A practitioner's guide to thermal infrared remote sensing of rivers and streams: recent advances, precautions and considerations
Bibliographic record
Abstract
Stream temperature is a key habitat variable controlling all physical and biological river processes. In light of the threat of climate change to fluvial environments, growing importance is being placed on the need to gain a better understanding of stream temperature dynamics. However, many current or historic stream temperature datasets are of very low spatial resolution. Such in situ measurements are often unable to provide the fine scale information on longitudinal or lateral temperature patterns necessary for understanding links between thermal heterogeneity and fluvial processes. In recent years, attention has therefore turned to the use of thermal infrared (TIR) remote sensing in order to acquire 2D stream temperature data at ecologically meaningful scales. While TIR remote sensing is a relatively mature technology in its own right, its application in fluvial environments is accompanied by a range of limitations and considerations that must be respected in order to ensure the acquisition of reasonable quality data. It is only in recent years that researchers have been started to shift from detailing the technical aspects of TIR imaging of river environments toward describing its application for river management and fundamental fluvial science. We critically review this recent research, demonstrating the utility of TIR for applied river temperature research. We also provide a detailed guide to the practical use of TIR in river environments with a view to further stimulating its use for advancing stream temperature science. WIREs Water 2016, 3:251–268. doi: 10.1002/wat2.1135 This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Science of Water > Hydrological Processes Science of Water > Water Quality
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.046 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".