Abstract
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.
Highlights
The current climate change causes serious difficulties for forests and keeping track of natural hazards such as pest outbreaks represents a major challenge for forest management and for the future of forest ecosystems [1,2,3,4]
Näsi et al [4] distinguished between healthy, infested, and dead trees with an overall accuracy of 76% and Näsi et al [19] with 81% overall accuracy using hyperspectral unmanned aerial vehicle (UAV)-borne images in Finland; they did not distinguish between healthy trees and those in an early stage of infestation
Our study complements those by Stoyanova et al and Brovkina et al [21,22] who investigated UAV-based detection capabilities on mosaics based on images acquired during a single mission
Summary
The current climate change causes serious difficulties for forests and keeping track of natural hazards such as pest outbreaks represents a major challenge for forest management and for the future of forest ecosystems [1,2,3,4]. Coniferous forests, suffering besides pest attacks from frequent windstorms and droughts, may join the ranks of endangered ecosystems in the foreseeable future. The probability of bark beetle attack increases after long periods of drought, which debilitates the natural defences of the trees. An overabundance of bark beetle affects the production of wood matter; it affects other forest functions [7] such as water retention or carbon sequestration, nutrients storage [8], or biodiversity [9]. Besides such environmental concerns, there are economic impacts. A bark beetle outbreak causes a significant drop in the value of wood and, even more importantly, imposes significant costs associated with its consequences and recovery [10]
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