic method for mapping and monitoring deadwood. The combination of fine-resolution remote sensing and deep learning techniques have a potential to provide exactly this. Unfortunately, due to the typical location of lying deadwood under the canopy, this is a challenging task as the visibility of the lying trees is limited notably with optical remote sensing techniques. Therefore, laser scanning data seems to be the most appropriate for this purpose as it can penetrate the canopy to some extent and hence gather data from a forest floor.

In this work we aim at the development of methods enabling detection of lying deadwood at the national scale in protected forests and we focus on the presence of deadwood in 15-meter-radius circular plots. To achieve this goal, we use Airborne Laser Scanning (ALS) data that is publicly available for the whole Denmark and, as a reference, almost 6000 forestry plots acquired as a part of the Danish national habitats monitoring program. The binary classification into plots that contain deadwood and the ones that do not is performed using SparseCNN deep neural network. In this study we showed that it is possible to detect plots having deadwood with an overall accuracy of around 61%. However, the accuracy of the classifier depends on the volume of the deadwood present in a plot.