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RegisTree: a registration algorithm to enhance forest inventory plot georeferencing

  • Maryem Fadili
  • Jean-Pierre Renaud
  • Jerome Bock
  • Cédric VegaEmail author
Research Paper
Part of the following topical collections:
  1. Forest information for bioeconomy outlooks at European level

Abstract

Key message

The accuracy of remote sensing-based models of forest attributes could be improved by controlling the spatial registration of field and remote sensing data. We have demonstrated the potential of an algorithm matching plot-level field tree positions with lidar canopy height models and derived local maxima to achieve a precise registration automatically.

Context

The accuracy of remote sensing-based estimates of forest parameters depends on the quality of the spatial registration of the training data.

Aims

This study introduces an algorithm called RegisTree to correct field plot positions by matching a spatialized field tree height map with lidar canopy height models (CHMs).

Methods

RegisTree is based on a point (field positions) to surface (CHM) adjustment approach modified to ensure that at least one field tree position corresponds to CHM local maxima.

Results

RegisTree has been validated with respect to positioning errors and the performance of lidar-derived estimation of plot volume. Overall, RegisTree enabled to register field plots surveyed in a range of forest conditions with a precision of 1.5 m (± 1.23 m), but a higher performance for conifer plots, and a limited efficiency in homogeneous stands, having similar heights. Improved plot positions were found to have a limited impact on volume predictions under the range of tested conditions, with a gain up to 1.3%.

Conclusion

RegisTree could be used to improve the forest plot position from field surveys collected with low-grade GPS and to contribute to the development of processing chains of 3D remote sensing-based models of forest parameters.

Keywords

Forest inventory Lidar Plot positioning Registration algorithm Forest parameter estimation 

Notes

Acknowledgements

The authors would like to thank the Office National des Forêt (ONF) for providing lidar and Field data for St-Gobain, Compiègne, and Darney.

Fundings

Maryem Fadili has been funded by the DIABOLO—Distributed, Integrated and Harmonised Forest Information for Bioeconomy Outlooks—project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 633464 (project duration: 1 March 2015 to 28 February 2019; coordinator, Natural Resources Institute Finland (Luke)). Part of the data (Vosges, Aillon, Bure) has been acquired in the Framework of the project FORESEE funded by the French National Research Agency (ANR-2010-BIOE-008). ONF Département RDI and IGN Laboratory of Forest Inventory (LIF) are supported by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Andersen H-E, Clarkin T, Winterberger K, Strunk J (2009) An accuracy assessment of positions obtained using survey- and recreational-grade Global Positioning System receivers across a range of forest conditions within the Tanana Valley of interior Alaska. West J Appl For 24:128–136Google Scholar
  2. Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models. Int Arch Photogramm Remote Sens Spat Inf Sci 33:110–117 Part B4/1 Google Scholar
  3. Baltsavias EP (1999) Airborne laser scanning: basis relations and formulas. ISPRS J Photogramm Remote Sens 54:199–214CrossRefGoogle Scholar
  4. Bock J, Piboule A, Jolly A (2017) TidALS: trunk identification in dense Airborne Laser Scanner data to estimate. In: Silvilaser conference, October 10–12, 2017, Blacksburg, Virginia, USAGoogle Scholar
  5. Bouvier M, Durrieu S, Fournier RA, Renaud J-P (2015) Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sens Environ 156:322–334CrossRefGoogle Scholar
  6. Danskin SD, Bettinger P, Jordan TR, Cieszewski C (2009) A comparison of GPS performance in a southern hardwood forest: exploring low-cost solutions for forestry applications. South J Appl For 33:9–16Google Scholar
  7. Deleuze C, Morneau F, Renaud J -P, Vivien Y, Rivoire M, Santenoise P, Longuetaud F, Mothe F, Hervé JC, Vallet P (2014) Estimer le volume total d’un arbre, quelles que soient l’essence, la taille, la sylviculture, la station. RDV techniques ONF 44: 22–32Google Scholar
  8. Dorigo W, Hollaus M, Wagner W, Schadauer K (2010) An application-oriented automated approach for registration of forest inventory and airborne laser scanning data. Int J Remote Sens 31:1133–1153CrossRefGoogle Scholar
  9. Fadili M, Renaud JP, Bock J, Vega C (2019) RegisTree: a registration algorithm to enhance forest inventory plot georeferencing. V1. Zenodo. [Dataset].  https://doi.org/10.5281/zenodo.2577140
  10. Favorskaya MN, Jain LC (2017) Overview of LiDAR technologies and equipment for land cover scanning In Handbook on advances in remote sensing and geographic information systems: paradigms and applications in forest landscape modeling, intelligent systems reference library. Springer International Publishing, 122, pp 19–68Google Scholar
  11. Gobakken T, Næsset E (2009) Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Can J For Res 39:1036–1052.  https://doi.org/10.1139/X09-025
  12. Hauglin M, Lien V, Næsset E, Gobakken T (2014) Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data. Int J Remote Sens 35:3135–3149CrossRefGoogle Scholar
  13. Johnson KD, Birdsey R, Finley AO, Swantaran A, Dubayah R, Wayson C, Riemann R (2014) Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system. Carbon Balance Manage 9:3CrossRefGoogle Scholar
  14. Kane VR, McGaughey RJ, Bakker JD et al (2010) Comparisons between field- and LiDAR-based measures of stand structural complexity. Can J For Res 40:761–773CrossRefGoogle Scholar
  15. Khosravipour A, Skidmore AK, Wang T, Isenburg M, Khoshelham K (2015) Effect of slope on treetop detection using a LiDAR Canopy Height Model. ISPRS J Photogramm Remote Sens 104:44–52CrossRefGoogle Scholar
  16. Korpela I, Tuomola T, Välimäki E (2007) Mapping forest plots: an efficient method combining photogrammetry and field triangulation. Silva Fenn 41:457–469CrossRefGoogle Scholar
  17. Magnussen S, Næsset E, Gobakken T, Frazer G (2012) A fine-scale model for area-based predictions of tree-size-related attributes derived from LiDAR canopy heights. Scand J For Res 27:312–322CrossRefGoogle Scholar
  18. McRoberts RE, Tomppo EO (2007) Remote sensing support for national forest inventories. Remote Sens Environ 110:412–419CrossRefGoogle Scholar
  19. McRoberts RE, Chen Q, Walters BF, Kaisershot DJ (2018) The effects of global positioning system receiver accuracy on airborne laser scanning-assisted estimates of aboveground biomass. Remote Sens Environ 207:42–49CrossRefGoogle Scholar
  20. Monnet J-M, Mermin É (2014) Cross-correlation of diameter measures for the co-registration of forest inventory plots with airborne laser scanning data. Forests 5:2307–2326CrossRefGoogle Scholar
  21. Nakajima H (2016) Plot location errors of National Forest Inventory: related factors and adverse effects on continuity of plot data. J For Res 21:300–305.  https://doi.org/10.1007/s10310-016-0538-1
  22. Næsset E (2007) Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scand J For Res 22:433–442CrossRefGoogle Scholar
  23. Næsset E, Jonmeister T (2002) Assessing point accuracy of DGPS under forest canopy before data acquisition, in the field and after postprocessing. Scand J For Res 17:351–358CrossRefGoogle Scholar
  24. Neter J, Wasserman W, Kutner MH (1985) Applied linear statistical models (2nd ed.). Irwin, New YorkGoogle Scholar
  25. O'Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690CrossRefGoogle Scholar
  26. Olofsson K, Lindberg E, Holmgren J (2008) A method for linking field-surveyed and aerial-detected single trees using cross correlation of position images and the optimization of weighted tree list graphs In proceeding of Silvilaser 2008, Sept 17-19, 2008 – Edinburgh, UK, pp 95–104Google Scholar
  27. Picard RR, Cook RD (1984) Cross-validation of regression models. J Am Stat Assoc 79:575–583CrossRefGoogle Scholar
  28. Pinto da Costa J (2015) Rankings and preferences—new results in weighted correlation and weighted principal component analysis, SpringerBriefs in Statistics, 95 pp. Google Scholar
  29. Ransom MD, Rhynold J, Bettinger P (2010) Performance of mapping-grade GPS receivers in southeastern forest conditions. RURALS: Review of Undergraduate Research in Agricultural and Life Sciences: Vol 5: Iss 1, Article 2Google Scholar
  30. Tomppo E, Olsson H, Ståhl G, Nilsson M, Hagner O, Katila M (2008) Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens Environ 112:1982–1999CrossRefGoogle Scholar
  31. Valbuena R, Mauro F, Rodriguez-Solano R, Manzanera JA (2010) Accuracy and precision of GPS receivers under forest canopies in a mountainous environment. Span J Agric Res 8:1047–1057CrossRefGoogle Scholar
  32. Véga C, Durrieu S (2011) Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: application to a mountainous forest with heterogeneous stands. Int J Appl Earth Obs Geoinf 13:646–656CrossRefGoogle Scholar
  33. Vega C, Hamrouni A, El Mokhtari S, Morel J, Bock J, Renaud J-P, Bouvier M, Durrieu S (2014) PTrees: a point-based approach to forest tree extraction from lidar data. Int J Appl Earth Obs Geoinf 33:98–108CrossRefGoogle Scholar
  34. Véga C, Renaud J-P, Durrieu S, Bouvier M (2016) On the interest of penetration depth, canopy area and volume metrics to improve Lidar-based models of forest parameters. Remote Sens Environ 175:32–42CrossRefGoogle Scholar
  35. Wasser L, Day R, Chasmer L, Taylor A (2013) Influence of vegetation structure on Lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions. PLoS One 8:e54776CrossRefGoogle Scholar
  36. White JC, Stepper C, Tompalski P, Coops NC, Wulder MA (2015a) Comparing ALS and image-based point cloud metrics and modelled forest inventory attributes in a complex coastal forest environment. Forests 6:3704–3732CrossRefGoogle Scholar
  37. White JC, Arnett JTTR, Wulder MA, Tompalski P, Coops NC (2015b) Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach. Can J For Res 45:1498–1513CrossRefGoogle Scholar
  38. Wing MG, Eklund A (2007) Performance comparison of a low-cost mapping grade global positioning systems (GPS) receiver and consumer grade GPS receiver under dense forest canopy. J For 105:9–14Google Scholar
  39. Wulder M (1998) Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Prog Phys Geogr 22:449–476CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  • Maryem Fadili
    • 1
  • Jean-Pierre Renaud
    • 2
  • Jerome Bock
    • 3
  • Cédric Vega
    • 1
    Email author
  1. 1.Laboratoire d’Inventaire ForestierInstitut National de l’Information Géographique et Forestière (IGN)NancyFrance
  2. 2.Département RDIOffice National des Forêts (ONF)Villers lès NancyFrance
  3. 3.Département RDIOffice National des Forêts (ONF)ChambéryFrance

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