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Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

Wang, Zhihui and Wang, Tiejun and Darvishzadeh, Roshanak and Skidmore, Andrew and Jones, Simon and Suarez, Lola and Woodgate, William and Heiden, Uta and Heurich, Marco and Hearne, John (2016) Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest. Remote Sensing, 8 (6), pp. 1-20. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs8060491 ISSN 2072-4292

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Official URL: http://www.mdpi.com/2072-4292/8/6/491


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. Our findings demonstrated the feasibility of using hyperspectral vegetation indices to estimate %N in a mixed temperate forest which may relate to the effect of the physical basis of nitrogen absorption features on canopy reflectance, or the biological links between nitrogen, chlorophyll, and canopy structure.

Item URL in elib:https://elib.dlr.de/110401/
Document Type:Article
Title:Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wang, Zhihuiz.wang-1 (at) utwente.nlhttps://orcid.org/0000-0003-1064-7820
Wang, Tiejunt.wang (at) utwente.nlUNSPECIFIED
Darvishzadeh, Roshanakr.darvish (at) utwente.nlUNSPECIFIED
Skidmore, Andrewa.k.skidmore (at) utwente.nlUNSPECIFIED
Jones, Simonrmit university, australiaUNSPECIFIED
Suarez, Lolarmit university, australiaUNSPECIFIED
Woodgate, Williamrmit university, australiaUNSPECIFIED
Heiden, Utauta.heiden (at) dlr.deUNSPECIFIED
Heurich, Marcobavarian forest national park, department of researchUNSPECIFIED
Hearne, Johnrmit university, australiaUNSPECIFIED
Date:June 2016
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs8060491
Page Range:pp. 1-20
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:canopy foliar nitrogen; vegetation indices; hyperspectral data; mixed forest; plant traits
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Fernerkundung der Landoberfläche (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Heiden, Dr.rer.nat. Uta
Deposited On:11 Jan 2017 10:38
Last Modified:14 Dec 2019 04:26

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