Yifang Shi aimed to accurately map the species of individual trees using multi-source remotely sensed data, including aerial photographs, airborne LiDAR and hyperspectral data. Yifang is a PhD student in the department of Natural Rescources. Her promotion was on Friday 31 January, she got her PhD cum laude. Her supervisor is prof.dr. A.K. Skidmore from the Faculty of Geo-Information Science and Earth Observation. Earlier, she won the Publication Award.
The accurate identification of tree species is critical for the management of forest ecosystems. Mapping of tree species is an important task as it can assist a wide range of environmental applications, such as biodiversity monitoring, ecosystem services assessment, invasive species detection, and sustainable forest management. However, individual tree species classification in natural mixed forests, as it is typical in central Europe, is still a challenging task. An in-depth understanding of the relationship between species-specific features and remote sensing observations for tree species classification needs further investigation.
The research in the thesis firstly evaluated the performance of geometric and radiometric metrics from airborne LiDAR data under leaf-on and leaf-off conditions for individual tree species discrimination. Then, the thesis examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping. To explore more valuable species-specific features, the thesis consequently integrated three plant functional traits (i.e. equivalent water thickness, leaf mass per area and leaf chlorophyll) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species. Three selected plant functional traits were accurately retrieved using radiative transfer model and further improved the accuracy of tree species classification. Eventually, the thesis focused on an important tree species – silver fir, and accurately mapped individuals of this species based on one-class classifiers using integrated airborne hyperspectral and LiDAR data. The mapping results provided the references locating the areas with a high occurrence probability of silver fir trees and hence increase the efficiency in subsequent field campaigns for forest management and biodiversity monitoring.
This thesis explored the potential of various remotely sensed datasets for individual tree species mapping. The methodologies and findings in this thesis can be applied in the mapping of other tree species, which enriches the knowledge of species-specific characteristics and related remotely sensed signatures. The emerging of UAVs and the upcoming hyperspectral missions such as EnMAP and HySPIRI deliver valuable datasets with multi-scale coverage and revisit observations, which can be used for mapping the diversity of tree species at stand or regional level.