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Open AccessArticle

The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

1
Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha - Suchdol, 165 00 Prague, Czech Republic
2
Department of Forest Management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Praha - Suchdol, 165 00 Prague, Czech Republic
3
The Krkonose Mountains National Park Administration, Dobrovského 3, 543 01 Vrchlabí, Czech Republic
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1561; https://doi.org/10.3390/rs11131561
Received: 21 May 2019 / Revised: 27 June 2019 / Accepted: 29 June 2019 / Published: 2 July 2019
(This article belongs to the Section Forest Remote Sensing)
The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue and presents a major challenge for forest management. A timely detection of bark beetle infestation is therefore necessary to reduce losses. Besides wood production, a bark beetle outbreak affects the forest ecosystem in many other ways including the water cycle, nutrient cycle, or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed in the unmanaged zone of the Krkonoše Mountains National Park in the northern part of the Czech Republic where the natural spreading of bark beetle is slow and, therefore, allow us to continuously monitor the infested trees that are, in contrast to managed forests, not being removed. The aim of this work is to evaluate possibilities of unmanned aerial vehicle (UAV)-mounted low-cost RGB and modified near-infrared sensors for detection of different stages of infested trees at the individual level, using a retrospective time series for recognition of still green but already infested trees (so-called green attack). A mosaic was created from the UAV imagery, radiometrically calibrated for surface reflectance, and five vegetation indices were calculated; the reference data about the stage of bark beetle infestation was obtained through a combination of field survey and visual interpretation of an orthomosaic. The differences of vegetation indices between infested and healthy trees over four time points were statistically evaluated and classified using the Maximum Likelihood classifier. Achieved results confirm our assumptions that it is possible to use a low-cost UAV-based sensor for detection of various stages of bark beetle infestation across seasons; with increasing time after infection, distinguishing infested trees from healthy ones grows easier. The best performance was achieved by the Greenness Index with overall accuracy of 78%–96% across the time periods. The performance of the indices based on near-infrared band was lower. View Full-Text
Keywords: bark beetle detection; spectral change; UAVs; green attack; forest infestation; near infrared (NIR); visible spectrum bark beetle detection; spectral change; UAVs; green attack; forest infestation; near infrared (NIR); visible spectrum
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MDPI and ACS Style

Klouček, T.; Komárek, J.; Surový, P.; Hrach, K.; Janata, P.; Vašíček, B. The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation. Remote Sens. 2019, 11, 1561.

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