Segmentation of forest patches and estimation of canopy cover using 3D information from stereo photogrammetry

3D information extracted by image matching of aerial images, so called image-based point clouds, have been found to provide accurate vegetation height measurements. This has led to an increased interest from the vegetation mapping community, since aerial images are an affordable alternative to airborne laser scanner (ALS) data. In Sweden, this is especially interesting due to the National Mapping Agency’s decision to derive 3D information from annually acquired aerial imagery, starting in 2016. Previous studies have shown that image-based point cloud data derived from standard stereo aerial images is of potential use for forest inventory and change detection. In this thesis, the focus is on exploring the utility of image-based point clouds, and surface models, for vegetation mapping; more specifically, it explores segmentation of vegetation patches based on height above ground, estimation of tree height, and estimation of vertical canopy cover. The studies were conducted in a study area located in the hemi-boreal zone of southern Sweden. Segmentation based on canopy height models (CHMs) derived by image matching combined with a digital elevation model (DEM) from ALS data was found to deliver polygons within which tree height varied with a few meters. Tree height was estimated using height percentiles derived from the CHM and the results were similar to previous studies using image-based point clouds. Estimation of vertical canopy cover resulted in low accuracy due to underestimation when the canopy cover was sparse, and overestimation when the canopy cover was dense, while behaving linearly at approximately 15 – 85 % canopy cover. Dominant tree species influenced the results of estimation of tree height, as well as vertical canopy cover. Vegetation mapping using image-based point cloud data holds great potential and further research is needed to gain knowledge of appropriate methods and limitations