RegisTree: a registration algorithm to enhance forest inventory plot georeferencing
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.
The accuracy of remote sensing-based estimates of forest parameters depends on the quality of the spatial registration of the training data.
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).
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.
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%.
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.
KeywordsForest inventory Lidar Plot positioning Registration algorithm Forest parameter estimation
The authors would like to thank the Office National des Forêt (ONF) for providing lidar and Field data for St-Gobain, Compiègne, and Darney.
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Neter J, Wasserman W, Kutner MH (1985) Applied linear statistical models (2nd ed.). Irwin, New YorkGoogle Scholar
- 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
- 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
- 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
- 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