Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera

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 (Less)

[1]  Kalifa Goïta,et al.  Characterization of land surface thermal structure from NOAA-AVHRR data over a northern ecosystem , 1997 .

[2]  Huixin Zhou,et al.  New improved nonuniformity correction for infrared focal plane arrays , 2005 .

[3]  Luis Alonso,et al.  Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer , 2015, Remote. Sens..

[4]  S. L. Barr,et al.  Investigating the performance of a low-cost thermal imager for forestry applications , 2016, Remote Sensing.

[5]  Andrew Baird,et al.  Modelling soil temperatures in northern peatlands , 2008 .

[6]  Georg Leitinger,et al.  Implications of atmospheric conditions for analysis of surface temperature variability derived from landscape-scale thermography , 2016, International Journal of Biometeorology.

[7]  Joseph A. Shaw,et al.  Correcting for focal-plane-array temperature dependence in microbolometer infrared cameras lacking thermal stabilization , 2013 .

[8]  Kazunobu Ishii,et al.  Correction of Low-altitude Thermal Images applied to estimating Soil Water Status , 2007 .

[9]  Dar A. Roberts,et al.  Continuous, long-term, high-frequency thermal imaging of vegetation: Uncertainties and recommended best practices , 2016 .

[10]  Helge Aasen,et al.  Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to application , 2018 .

[11]  Nicolas Virlet,et al.  Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration , 2016, Precision Agriculture.

[12]  H. B. Mitchell Image Fusion: Theories, Techniques and Applications , 2010 .

[13]  Karsten Schulz,et al.  Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system , 2017, International journal of remote sensing.

[14]  Eija Honkavaara,et al.  Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows , 2018, Remote. Sens..

[15]  H. Nieto,et al.  Crop water stress maps for an entire growing season from visible and thermal UAV imagery , 2016 .

[16]  Pablo J. Zarco-Tejada,et al.  Estimating evaporation with thermal UAV data and two-source energy balance models , 2016 .

[17]  Juan C. Suárez,et al.  Use of Miniature Thermal Cameras for Detection of Physiological Stress in Conifers , 2017, Remote. Sens..

[18]  David Hernández-López,et al.  Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture , 2017, Sensors.

[19]  Boguslaw Wiecek,et al.  New approach to thermal drift correction in microbolometer thermal cameras , 2015 .

[20]  YangQuan Chen,et al.  Survey of thermal infrared remote sensing for Unmanned Aerial Systems , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[21]  F. Meier,et al.  Atmospheric correction of thermal-infrared imagery of the 3-D urban environment acquired in oblique viewing geometry , 2010 .

[22]  Jean-Pierre Lagouarde,et al.  A two parameter model to simulate thermal infrared directional effects for remote sensing applications , 2016 .

[23]  K. Moffett,et al.  Remote Sens , 2015 .

[24]  Meelis Mölder,et al.  Excess resistance of bog surfaces in central Sweden , 2002 .

[25]  Pablo J. Zarco-Tejada,et al.  Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery , 2009 .

[26]  Robert Olbrycht,et al.  Thermal drift compensation method for microbolometer thermal cameras. , 2012, Applied optics.

[27]  Stuart Barr,et al.  UAV-BORNE THERMAL IMAGING FOR FOREST HEALTH MONITORING: DETECTION OF DISEASE-INDUCED CANOPY TEMPERATURE INCREASE , 2015 .

[28]  José Emilio Meroño de Larriva,et al.  Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles , 2018, Remote. Sens..

[29]  H. Budzier,et al.  Calibration of uncooled thermal infrared cameras , 2015 .

[30]  Yangquan Chen,et al.  Thermal remote sensing with an autonomous unmanned aerial remote sensing platform for surface stream temperatures , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[31]  N. Horny,et al.  FPA camera standardisation , 2003 .

[32]  Dan B. Goldman,et al.  Vignette and exposure calibration and compensation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[33]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Hans-Michael Kaltenbach,et al.  A Concise Guide to Statistics , 2011 .

[35]  nasa,et al.  LANDSAT data users handbook , 2013 .

[36]  Pablo J. Zarco-Tejada,et al.  Almond tree canopy temperature reveals intra-crown variability that is water stress-dependent , 2012 .

[37]  Sharon A. Robinson,et al.  Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds , 2014, Remote. Sens..

[38]  Christopher J. Still,et al.  Canopy skin temperature variations in relation to climate, soil temperature, and carbon flux at a ponderosa pine forest in central Oregon , 2016 .

[39]  Nicholas G. Paulter,et al.  Tasking on Natural Statistics of Infrared Images , 2016, IEEE Transactions on Image Processing.

[40]  Kathy Steppe,et al.  Optimizing the Processing of UAV-Based Thermal Imagery , 2017, Remote. Sens..