Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects |
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| Institution: | 1. Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, 00076 Aalto, Finland;2. School of Biology, Newcastle University, Ridley Building 2, Newcastle Upon Tyne, NE1 7RU, UK;3. University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland;4. Department of Geography, University College London, London WC1E 6BT, UK;5. Aalto University, School of Electrical Engineering, Department of Electronics and Nanoengineering, P.O. Box 15500, 00076 Aalto, Finland;1. Irstea, UMR ITAP, 361 rue J.F. Breton, 34196 Montpellier, France;2. Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia;3. Ophthalmology, University of Melbourne, Department of Surgery, Melbourne, Australia;4. Irstea, UMR TETIS, Maison de la Télédétection, 500 rue J.F. Breton, 34093 Montpellier, France |
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| Abstract: | A statistical relationship between canopy mass-based foliar nitrogen concentration (%N) and canopy bidirectional reflectance factor (BRF) has been repeatedly demonstrated. However, the interaction between leaf properties and canopy structure confounds the estimation of foliar nitrogen. The canopy scattering coefficient (the ratio of BRF and the directional area scattering factor, DASF) has recently been suggested for estimating %N as it suppresses the canopy structural effects on BRF. However, estimation of %N using the scattering coefficient has not yet been investigated for longer spectral wavelengths (>855 nm). We retrieved the canopy scattering coefficient for wavelengths between 400 and 2500 nm from airborne hyperspectral imagery, and then applied a continuous wavelet analysis (CWA) to the scattering coefficient in order to estimate %N. Predictions of %N were also made using partial least squares regression (PLSR). We found that %N can be accurately retrieved using CWA (R2 = 0.65, RMSE = 0.33) when four wavelet features are combined, with CWA yielding a more accurate estimation than PLSR (R2 = 0.47, RMSE = 0.41). We also found that the wavelet features most sensitive to %N variation in the visible region relate to chlorophyll absorption, while wavelet features in the shortwave infrared regions relate to protein and dry matter absorption. Our results confirm that %N can be retrieved using the scattering coefficient after correcting for canopy structural effect. With the aid of high-fidelity airborne or upcoming space-borne hyperspectral imagery, large-scale foliar nitrogen maps can be generated to improve the modeling of ecosystem processes as well as ecosystem-climate feedbacks. |
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| Keywords: | Foliar nitrogen Forest canopy structure Hyperspectral remote sensing Essential biodiversity variables |
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