Comparison of Different Remotely Sensed Data Sources for Detection of Presence of Standing Dead Trees Using a Tree-Based Approach

Abstract

Deadwood is an important key ecological element for forest ecosystem biodiversity. Its low occurrence, especially in managed forests, makes inventory through field campaigns challenging. Remote sensing can provide a more objective and systematic approach to detect deadwood for large areas. Traditional area-based approaches have, however, shown limitations when it comes to predicting rare objects such as standing dead trees (SDT). To overcome this limitation, this study proposes a tree-based approach that uses a local maxima function to identify trees from airborne laser scanning (ALS) and optical data, and predict their status, i.e., living or dead, from normalized difference vegetation index (NDVI). NDVI was calculated from aerial images (hyperspectral and simulated aerial image) and from satellite images (PlanetScope and Sentinel-2). By comparing the different remotely sensed data sources, we aimed to assess the impact of spatial and spectral resolutions in the prediction of SDT. The presence/absence of SDT was perfectly predicted by combining trees identified using ALS-derived canopy height models with spatial resolutions between 0.75 m and 1 m and a search window size of 3 pixels, and NDVI computed from aerial images to predict their status. The presence/absence of SDT was not predicted as accurately when using NDVI computed from satellite images. A root-mean-square deviation of around 35 trees ha−1 was obtained when predicting the density of SDT with NDVI from aerial images and around 60 trees ha−1 with NDVI from satellite images. The tree-based approach presented in this study shows great potential to predict the presence of SDT over large areas.