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Technical Report: Unmanned Helicopter Solution for Survey-Grade Lidar and Hyperspectral Mapping

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Abstract

Recent development of light-weight unmanned airborne vehicles (UAV) and miniaturization of sensors provide new possibilities for remote sensing and high-resolution mapping. Mini-UAV platforms are emerging, but powerful UAV platforms of higher payload capacity are required to carry the sensors for survey-grade mapping. In this paper, we demonstrate a technological solution and application of two different payloads for highly accurate and detailed mapping. The unmanned airborne system (UAS) comprises a Scout B1-100 autonomously operating UAV helicopter powered by a gasoline two-stroke engine with maximum take-off weight of 75 kg. The UAV allows for integrating of up to 18 kg of a customized payload. Our technological solution comprises two types of payload completely independent of the platform. The first payload contains a VUX-1 laser scanner (Riegl, Austria) and a Sony A6000 E-Mount photo camera. The second payload integrates a hyperspectral push-broom scanner AISA Kestrel 10 (Specim, Finland). The two payloads need to be alternated if mapping with both is required. Both payloads include an inertial navigation system xNAV550 (Oxford Technical Solutions Ltd., United Kingdom), a separate data link, and a power supply unit. Such a constellation allowed for achieving high accuracy of the flight line post-processing in two test missions. The standard deviation was 0.02 m (XY) and 0.025 m (Z), respectively. The intended application of the UAS was for high-resolution mapping and monitoring of landscape dynamics (landslides, erosion, flooding, or crops growth). The legal regulations for such UAV applications in Switzerland and Slovakia are also discussed.

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References

  • Aasen, H., Burkart, A., Bolten, A., & Bareth, G. (2015). Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing, 108(10), 245–259.

    Article  Google Scholar 

  • Bareth, G., Aasen, H., Bendig, J., Gnyp, M. L., Bolten, A., Jung, A., et al. (2015). Hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements. Photogrammetrie Fernerkundung Geoinformation, 1, 69–79.

    Article  Google Scholar 

  • Barnard, J. (2007). Small UAV (< 150 kg TOW) Command, control and communication issues. Technical Report. Institution of Engineering and Technology.

  • Black, M., Riley, T. R., Ferrier, G., Fleming, T. R., & Fretwell, P. T. (2016). Automated lithological mapping using airborne hyperspectral thermal infrared data: A case study from Anchorage Island, Antarctica. Remote Sensing of Environment, 176, 225–241.

    Article  Google Scholar 

  • Clarke, R., & Moses, L. B. (2014). The regulation of civilian drones’ impacts on public safety. Computer Law and Security Review, 30(3), 263–285.

    Article  Google Scholar 

  • Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97.

    Article  Google Scholar 

  • Eck, C. & Imbach, B. (2011). Aerial magnetic sensing with an UAV helicopter. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Zürich, Switzerland, Vol. XXXVIII-1/C22(1).

  • Eitel, J. U. H., Höfle, B., Vierling, L. A., Abellán, A., Asner, G. P., Deems, J. S., et al. (2016). Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences. Remote Sensing of Environment, 186, 372–392.

    Article  Google Scholar 

  • Federal Office of Civil Aviation (FOCA). (2016). Drones and aircraft models. https://www.bazl.admin.ch/bazl/en/home/good-to-know/drones-and-aircraft-models.html. Accessed 09 Dec 2016.

  • Gallay, M., Eck, C., Zgraggen, C., Kaňuk, J., & Dvorný, E. (2016a). High resolution airborne laser scanning and hyperspectral imaging with a small UAV platform. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLI-B1, 823–827. https://doi.org/10.5194/isprs-archives-xli-b1-823-2016.

    Article  Google Scholar 

  • Gallay, M., Hochmuth, Z., Kaňuk, J., & Hofierka, J. (2016b). Geomorphometric analysis of cave ceiling channels mapped with 3D terrestrial laser scanning. Hydrology and Earth System Sciences, 20, 1827–1849.

    Article  Google Scholar 

  • Gandor, F., Rehak, M., & Skaload, J. (2015). Photogrammetric mission planner for RPAS. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XL-1/W4, 61–65.

    Article  Google Scholar 

  • Hofierka, J., & Knutová, M. (2015). Simulating spatial aspects of a flash flood using the Monte Carlo method and GRASS GIS: A case study of the Malá Svinka Basin (Slovakia). Open Geosciences, 7(1), 118–125.

    Article  Google Scholar 

  • Hofierka, J., Lacko, M., & Zubal, S. (2017). Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP. Computers and Geosciences, 107, 20–27.

    Article  Google Scholar 

  • Jaud, M., Le Dantec, N., Ammann, J., Grandjean, P., Constantin, D., Akhtman, Y., et al. (2018). Direct georeferencing of a pushbroom, lightweight hyperspectral system for mini-UAV applications. Remote Sensing, 10(2), 204. https://doi.org/10.3390/rs10020204.

    Article  Google Scholar 

  • Jozkow, G., Toth, C., & Grejner-Brzezinska, D. (2016). UAS topographic mapping with Velodyne lidar sensor. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-1, 201–208.

    Article  Google Scholar 

  • Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35–45.

    Article  Google Scholar 

  • Kaňuk, J., Gallay, M., & Hofierka, J. (2015). Generating time series of virtual 3-D city models using a retrospective approach. Landscape and Urban Planning, 139, 40–53.

    Article  Google Scholar 

  • Mandlburger, G., Glira, P., & Pfeifer, N. (2015). UAS-borne lidar for mapping complex terrain and vegetation structure. GIM International, 29(7), 30–33.

    Google Scholar 

  • Mandlburger, G., Pfennigbauer, M., Wieser, M., Riegl, U., & Pfeifer, N. (2016). Evaluation of a novel UAV-borne topo-bathymetric laser profiler. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS Archives, 2016-January, 933–939.

    Article  Google Scholar 

  • Morsdorf, F., Eck, C., Zgraggen, C., Imbach, B., Schneider, F. D., & Kükenbrink, D. (2017). UAV-based LiDAR acquisition for the derivation of high-resolution forest and ground information. Leading Edge, 36(7), 566–570.

    Article  Google Scholar 

  • Nex, F., & Remondino, F. (2014). UAV for 3D mapping applications: A review. Applied Geomatics, 6(1), 1–15.

    Article  Google Scholar 

  • Pfennigbauer, M., Wolf, C., Weinkopf, J., & Ullrich, A. (2014). Online waveform processing for demanding target situations. Proceedings of SPIE The International Society for Optical Engineering. https://doi.org/10.1117/12.2052994.

    Article  Google Scholar 

  • Riegl. (2016). RIEGL laser measurement systems GmbH. RIEGL VUX-1 Data Sheet. http://www.riegl.com/. Accessed 20 Dec 2017.

  • Ryan, J. P., Davis, C. O., Tufillaro, N. B., Kudela, R. M., & Gao, B.-C. (2014). Application of the hyperspectral imager for the coastal ocean to phytoplankton ecology studies in Monterey Bay, CA, USA. Remote Sensing, 6, 1007–1025.

    Article  Google Scholar 

  • Sankey, T., Donager, J., McVay, J., & Sankey, J. B. (2017). UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sensing of Environment, 195, 30–43.

    Article  Google Scholar 

  • Shendryk, I., Tulbure, M., Broich, M., McGrath, A., Alexandrov, S., & Keith, D. (2016). Mapping tree health using airborne laser scans and hyperspectral imagery: a case study for a floodplain eucalypt forest. Geophysical Research Abstracts, 18, EGU2016-355. Available online: http://meetingorganizer.copernicus.org/EGU2016/EGU2016-355.pdf. Accessed 09 Dec 2016.

  • Sima, A., Baeck, P.-J., Delalieux, S., Livens, S., Blommaert, J., Delauré, B., & Boonen, M. (2016). A new COmpact hyperSpectral Imaging system (COSI) for UAS. Geophysical Research Abstracts, 18, EGU2016-5504. http://meetingorganizer.copernicus.org/EGU2016/EGU2016-5504.pdf. Accessed 09 Dec 2016.

  • Torresan, C., Berton, A., Carotenuto, F., Di Gennaro, S. F., Gioli, B., Matese, A., et al. (2017). Unmanned aerial vehicles for environmental applications. International Journal of Remote Sensing, 38(8–10), 2029–2036.

    Google Scholar 

  • Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 22–36.

    Article  Google Scholar 

  • Transport Authority. (2016). Podmienky vykonania letu UAV (drony). http://letectvo.nsat.sk/2015/08/25/podmienky-vykonania-letu-uav/. Accessed 09 Dec 2017.

  • Villa, T. F., Gonzalez, F., Miljievic, B., Ristovski, Z. D., & Morawska, L. (2016). An over view of small unmanned aerial vehicles for air quality measurements: Present applications and future prospectives. Sensors, 16(7), 1072.

    Article  Google Scholar 

  • Wallace, L., Watson, C., & Lucieer, A. (2014). Detecting pruning of individual stems using airborne laser scanning data captured from an unmanned aerial vehicle. International Journal of Applied Earth Observation and Geoinformation, 30, 76–85.

    Article  Google Scholar 

  • Watts, A. C., Ambrosia, V. G., & Hinkley, E. A. (2012). Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sensing, 4, 1671–1692.

    Article  Google Scholar 

  • WeControl. (2017a). Autopilot wePilot300. http://www.wecontrol.ch/products/wePilot3000/flyer_wePilot3000.pdf. Accessed 19 Dec 2017.

  • WeControl. (2017b). Ground control station weGCS http://www.wecontrol.ch/products/weGCS/flyer_weGCS.pdf. Accessed 19 Dec 2017.

  • Wieser, M., Mandlburger, G., Hollaus, M., Otepka, J., Glira, P., & Pfeifer, N. (2017). A case study of UAS borne laser scanning for measurement of tree stem diameter. Remote Sensing, 9(11), art. no. 1154.

    Article  Google Scholar 

  • Yang, B., & Chen, C. (2015). Automatic registration of UAV-borne sequent images and LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 262–274.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Guillén-Climent, M. L., Hernández-Clemente, R., Catalina, A., González, M. R., & Martín, P. (2013). Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agricultural and Forest Meteorology, 171–172(4), 281–294.

    Article  Google Scholar 

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Acknowledgements

Production of the presented UAV and development of the lidar and hyperspectral payloads was financed within two projects of the University Science Park TECHNICOM for innovative applications supported by knowledge technologies—phase 1 and phase 2 (ITMS 26220220182, ITMS2014 + 313011D232). The projects were co-funded by the European Union Structural Funds and the Ministry of Education, Science, Research and Sport of the Slovak Republic, the executive authority for the Operational Programme Research and Development. The research presented in this paper was funded by the Slovak Research and Development Agency within the scientific project APVV-15-0054 and financial support was also provided by the Slovak Research Grant Agency within the projects VEGA 1/0474/16 and VEGA 1/0963/17. We would like to thank Benedikt Imbach and Christoph Fallegger for assistance in preparing the flight missions. We also want to thank the Specim team for the support with the hyperspectral data processing. Our special thanks go to the reviewers and the editor for their comments which helped us to better convey the conducted research to the reader.

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Correspondence to Michal Gallay.

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Kaňuk, J., Gallay, M., Eck, C. et al. Technical Report: Unmanned Helicopter Solution for Survey-Grade Lidar and Hyperspectral Mapping. Pure Appl. Geophys. 175, 3357–3373 (2018). https://doi.org/10.1007/s00024-018-1873-2

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