{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:18:52Z","timestamp":1776118732866,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,5,12]],"date-time":"2017-05-12T00:00:00Z","timestamp":1494547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The current standard procedure for aligning thermal imagery with structure-from-motion (SfM) software uses GPS logger data for the initial image location. As input data, all thermal images of the flight are rescaled to cover the same dynamic scale range, but they are not corrected for changes in meteorological conditions during the flight. This standard procedure can give poor results, particularly in datasets with very low contrast between and within images or when mapping very complex 3D structures. To overcome this, three alignment procedures were introduced and tested: camera pre-calibration, correction of thermal imagery for small changes in air temperature, and improved estimation of the initial image position by making use of the alignment of RGB (visual) images. These improvements were tested and evaluated in an agricultural (low temperature contrast data) and an afforestation (complex 3D structure) dataset. In both datasets, the standard alignment procedure failed to align the images properly, either by resulting in point clouds with several gaps (images that were not aligned) or with unrealistic 3D structure. Using initial thermal camera positions derived from RGB image alignment significantly improved thermal image alignment in all datasets. Air temperature correction had a small yet positive impact on image alignment in the low-contrast agricultural dataset, but a minor effect in the afforestation area. The effect of camera calibration on the alignment was limited in both datasets. Still, in both datasets, the combination of all three procedures significantly improved the alignment, in terms of number of aligned images and of alignment quality.<\/jats:p>","DOI":"10.3390\/rs9050476","type":"journal-article","created":{"date-parts":[[2017,5,12]],"date-time":"2017-05-12T11:03:53Z","timestamp":1494587033000},"page":"476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":113,"title":["Optimizing the Processing of UAV-Based Thermal Imagery"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1592-9299","authenticated-orcid":false,"given":"Wouter","family":"Maes","sequence":"first","affiliation":[{"name":"Laboratory of Hydrology and Water Management (LHWM), Department of Forest and Water Management, Ghent University, Coupure Links 653\u2014Bl. A, BE-9000 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-2376","authenticated-orcid":false,"given":"Alfredo","family":"Huete","sequence":"additional","affiliation":[{"name":"Ecosystem Dynamics Health and Resilience, Climate Change Cluster, University of Technology, Sydney (UTS), 745 Harris Street, Broadway NSW 2007, Australia"}]},{"given":"Kathy","family":"Steppe","sequence":"additional","affiliation":[{"name":"Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Ghent University, Coupure Links 653\u2014Bl. A, BE-9000 Ghent, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11263-007-0107-3","article-title":"Modeling the world from Internet photo collections","volume":"80","author":"Snavely","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carrivick, J.L., Smith, M.W., and Quincey, D.J. (2016). Structure from Motion in the Geosciences, Wiley-Blackwell.","DOI":"10.1002\/9781118895818"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic structure from motion: A new development in photogrammetric measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u2018Structure-from-Motion\u2019 photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.geomorph.2016.06.027","article-title":"Erosion processes in calanchi in the Upper Orcia Valley, Southern Tuscany, Italy based on multitemporal high-resolution terrestrial LiDAR and UAV surveys","volume":"269","author":"Neugirg","year":"2016","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.3390\/rs70201736","article-title":"Time series analysis of landslide dynamics using an Unmanned Aerial Vehicle (UAV)","volume":"7","author":"Turner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1177\/0309133313515293","article-title":"Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography","volume":"38","author":"Lucieer","year":"2014","journal-title":"Prog. Phys. Geogr."},{"key":"ref_9","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shi, Y.Y., Thomasson, J.A., Murray, S.C., Pugh, N.A., Rooney, W.L., Shafian, S., Rajan, N., Rouze, G., Morgan, C.L.S., and Neely, H.L. (2016). Unmanned Aerial Vehicles for high-yhroughput phenotyping and agronomic research. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0159781"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mikita, T., Janata, P., and Surovy, P. (2016). Forest Stand Inventory Based on Combined Aerial and Terrestrial Close-Range Photogrammetry. Forests, 7.","DOI":"10.3390\/f7080165"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/LGRS.2016.2584109","article-title":"Individual tree delineation in windbreaks using airborne-laser-scanning data and unmanned aerial vehicle stereo images","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.biombioe.2016.02.013","article-title":"Productivity, stand dynamics and the selection effect in a mixed willow clone short rotation coppice plantation","volume":"87","author":"Dillen","year":"2016","journal-title":"Biomass Bioenerg."},{"key":"ref_15","first-page":"204","article-title":"Obtaining biophysical measurements of woody vegetation from high resolution digital aerial photography in tropical and arid environments: Northern Territory, Australia","volume":"52","author":"Staben","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wallace, L., Lucieer, A., Malenovsky, Z., Turner, D., and Vopenka, P. (2016). Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds. Forests, 7.","DOI":"10.3390\/f7030062"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"186","DOI":"10.4314\/sajg.v5i2.7","article-title":"Unmanned Aerial Vehicle (UAV) Photogrammetry Produces Accurate High-resolution Orthophotos, Point Clouds and Surface Models for Mapping Wetlands","volume":"5","author":"Boon","year":"2016","journal-title":"S. Afr. J. Geomat."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2016.05.019","article-title":"Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry","volume":"183","author":"Cunliffe","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5440","DOI":"10.1109\/TGRS.2016.2565471","article-title":"Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV)","volume":"54","author":"Honkavaara","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11119-014-9360-y","article-title":"Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle","volume":"15","author":"Calderon","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.3389\/fpls.2016.01131","article-title":"A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding","volume":"7","author":"Tattaris","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1007\/s11119-013-9322-9","article-title":"Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard","volume":"14","author":"Nicolas","year":"2013","journal-title":"Precis. Agric."},{"key":"ref_23","first-page":"94","article-title":"Applicability and limitations of using the crop water stress index as an indicator of water deficits in citrus orchards","volume":"198","author":"Fereres","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s00271-012-0382-9","article-title":"Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV)","volume":"30","author":"Baluja","year":"2012","journal-title":"Irrig. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"697","DOI":"10.5194\/hess-20-697-2016","article-title":"Estimating evaporation with thermal UAV data and two-source energy balance models","volume":"20","author":"Hoffmann","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ortega-Farias, S., Ortega-Salazar, S., Poblete, T., Kilic, A., Allen, R., Poblete-Echeverria, C., Ahumada-Orellana, L., Zuniga, M., and Sepulveda, D. (2016). Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV). Remote Sens., 8.","DOI":"10.3390\/rs8080638"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sep\u00falveda-Reyes, D., Ingram, B., Bardeen, M., Z\u00fa\u00f1iga, M., Ortega-Far\u00edas, S., and Poblete-Echeverr\u00eda, C. (2016). Selecting canopy zones and thresholding approaches to assess grapevine water status by using aerial and ground-based thermal imaging. Remote Sens., 8.","DOI":"10.3390\/rs8100822"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2380","DOI":"10.1016\/j.rse.2009.06.018","article-title":"Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery","volume":"113","author":"Berni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s40490-015-0044-9","article-title":"Use of UAV or remotely piloted aircraft and forward-looking infrared in forest, rural and wildland fire management: Evaluation using simple economic analysis","volume":"45","author":"Christensen","year":"2015","journal-title":"N. Z. J. For. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gonzalez, L.F., Montes, G.A., Puig, E., Johnson, S., Mengersen, K., and Gaston, K.J. (2016). Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors, 16.","DOI":"10.3390\/s16010097"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1002\/wsb.629","article-title":"Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system","volume":"40","author":"Chretien","year":"2016","journal-title":"Wildl. Soc. Bull."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1016\/j.scitotenv.2016.05.142","article-title":"Soil moisture controls on phenology and productivity in a semi-arid critical zone","volume":"568","author":"Cleverly","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4003","DOI":"10.3390\/rs6054003","article-title":"Spatial co-registration of ultra-high resolution visible, multispectral and thermal images acquired with a micro-UAV over Antarctic moss beds","volume":"6","author":"Turner","year":"2014","journal-title":"Remote Sens."},{"key":"ref_34","unstructured":"Grenzdorffer, G., and Bill, R. (2013). Generation of Multitemporal Thermal Orthophotos from UAV Data in Uav-G2013, Copernicus Gesellschaft Mbh."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Raza, S.-E.-A., Smith, H.K., Clarkson, G.J.J., Taylor, G., Thompson, A.J., Clarkson, J., and Rajpoot, N.M. (2014). Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0097612"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.isprsjprs.2014.07.015","article-title":"A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs","volume":"104","author":"Yahyanejad","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","unstructured":"Jones, H.G. (1992). Plants and microclimate. a Quantitative Approach to Environmental Plant Physiology, Cambridge University Press. [2nd ed.]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4671","DOI":"10.1093\/jxb\/ers165","article-title":"Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: A review","volume":"63","author":"Maes","year":"2012","journal-title":"J. Exp. Bot."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1016\/j.ecolmodel.2011.08.028","article-title":"Does energy dissipation increase with ecosystem succession? Testing the ecosystem exergy theory combining theoretical simulations and thermal remote sensing observations","volume":"23\u201324","author":"Maes","year":"2011","journal-title":"Ecol. Model."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF00117455","article-title":"Radiative surface-temperature and energy-balance of a wheat canopy, 1: Comparison of radiative and aerodynamic canopy temperature","volume":"36","author":"Huband","year":"1986","journal-title":"Bound.-Layer Meteorol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/S0034-4257(00)00171-1","article-title":"A comparative study of land surface emissivity retrieval from NOAA data","volume":"75","author":"Sobrino","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s13280-015-0685-1","article-title":"Contributions of a global network of tree diversity experiments to sustainable forest plantations","volume":"45","author":"Verheyen","year":"2016","journal-title":"Ambio"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"26","DOI":"10.5091\/plecevo.2013.803","article-title":"Assessment of the functional role of tree diversity: The multi-site FORBIO experiment","volume":"146","author":"Verheyen","year":"2013","journal-title":"Plant Ecol. Evol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1111\/1365-2664.12721","article-title":"Biodiversity as insurance for sapling survival in experimental tree plantations","volume":"53","author":"Verheyen","year":"2016","journal-title":"J. Appl. Ecol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.3390\/rs4051462","article-title":"Sensor correction of a 6-Band multispectral imaging sensor for UAV remote sensing","volume":"4","author":"Kelcey","year":"2012","journal-title":"Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"11933","DOI":"10.3390\/rs70911933","article-title":"The impact of the calibration method on the accuracy of point clouds derived using Unmanned Aerial Vehicle multi-view stereopsis","volume":"7","author":"Harwin","year":"2015","journal-title":"Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1071\/FP14021","article-title":"Early detection of Psa infection in kiwifruit by means of infrared thermography at leaf and orchard scale","volume":"41","author":"Maes","year":"2014","journal-title":"Funct. Plant Biol."},{"key":"ref_49","first-page":"1105","article-title":"Design of orientation estimation system by inertial and magnetic sensors; Proceedings of the Institution of Mechanical Engineers","volume":"228","author":"Miao","year":"2013","journal-title":"J. Aerosp. Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/5\/476\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:35:39Z","timestamp":1760207739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/5\/476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,12]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2017,5]]}},"alternative-id":["rs9050476"],"URL":"https:\/\/doi.org\/10.3390\/rs9050476","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,5,12]]}}}