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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Features predisposing forest to bark beetle outbreaks and their dynamics during drought

Forest Ecology and Management, Journal Year: 2022, Volume and Issue: 523, P. 120480 - 120480

Published: Sept. 7, 2022

Climate change is estimated to increase the risk of bark beetle (Ips typographus L.) mass outbreaks in Norway Spruce (Picea abies (L.) Karst) forests. Habitats that are thermally suitable for beetles may expand, and an frequency intensity droughts can promote drought stress on host trees. Drought affects tree vigor unison with environmental features it influences local predisposition forest stands attacks. We aimed study how various influence attacks during a year following years more normal weather conditions but higher populations. included representing stand attributes, topography, soil type wetness, proximity clear-cuts previous attacks, machine learning algorithm (random forest) was applied variation across 48,600 km2 area SE Sweden. Forest increased attack were distinguished high accuracy both conditions. The results show periods, spruce mixed coniferous forests had elevated attack, while mix deciduous trees lower risk. Forests average canopy height strongly predisposed However, similar between height, suggesting periods younger be importance moisture position within landscape highlighted as important year. Identifying areas risk, supported by information control drought, could aid adaptation strategies management intervention efforts. conclude geospatial data have potential further support digitalization industry, facilitating development methods capable quantify dynamics controlling context. Corresponding help direct actions effectively offer decision-making changing climate.

Language: Английский

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Patterns, drivers and detectability of infestation symptoms following attacks by the European spruce bark beetle

Journal of Pest Science, Journal Year: 2022, Volume and Issue: 96(1), P. 403 - 414

Published: April 15, 2022

Abstract Recent outbreaks of the European spruce bark beetle ( Ips typographus ) in Norway Picea abies forests Central Europe highlight importance timely detection and sanitation infested trees for pest management efficacy. This study provides novel quantitative evidence on manifestation infestation symptoms their visual detectability, to guide accelerated, optimized terrestrial monitoring, as well establishing benchmarks potential alternative (e.g. sensor-based) monitoring approaches. We employed bi-weekly, individual tree-level assessments 85 hectares spruce-dominated unmanaged forest over a 2-year period south-western Germany (detecting total 1,176 trees). By applying decision tree-type models, we quantified predictive power observed correlation with environmental factors time. Terrestrial accuracy timeliness were high, suggestive being sufficient suppress I. outbreak propagation by subsequent felling. Among six studied symptoms, boring dust occurred most frequently (in 82% correctly detected infestations) is suitable detection. Total symptom abundance was best explained two site parameters (slope, Standardized Precipitation-Evapotranspiration-Index) population density, while it widely independent tree Though varied time among trees, patterns clearly identified. For instance, infestations spring critical be detected, increasing crown discoloration defoliation facilitated late summer autumn. Findings further imply that hibernation would optimally already felling applied before November.

Language: Английский

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Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(13), P. 3135 - 3135

Published: June 29, 2022

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored detection European spruce bark beetle (Ips typographus, L.) at individual tree crown level using multispectral satellite images. Moreover, possibility tracking progression outbreak over time multitemporal data. Sentinel-2 data acquired during summer 2020 a beetle–infested area in Italian Alps were used for mapping time, while airborne lidar to automatically detect crowns classify species. Mapping carried out support vector machine classifier with input vegetation indices extracted from The results showed that it was possible two stages (i.e., early, late) an overall accuracy 83.4%. how is technically track evolution almost bi-weekly period crowns. outcomes paper are useful both management ecological perspective: allows forest managers map different spatial accuracy, maps describing could be further studies related behavior beetles.

Language: Английский

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Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: June 23, 2023

Bark beetle infestations are among the most substantial forest disturbance agents worldwide. Moreover, as a consequence of global climate change, they have increased in frequency and size number affected areas. Controlling bark outbreaks requires consistent operational monitoring, is possible using satellite data. However, while many satellite-based approaches been developed, full potential dense, multi-sensor time series has yet to be fully explored. Here, for first time, we used all available multispectral data from Landsat Sentinel-2, Sentinel-1 SAR data, combinations thereof detect Bavarian Forest National Park. Based on multi-year reference dataset annual infested areas, assessed separability between healthy forests various vegetation indices calculated We two compute infestation probability different datasets: Bayesian conditional probabilities, based best-separating index each type, random regression, type. Five sensor configurations were tested their detection capabilities: alone, Sentinel-2 combined, types combined. The best overall results terms spatial accuracy achieved with (max. accuracy: 0.93). detections also closest onset estimated year. detected areas larger contiguous patches higher reliability compared smaller patches. somewhat inferior those 0.89). While yielding similar results, combination did not provide any advantages over or alone 0.87), was unable 0.62). combined three achieve satisfactory either 0.67). Spatial accuracies typically probabilities than forest-derived but latter resulted earlier detections. approach presented herein provides flexible pipeline well-suited monitoring outbreaks. Furthermore, it can applied other types.

Language: Английский

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Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108665 - 108665

Published: Jan. 23, 2024

Language: Английский

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Detection of green attack and bark beetle susceptibility in Norway Spruce: Utilizing PlanetScope Multispectral Imagery for Tri-Stage spectral separability analysis

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 560, P. 121838 - 121838

Published: March 16, 2024

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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Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

ACM Computing Surveys, Journal Year: 2023, Volume and Issue: 56(4), P. 1 - 40

Published: Sept. 24, 2023

Bark beetle outbreaks can have serious consequences on forest ecosystem processes, biodiversity, structure and function, economies. Thus, accurate timely detection of bark infestations in the early stage (known as green-attack detection) is crucial to mitigate further impact, develop proactive management activities, minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep (DL)) provide a great alternative current approaches that primarily rely aerial surveys field surveys, which be impractical over vast areas. Existing exploit RS ML/DL exhibit substantial diversity due wide range factors involved. This article provides comprehensive review past advances from three primary perspectives: host interactions, RS, ML/DL. In contrast prior efforts, this encompasses all systems emphasizes methods investigate their strengths weaknesses. We parse existing literature based multi- or hyperspectral analyses distill knowledge species attack phases emphasis stages attacks, trees, study regions, platforms sensors, spectral/spatial/temporal resolutions, spectral signatures, vegetation indices, ML approaches, schemes, task categories, models, algorithms, classes/clusters, features, DL networks architectures. Although DL-based random algorithm showed promising results, highlighting potential detect subtle changes across visible, thermal, short-wave infrared effectiveness remains limited, high uncertainties persist distinctions between healthy attacked trees. To inspire novel solutions these shortcomings, we delve into principal challenges opportunities different perspectives, enabling deeper understanding state research guiding future directions.

Language: Английский

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