Next Article in Journal
Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors
Previous Article in Journal
Evaluating Concentrated Flowpaths in Riparian Forest Buffer Contributing Areas Using LiDAR Imagery and Topographic Metrics
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles

1
Department of Graphic Engineering and Geomatics, University of Cordoba, Campus de Rabanales, 14071 Córdoba, Spain
2
Departamento de Gestión Forestal Ambiental, University of Talca, 3460000 Talca, Chile
3
Department of Engineering, University of Almeria, La Cañada, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 615; https://doi.org/10.3390/rs10040615
Received: 23 March 2018 / Revised: 23 March 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
  |  
PDF [20453 KB, uploaded 3 May 2018]
  |  

Abstract

The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 °C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1° C compared to those where no drift correction was applied. View Full-Text
Keywords: UAV; uncooled thermal sensor; precision agriculture; thermal orthomosaic UAV; uncooled thermal sensor; precision agriculture; thermal orthomosaic
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Mesas-Carrascosa, F.-J.; Pérez-Porras, F.; Meroño de Larriva, J.E.; Mena Frau, C.; Agüera-Vega, F.; Carvajal-Ramírez, F.; Martínez-Carricondo, P.; García-Ferrer, A. Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles. Remote Sens. 2018, 10, 615.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top