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

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 (2018)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2018

Journal

Remote Sensing of Environment Elsevier

Book Volume: 211

Pages Range: 13-25

DOI: 10.1016/j.rse.2018.03.038

Authors with CRIS profile

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

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. https://dx.doi.org/10.1016/j.rse.2018.03.038

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.

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