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Challenges and best practices for deriving temperature data from an uncalibrated UAV thermal infrared camera

Author:
  • Julia Kelly
  • Natascha Kljun
  • Per Ola Olsson
  • Laura Mihai
  • Bengt Liljeblad
  • Per Weslien
  • Leif Klemedtsson
  • Lars Eklundh
Publishing year: 2019-03-01
Language: English
Pages:
Publication/Series: Remote Sensing
Volume: 11
Issue: 5
Document type: Journal article
Publisher: MDPI AG

Abstract english

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ×0.5 °C), the accuracy declined to ×5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera's automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these systems

Keywords

  • Computer Vision and Robotics (Autonomous Systems)
  • Physical Geography
  • Environmental Sciences
  • Calibration
  • FLIR
  • NUC
  • Radiometric
  • Remote sensing
  • Temperature
  • Thermal infrared
  • UAS
  • UAV
  • Vignetting

Other

Published
  • GREEN GAP: GREENhouse GAs flux uPscaling - improved understanding of key ecosystem processes using remote sensing and ground-based measurements
  • CarboScale : Scaling carbon flux from towers to the northern landscape
  • ISSN: 2072-4292
E-mail: lars [dot] eklundh [at] nateko [dot] lu [dot] se

Professor

Dept of Physical Geography and Ecosystem Science

+46 46 222 96 55

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Teaching staff

Dept of Physical Geography and Ecosystem Science

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Department of Physical Geography and Ecosystem Science
Lund University
Sölvegatan 12
S-223 62 Lund
Sweden

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