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Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels

The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

Titel: Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels
Verfasser: Lehmann, Jan
Nieberding, Felix GND
Prinz, Torsten GND
Knoth, Christian
Organisation: FB 14: Geowissenschaften
Dokumenttyp: Artikel
Medientyp: Text
Erscheinungsdatum: 02.03.2015
Publikation in MIAMI: 19.03.2015
Datum der letzten Änderung: 16.04.2019
Quelle: Forests 6 (2015) 3, 594-612
Schlagwörter: autonomous flying; beetle infection; drone; GIS; NDVI; object-based image analysis; OBIA; UAV
Fachgebiete: Geowissenschaften, Geologie
Lizenz: CC BY 4.0
Sprache: Englisch
Anmerkungen: Finanziert durch den Open-Access-Publikationsfonds 2014/2015 der Deutschen Forschungsgemeinschaft (DFG) und der Westfälischen Wilhelms-Universität Münster (WWU Münster).
Format: PDF-Dokument
URN: urn:nbn:de:hbz:6-00329474874
DOI: doi:10.3390/f6030594
ISSN: 1999-4907