Understanding bark beetle outbreaks: exploring the impact of changing temperature regimes, droughts, forest structure, and prospects for future forest pest management

Reviews in Environmental Science and Bio/Technology, Journal Year: 2024, Volume and Issue: 23(2), P. 257 - 290

Published: May 23, 2024

Abstract Climate change has increased the susceptibility of forest ecosystems, resulting in escalated decline globally. As one largest biomasses Northern Hemisphere, Eurasian boreal forests are subjected to frequent drought, windthrow, and high-temperature disturbances. Over last century, bark beetle outbreaks have emerged as a major biotic threat these forests, extensive tree mortality. Despite implementing various management strategies mitigate populations reduce mortality, none been effective. Moreover, altered disturbance regimes due changing climate facilitated success attacks with shorter multivoltine life cycles, consequently inciting more beetle-caused This review explores population dynamics context change, stand dynamics, strategies. Additionally, it examines recent advancements like remote sensing canine detection infested trees focuses on cutting-edge molecular approaches including RNAi-nanoparticle complexes, RNAi-symbiotic microbes, sterile insect technique, CRISPR/Cas9-based methods. These diverse novel potential effectively address challenges associated managing beetles improving health response climate.

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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Remote sensing forest health assessment – a comprehensive literature review on a European level

Central European Forestry Journal, Journal Year: 2025, Volume and Issue: 71(1), P. 14 - 39

Published: Feb. 1, 2025

Abstract Forest health assessments (FHA) have been carried out at European level since the 1980s in order to identify forest damage. The annual surveys are usually conducted without use of remote sensing tools. However, increasing availability observations potentially allows conduct FHA more wide-spread, often, or comprehensive and comparable way. This literature review systematically evaluated 110 studies from 2015 2022 that for Europe. purpose was determine (1) which tree species were studied; (2) what types damage evaluated; (3) whether levels distinguished according standard International Co-operative Program on Assessment Monitoring Air Pollution Effects Forests (ICP-Forest); (4) automation; (5) findings applicable a systematic FHA. results show spruce is most studied species. Damage caused by bark beetles drought predominantly studied. In only 2 classified. Only four able perform identifying individual trees, classifying their levels. None investigated suitability approach assessments. result surprising programs such as SEMEFOR analyzed potential already 1990s. We conclude new satellite systems advances artificial intelligence machine learning should be translated into practice ICP standards.

Language: Английский

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Multispectral drone images for the early detection of bark beetle infestations: assessment over large forest areas in the Italian South-Eastern Alps

Frontiers in Forests and Global Change, Journal Year: 2025, Volume and Issue: 8

Published: Feb. 27, 2025

Introduction European forests face increasing threats from climate change-induced stressors, which create favorable conditions for bark beetle outbreaks. The most critical spruce forest pest in Europe is the Spruce Bark Beetle ( Ips typographus L.). Effective management of this beetles’ outbreaks necessitates timely detection recently attacked trees, challenging given difficulty identifying symptoms on infested tree crowns, especially over large areas. This study assessed detectability trees dominated areas (20–60 ha) using high-resolution drone multispectral imagery. Methods A sensor mounted an Unmanned Aerial Vehicle (UAV) was used to capture images investigated stands weekly during June 2023. These were compute reflectance all single derive vegetation indices, and then compare these between healthy ones. Results results showed that it possible separate spectral features final developmental stage first generation, despite limitations due difficulties image processing best performing indices included NDRE (Normalized Difference Red Edge index) GNDVI (Green Normalized Vegetation Index), allowed earlier separation trees. Discussion shows use UAV imagery can present some when early larger integration sensors focused narrower windows around Red-Edge Green bands other remote sensing methods (e.g., satellite imagery) could help overcome improve early-detection proposed approach will increase understanding factors consider with techniques. In particular, add insights upscaling spatial scales, providing useful guidance suffering

Language: Английский

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Examining the potential for early detection of spruce bark beetle attacks using multi-temporal Sentinel-2 and harvester data

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 205, P. 352 - 366

Published: Oct. 25, 2023

Forests are invaluable terrestrial ecosystems with considerable economic, ecological, and environmental benefits. Bark beetles have been recognized as one of the major causes forest disturbance, climate change can exacerbate their impact, leading to more tree mortality. Early detection bark beetle attacks is vital reduce loss devastating consequences. This study examines potential for early European spruce (Ips typographus L.) in southeastern Sweden using comprehensive harvester data time series Sentinel-2 images, 2015–2021. Specifically, it aims at 1) determining most pronounced wavelength bands vegetation indices (VIs) detection, 2) number attacked trees a pixel required enable 3) testing three approaches, Detecting Breakpoints Estimating Segments Trend (DBEST), Mean-Level-Shift (MLS), Cumulative Sum (CUSUM) investigate attacks. The greatest separation reflectance between healthy pixels, from first swarming peak (May 2018) till harvesting (April 2019), was observed SWIR1 (0.018) SWIR2 (0.011) followed by red-edge (0.008), red (0.007), NIR (0.005), green band (0.004). blue showed least (0.003). All VIs base level after this prominent NDRS an increase 0.14, NDWI (-0.13), CCI (-0.11) NDVI (-0.09), all decreasing values. responses relation increased gradually pixels having ten infested trees, strongest response 9 14 trees. Pixels including than did not show any further substantial VIs. DBEST, on average, indicated that infestation impact detectable month 15–31 days precision. MLS CUSUM up two months' accuracies were ranked next. superior performance compared NDWI. based smoothed influence noise missing cannot be directly applied near real-time method.

Language: Английский

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Kernel-Based Early Detection of Forest Bark Beetle Attack Using Vegetation Indices Time Series of Sentinel-2

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 12868 - 12877

Published: Jan. 1, 2024

The European spruce bark beetle ( Ips typographus L.) is a biotic disturbance that devastates forest environmental services, and its activities are exacerbated due to climate change. Accordingly, researchers seek workflows using remote sensing imagery for detection in the early stage of attack, enabling proactive management. Most previous studies attempted detect attacks with pixel-based approaches. This study explores applicability pixels' spatial information, kernels, south Sweden. Four vegetation indices, Normalized Difference Vegetation Index (NDVI), Water (NDWI), Distance Red SWIR (NDRS), Chlorophyll Carotenoid (CCI), were derived from Sentinel-2 images time-series coefficient variation (CV) calculated, followed by interpolation smoothing eliminate gaps reduce noise. CV time series fed change algorithm called Detecting Breakpoints Estimating Segments Trend (DBEST). Detection accuracies ranged 83.80% 87.89%, highest related NDVI, NDRS. dates mainly fell June July, 6–7 weeks after swarming. NDRS performed slightly better detecting earlier, an average date 29th June. NDVI obtained higher pine, spruce, mixed conifer forests nonwetland areas, dominating area. In general, increased as number attacked trees pixels kernels. Results demonstrated kernel-based attack detection, which can elucidate new paradigm studies.

Language: Английский

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Drone-based early detection of bark beetle infested spruce trees differs in endemic and epidemic populations

Frontiers in Forests and Global Change, Journal Year: 2024, Volume and Issue: 7

Published: June 11, 2024

Introduction European forests face increasing threats due to climate change-induced stressors, which create the perfect conditions for bark beetle outbreaks. The most important spruce forest pest in Europe is Spruce Bark Beetle ( Ips typographus L.). Effective management of I. outbreaks necessitates timely detection recently attacked trees, challenging given difficulty spotting symptoms on infested tree crowns. population density one many factors that can affect infestation rate and development. This study compares appearance early endemic epidemic populations using highresolution Unmanned Aerial Vehicles (UAV) multispectral imagery. Methods In spring 2022, host colonization by beetles was induced groups trees growing 10 sites Southern Alps, characterized different (5 5 endemic). A sensor mounted a drone captured images once every 2 weeks, from May August 2022. analyses set vegetational indices allowed actual trees’ reflectance features be observed at each site, comparing them with those unattacked trees. Results show high triggers more rapid intense response regarding emergence symptoms. Infested were detected least 1 month before became evident human eye (red phase) sites, while this not possible sites. Key performing vegetation included NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjust Index, correction factor 0.44), NDRE Red Edge index). Discussion early-detection approach could allow automatic diagnosis beetles’ infestations provide useful guidance areas suffering

Language: Английский

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: An eXplainable Framework to Map Bark Beetle Infestation in Sentinel-2 Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2023, Volume and Issue: 16, P. 10050 - 10066

Published: Jan. 1, 2023

Recent long spells of high temperatures and drought-hit summers have fostered the conditions for an unprecedented outbreak bark beetles in Europe. This phenomenon has ruined vast swathes European conifer forests creating a need amongst forest managers to find effective methods gather information about mapping beetle infestation hotspots. Sentinel-2 data been recently established as alternative field surveys certain inventory tasks. Hence, this study explores achievements machine learning perform hotspots images. In particular, we investigate accuracy performance spectral classifier that is learned task by leveraging vegetation indices performing self-training. We use dataset images acquired non-overlapping scenes from North-east France October 2018. The selected host different sizes, which originate mass reproduction 2018 infestation. stage accounting ground truth masks subset imagery (training set). goal produce prediction remaining (working Moreover, eXplainable Artificial Intelligence technique relevance explain effect both self-training on decisions.

Language: Английский

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Detailed validation of large-scale Sentinel-2-based forest disturbance maps across Germany

Forestry An International Journal of Forest Research, Journal Year: 2024, Volume and Issue: unknown

Published: July 11, 2024

Abstract Monitoring forest areas with satellite data has become a vital tool to derive information on disturbances in European forests at large scales. An extensive validation of generated maps is essential evaluate their potential and limitations detecting various disturbance patterns. Here, we present the results for four study Germany using Sentinel-2 from 2018 2022. We apply time series filtering method map annual larger than 0.1 ha based spectral clustering change magnitude. The presented part research design precursor national German monitoring system. In this context, are used estimate affected timber volume related economic losses. To better understand thematic accuracies reliability area estimates, performed an independent product 20 sets embedded our comprising total 11 019 sample points. collected reference datasets expert interpretation high-resolution aerial imagery, including dominant tree species, cause, severity level. Our achieves overall accuracy 99.1 ± 0.1% separating disturbed undisturbed forest. This mainly indicative forest, as that class covers 97.2% area. For class, user’s 84.4 2.0% producer’s 85.1 3.4% similar indicate estimated accurately. However, 2022, observe overestimation extent, which attribute high drought stress year leading false detections, especially around edges. varies widely among seems severity, patch size. User’s range 31.0 8.4% 98.8 1.3%, while 60.5 37.3% 100.0 0.0% across sets. These variations highlight single local set not representative region diversity patterns, such Germany. emphasizes need assess large-scale products many different possible, cover sizes, severities, causes.

Language: Английский

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An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 183 - 199

Published: Jan. 1, 2025

Language: Английский

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