publication . Article . Other literature type . 2018

METHODOLOGY FOR DIRECT REFLECTANCE MEASUREMENT FROM A DRONE: SYSTEM DESCRIPTION, RADIOMETRIC CALIBRATION AND LATEST RESULTS

Markelin, L.; Suomalainen, J.; Hakala, T.; Oliveira, R. A.; Viljanen, N.; Näsi, R.; Scott, B.; Theocharous, T.; Greenwell, C.; Fox, N.; ...
Open Access English
  • Published: 01 Sep 2018 Journal: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (issn: 1682-1750, eissn: 2194-9034, Copyright policy)
  • Publisher: Copernicus Publications
Abstract
<strong>Abstract.</strong> We study and analyse performance of a system for direct reflectance measurements from a drone. Key instruments of the system are upwards looking irradiance sensor and downwards looking imaging spectrometer. Requirement for both instruments is that they are radiometrically calibrated, the irradiance sensor has to be horizontally stabilized, and the sensors needs to be accurately synchronized. In our system, irradiance measurements are done with <i>FGI Aerial Image Reference System</i> (FGI AIRS), which uses novel optical levelling methodology and can compensate sensor tilting up to 15°. We performed SI-traceable spectral and radiance calibration of FPI hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). After the calibration, the radiance accuracy of different channels was between ±4% when evaluated with independent test data. Sensors response to radiance proved to be highly linear and was on average 0.9994 for all channels. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI and highlighted the importance of accurate calibration. The drone-based direct reflectance measurement system showed promising results with imagery collected over Jokioinen agricultural grass test site, Finland. AIRS-based image- and band wise image adjustment provided homogenous and seamless image mosaics even under varying illumination conditions and under clouds.
Subjects
free text keywords: lcsh:Technology, lcsh:T, lcsh:Engineering (General). Civil engineering (General), lcsh:TA1-2040, lcsh:Applied optics. Photonics, lcsh:TA1501-1820, Irradiance, Calibration, Radiometric calibration, Remote sensing, Imaging spectrometer, Environmental science, Hyperspectral imaging, Radiance, Aerial image, System of measurement

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Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution. Precision Agriculture, Vol. 19(1), pp. 134-146.

https://doi.org/10.1007/s11119-017-9504-y Hakala, T., Honkavaara, E., Saari, H., Mäkynen, J., Kaivosoja, J., Pesonen, L., Pölönen, I., 2013. Spectral imaging from UAVs under varying illumination conditions. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL-1/W2, pp. 189-194.

https://doi.org/10.5194/isprsarchives-XL-1-W2-189-2013 Hakala, T., Markelin, L., Honkavaara, E., Scott, B., Theocharous, T., Nevalainen, O., Näsi, R., Suomalainen, J., Viljanen, N., Greenwell, C., Fox, N., 2018. Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization. Sensors, Vol. 18(5), 1417. https://doi.org/10.3390/s18051417 Honkavaara, E., Hakala, T., Markelin, L., Jaakkola, A., Saari, H., Ojanen, H., Pölönen, I., Tuominen, S., Näsi, R., Rosnell, T., Viljanen, N., 2014. Autonomous hyperspectral UAS photogrammetry for environmental monitoring applications. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XL-1, pp. 155-159.

https://doi.org/10.5194/isprsarchives-XL-1-155-2014 Honkavaara, E., Hakala, T., Markelin, L., Rosnell, T., Saari, H., Mäkynen, J., 2012. A Process for Radiometric Correction of UAV Image Blocks. Photogrammetrie - Fernerkundung - Geoinformation, Vol. 2012(2), https://doi.org/10.1127/1432-8364/2012/0106 Näsi, R., Honkavaara, E., Blomqvist, M., LyytikäinenSaarenmaa, P., Hakala, T., Viljanen, N., Kantola, T., Holopainen, M., 2018a. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban Forestry & Urban Greening, Vol.

30, pp. 72-83. https://doi.org/10.1016/j.ufug.2018.01.010 Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., Viljanen, N., Kantola, T., Tanhuanpää, T., Holopainen, M., 2015. Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sensing, Vol. 7, pp.

15467-15493. https://doi.org/10.3390/rs71115467 Näsi, R., Viljanen, N., Kaivosoja, J., Alhonoja, K., Markelin, L., Hakala, T., Honkavaara, E., 2018b. Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features. Remote Sensing, Vol. 10(7), 1082. https://doi.org/10.3390/rs10071082 Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyyppä, J., Saari, H., Pölönen, I., Imai, N., Tommaselli, A., 2017. Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sensing Vol. 9(3), 185.

https://doi.org/10.3390/rs9030185 Oliveira, R.A., Khoramshahi, E., Suomalainen, J., Hakala, T., Viljanen, N., Honkavaara, E., 2018. Real-time and post processed georeferencing for hyperspectral drone remote sensing. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-2, pp. 789-795. https://doi.org/10.5194/isprs-archives-XLII-2-789- 2018 Saari, H., Pölönen, I., Salo, H., Honkavaara, E., Hakala, T., Holmlund, C., Mäkynen, J., Mannila, R., Antila, T., Akujärvi, A., 2013. Miniaturized hyperspectral imager calibration and UAV flight campaigns. In: Proc. SPIE 8889, Sensors, Systems, and Next-Generation Satellites XVII, 88891O (24 October 2013).

https://doi.org/10.1117/12.2028972 Schaepman-Strub, G., Schaepman, M.E., Painter, T.H., Dangel, S., Martonchik, J.V., 2006. Reflectance quantities in optical remote sensing-definitions and case studies. Remote Sensing of Environment Vol. 103(1), pp. 27-42.

https://doi.org/10.1016/j.rse.2006.03.002 Suomalainen, J., Hakala, T., Honkavaara, E., 2017. Measuring incident irradiance on-board an unstable UAV platform - First results on virtual horizontalization of multiangle measurement.

In: Online Proceedings of the International Conference on Unmanned Aerial Vehicles in Geomatics, ISPRS, Bonn, Germany, 4-7 September, 2017.

von Bueren, S.K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M.P., Yule, I.J., 2015. Deploying four optical UAV-based sensors over grassland: challenges and limitations.

Biogeosciences, Vol. 12, pp. 163-175.

https://doi.org/10.5194/bg-12-163-2015

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