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

Type Article
Date 2018-06
Language English
Author(s) Ewald Michael1, Aerts Raf2, Lenoir Jonathan3, Fassnacht Fabian Ewald1, Nicolas Manuel4, Skowronek Sandra5, Piat Jerome4, Honnay Olivier2, Garzon-Lopez Carol Ximena3, 6, Feilhauer Hannes5, Van De Kerchove Ruben7, Somers Ben8, Hattab Tarek3, 9, Rocchini Duccio10, 11, 12, Schmidtlein Sebastian1
Affiliation(s) 1 : Karlsruhe Inst Technol, Inst Geog & Geoecol, Kaiserstr 12, D-76131 Karlsruhe, Germany.
2 : Katholieke Univ Leuven, Biol Dept, Kasteelpk Arenberg 31-2435, B-3001 Leuven, Belgium.
3 : Univ Picardie Jules Verne, UMR CNRS 7058, EDYSAN, UR Ecol & Dynam Syst Anthropises, 1 Rue Louvels, F-80037 Amiens 1, France.
4 : Off Natl Forets, Dept Rech & Dev, F-77300 Fontainebleau, France.
5 : FAU Erlangen Nuremberg, Inst Geog, Wetterkreuz 15, D-91058 Erlangen, Germany.
6 : Univ Los Andes, Ecol & Vegetat Physiol Grp EcoFiv, Cr 1E 18A, Bogota, Colombia.
7 : VITO Flemish Inst Technol Res, Boeretang 200, B-2400 Mol, Belgium.
8 : Katholieke Univ Leuven, Dept Earth & Environm Sci, Celestijnenlaan 200E, B-3001 Leuven, Belgium.
9 : Inst Francais Rech Exploitat Mer, UMR MARBEC, Ave Jean Monnet CS, Sete, France.
10 : Fdn Edmund Mach, Res & Innovat Ctr, Dept Biodivers & Mol Ecol, Via E Mach 1, I-38010 San Michele All Adige, TN, Italy.
11 : Univ Trento, Ctr Agr Food Environm, Via E Mach 1, I-38010 San Michele All Adige, TN, Italy.
12 : Univ Trento, Ctr Integrat Biol, Via Sommarive 14, I-38123 Povo, TN, Italy.
Source Remote Sensing Of Environment (0034-4257) (Elsevier Science Inc), 2018-06 , Vol. 211 , P. 13-25
DOI 10.1016/j.rse.2018.03.038
WOS© Times Cited 10
Keyword(s) Remote sensing, Canopy biochemistry, APEX, Hyperspectral imagery, Leaf traits, Leaf nutrient content, Data fusion, Forest ecosystem

Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (Nmass) and phosphorus concentrations (Pmass) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of Nmass and Pmass. We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for Nmass (R2cv 0.31 vs. 0.41, % RSMEcv 3.3 vs. 3.0) and Pmass (R2cv 0.45 vs. 0.63, % RSMEcv 15.3 vs. 12.5). The predictive performances of Nmass models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R2cv values ranged from 0.26 for Nmass to 0.54 for Pmass indicating considerable covariation between biochemical traits and forest structural properties. Nmass was negatively related to the spatial heterogeneity of canopy density, whereas Pmass was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents.

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Ewald Michael, Aerts Raf, Lenoir Jonathan, Fassnacht Fabian Ewald, Nicolas Manuel, Skowronek Sandra, Piat Jerome, Honnay Olivier, Garzon-Lopez Carol Ximena, Feilhauer Hannes, Van De Kerchove Ruben, Somers Ben, Hattab Tarek, Rocchini Duccio, Schmidtlein Sebastian (2018). LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy. Remote Sensing Of Environment, 211, 13-25. Publisher's official version : , Open Access version :