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Mapping European spruce bark beetle infestation at its early phase using gyrocopter-mounted hyperspectral data and field measurements

  • The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices).The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.show moreshow less

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Metadaten
Author:Florian M. HellwigORCiDGND, Martyna A. Stelmaszczuk-Górska, Clémence Dubois, Marco Wolsza, Sina C. Truckenbrodt, Herbert Sagichewski, Sergej Chmara, Lutz Bannehr, Angela Lausch, Christiane Schmullius
URN:urn:nbn:de:bvb:384-opus4-1287258
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/128725
ISSN:2072-4292OPAC
Parent Title (English):Remote Sensing
Publisher:MDPI
Place of publication:Basel
Type:Article
Language:English
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2026/03/05
Volume:13
Issue:22
First Page:4659
DOI:https://doi.org/10.3390/rs13224659
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Lehrstuhl für Regionales Klima und Hydrologie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung