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Remote Sensing
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High-Resolution Thermal Imaging for Vegetation Monitoring
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High-Resolution Thermal Imaging for Vegetation Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (22 March 2019) | Viewed by 13073

Special Issue Editors

Dr. Jose A. Jiménez-Berni

E-Mail Website
Guest Editor
Institute for Sustainable Agriculture (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Avenida Menéndez Pidal s/n, Campus Alameda del Obispo, 14004 Córdoba, Spain
Interests: remote sensing; plant phenotyping; crop physiology; LiDAR; hyperspectral; thermal
Special Issues, Collections and Topics in MDPI journals
Dr. Pablo J. Zarco-Tejada

grade E-Mail Website
Guest Editor
QuantaLab Remote Sensing Laboratory, Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo, s/n, E-14004 Córdoba, Spain
Interests: hyperspectral, thermal, manned platforms; Unmanned Aerial Vehicles (UAV, UAS, RPAS); fluorescence; radiative transfer models
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thermal infrared remote sensing provides a unique tool for understanding physical processes involving vegetation and atmosphere interactions. Measuring plant temperatures enables modelling of the energy balance, providing insights of the surface's properties and physiological processes in vegetation, mainly related to the plant water status.  

Recent advances in the development of thermal infrared sensors provide cost and size reductions of thermal imagers, which are allowing their application in manned and unmanned aerial platforms. These new sensors are providing unprecedented spatial and temporal resolutions, resulting in novel applications for the monitoring of the land surface, with particular interest for crops and natural vegetation. However, the use of these new sensors also presents unique challenges regarding sensor calibration, atmospheric correction, georeferencing and data handling.

This Special Issue seeks to attract manuscripts dealing with novel applications of high-resolution thermal imaging for a range of applications in vegetation monitoring, both in crops and natural vegetation. We encourage potential authors to submit studies involving the use of very high-resolution imagery, on-board of manned and unmanned aerial platforms. Manuscripts covering acquisition and calibration aspects of thermal imaging are also welcome.

Dr. Jose A. Jiménez-Berni
Dr. Pablo J. Zarco-Tejada
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • New thermal imaging sensors.
  • Thermal infrared image processing, calibration and correction.
  • Thermal imaging platforms: ground and airborne, manned and unmanned.
  • Applications of thermal imaging for plant monitoring: biotic and abiotic stress detection, phenomics, physiological modelling.
  • Integration of thermal imaging and other sensing technologies: multispectral, hyperspectral, active sensing (LiDAR, RADAR), chlorophyll fluorescence.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

13 pages, 3015 KiB  
Article
Automatic Wheat Ear Counting Using Thermal Imagery
by Jose A. Fernandez-Gallego, Ma. Luisa Buchaillot, Nieves Aparicio Gutiérrez, María Teresa Nieto-Taladriz, José Luis Araus and Shawn C. Kefauver
Remote Sens. 2019, 11(7), 751; https://doi.org/10.3390/rs11070751 - 28 Mar 2019
Cited by 41 | Viewed by 5320
Abstract
Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, [...] Read more.
Ear density is one of the most important agronomical yield components in wheat. Ear counting is time-consuming and tedious as it is most often conducted manually in field conditions. Moreover, different sampling techniques are often used resulting in a lack of standard protocol, which may eventually affect inter-comparability of results. Thermal sensors capture crop canopy features with more contrast than RGB sensors for image segmentation and classification tasks. An automatic thermal ear counting system is proposed to count the number of ears using zenithal/nadir thermal images acquired from a moderately high resolution handheld thermal camera. Three experimental sites under different growing conditions in Spain were used on a set of 24 varieties of durum wheat for this study. The automatic pipeline system developed uses contrast enhancement and filter techniques to segment image regions detected as ears. The approach is based on the temperature differential between the ears and the rest of the canopy, given that ears usually have higher temperatures due to their lower transpiration rates. Thermal images were acquired, together with RGB images and in situ (i.e., directly in the plot) visual ear counting from the same plot segment for validation purposes. The relationship between the thermal counting values and the in situ visual counting was fairly weak (R2 = 0.40), which highlights the difficulties in estimating ear density from one single image-perspective. However, the results show that the automatic thermal ear counting system performed quite well in counting the ears that do appear in the thermal images, exhibiting high correlations with the manual image-based counts from both thermal and RGB images in the sub-plot validation ring (R2 = 0.75–0.84). Automatic ear counting also exhibited high correlation with the manual counting from thermal images when considering the complete image (R2 = 0.80). The results also show a high correlation between the thermal and the RGB manual counting using the validation ring (R2 = 0.83). Methodological requirements and potential limitations of the technique are discussed. Full article
(This article belongs to the Special Issue High-Resolution Thermal Imaging for Vegetation Monitoring)
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16 pages, 3933 KiB  
Article
Can UAV-Based Infrared Thermography Be Used to Study Plant-Parasite Interactions between Mistletoe and Eucalypt Trees?
by Wouter H. Maes, Alfredo R. Huete, Michele Avino, Matthias M. Boer, Remy Dehaan, Elise Pendall, Anne Griebel and Kathy Steppe
Remote Sens. 2018, 10(12), 2062; https://doi.org/10.3390/rs10122062 - 19 Dec 2018
Cited by 44 | Viewed by 6705
Abstract
Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that [...] Read more.
Some of the remnants of the Cumberland Plain woodland, an endangered dry sclerophyllous forest type of New South Wales, Australia, host large populations of mistletoe. In this study, the extent of mistletoe infection was investigated based on a forest inventory. We found that the mistletoe infection rate was relatively high, with 69% of the Eucalyptus fibrosa and 75% of the E. moluccana trees being infected. Next, to study the potential consequences of the infection for the trees, canopy temperatures of mistletoe plants and of infected and uninfected trees were analyzed using thermal imagery acquired during 10 flights with an unmanned aerial vehicle (UAV) in two consecutive summer seasons. Throughout all flight campaigns, mistletoe canopy temperature was 0.3–2 K lower than the temperature of the eucalypt canopy it was growing in, suggesting higher transpiration rates. Differences in canopy temperature between infected eucalypt foliage and mistletoe were particularly large when incoming radiation peaked. In these conditions, eucalypt foliage from infected trees also had significantly higher canopy temperatures (and likely lower transpiration rates) compared to that of uninfected trees of the same species. The study demonstrates the potential of using UAV-based infrared thermography for studying plant-water relations of mistletoe and its hosts. Full article
(This article belongs to the Special Issue High-Resolution Thermal Imaging for Vegetation Monitoring)
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