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Drone-based area scanning of vegetation fluorescence height profiles using a miniaturized hyperspectral lidar system

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Abstract

We have developed a compact hyperspectral lidar system based on a continuous-wave (CW) 445 nm diode laser and a double Scheimpflug imaging arrangement. The light-weight construction allows the integration of the system on a commercial drone. Airborne, range-resolved spatial imaging of vegetation fluorescence is demonstrated.

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References

  1. Y.C. Xie, Z.Y. Sha, M. Yu, Remote sensing imagery in vegetation mapping: a review. J. Plant Ecol. 1, 9 (2008)

    Article  Google Scholar 

  2. D. Blondeau-Patissier, J.F. Gower, A.G. Dekker, S.R. Phinn, V.E. Brando, A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Prog. Oceanogr. 123, 123 (2014)

    Article  ADS  Google Scholar 

  3. H.K. Lichtenthaler, U. Rinderle, The role of chlorophyll fluorescence in the detection of stress conditions in plants. CRC Crit. Rev. Anal. Chem. 19, S29 (1988)

    Article  Google Scholar 

  4. E.H. Murchie, T. Lawson, Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. J. Exp. Bot. 64, 3983–3998 (2013)

    Article  Google Scholar 

  5. I. Moya, A new instrument for passive remote sensing. 1. Measurements of sunlight induced fluorescence. Remote Sens. Environ. 91, 196 (2004)

    Article  ADS  Google Scholar 

  6. M. Meroni, M. Rossini, L. Guanter, L. Alonso, U. Rascher, R. Colombo, J. Moreno, Remote sensing of solar-induced chlorophyll fluorescence: review of methods and applications. Remote Sens. Environ. 113, 2037 (2009)

    Article  ADS  Google Scholar 

  7. U. Rascher, L. Alonso, A. Burkart, C. Cilia, S. Cogliati, R. Colombo, A. Damm, M. Drusch, L. Guanter, J. Hanus, Sun-induced fluorescence—a new probe of photosynthesis: first maps from the imaging spectrometer HyPlant. Glob. Change Biol. 21, 4673–4684 (2015)

    Article  ADS  Google Scholar 

  8. K. Guan, J.A. Berry, Y. Zhang, J. Joiner, L. Guanter, G. Badgley, D.B. Lobell, Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence. Glob. Change Biol. 22, 716–726 (2016)

    Article  ADS  Google Scholar 

  9. L. Guanter, Y. Zhang, M. Jung, J. Joiner, M. Voigt, J.A. Berry, C. Frankenberg, A.R. Huete, P. Zarco-Tejada, J.-E. Lee, Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. 111, E1327–E1333 (2014)

    Article  Google Scholar 

  10. C. Frankenberg et al., New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011)

    Article  ADS  Google Scholar 

  11. J. Joiner, L. Guanter, R. Lindstrot, M. Voigt, A.P. Vasilkov, E.M. Middleton, K.F. Huemmrich, Y. Yoshida, C. Frankenberg, Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013)

    Article  Google Scholar 

  12. ESA, Report for mission selection, FLEX, ESA SP-1330/2, European Space Agency, Noordwiik, The Netherlands (2015)

  13. S. Svanberg, Fluorescence lidar monitoring of vegetation status. Phys. Scr. T58, 79 (1995)

    Article  ADS  Google Scholar 

  14. S. Svanberg, Fluorescence spectroscopy and imaging of lidar targets, Chap. 7, in Laser Remote Sensing, ed. by T. Fujii, T. Fukuchi (CRC Press, Boca Raton, 2005), pp. 433–467

    Chapter  Google Scholar 

  15. G.Y. Zhao, Z. Duan, L. Ming, Y.Y. Li, R.P. Chen, J.D. Hu, S. Svanberg, Y.L. Han, Reflectance and fluorescence characterization of maize species using laboratory measurements and lidar remote sensing. Appl. Opt. 55, 5273 (2016)

    Article  ADS  Google Scholar 

  16. Z. Duan, T. Peng, S.M. Zhu, M. Lian, Y.Y. Li, W. Fu, J.B. Xiong, S. Svanberg, Q.Z. Zhao, J.D. Hu, G.Y. Zhao, Optical characterization of Chinese hybrid rice using laser-induced fluorescence techniques—laboratory and remote-sensing measurements. Appl. Opt. 57, 3481 (2018)

    Article  ADS  Google Scholar 

  17. G.A. Chapelle, L.A. Franks, D.A. Jessup, Aerial testing of a KrF laser-based fluorosensor. Appl. Opt. 22, 3382 (1983)

    Article  ADS  Google Scholar 

  18. V. Drozdowska, Seasonal and spatial variability of surface seawater fluorescence properties in the Baltic and Nordic Seas: results of lidar experiments. Oceanologia 49, 59–69 (2007)

    Google Scholar 

  19. J. Cuesta, P. Chazette, T. Allouis, P.H. Flamant, S. Durrieu, J. Sanak, P. Genau, D. Guyon, D. Loustau, C. Flamant, Observing the forest canopy with a new ultra-violet compact airborne lidar. Sensors (Basel) 10, 7386 (2010)

    Article  Google Scholar 

  20. A. Ounis, J. Bach, A. Mahjoub, F. Daumard, I. Moya, Y. Goulas, A new airborne lidar for remote sensing of canopy fluorescence and vertical profile, in EPJ Web of Conferences (2016), p. 25019

  21. M.A. Lefsky, W.B. Cohen, G.G. Parker, D.J. Harding, Lidar remote sensing for ecosystem studies: lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. Bioscience 52, 19 (2002)

    Article  Google Scholar 

  22. M.A. Wulder, J.C. White, R.F. Nelson, E. Næsset, H.O. Ørka, N.C. Coops, T. Hilker, C.W. Bater, T. Gobakken, Lidar sampling for large-area forest characterization: a review. Remote Sens. Environ. 121, 196 (2012)

    Article  ADS  Google Scholar 

  23. M. Maltamo, E. Næsset, J. Vauhkonen, Forestry applications of airborne laser scanning, concepts and case studies. Manag. Ecosyst. 27, 2014 (2014)

    Google Scholar 

  24. H. Churnside, Review of profiling oceanographic lidar. Opt. Eng. 53, 051405 (2014)

    Article  ADS  Google Scholar 

  25. L. Tang, G.F. Shao, Drone remote sensing for forestry research and practices. J. For. Res. 26, 791 (2015)

    Article  Google Scholar 

  26. K. Whitehead, C.H. Hugenholtz, Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges. J. Unmanned Veh. Syst. 2, 69 (2014)

    Article  Google Scholar 

  27. S. Nakamura, Background story of the invention of efficient InGaN blue-light-emitting diodes (Nobel lecture). Angew. Chem. Int. Ed. 54, 7770–7788 (2015)

    Article  Google Scholar 

  28. M. Brydegaard, A. Merdasa, A. Gebru, H. Jayaweera, S. Svanberg, Realistic instrumentation platform for active and passive optical remote sensing. Appl. Spectrosc. 70, 372 (2016)

    Article  ADS  Google Scholar 

  29. M. Brydegaard, A. Gebru, S. Svanberg, Super resolution laser radar with blinking atmospheric particles—application to interacting flying insects. Prog. Electromagn. Res. 147, 141 (2014)

    Article  Google Scholar 

  30. E. Malmqvist, S. Jansson, S. Torok, M. Brydegaard, Effective parameterization of laser radar observations of atmospheric fauna. IEEE J. Select. Top. Quantum Electron. 22, 327 (2016)

    Article  ADS  Google Scholar 

  31. S.M. Zhu, E. Malmqvist, W.S. Li, S. Jansson, Y.Y. Li, Z. Duan, K. Svanberg, H.Q. Feng, Z.W. Song, G.Y. Zhao, M. Brydegaard, S. Svanberg, Insect abundance over Chinese rice fields in relation to environmental parameters, studied with a polarization-sensitive CW near-IR lidar system. Appl. Phys. B 123, 211 (2017). https://doi.org/10.1007/s00340-017-6784-x

    Article  ADS  Google Scholar 

  32. G.Y. Zhao, M. Ljungholm, E. Malmqvist, G. Bianco, L.A. Hansson, S. Svanberg, M. Brydegaard, Inelastic hyperspectral lidar for profiling aquatic ecosystems. Lasers Photonics Rev. (2016). https://doi.org/10.1002/lpor.201600093

    Article  Google Scholar 

  33. G. Zhao, E. Malmqvist, K. Rydhmer, A. Strand, G. Bianco, L.-A. Hansson, S. Svanberg, M. Brydegaard, Inelastic hyperspectral lidar for aquatic monitoring and landscape plant scanning test, ILRC28. Bucharest 176, 1003 (2017)

    Google Scholar 

  34. J. Erdkamp, J. Marriage, Theodor Scheimpflug—the life and work of the man who gave us that rule. Photographica World 3, 29–38 (2012)

    Google Scholar 

  35. S. Svanberg, LIDAR, in Springer Handbook of Lasers and Optics, 2nd edn. ed. by F. Träger (Springer, Heidelberg, 2012), p. 1146

    Google Scholar 

  36. E. Malmqvist, M. Brydegaard, M. Aldén, J. Bood, Scheimpflug lidar for combustion diagnostics. Opt. Express 26, 14842 (2018)

    Article  ADS  Google Scholar 

  37. L. Mei, M. Brydegaard, Atmospheric aerosol monitoring by an elastic Scheimpflug lidar system. Opt. Express 23, A1613–A1628 (2015)

    Article  ADS  Google Scholar 

  38. M. Brydegaard, E. Malmqvist, S. Jansson, J. Larsson, S. Török, G. Zhao, The Scheimpflug lidar method, in Proceedings of SPIE, vol. 10406 (2017)

  39. J.M. Romero, G.B. Cordon, M.G. Lagorio, Modeling re-absorption of fluorescence from the leaf to the canopy level. Remote Sens. Environ. 204, 138–146 (2018)

    Article  ADS  Google Scholar 

  40. H. Edner, J. Johansson, S. Svanberg, E. Wallinder, G. Cecchi, L. Pantani, Fluorescence lidar monitoring of the Arno river, EARSEL. Adv. Remote Sens. 1, 42 (1992)

    ADS  Google Scholar 

  41. A.J. Lawaetz, C.A. Stedmon, Fluorescence intensity calibration using the Raman scatter peak of water. Appl. Spectrosc. 63, 936–940 (2009)

    Article  ADS  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the continuing support from Professors Sailing He and Guofu Zhou. We are also very grateful to Ying Li, Ying Li, and Jinlei Wang for assistance in the measurements and Klas Rydhmer, Alfred Strand, and Mikael Ljungholm for contributions in the early work on hyperspectral Scheimpflug systems. This work was supported by the Guangdong Province Innovation Research Team Program (2010001D0104799318), the National Science Foundation of China (61705069), the Chinese Ministry of Science and Technology through the National Key Research and Development Program of China (2018YFC1407503), and by Spectraray Inc.

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Authors and Affiliations

  1. Center for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University, University City Campus, Guangzhou, 510006, China

    Xun Wang, Zheng Duan, Sune Svanberg & Guangyu Zhao

  2. Department of Physics, Lund University, 221 00, Lund, Sweden

    Mikkel Brydegaard & Sune Svanberg

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  1. Xun Wang
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  2. Zheng Duan
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  4. Sune Svanberg
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  5. Guangyu Zhao
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Correspondence to Sune Svanberg.

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Wang, X., Duan, Z., Brydegaard, M. et al. Drone-based area scanning of vegetation fluorescence height profiles using a miniaturized hyperspectral lidar system. Appl. Phys. B 124, 207 (2018). https://doi.org/10.1007/s00340-018-7078-7

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