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Magdalena Smigaj

Researcher at Newcastle University

Publications -  14
Citations -  499

Magdalena Smigaj is an academic researcher from Newcastle University. The author has contributed to research in topics: Carbon neutrality & Thermography. The author has an hindex of 6, co-authored 13 publications receiving 180 citations. Previous affiliations of Magdalena Smigaj include Kyushu University.

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Will climate mitigation ambitions lead to carbon neutrality? An analysis of the local-level plans of 327 cities in the EU

TL;DR: In this article, the authors present a comparative analysis of the mitigation targets of 327 European cities, as declared in their local climate plans, and analyze whether the type of plan, city size, membership of climate networks, and its regional location are associated with different levels of mitigation ambition.
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The benefits and negative impacts of citizen science applications to water as experienced by participants and communities

TL;DR: The authors reviewed 549 publications concerning citizen science applications in the water sciences to examine personal benefits and motivations, and wider community benefits, and revealed that more consideration should be given to how these benefits interrelate and how they build community capitals to foster their realization in citizen science water projects.
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Canopy temperature from an Unmanned Aerial Vehicle as an indicator of tree stress associated with red band needle blight severity.

TL;DR: In this paper, the authors investigated the use of UAV-borne thermal systems for detecting disease-induced canopy temperature increase and explored the influence of the imaging time and weather conditions on the detected relationship.
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Uav-borne thermal imaging for forest health monitoring: detection of disease-induced canopy temperature increase

TL;DR: In this paper, the authors used a fixed-wing UAV-borne thermal system for monitoring disease-induced canopy temperature rise in a Scots pine stand, where the camera calibration was performed revealing a significant overestimation of the temperature readings and a non-uniformity (exceeding 1 K) across the imagery.
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Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

TL;DR: A detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results, revealed that a crown-shaped kernel consistently generates better results than other variants, whereas weighting and adaptiveness do not warrant improvements.