Drone remote sensing for forestry research and practices

Journal of Forestry Research volume 26, pages791–797(2015)Cite this article

Abstract

Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing initiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, measuring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it is important to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

References

  1. Alberts K (2012) Landsat data characteristics and holdings. A presentation of USGS Landsat Ground System Lead (http://www.slideserve.com/keahi/landsat-data-characteristics-and-holdings)

  2. Ambrosia V, Hutt M, Lulla K (2011a) Special issue: unmanned airborne systems (UAS) for remote sensing applications. Geocarto Int 26(2):69–70

    Article  Google Scholar 

  3. Ambrosia V, Wegener S, Zajkowski T, Sullivan D, Buechel S, Enomoto F, Lobitz B, Johan S, Brass J, Hinkley E (2011b) The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010). Geocarto Int 26(2):85–101

    Article  Google Scholar 

  4. Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11(3):138–146

    Article  Google Scholar 

  5. Arano KG, Munn IA (2006) Evaluating forest management intensity: a comparison among major forest landowner types. Forest Policy Econ 9(3):237–248

    Article  Google Scholar 

  6. Arroyo LA, Pascual C, Manzanera JA (2008) Fire models and methods to map fuel types: the role of remote sensing. For Ecol Manag 256(6):1239–1252

    Article  Google Scholar 

  7. Asner G, Knapp D, Broadbent P, Keller M, Silva J (2005) Selective logging in the Brazilian Amazon. Science 310(5747):480–482

    CAS  Article  PubMed  Google Scholar 

  8. Bai GX, Wang YY, Dai LM, Liu SR, Tang LN, Shao GF (2015) Market-oriented forestry in China promotes forestland productivity. New Forest 46(1):1–6

    Article  Google Scholar 

  9. Berni JAJ, Zarco-Tejada PJ, Suarez L, Fereres E (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47(3):722–738

    Article  Google Scholar 

  10. Boyd DS, Danson FM (2005) Satellite remote sensing of forest resources: three decades of research development. Prog Phys Geogr 29(1):1–26

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Dandois JP, Ellis EC (2013) High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens Environ 136:259–276

    Article  Google Scholar 

  13. Dubayah RO, Drake JB (2000) Lidar remote sensing for forestry. J Forest 98(6):44–46

    Google Scholar 

  14. Dudek M, Tomczyk P, Wygonik P, Korkosz M, Bogusz P, Lis B (2013) Hybrid fuel cell—battery system as a main power unit for small unmanned aerial vehicles (UAV). Int J Electrochem Sci 8(6):8442–8463

    CAS  Google Scholar 

  15. Felderhof L, Gillieson D (2011) Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops. Can J Remote Sens 37(4):376–386

    Article  Google Scholar 

  16. Frolking S, Palace MW, Clark DB, Chambers JQ, Hugart HH, Hurtt GC (2009) Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure, J Geophys Res, 114: G00E02, doi: 10.1029/2008JG000911

  17. Getzin S, Wiegand K, Schöning I (2012) Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol Evol 3(2):397–404

    Article  Google Scholar 

  18. Hinkley E, Zajkowski T (2011) USDA forest service-NASA: unmanned aerial systems demonstrations—pushing the leading edge in fire mapping. Geocarto Int 26(2):103–111

    Article  Google Scholar 

  19. Koch B (2010) Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS J Photogramm Remote Sens 65(6):581–590

    Article  Google Scholar 

  20. Koh LP, Wich SA (2012) Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop Conserv Sci 5:121–132

    Google Scholar 

  21. Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) Lidar remote sensing for ecosystem studies. Bioscience 52(1):19–30

    Article  Google Scholar 

  22. Lisein J, Pierrot-Deseilligny M, Bonnet S, Lejeune P (2013) A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 4(4):922–944

    Article  Google Scholar 

  23. Lu DS (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27(7):1297–1328

    Article  Google Scholar 

  24. Lyons EH (1966) Fixed air-base 70 mm photography, a new tool for forest sampling. For Chron 42(4):420–431

    Article  Google Scholar 

  25. Martin ME, Aber JD (1997) High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes. Ecol Appl 7(2):431–443

    Article  Google Scholar 

  26. Martinez-de Dios JR, Merino L, Caballero F, Ollero A (2011) Automatic forest-fire measuring using ground stations and unmanned aerial systems. Sensors 11(6):6328–6353

    PubMed Central  Article  PubMed  Google Scholar 

  27. Merino L, Caballero F, Martínez-de-Dios JR, Iván M, Aníbal O (2012) An unmanned aircraft system for automatic forest fire monitoring and measurement. J Intell Rob Syst 65(1–4):533–548

    Article  Google Scholar 

  28. Paneque-Galvez J, McCall MK, Napoletano BM, Wich SA, Koh LP (2014) Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas. Forests 5(6):1481–1507

    Article  Google Scholar 

  29. Pope I, Bowen D, Harbor J, Shao GF, Zanotti L, Burniske G (2015) Deforestation of montane cloud forest in the central highlands of Guatemala: contributing factors and implications for sustainability in Q’eqchi’ communities. Int J Sustain Dev World Ecol 22:201–212

    Article  Google Scholar 

  30. Saari H, Antila T, Holmlund C, Mäkynen J, Ojala K, Toivanen H, Pellikka I, Tuominen S, Pesonen L, Heikkilä J (2011) Unmanned aerial vehicle (UAV) operated spectral camera system for forest and agriculture applications. In: Proceedings of the SPIE, 8174: id 81740H

  31. Shahbazi M, Théau J, Ménard P (2014) Recent applications of unmanned aerial imagery in natural resource management. GISci Remote Sens. doi:10.1080/15481603.2014.926650

    Google Scholar 

  32. Shao GF (2012a) Remote sensing. In: El-Shaarawi A-H, Piegorsch W (eds) Encyclopedia of environmetrics, 2nd edn. Wiley, Chichester, pp 2187–2193

    Google Scholar 

  33. Shao GF (2012b) Satellite data. In: El-Shaarawi A-H, Piegorsch W (eds) Encyclopedia of environmetrics, 2nd edn. Wiley, Chichester, pp 2390–2395

    Google Scholar 

  34. Shao GF (2015) Optical remote sensing. In: Richardson D (ed) The international encyclopedia of geography: people, the earth, environment, and technology, 2nd edn. Wiley, Chichester, pp 2390–2395

    Google Scholar 

  35. Shao GF, Zhao G, Zhao SD, Shugart HH, Wang SX, Schaller J (1996) Forest cover types derived from landsat TM imagery for Changbai Mountain Area of China. Can J For Res 26(2):206–216

    Article  Google Scholar 

  36. Siebert S, Teizer J (2014) Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system. Autom Constr 41:1–14

    Article  Google Scholar 

  37. Tang LN, Shao GF, Dai LM (2009) Roles of digital technology in China’s sustainable forestry development. Int J Sustain Dev World Ecol 16(2):94–101

    Article  Google Scholar 

  38. Tang LN, Shao GF, Piao ZJ, Dai LM, Jenkins MA, Wang SX, Wu G, Wu JG, Zhao JZ (2010) Forest degradation deepens around and within protected areas in East Asia. Biol Conserv 143(5):1295–1298

    Article  Google Scholar 

  39. Thakur T, Swamy SL, Nain AS (2014) Composition, structure and diversity characterization of dry tropical forest of Chhattisgarh using satellite data. J For Res 25(4):819–825

    Article  Google Scholar 

  40. Treitz PM, Howarth PJ (1999) Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Prog Phys Geogr 23(3):359–390

    Article  Google Scholar 

  41. Tulldahl HM, Larsson H. 2014. Lidar on small UAV for 3D mapping. In: Proceedings of SPIE sponsored conference on electro-optical remote sensing, photonic technologies, and applications VIII; and military applications in hyperspectral imaging and high spatial resolution sensing II. Amsterdam, Netherlands, 22–23 Sep 2014

  42. Turner DP, Cohen WB, Kennedy RE, Karin S (1999) Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens Environ 70(1):52–68

    Article  Google Scholar 

  43. Wallace L, Watson C, Lucieer A (2014) Detecting pruning of individual stems using airborne laser scanning data captured from an unmanned aerial vehicle. Int J Appl Earth Obs Geoinf 30:76–85

    Article  Google Scholar 

  44. Wang YY, Bai GX, Shao GF, Cao YK (2014) An analysis of potential investment returns and their determinants of poplar plantations in state-owned forest enterprises of China. New Forest 45(2):251–264

    Article  Google Scholar 

  45. Watts AC, Ambrosia VG, Hinkley EA (2012) Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens 4(6):1671–1692

    Article  Google Scholar 

  46. Wing MG, Burnett J, Sessions J, Brungardt J, Cordell V, Dobler D, Wilson D (2013) Eyes in the sky: remote sensing technology development using small unmanned aircraft systems. J Forest 111(5):341–347

    Article  Google Scholar 

  47. Wing MG, Burnett JD, Sessions J (2014) Remote sensing and unmanned aerial system technology for monitoring and quantifying forest fire impacts. Int J Remote Sens Appl 4(1):18–35

    Article  Google Scholar 

  48. Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006) Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. For Ecol Manag 221(1–3):27–41

    Article  Google Scholar 

  49. Zaloga SJ (2008) Unmanned aerial vehicles: Robotic air warfare 1917–2007. Osprey Publishing, New York

    Google Scholar 

  50. Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P (2014) Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55:89–99

    Article  Google Scholar 

  51. Zhang H, Jim CY (2013) Species adoption for sustainable forestry in Hong Kong’s degraded countryside. Int J Sustain Dev World Ecol 20(6):484–503

    Article  Google Scholar 

  52. Zhao G, Shao GF, Reynolds K, Wimberly MC, Warner T, Moser JM, Rennolls K, Magnussen S, Kohl M, Andersen HE, Mendoza GA, Dai LM, Huth A, Zhang LJ, Brey J, Sun YJ, Ye RH, Martin BA, Li FR (2005) Digital forestry: a white paper. J Forest 103(1):47–50

    Google Scholar 

  53. Zhu ZL, Evans DL (1994) U.S. forest types and predicted percent forest cover from AVHRR data. Photogramm Eng Remote Sens 60(5):525–531

    Google Scholar 

Download references

Author information

Affiliations

  1. Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China

    Lina Tang

  2. Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN, 47907, USA

    Guofan Shao

Authors
  1. Lina Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guofan Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guofan Shao.

Additional information

The online version is available at http://www.springerlink.com

Corresponding editor: Chai Ruihai

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Drone
  • Remote sensing
  • UAV
  • UAS
  • UA
  • RPA
  • Forest