Published August 23, 2021
| Version v1
Dataset
Open
Global dataset of co-incident TLS-derived and harvested tree biomass
Authors/Creators
- 1. CAVElab, Computational and Applied Vegetation Ecology, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium // PLECO, Plants and Ecosystems, Faculty of Science, Antwerp University, Wilrijk, Belgium
- 2. CAVElab, Computational and Applied Vegetation Ecology, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
- 3. PLECO, Plants and Ecosystems, Faculty of Science, Antwerp University, Wilrijk, Belgium
- 4. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
- 5. Department of Geography, University College London, London, UK
- 6. Department of Geography, University College London, London, UK // NERC NCEO-UCL
- 7. Independent developer for free software - Aachener Str. 5d, 56072 Koblenz, Germany
- 8. Swiss Federal Institute WSL, Zurichstrasse 111, CH-8903 Birmensdorf, Switzerland
- 9. Wageningen University, Laboratory of Geo-Information Science and Remote Sensing, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
- 10. AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier, France
- 11. Department of Forest Management, Centre for Agricultural Research in Suriname (CELOS), Prof.Dr.Ir.J.Ruinardlaan 1, Paramaribo, Suriname
- 12. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA // NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD, United States
- 13. AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier, France // Plant Systematic and Ecology Laboratory (LaBosystE), Department of Biology, Higher Teachers' Training College, University of Yaounde I, P.O. Box 047, Yaoundé Cameroon
- 14. School of Ecosystem and Forest Sciences, The University of Melbourne, Victoria 3010, Australia
Description
This dataset was used to producee the figures and statistics of the publication "Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions".
This dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns. Elaborate method descriptions can be found in the publication or in the following original publications:
- Burt, A., Boni Vicari, M., da Costa, A. C. L., Coughlin, I., Meir, P., Rowland, L., et al. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science 8, 201458. doi:10.1098/rsos.201458
- Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., et al. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution 6, 198–208. doi:10.1111/2041-210X.12301
- Demol, M., Calders, K., Krishna Moorthy, S. M., Van den Bulcke, J., Verbeeck, H., and Gielen, B. (2021). Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning. Trees 35, 671–684. doi:10.1007/s00468-020-02067-7
- Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., et al. (2018). Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution 9, 223–234. doi:10.1111/2041-210X.12904
- Hackenberg, J., Wassenberg, M., Spiecker, H., and Sun, D. (2015). Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6, 1274–1300. doi:10.3390/ f6041274
- Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., and Mathys, L. (2021). Aboveground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 1–16doi:10.1093/aob/mcab002
- Lau, A., Calders, K., Bartholomeus, H., Martius, C., Raumonen, P., Herold, M., et al. (2019). Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana. Forests 10, 527. doi:10.3390/f10060527
- Momo Takoudjou, S., Ploton, P., Sonke, B., Hackenberg, J., Griffon, S., Coligny, F., et al. (2018). Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods in Ecology and Evolution 9, 905–916.
doi:10.1111/2041-210X.12933 - Stovall, A. E., Vorster, A. G., Anderson, R. S., Evangelista, P. H., and Shugart, H. H. (2017). Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sensing of Environment 200, 31–42. doi:10.1016/j.rse.2017.08.013
Files
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Additional details
Funding
- European Commission
- RINGO - Readiness of ICOS for Necessities of integrated Global Observations 730944
- European Commission
- FODEX - Tropical Forest Degradation Experiment 757526
- UK Research and Innovation
- Weighing trees with lasers: reducing uncertainty in tropical forest biomass and allometry NE/N00373X/1
- European Commission
- 3D-FOGROD - Understanding forest growth dynamics using novel 3D measurements and modelling approaches 835398