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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Current and future control of the wood‐boring pest Anoplophora glabripennis

Insect Science, Journal Year: 2023, Volume and Issue: 30(6), P. 1534 - 1551

Published: March 21, 2023

Abstract The Asian longhorn beetle (ALB) Anoplophora glabripennis is one of the most successful and feared invasive insect species worldwide. This review covers recent research concerning distribution damage caused by ALB, as well major efforts to control manage ALB in China. destruction range have continued expand over past decade worldwide, number interceptions has remained high. Detection monitoring methods for early discovery diversified, with advances semiochemical using satellite remote sensing Ecological China involves planting mixtures preferred resistant tree species, this practice can prevent outbreaks. In addition, strategies chemical biological achieved promising results during last China, especially development insecticides targeting different stages applying Dastarcus helophoroides Dendrocopos biocontrol agents. Finally, we analyze recommendations prevention management based on native area research. information will hopefully help some invaded areas where target containment ALB.

Language: Английский

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Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications

Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 1975 - 1975

Published: Sept. 1, 2024

Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way realize the accurate management decision support of production processes using modern information technology, is becoming an effective method solving these In particular, combination remote sensing technology machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive systematic reviews on integrated application two technologies. For this reason, study conducts literature search Web Science, Scopus, Google Scholar, PubMed databases analyzes in PA over last 10 years. The found that: (1) because their varied characteristics, different types data exhibit significant differences meeting needs PA, which hyperspectral most widely used method, accounting more than 30% results. UAV offers greatest potential, about 24% data, showing upward trend. (2) Machine displays obvious advantages promoting development vector algorithm 20%, followed by random forest algorithm, 18% methods used. addition, also discusses main challenges faced currently, such difficult problems regarding acquisition processing high-quality model interpretation, generalization ability, considers future trends, intelligence automation, strengthening international cooperation sharing, sustainable transformation achievements. summary, can provide ideas references combined with promote

Language: Английский

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Spatial, spectral and temporal insights: harnessing high-resolution satellite remote sensing and artificial intelligence for early monitoring of wood boring pests in forests

Plant Stress, Journal Year: 2024, Volume and Issue: 11, P. 100381 - 100381

Published: Feb. 2, 2024

Globally, biotic factors like insect pests and diseases as well abiotic fire, windstorms, droughts influence the global forest ecosystem. Wood-boring (WBPs) pose a considerable threat to ecosystems worldwide owing their capacity of remaining unnoticed during early stages, resulting in vast pervasive infestations later eventually significant tree death. Therefore, it is crucial promptly effectively treat early-stage WBPs by timely detection. The prompt detection requires use advanced effective methods, such remote sensing. This paper provides an overview many uses several sensing devices, platforms, algorithms context monitoring infestations. Modern lightweight sensors light ranging (LiDAR), hyperspectral imagers, thermal cameras, radio (Radar) combined with unmanned aerial vehicles (UAVs) versatile capabilities offer comprehensive method for gathering data. purpose this study examine current capabilities, limits, potential future advancements accurately identifying WBPs.

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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Non-destructive estimation of wood-boring pest density in living trees using X-ray imaging and edge computing techniques

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 233, P. 110183 - 110183

Published: March 6, 2025

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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Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(20), P. 4928 - 4928

Published: Oct. 12, 2023

The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there evidence of increases affected areas due to climate warming. Effective forest monitoring methods are urgently needed providing timely data on health status conducting management operations that aim prepare mitigate damage beetle. Unoccupied aircraft systems (UASs) combination with machine learning image analysis have emerged as powerful tool fast-response health. This research aims assess effectiveness deep neural networks (DNNs) identifying infestations at individual level from UAS images. study compares efficacy RGB, multispectral (MS), hyperspectral (HS) imaging, evaluates various network structures each type. findings reveal MS HS images perform better than RGB A 2D-3D-CNN model trained proves be best detecting infested trees, an F1-score 0.759, while dead healthy F1-scores 0.880 0.928, respectively. also demonstrates tested classifier outperform state-of-the-art You Only Look Once (YOLO) module, effective analyzer can implemented integrating YOLO DNN model. current provides foundation further exploration imaging disturbances time, which play crucial role efforts combat large-scale outbreaks. highlights potential remote sensing mitigating impacts biotic stresses. It offers valuable insights into DNNs using UAS-based technology.

Language: Английский

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Bark beetle pre-emergence detection using multi-temporal hyperspectral drone images: Green shoulder indices can indicate subtle tree vitality decline

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 216, P. 200 - 216

Published: Aug. 8, 2024

Forest stress monitoring and in-time identification of forest disturbances are important to improve resilience climate change. Fast-developing drone techniques hyperspectral imagery provide tools for understanding the decline process under contribute focused monitoring. This study explored developed early detection caused by European spruce bark beetle Ips typographus (L.), before offspring emergence, which is crucial in controlling spread but has been shown be challenging. challenges highest possible detectability infested trees using a system that provided images with very high spectral, spatial, temporal resolutions Southern Finland. Images were acquired bi-weekly, four times (T1, T2, T3, T4), covering 8 weeks from being attacked first filial generation (F1) beginning second (F2) brood emergence. Very low separability was observed reflectance healthy trees, derivative captured vitality changes, green shoulder region (wavelengths 490–550 nm) exhibiting all wavelengths (400–1700 nm). We discovered peak valley values curves consistently shifted longer infestation time. Based on this finding, we indices. The rates 0.24–0.31 0.76–0.83 T3 T4, higher than commonly used VIs such as Photochemical Reflectance Index Red Edge Inflection Position, 0.69 0.34 respectively. also proposed simplified indices three bands can multispectral cameras satellite large area health. concluded infestations month after attack, then rapidly increased highlighted great potential quantifying photochemical functioning vegetation stress. methodology potentially applied forests declining various sources disturbances, infestations, diseases drought.

Language: Английский

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Building integrated plant health surveillance: a proactive research agenda for anticipating and mitigating disease and pest emergence

CABI Agriculture and Bioscience, Journal Year: 2024, Volume and Issue: 5(1)

Published: Aug. 17, 2024

Abstract In an era marked by rapid global changes, the reinforcement and modernization of plant health surveillance systems have become imperative. Sixty-five scientists present here a research agenda for enhanced modernized to anticipate mitigate disease pest emergence. Our approach integrates wide range scientific fields (from life, social, physical engineering sciences) identifies key knowledge gaps, focusing on anticipation, risk assessment, early detection, multi-actor collaboration. The directions we propose are organized around four complementary thematic axes. first axis is anticipation emergence, encompassing innovative forecasting, adaptive potential, effects climatic cropping system changes. second addresses use versatile broad-spectrum tools, including molecular or imaging diagnostics supported artificial intelligence, monitoring generic matrices such as air water. third focuses known pests from new perspectives, i.e., using novel approaches detect species but also anticipating detecting, within species, populations genotypes that pose higher risk. fourth advocates management commons through establishment cooperative long-term data-driven alert information dissemination. We stress importance integrating data multiple sources open science databases metadata, alongside developing methods interpolating extrapolating incomplete data. Finally, advocate Integrated Health Surveillance in One context, favoring tailored solutions problems recognizing interconnected risks plants, humans, animals environment, food insecurity, pesticide residues, environmental pollution alterations ecosystem services.

Language: Английский

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