Bark Beetle Outbreaks in Europe: State of Knowledge and Ways Forward for Management

Current Forestry Reports, Journal Year: 2021, Volume and Issue: 7(3), P. 138 - 165

Published: July 28, 2021

Language: Английский

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Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft

Urban forestry & urban greening, Journal Year: 2018, Volume and Issue: 30, P. 72 - 83

Published: Jan. 12, 2018

Climate-related extended outbreaks and range shifts of destructive bark beetle species pose a serious threat to urban boreal forests in North America Fennoscandia. Recent developments low-cost remote sensing technologies offer an attractive means for early detection management environmental change. They are great interest the actors responsible monitoring managing forest health. The objective this investigation was develop, assess, compare automated procedures based on novel, hyperspectral imaging technology identification infestations at individual tree level forests. A camera tunable Fabry-Pérot interferometer operated from small, unmanned airborne vehicle (UAV) platform small Cessna-type aircraft platform. This study compared aspects using UAV datasets with spatial extent few hectares (ha) ground sample distance (GSD) 10–12 cm data covering areas several km2 having GSD 50 cm. An empirical assessment mature Norway spruce (Picea abies L. Karst.) trees suffering infestation (representing different colonization phases) by European (Ips typographus L.) carried out Lahti, city southern Finland. Individual spruces were classified as healthy, infested, or dead. For entire test area, best results overall accuracy 79% (Cohen's kappa: 0.54) when three crown color classes (green yellow gray dead). two (healthy, dead) same 93% (kappa: 0.77). finer resolution dataset provided better results, 81% 0.70), 73% 0.56) smaller sub-area. showed that analysis calibrated imagery potential affordable timely assessments health condition vulnerable

Language: Английский

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Living with bark beetles: impacts, outlook and management options

From science to policy, Journal Year: 2019, Volume and Issue: unknown

Published: April 4, 2019

Language: Английский

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Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)

Remote Sensing of Environment, Journal Year: 2021, Volume and Issue: 255, P. 112240 - 112240

Published: Jan. 20, 2021

The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects forests. A crucial measure in control removal infested trees before beetles leave bark, which generally happens end June. However, stressed tree crowns do not show any significant color changes visible spectrum at this early-stage infestation, making early detection difficult. In order to detect related forest stress an stage, we investigated differences radar and spectral signals healthy trees. How characteristics changed over time was analyzed for whole vegetation season, covered period attacks (April), infestation (‘green-attacks’, May July), middle late-stage (August October). results that already existed beginning attacks. separability between samples did change significantly during ‘green-attack’ stage. indicate were had signatures differed from ones. These stress-induced could be more efficient indicators infestations than symptoms. study used Sentinel-1 2 images a test site southern Sweden April October 2018 2019. red SWIR bands Sentinel-2 showed highest samples. backscatter additional contributed only slightly Random Forest classification models. We therefore propose Normalized Distance Red & (NDRS) index as new based on our observations linear relationship bands. This identified with accuracies 0.80 0.88 attacks, 0.82 0.81 0.91 middle- infestations. are higher those attained by established indices aimed detection, such Difference Water Index, Ratio Drought Disease Stress Index. By using proposed method, highlight potential NDRS estimate vulnerability season.

Language: Английский

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A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(3), P. 562 - 562

Published: Jan. 25, 2022

Central Europe was hit by several unusually strong periods of drought and heat between 2018 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast areas Germany. We present the first assessment canopy cover loss Germany for period January 2018–April 2021. Our approach makes use dense Sentinel-2 Landsat-8 time-series data. computed disturbance index (DI) from tasseled cap components brightness, greenness, wetness. Using quantiles, we generated monthly DI composites calculated anomalies a reference (2017). From resulting map, statistics administrative entities. results show 501,000 ha Germany, large regional differences. The losses largest central reached up two-thirds coniferous some districts. map has high spatial (10 m) temporal (monthly) resolution can be updated at any time.

Language: Английский

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Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 287, P. 113484 - 113484

Published: Feb. 3, 2023

Detecting disease- or insect-infested forests as early possible is a classic application of remote sensing. Under conditions climate change and global warming, outbreaks the European spruce bark beetle (Ips typographus, L.) are threatening related timber industry across Europe, detection infestations important for damage control. Infested trees without visible discoloration (green attack) have been identified using multispectral images, but how green attacks can be detected still unknown. This study aimed to determine when infested start show an abnormal spectral response compared with healthy trees, quantify detectability during infestation process. Pheromone bags were used attract beetles in controlled experiment, subsequent assessed field on weekly basis. In total, 977 monitored, including 208 attacked trees. Multispectral drone images obtained before insect attacks, representing different periods infestation. Individual tree crowns (ITC) delineated by marker-controlled watershed segmentation, average reflectance ITCs was analyzed based duration The driving factors examined. We propose new Multiple Ratio Disease–Water Stress Indices (MR-DSWIs) vegetation indices (VI) detecting infestations. defined VI range 5–95% tree, value outside that tree. Detection rates always higher than observed field, newly proposed MR-DSWIs more established VIs. Infestations detectable at 5 10 weeks after attack rate 15% 90%, respectively, from images. Weeks 5–10 therefore represent suitable period methodology map stage.

Language: Английский

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Early detection of bark beetle (Ips typographus) infestations by remote sensing – A critical review of recent research

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 556, P. 121595 - 121595

Published: Feb. 16, 2024

Bark beetle disturbances increasingly threaten structure and functionality of temperate boreal forests globally. The early detection bark beetle-infested trees, i.e. before beetles' emergence from the breeding tree, is essential for an effective outbreak mitigation. Terrestrial control surveys as traditionally employed infestation detection, however, are resource-intensive approach their limits in difficult terrain during mass outbreaks. Developments remote sensing algorithms giving hope that early-infested trees will be detectable remotely, thereby improving success management efficacy. Yet, a comprehensive quantitative evaluation approaches currently being developed lacking to date. This review synthesises state-of-the-art recent research on (or green-attack) by sensing, places it context with underlying biological constraints, technical opportunities potential applications. Since each beetle-host tree system has specific characteristics detectability, we focus greatest impact European forests, spruce (Ips typographus), which attacks Norway (Picea abies). By screening published within period 2000–2022, included 26 studies our analyses. All reviewed were purely exploratory, testing variety data and/or classification relatively limited spatial temporal coverage. Among tested platforms sensor types, satellite multispectral imagery most frequently investigated. Promising spectral wavelength range or index highly varied among regions. Timeliness accuracy found insufficient efficient management, regardless platform, type, resolution applied. main reasons preventing better performance include rapid development I. typographus combination delayed variable vitality response crown, frequent cloud cover spruce-dominated regions across Europe. In conclusion, current survey methods cannot yet replace terrestrial timely management. Nevertheless, they might supportive either back-up regular surveys, situations, e.g. detect hibernation accessibility, extensively managed without sufficient capacity. We suggest term 'early detection' used consistently synonym 'pre-emergence avoid ambiguity. Finally, provide recommendations future based lessons learned analysed, namely use more rigorous targeted study design, ensure interdisciplinarity, communicate results explicitly.

Language: Английский

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Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

Remote Sensing, Journal Year: 2019, Volume and Issue: 11(6), P. 643 - 643

Published: March 16, 2019

Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially Central Siberia. Determining tree stage based on shape, texture and colour crown unmanned aerial vehicle (UAV) images could help assess forest health a faster cheaper way. However, this task is challenging since (i) fir trees at different stages coexist overlap canopy, (ii) distribution nature irregular hence distinguishing between crowns hard, even for human eye. Motivated by latest advances computer vision machine learning, work proposes two-stage solution: In first stage, we built detection strategy that finds regions input UAV image are more likely contain crown, second developed new convolutional neural network (CNN) architecture predicts each candidate region. Our experiments show proposed approach shows satisfactory results Red, Green, Blue (RGB) areas state reserve “Stolby” (Krasnoyarsk, Russia).

Language: Английский

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Sentinel‐2 accurately maps green‐attack stage of European spruce bark beetle (Ips typographus, L.) compared with Landsat‐8

Remote Sensing in Ecology and Conservation, Journal Year: 2018, Volume and Issue: 5(1), P. 87 - 106

Published: Aug. 25, 2018

Abstract Natural disturbances induced by insect outbreaks have increased in forest ecosystems over the past decades. To minimize economic loss and prevent a mass outbreak, early detection of bark beetle green attack – period when trees yet to show visual signs infestation stress is therefore crucial effective timely management. In this study, we evaluated ability spectral vegetation indices extracted from Landsat‐8 Sentinel‐2 imagery map using principal component analysis ( PCA ) partial least square discriminate PLS ‐ DA ). A recent produced through interpretation high‐resolution aerial photographs validated final output maps. Leaf measurements alongside total chlorophyll nitrogen concentration, leaf water content dry matter were measured assess impact on foliar properties. We observed that majority SVI s) calculated Sentinel‐2, particularly red‐edge dependent NDRE 2 3) water‐related SR SWIR , NDWI DSWI LWCI ), able healthy infested plots. contrast, only RDI between plots efficiently. The number pixels identified as harboring matched with ground truth data (aerial photography) was higher for (67%) than (36%) s, indicating elevated sensitivity changes attack. also determined significantly P < 0.05) green‐attacked trees. Our study highlights potential infestations production reliable maps at green‐attack stage.

Language: Английский

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

Remote Sensing, Journal Year: 2019, Volume and Issue: 11(13), P. 1561 - 1561

Published: July 2, 2019

The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue presents a major challenge for forest management. A timely detection of infestation is therefore necessary to reduce losses. Besides wood production, outbreak affects the ecosystem in many other ways including water cycle, nutrient or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed unmanaged zone Krkonoše Mountains National Park northern part Czech Republic where natural spreading slow and, therefore, allow us continuously monitor infested trees are, contrast managed forests, not being removed. aim this work evaluate possibilities unmanned aerial vehicle (UAV)-mounted low-cost RGB modified near-infrared sensors different stages at individual level, using retrospective time series recognition still green but already (so-called attack). mosaic created from UAV imagery, radiometrically calibrated surface reflectance, five vegetation indices were calculated; reference data about stage obtained through combination field survey visual interpretation an orthomosaic. differences between healthy over four points statistically evaluated classified Maximum Likelihood classifier. Achieved results confirm our assumptions it possible use UAV-based sensor various across seasons; with increasing after infection, distinguishing ones grows easier. best performance achieved by Greenness Index overall accuracy 78%–96% periods. based on band lower.

Language: Английский

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