Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects
The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlin...
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doaj-b2b1d4a828df43bdbc969de903b944272021-08-26T14:17:48ZengMDPI AGRemote Sensing2072-42922021-08-01133255325510.3390/rs13163255Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind EffectsYoann Malbéteau0Kasper Johansen1Bruno Aragon2Samir K. Al-Mashhawari3Matthew F. McCabe4Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaHydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaHydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaHydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaHydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi ArabiaThe miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics.https://www.mdpi.com/2072-4292/13/16/3255thermal infraredunmanned aerial vehicleUAVwindflight directionorthomosaic |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yoann Malbéteau Kasper Johansen Bruno Aragon Samir K. Al-Mashhawari Matthew F. McCabe |
spellingShingle |
Yoann Malbéteau Kasper Johansen Bruno Aragon Samir K. Al-Mashhawari Matthew F. McCabe Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects Remote Sensing thermal infrared unmanned aerial vehicle UAV wind flight direction orthomosaic |
author_facet |
Yoann Malbéteau Kasper Johansen Bruno Aragon Samir K. Al-Mashhawari Matthew F. McCabe |
author_sort |
Yoann Malbéteau |
title |
Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects |
title_short |
Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects |
title_full |
Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects |
title_fullStr |
Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects |
title_full_unstemmed |
Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects |
title_sort |
overcoming the challenges of thermal infrared orthomosaics using a swath-based approach to correct for dynamic temperature and wind effects |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-08-01 |
description |
The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics. |
topic |
thermal infrared unmanned aerial vehicle UAV wind flight direction orthomosaic |
url |
https://www.mdpi.com/2072-4292/13/16/3255 |
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