Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter
Abstract
:1. Introduction
1.1. Current Research in the Remote Sensing of Floating Plastic Litter
1.2. TIR Remote Sensing
1.3. Thermal Radiance Transfer Model
2. Materials and Methods
2.1. FLIR Camera and Image Processing
- i.
- Flatfield, corrected for comparing the DN values of targets in UAV images;
- ii.
- Uncorrected for deriving delta in UAV images, with DN(water) taken close to DN(target);
- iii.
- Uncorrected for DN(water) in the UAV images, taken from the center of the images;
- iv.
- Uncorrected for FLIR images taken in the laboratory, with targets in the center of the view.
2.2. NIR and RGB Cameras
2.3. Temperature, Light Intensity, and Humidity
2.3.1. Measurements
2.3.2. Sensor Details
2.4. UAV Surveys
- PET bottles, clear (0.5 L);
- PET bottles, clear (2 L);
- EPS foam board, white (thickness 5 cm);
- EPS foam board, blue (thickness 3 cm);
- HDPE milk bottles, semi-transparent, white (2.3 L);
- LDPE/HDPE binbag, black, two thin layers;
- PE tarpaulin, white, single-layer;
- Aluminum foil (wrapped around 3);
- Wooden tree trunk disk (thickness 4 cm, radius 29 cm).
2.5. Atmospheric Parameters from ERA5
2.6. FLIR Measurements in the Laboratory
2.7. Biofouling Experiment
3. Results
3.1. Assessing the FLIR Camera Response
3.2. Response of the FLIR Camera to Background TIR Radiance
3.3. Temperatures
3.3.1. Environmental Temperatures
3.3.2. Surface Temperatures of Floating Plastic
3.4. FLIR Signals of Floating Plastic
3.5. Background TIR Radiance over the Open Ocean
3.6. Biofouling
4. Discussion
4.1. Measurements
4.2. Questions Answered
- (A)
- Little or no daylight. At night and in the early morning (surveys 1, 2, and 4) all targets looked cooler than water, reflecting the cold background radiance from the higher atmosphere. The cooler the background radiance, the more negative the DN difference and delta. As the presence of clouds increased the sky’s thermal radiance, we saw the largest |delta| under a clear sky. Increased cloud cover and low cloud cover height appeared to reduce |delta| more than warmer air from the surface to a 111-meter altitude. In this scenario, the TIR signal of floating plastic was dominated by the reflectance of cold background radiance, controlled by low cloud cover and cloud base height.
- (B)
- Daylight. During survey 3, at around noon, although the sky was overcast, sunlight warmed the targets and all logged kinetic surface temperatures were above water temperature, with some above air temperature. The latter did not include clear plastic bottles, but the targets were possibly not deployed for long enough to see a strong greenhouse effect. The black binbag looked warmest in the TIR image, relating to the enhanced absorption of light by dark colors. In the Aegean Sea survey, the clear PET bottles looked brighter than the binbags [7], this could be because the binbags were light blue and not a dark colour. White EPS looked the coolest (although the logged temperature was the highest) which would indicate low thermal emissivity. In scenario B, the TIR signal of most floating plastic was dominated by their raised surface temperatures.
- (1)
- Their surface temperature is different from Tw.
- (2)
- Their emissivity is different from εw, which is close to one.
- (3)
- We found Tw to be spatially homogeneous, providing a suitable background.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix A.1. Snapshots
Appendix A.2. Temperatures
Appendix A.3. DN
Appendix A.4. Delta
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Survey | Day (2021) | LT | Sky Condition | Sea State | |
---|---|---|---|---|---|
1 | 1 April | day | 07:40 | Cloudy | smooth |
2 | 23 April | night | 04:14 | Overcast (no stars) | slight |
3 | 3 August | day | 12:01 | Overcast (100% cloud cover) | calm (rippled) |
4 | 4 August | night | 01:41 | Clear sky (stars and red moon) | calm (smooth) |
Tair, 111 m | STRD | LCC | CBH | |
---|---|---|---|---|
Survey | (°C) | (106 J/m2) | (0–1) | km |
1 | 1.9 | 1.0395 | 0.81 | 0.9231 |
2 | 5.0 | 1.1465 | 0.79 | 0.5344 |
3 | 14.6 | 1.2696 | 0.82 | 0.9115 |
4 | 13.7 | 1.1582 | 0.30 | 1.5682 |
Tair (mean ± std) (°C) | Tw (°C) | p1 | p2 | R2 | RMSE (DN) |
---|---|---|---|---|---|
19.8 ± 0.2 | 6 to 35 | 24.9 ± 0.7 | 6852 ± 15 | 1.00 | 10 |
22 ± 1 | 4 to 35 | 22.9 ± 0.5 | 6922 ± 12 | 1.00 | 13 |
19.7 ± 0.3 | −9 to 1 | 31 ± 3 | 6820 ± 10 | 0.98 | 18 |
Handheld | Datalogger | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Survey | Tair,2m | Tw | Tsand | Wind | iButton Tair,2m | HOBO Tair,2m | iButton Tair,30m | iButton Tw | iButton RH% | HOBO I (lux) |
1 | 5.7 | 5.6 | 6.0 | 5 | 6.1 | 5.9 | 7.6 | 6.1 | 66.8 | 5511 |
2 | 7.4 | 7.3 | 6.8 | 1 | 6.6 | 6.4 | 7.1 | 7.6 | 87.6 | 0 |
3 | 17.0 | 13.2 | 17.7 | 3–16 | 19.0 | 19.2 | 17.9 | 14.1 | 66.0 | 14,467 |
4 | 13.5 | 12.7 | 13.9 | 2–6 | 11.6 | x | 12.6 | 13.6 | 98.8 | 0 |
Temperature (°C) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Survey | Water | PET S | PET L | EPS White | EPS Blue | HDPE | Binbag | Tarp | Alu | Air, 2 m |
1 | 6.1 | 6.1 | 6.6 | 5.6 | 6 | 5.8 | 6.1 | 6.1 | 6.1 | 6.1 |
2 | 7.6 | 7.1 | 6.6 | 6.8 | 6.6 | 6.1 | 7.5 | 7.6 | 7.4 | 6.6 |
3 | 14.1 | 15.1 | 17.6 | 24.6 | 22.6 | 24.1 | 15.6 | 14.8 | 22.7 | 19.0 |
4 | 13.6 | 12.1 | 11.1 | 11.0 | 11.1 | 11.7 | 12.1 | 12.6 | 11.6 | 11.6 |
(a) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
FLIR kDN | ||||||||||
S | Water | PET S | PET L | EPS White | EPS Blue | HDPE | Binbag | Tarpaulin | Alu | Wood |
1 | 6.805 | 6.785 | 6.784 | 6.729 | 6.754 | 6.79 | 6.799 | NaN | 6.56 | NaN |
2 | 6.862 | 6.83 | 6.827 | 6.772 | 6.783 | 6.837 | NaN | NaN | 6.641 | NaN |
3 | 6.979 | 7.004 | 6.995 | 6.956 | 7.089 | 6.978 | 7.261 | NaN | 6.745 | 7.045 |
4 | 6.795 | 6.751 | 6.747 | 6.697 | 6.687 | 6.753 | 6.768 | 6.691 | 5.647 | 6.769 |
(b) | ||||||||||
Delta (DN) | ||||||||||
S | PET S | PET L | EPS White | EPS Blue | HDPE | Binbag | Tarpaulin | Alu | Wood | |
1 | −21 | −20 | −71 | −48 | −14 | −9 | NaN | −255 | NaN | |
2 | −29 | −31 | −85 | −73 | −23 | NaN | NaN | −191 | NaN | |
3 | 23 | 14 | −21 | 102 | 14 | 265 | NaN | −373 | 85 | |
4 | −41 | −44 | −105 | −96 | −38 | −24 | −96 | −1073 | −26 | |
(c) | ||||||||||
Delta (DN) | ||||||||||
S | PET S | PET L | EPS White | EPS Blue | HDPE | Binbag | Tarpaulin | Alu | Wood | |
1 | −25 | −23 | −92 | −56 | −16 | −12 | NaN | −262 | NaN | |
2 | −34 | −37 | −95 | −82 | −26 | NaN | NaN | −219 | NaN | |
3 | 30 | 22 | −24 | 114 | 18 | 294 | NaN | −245 | 90 | |
4 | −51 | −55 | −133 | −114 | −46 | −36 | −110 | −1214 | −31 |
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Goddijn-Murphy, L.; Williamson, B.J.; McIlvenny, J.; Corradi, P. Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter. Remote Sens. 2022, 14, 3179. https://doi.org/10.3390/rs14133179
Goddijn-Murphy L, Williamson BJ, McIlvenny J, Corradi P. Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter. Remote Sensing. 2022; 14(13):3179. https://doi.org/10.3390/rs14133179
Chicago/Turabian StyleGoddijn-Murphy, Lonneke, Benjamin J. Williamson, Jason McIlvenny, and Paolo Corradi. 2022. "Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter" Remote Sensing 14, no. 13: 3179. https://doi.org/10.3390/rs14133179
APA StyleGoddijn-Murphy, L., Williamson, B. J., McIlvenny, J., & Corradi, P. (2022). Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter. Remote Sensing, 14(13), 3179. https://doi.org/10.3390/rs14133179