Microthermal variability in a Welsh upland stream

Gangi, Laura; Hannah, David M.; Weiler, Markus

doi:10.1002/9781118643525.ch14

Cited by 1 publication

(1 citation statement)

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“…The simultaneous acquisition of river temperature data using ground‐based/handheld TIR cameras also holds potential for enhancing TIR data collection using drones. Indeed, recent research (e.g., Bonar & Petre, ; Gangi, Hannah, & Weiler, ; Hare, Briggs, Rosenberry, Boutt, & Lane, ) has demonstrated that such methods are both a cost‐effective and relatively simple technique for acquiring spatially explicit river temperature data. The collection of ground‐based temperature data at the same time as sUAS imagery surveys may therefore prove useful for compensating for drift observed here.…”

Section: Discussionmentioning

confidence: 99%

Assessing the potential of drone‐based thermal infrared imagery for quantifying river temperature heterogeneity

Dugdale

Kelleher

Malcolm

et al. 2019

Hydrological Processes

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Climate change is altering river temperature regimes, modifying the dynamics of temperature‐sensitive fishes. The ability to map river temperature is therefore important for understanding the impacts of future warming. Thermal infrared (TIR) remote sensing has proven effective for river temperature mapping, but TIR surveys of rivers remain expensive. Recent drone‐based TIR systems present a potential solution to this problem. However, information regarding the utility of these miniaturised systems for surveying rivers is limited. Here, we present the results of several drone‐based TIR surveys conducted with a view to understanding their suitability for characterising river temperature heterogeneity. We find that drone‐based TIR data are able to clearly reveal the location and extent of discrete thermal inputs to rivers, but thermal imagery suffers from temperature drift‐induced bias, which prevents the extraction of accurate temperature data. Statistical analysis of the causes of this drift reveals that drone flight characteristics and environmental conditions at the time of acquisition explain ~66% of the variance in TIR sensor drift. These results shed important light on the factors influencing drone‐based TIR data quality and suggest that further technological development is required to enable the extraction of robust river temperature data. Nonetheless, this technology represents a promising approach for augmenting in situ sensor capabilities and improved quantification of advective inputs to rivers at intermediate spatial scales between point measurements and “conventional” airborne or satellite remote sensing.

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Section: Discussionmentioning

confidence: 99%