Remote sensing of bark beetle damage in Norway spruce individual tree canopies using thermal infrared and airborne laser scanning data fusion

Zakrzewska, Agata; Kopeć, Dominik

doi:10.1016/j.fecs.2022.100068

Cited by 4 publications

(1 citation statement)

References 54 publications

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“…Among them, RGB sensors take images in the visible range of the spectrum (i.e., three channels-red, green, and blue) and have been extensively used for many applications, owing to their low cost and high resolution [10]. On the other hand, thermal sensors capture images that measure surface temperatures, which is beneficial for numerous applications, e.g., forest fire monitoring [11] or detection of insect-induced canopy temperature increases [12], for instance, those created by bark beetle infestations of Norway spruce (Pinus abies) trees [13]. To facilitate monitoring in Geographic Information Systems (GIS) for such applications, drone-collected images need to be orthorectified and stitched together to generate an orthophoto mosaic (hereafter orthomosaic).…”

Section: Introductionmentioning

confidence: 99%

Orthomosaicking Thermal Drone Images of Forests via Simultaneously Acquired RGB Images

et al. 2023

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Operational forest monitoring often requires fine-detail information in the form of an orthomosaic, created by stitching overlapping nadir images captured by aerial platforms such as drones. RGB drone sensors are commonly used for low-cost, high-resolution imaging that is conducive to effective orthomosaicking, but only capture visible light. Thermal sensors, on the other hand, capture long-wave infrared radiation, which is useful for early pest detection among other applications. However, these lower-resolution images suffer from reduced contrast and lack of descriptive features for successful orthomosaicking, leading to gaps or swirling artifacts in the orthomosaic. To tackle this, we propose a thermal orthomosaicking workflow that leverages simultaneously acquired RGB images. The latter are used for producing a surface mesh via structure from motion, while thermal images are only used to texture this mesh and yield a thermal orthomosaic. Prior to texturing, RGB-thermal image pairs are co-registered using an affine transformation derived from a machine learning technique. On average, the individual RGB and thermal images achieve a mutual information of 0.2787 after co-registration using our technique, compared to 0.0591 before co-registration, and 0.1934 using manual co-registration. We show that the thermal orthomosaic generated from our workflow (1) is of better quality than other existing methods, (2) is geometrically aligned with the RGB orthomosaic, (3) preserves radiometric information (i.e., surface temperatures) from the original thermal imagery, and (4) enables easy transfer of downstream tasks—such as tree crown detection from the RGB to the thermal orthomosaic. We also provide an open-source tool that implements our workflow to facilitate usage and further development.

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Section: Introductionmentioning

confidence: 99%

Orthomosaicking Thermal Drone Images of Forests via Simultaneously Acquired RGB Images

et al. 2023

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Thermal Infrared Remote Sensing of Stress Responses in Forest Environments: a Review of Developments, Challenges, and Opportunities

Smigaj,

Agarwal,

Bartholomeus

et al. 2023

Curr. For. Rep.

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Purpose of Review The successful application of thermal infrared (TIR) remote sensing in the agricultural domain, largely driven by the arrival of new platforms and sensors that substantially increased thermal data resolution and availability, has sparked interest in thermography as a tool for monitoring forest health. In this review, we take a step back to reflect on what physiological responses are reflected in leaf and canopy temperature and summarise research activities on TIR remote sensing of stress responses in forest environments, highlighting current methodological challenges, open questions, and promising opportunities. Recent Findings This systematic literature review showed that whilst the focus still remains on satellite imagery, Uncrewed Aerial Vehicles (UAVs) are playing an increasingly important role in testing the capabilities and sensitivity to stress onset at the individual tree level. To date, drought stress has been the focal point of research, largely due to its direct link to stomatal functioning at leaf level. Though, research into thermal responses to other stressors, e.g. pathogens, is also gaining momentum. Summary Disentangling stress-induced canopy temperature variations from environmental factors and structural influences remains the main challenge for broader application of TIR remote sensing. Further development and testing of approaches for thermal data analysis, including their applicability for different tree species and sensitivity under different climatic conditions, are required to establish how TIR remote sensing can best complement existing forest health monitoring approaches.

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

Kautz,

Feurer,

Adler

2024

Forest Ecology and Management

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Remote sensing of bark beetle damage in Norway spruce individual tree canopies using thermal infrared and airborne laser scanning data fusion

Cited by 4 publications

References 54 publications

Orthomosaicking Thermal Drone Images of Forests via Simultaneously Acquired RGB Images

Orthomosaicking Thermal Drone Images of Forests via Simultaneously Acquired RGB Images

Thermal Infrared Remote Sensing of Stress Responses in Forest Environments: a Review of Developments, Challenges, and Opportunities

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

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