Automated Organization Profile

Independent developer for free software - Aachener Str. 5d, 56072 Koblenz, Germany

Current S-Index

1.9

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

1.0

Average Dataset Index per dataset

Total Datasets

2

Total datasets in this organization

Average FAIR Score

76.9%

Average FAIR Score per dataset

Total Citations

0

Total citations to the organization's datasets

Total Mentions

0

Total mentions of the organization's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Global dataset of co-incident TLS-derived and harvested tree biomass

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

Authors

  • Demol, Miro ;
  • Verbeeck, Hans ;
  • Gielen, Bert ;
  • Armston, John ;
  • Burt, Andrew ;
  • Disney, Mathias ;
  • Duncanson, Laura ;
  • Hackenberg, Jan ;
  • Kükenbrink, Daniel ;
  • Lau, Alvaro ;
  • Ploton, Pierre ;
  • Sewdien, Artie ;
  • Stovall, Atticus ;
  • Takoudjou, Stéphane Momo ;
  • Volkova, Liubov ;
  • Weston, Chris ;
  • Wortel, Verginia ;
  • Calders, Kim
0 Citations0 Mentions77% FAIR0.4 Dataset Index
10.5281/zenodo.52367622021

Global dataset of co-incident TLS-derived and harvested tree biomass

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

Authors

  • Demol, Miro ;
  • Verbeeck, Hans ;
  • Gielen, Bert ;
  • Armston, John ;
  • Burt, Andrew ;
  • Disney, Mathias ;
  • Duncanson, Laura ;
  • Hackenberg, Jan ;
  • Kükenbrink, Daniel ;
  • Lau, Alvaro ;
  • Ploton, Pierre ;
  • Sewdien, Artie ;
  • Stovall, Atticus ;
  • Takoudjou, Stéphane Momo ;
  • Volkova, Liubov ;
  • Weston, Chris ;
  • Wortel, Verginia ;
  • Calders, Kim
0 Citations0 Mentions77% FAIR0.4 Dataset Index
10.5281/zenodo.52367612021