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...

Authors: Lehmann, Jan Rudolf Karl
Nieberding, Felix
Prinz, Torsten
Knoth, Christian
Division/Institute:FB 14: Geowissenschaften
Document types:Article
Media types:Text
Publication date:2015
Date of publication on miami:19.03.2015
Modification date:16.04.2019
Edition statement:[Electronic ed.]
Source:Forests 6 (2015) 3, 594-612
Subjects:autonomous flying; beetle infection; drone; GIS; NDVI; object-based image analysis; OBIA; UAV
DDC Subject:550: Geowissenschaften, Geologie
License:CC BY 4.0
Language:English
Notes:Finanziert durch den Open-Access-Publikationsfonds 2014/2015 der Deutschen Forschungsgemeinschaft (DFG) und der Westfälischen Wilhelms-Universität Münster (WWU Münster).
Format:PDF document
ISSN:1999-4907
URN:urn:nbn:de:hbz:6-00329474874
Permalink:http://nbn-resolving.de/urn:nbn:de:hbz:6-00329474874
Other Identifiers:DOI: doi:10.3390/f6030594
Digital documents:forests-06-00594-v2.pdf

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.