LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy.

Journal article
(Original article)


Publication Details

Author(s): Ewald M, Aerts R, Lenoir J, Fassnacht F, Nicolas M, Skowronek S, Piat J, Honnay O, Garzon-Lopez C, Feilhauer H, Van De Kerchove R, Somers B, Hattab T, Rocchini D, Schmidtlein S
Journal: Remote Sensing of Environment
Publication year: 2018
Volume: 211
Pages range: 13-25
ISSN: 0034-4257
Language: English


FAU Authors / FAU Editors

Feilhauer, Hannes Dr.
Professur für Geographie (Fernerkundung und GIS)
Skowronek, Sandra
Institut für Geographie


How to cite

APA:
Ewald, M., Aerts, R., Lenoir, J., Fassnacht, F., Nicolas, M., Skowronek, S.,... Schmidtlein, S. (2018). LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy. Remote Sensing of Environment, 211, 13-25.

MLA:
Ewald, Michael, et al. "LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy." Remote Sensing of Environment 211 (2018): 13-25.

BibTeX: 

Last updated on 2018-07-08 at 23:08