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Forests 2015, 6(3), 594-612; doi:10.3390/f6030594

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

1
Institute of Landscape Ecology, University of Münster, Heisenbergstr.2, 48149 Münster, Germany
2
Institute of Geoinformatics, University of Münster, Heisenbergstr.2, 48149 Münster, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Dave Verbyla and Eric J. Jokela
Received: 3 February 2015 / Accepted: 15 February 2015 / Published: 2 March 2015
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Abstract

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. View Full-Text
Keywords: autonomous flying; beetle infection; drone; GIS; NDVI; object-based image analysis; OBIA; UAV autonomous flying; beetle infection; drone; GIS; NDVI; object-based image analysis; OBIA; UAV
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lehmann, J.R.K.; Nieberding, F.; Prinz, T.; Knoth, C. Analysis of Unmanned Aerial System-Based CIR Images in Forestry—A New Perspective to Monitor Pest Infestation Levels. Forests 2015, 6, 594-612.

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