Work

Identification of damage severity in Fraxinus excelsior L. trees caused by ash dieback using multisensory and multitemporal UAV imagery

Year: 2025

Type: article

Abstract: The extended spread of ash dieback in Europe has far-reaching consequences for Fraxinus excelsior L. populations. The progression of the disease leads to characteristic symptoms, particularly within the tree crowns. To date, assessing the damage severity of each individual tree typically requires in-field inspections. However, UAVs equipped with RGB, thermal, and multispectral sensors offer cost-effective and objective possibilities. This study relied on such analyses and focused on two ash seed orchards in Baden-Wuerttemberg, Germany, where visual inspections were compared with multisensorial data obtained in spring, summer and autumn of 2022 and 2023. The calculated RGB and multispectral vegetation indices were able to significantly discriminate between different degrees of damage due to ash dieback; in contrast, thermal data were less reliable and linked to different dynamics. Novel thresholds applied to the vegetation indices enabled a classification of mild and severe damage with an overall accuracy of 74.9 % for the multispectral index DVI (Difference Vegetation Index) and 73.0 % for the RGB index GRVI (Green-Red Vegetation Index). Combining RGB and multispectral indices further improved the overall accuracy to 77.2 %. The presented workflow offers forest practitioners an accessible toolset for evaluating the health status of ash populations affected by ash dieback. • Multisensorial UAV surveys during summer enable analysis of ash dieback severity. • RGB and multispectral indices can differentiate mild from severe ash dieback damage. • Thermal UAV data presents challenges in ash dieback damage severity analysis. • Vegetation index thresholds are suitable for classifying mild or severe damage. • Threshold application enables targeted forestry efforts to conserve ash trees.

Source: Forest Ecology and Management

DOI: 10.1016/j.foreco.2025.122660

Authors: Lisa Buchner, Anna-Katharina Eisen, Susanne Jochner-Oette

Institution: Catholic University of Eichstätt-Ingolstadt

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Life on land, Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,560