Monitoring ash dieback in British forests using hyperspectral remote sensing

Fungal ash dieback (Hymenoscyphus fraxineus) is posing an imminent threat to forest health in Europe. Using airborne hyperspectral imagery trained against 422 tree crowns of known species and ash dieback severity, we built PLS-DA and RF models that classified individual tree crowns (ITCs) into five species (>90% OA) and ash crowns into three disease severity classes (77% OA) respectively. Dark pixel filtering was found to improve the accuracy of species (+6%) but not disease classification. By incorporating automatic ITC segmentation and the classification models, we further demonstrated how species and fungal ash dieback can be mapped at a region scale for forest management and epidemiological research.

Remote Sensing in Ecology and Conservation, 2020

Link to Abstract and video presentation