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Construction of a Multisensor UAV System for Early Detection of Forest Pests

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Advances in Intelligent Systems and Computing V (CSIT 2020)

Abstract

The use of unmanned aerial vehicles (UAVs) is becoming increasingly important in recent years. The usage is versatile, as UAVs are able to carry various load including complex sensor systems. However, the usage in special applications can present an issue. There is only a small number of special sensors that are suitable for operation on unmanned aerial vehicles. Mostly proprietary devices are available, which, on the other hand, may not be entirely suitable for use in conjunction with other devices or specific type of UAV. In order to move remote sensing applications from the experimental phase to the real deployment, it is necessary to come up with new methodologies a technology that can be verified using UAVs. One such application is remote sensing of forest areas by means of special spectral cameras, to allow early detection of infested bark beetle vegetation to be carried out. To accomplish this, it was necessary to create a specialized system containing both hardware and software components.

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References

  1. Tang, L., Shao, G.: Drone remote sensing for forestry research and practices. J. For. Res. 26(4), 791–797 (2015). https://doi.org/10.1007/s11676-015-0088-y

    Article  Google Scholar 

  2. Banu, T.P., Borlea, G.F., Banu, C.: The use of drones in forestry. J. Environ. Sci. Eng. B 5(11), 557–562 (2016). https://doi.org/10.17265/2162-5263/2016.11.007. ISSN 21625263. Accessed 22 Dec 2019

    Article  Google Scholar 

  3. Colomina, I., Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 92, 79–97 (2014)

    Article  Google Scholar 

  4. Commission Delegated Regulation (EU) 2019/945 of 12 March 2019 on unmanned aircraft systems and on third-country operators of unmanned aircraft systems. In: European Union: Official Journal of the European Union, 2019, L 152/1

    Google Scholar 

  5. Shahbazi, M., Theau, J., Menard, P.: Recent applications of unmanned aerial imagery in natural resource management. GISci. Remote Sens. 51(4), 339–365 (2014)

    Article  Google Scholar 

  6. Eisenbeiss, H.: The potential of unmanned aerial vehicles for mapping. In: Fritsch, D. (ed.) Photogrammetrische Woche 2011, pp. 135–145. Wichmann Verlag, Heidelberg (2011)

    Google Scholar 

  7. Roth, L., Hund, A., Aasen, H.: PhenoFly Planning Tool: flight planning for high-resolution optical remote sensing with unmanned areal systems. Plant Methods 14, 116 (2018). https://doi.org/10.1186/s13007-018-0376-6

    Article  Google Scholar 

  8. Ghulam, A., Zhao-Liang, L.I., Qin, Q., Yimit, H., Wang, J.: Estimating crop water stress with ETM+ NIR and SWIR data. Agric. For. Meteorol. 148(11), 1679–1695 (2008). https://doi.org/10.1016/j.agrformet.2008.05.020. ISSN 01681923. Accessed 16 Feb 2020

    Article  Google Scholar 

  9. Abdullah, H., Darvishzadeh, R., Skidmore, A.K., Groen, T.A., Heurich, M.: European spruce bark beetle (Ips typographus, L.) green attack affects foliar reflectance and biochemical properties. Int. J. Appl. Earth Obs. Geoinf. 64, 199–209 (2018). https://doi.org/10.1016/j.jag.2017.09.009. ISSN 03032434. Accessed 4 Jun 2020

    Article  Google Scholar 

  10. MV1-D2048x1088-HS01-96-G2. Photonfocus. Photonfocus, Switzerland (2020). https://www.photonfocus.com/products/camerafinder/camera/mv1-d2048x1088-hs01-96-g2. Accessed 4 Jun 2020

  11. MV3-D640I-M01-144-G2. Photonfocus. Photonfocus, Switzerland (2020). https://www.photonfocus.com/products/camerafinder/camera/mv3-d640i-m01-144-g2. Accessed 4 Jun 2020

  12. WIRIS 2gn Gen. Photonfocus. Photonfocus, Switzerland (2020). https://workswell.cz/wiris/. Accessed 4 Jun 2020

  13. Geladi, P., Burger, J., Lestander, T.: Hyperspectral imaging: calibration problems and solutions. Chemom. Intell. Lab. Syst. 72(2), 209–217 (2004). Accessed 19 Jul 2020

    Article  Google Scholar 

  14. Barry, P., Coakley, R.: Field Accuracy Test of RPAS Photogrammetry, paper presented at UAV-g 2013, Zurich, Switzerland, 16 May 2013

    Google Scholar 

  15. Frey, J., Kovach, K., Stemmler, S., Koch, B.: UAV photogrammetry of forests as a vulnerable process. A sensitivity analysis for a structure from motion RGB-image pipeline. Remote Sens. 10(6), 912 (2018). https://doi.org/10.3390/rs10060912. ISSN 2072-4292. Accessed 11 Jun 2020

    Article  Google Scholar 

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Correspondence to Milan Novák , Jakub Geyer or Miloš Prokýšek .

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Novák, M. et al. (2021). Construction of a Multisensor UAV System for Early Detection of Forest Pests. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_78

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