ICOS Science Conference 2020: Day 2, Plenary

1. JULIA KELLY, NATASCHA KLJUN, LARS EKLUNDH, BENGT
LILJEBLADH, LEIF KLEMEDTSSON, PER-OLA OLSSON, PER
WESLIEN, XIANGHUA XIE
Thermal cameras: a new tool for
modelling and upscaling ecosystem
respiration?

2. Advantages of thermal cameras
July 2019August 2019
RGB image from UAV thermal image from UAV
5m 5m

3. Applications of thermal camera data
• Evapotranspiration modelling
• GPP modelling
• Vegetation drought stress indices
• Validating modelled leaf temperatures
• Species habitats/niches
• Impact of microclimatic variation on ecosystem processes

4. Precedent: satellite and airborne ER
modelling
• Successful ER modelling with satellite thermal imagery
• Limited by coarse resolution/complex processing
- Pixel size: 60 m to 1km
- Temporal resolution: 1 to 16 days

5. Does thermal camera data improve ER modelling?
Does temperature variation across space matter?
Thermal camera challenges?

6. Study area
• Hemi-boreal peatland
• Mycklemossen, Skogaryd Research Catchment, Sweden
• This study: 2018-2019
150m
Mycklemossen
Map and satellite image are from Lantmeteriet and Google Earth

7. Method
• Install thermal camera on mast, take images every 5min (Tsurf)
• Conduct dark chamber measurements within FOV of thermal camera
• Measure Tair and Tsoil during chamber measurements
• Compare ER model driven by Tair, Tsoil and Tsurf
• UAV flights over peatland collecting images of Tsurf with thermal
camera
• Use UAV Tsurf as input to ER model and produce ER maps over
larger area of peatland

8. Instruments
Thermal cameras:
Tower-based: FLIR A65
UAV-based: FLIR Vue Pro 640
and FLIR Vue Pro 640 R with
TEAX ThermalCapture
Calibrator
Spatial resolution: <7 cm
Accuracy: ±5 °C (tower-based
down to ±2.5 °C)
UAVs:
PitchUp Explorian 8
3DR Solo
FLIR A65
FLIR Vue Pro 640
FLIR Vue Pro R 640 with
ThermalCapture Calibrator Camera images from www.flir.com

9. Results
• ER models using Tsurf produce same accuracy as models based on Tair or Tsoil
- Tsurf can thus be used for upscaling
• ER model temperature-sensitivity depends on temperature metric used to drive the model
(Tsoil = highest, Tsurf = lowest sensitivity)
• Clear impact of 2018 drought on measured and modelled ER
- ER declines in 2018, more so for hummocks than hollows
- Peatland response to drought thus depends on proportion of hummocks and hollows
• No significant difference in Tsurf between hollows and hummocks
• Using Tsurf to model ER allows ER to be upscaled using UAV data
 All results figures are currently in a manuscript under review

10. Thermal camera challenges
• UAV-specific cameras need further development
- Stitching can be problematic
- Low resolution and slow shutter speed
- Effects of wind and camera temperature change
• Fixed cameras more accurate
• Collection of ancillary data
• Proprietary software/hardware

11. Useful resources for thermal users
• Kelly J., Kljun N., Olsson P-O., Mihai L., Liljebladh B., Weslien P.,
Klemedtsson L., Eklundh, L. (2019) Challenges and best practices for
deriving temperature data from an uncalibrated UAV thermal infrared
camera, Remote Sensing, 11(5), 567
• Aubrecht et al. (2016) Continuous, long-term, high-frequency thermal
imaging of vegetation: Uncertainties and recommended best practices,
Agricultural and Forest Meteorology, 228-229, pp. 315-326
• ThermStats R package (Senior et al. 2019)

12. Ongoing work
• Testing at other sites
• Upscaling to whole EC footprint area and validation with UAV data

13. Take home messages
• Benefit of using thermal: spatial data + upscaling
• Thermal data gives same ER model accuracy as Tair or Tsoil
• UAV thermal cameras still challenging
• ER model shape important to capture drought impact
’Modelling and upscaling ecosystem respiration using thermal imaging
and UAVs: application to a peatland during and after a hot drought’, Kelly J.,
Kljun N., Eklundh L., Liljebladh B., Klemedtsson L., Olsson P-O., Weslien P.,
Xianghua X., in revision

14. Thank you for listening!
julia.kelly@cec.lu.se