Tree Species Classification : Analyzing Multitemporal Satellite Imagery and Multispectral Airborne Laser Scanning Data

Sammanfattning: Tree species composition of forests affects the whole ecosystem and is part of the information needed for an efficient planning of forest management. This thesis explores how recent developments in remote sensing can provide more accurate tree species mapping. I try to answer the question of how the properties of these data can be used to derive more information on tree species. Out of the four papers in this thesis, two papers examine how multitemporal satellite imagery from the Sentinel-2 mission can be of use, and the other two papers investigate what properties of multispectral airborne laser scanning (MSALS) data that contain the most information on tree species. We applied a Bayesian method to multitemporal satellite imagery for tree species classification of pixels in the hemiboreal forest of Remningstorp in southwestern Sweden. The Bayesian method was applied to 142 Sentinel-2 images, and to a subset of images ranked and selected by the separability of tree species classes. The method was also compared to a Random Forest classifier for 45 Sentinel-2 images of boreal forest in mid-Sweden. The Bayesian method performed better for homogeneous tree species classes, while Random Forest performed better for heterogeneous classes. Data from two MSALS systems were used for classifying the tree species of individual trees. Optech Titan-X data were used to classify free-standing trees of nine species in Remningstorp. By using Riegl VQ-1560i-DW data, we performed a tree species classification in a more operational setting for three tree species in closed-canopy hemiboreal forest in Asa in southern Sweden. Multispectral intensity features provided a great improvement in classification accuracy in both cases, compared to using only structural features or combining them with monospectral intensity features. For Optech Titan-X, the green wavelength performed poorly, but for Riegl VQ-1560i-DW, the green wavelength provided the most information for separability, especially for birch (Betula spp.). There are two main conclusions in this thesis. The first is that Bayesian methods that updates probabilities as new observations are made provides an opportunity to automate the addition of satellite images for an updated classification. The second is that MSALS data provides more information on tree species than monospectral data and tree crown structure do, with the most information coming from the upper parts of the canopy. Nonetheless, what wavelengths of light that contribute most to tree species classification accuracy is highly dependent on what MSALS system that is used.