High resolution leaf fall monitoring using hemispherical NIR imagery

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Kirby, Jennifer (2020). High resolution leaf fall monitoring using hemispherical NIR imagery. University of Birmingham. Ph.D.

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Abstract

Leaf fall, coupled with adverse weather conditions, increases low adhesion on the rail network which in turn causes delays and disruption. In order to mitigate these issues, dedicated leaf fall forecasts are used by operators to target mitigation efforts. This research aims to improve these forecasts by improving real-time observations. This is achieved by designing and building a device that can record leaf fall at high spatial and temporal scales in real time. Specifically, a Raspberry Pi computer was adapted to capture near-infrared hemispherical images of the tree canopy throughout autumn. This sensor was compared with existing approaches and indicated that the low-cost approach provided a viable alternative, permitting deployments at an order of magnitude greater than what is currently achieved. The research concludes by contextualizing this approach within a suite of alternative low-cost sensors and approaches to improve autumn resilience on the railways.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):