Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest

Wang, Z, Wang, T, Darvishzadeh, R, Skidmore, A, Jones, S, Suarez, L, Woodgate, W, Heiden, U, Heurich, M and Hearne, J 2016, 'Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest', Remote Sensing, vol. 8, no. 6, 491, pp. 1-20.


Document type: Journal Article
Collection: Journal Articles

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Title Vegetation indices for mapping canopy foliar nitrogen in a mixed temperate forest
Author(s) Wang, Z
Wang, T
Darvishzadeh, R
Skidmore, A
Jones, S
Suarez, L
Woodgate, W
Heiden, U
Heurich, M
Hearne, J
Year 2016
Journal name Remote Sensing
Volume number 8
Issue number 6
Article Number 491
Start page 1
End page 20
Total pages 20
Publisher MDPI
Abstract Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of techniques. Vegetation indices (VIs) are a simple means of retrieving foliar nitrogen. Despite their popularity, few studies have been conducted to examine the utility of VIs for mapping canopy foliar nitrogen in a mixed forest context. In this study, we assessed the performance of 32 vegetation indices derived from HySpex airborne hyperspectral images for estimating canopy mass-based foliar nitrogen concentration (%N) in the Bavarian Forest National Park. The partial least squares regression (PLSR) was performed for comparison. These vegetation indices were classified into three categories that are mostly correlated to nitrogen, chlorophyll, and structural properties such as leaf area index (LAI). %N was destructively measured in 26 broadleaf, needle leaf, and mixed stand plots to represent the different species and canopy structure. The canopy foliar %N is defined as the plot-level mean foliar %N of all species weighted by species canopy foliar mass fraction. Our results showed that the variance of canopy foliar %N is mainly explained by functional type and species composition. The normalized difference nitrogen index (NDNI) produced the most accurate estimation of %N (R2CV = 0.79, RMSECV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R2CV = 0.76, RMSECV = 0.27). In addition, the mean NIR reflectance (800-850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R2CV = 0.73, RMSECV = 0.30). The PLSR model provided a less accurate estimation of %N (R2CV = 0.69, RMSECV = 0.32). We argue that the good performance of all three categories of vegetation indices in %N estimation can be attributed to the synergy among plant traits (i.e., canopy structure, leaf chemical and optical properties) while these traits may converge across plant species for evolutionary reasons.
Subject Photogrammetry and Remote Sensing
Keyword(s) Canopy foliar nitrogen
Vegetation indices
Hyperspectral data
Mixed forest
Plant traits
DOI - identifier 10.3390/rs8060491
Copyright notice © 2016 by the authors; licensee MDPI, Basel, Switzerland.' distributed under the terms and conditions of the Creative Commons
ISSN 2072-4292
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