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Remote Sens. 2013, 5(2), 891-908; doi:10.3390/rs5020891

Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations

1,* , 2
1 Department of Geography and Geology, Algoma University, Sault Ste. Marie, ON P6A 2G4, Canada 2 Department of Geography, Nipissing University, North Bay, ON P1B 8L7, Canada 3 Department of Computer Science and Mathematics, Nipissing University, North Bay, ON P1B 8L7, Canada 4 Instituto del Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Mazatlán, Sinaloa 82040, Mexico
* Author to whom correspondence should be addressed.
Received: 20 December 2012 / Revised: 6 February 2013 / Accepted: 16 February 2013 / Published: 22 February 2013
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The use of spectral response curves for estimating nitrogen (N) leaf concentrations generally has been found to be a challenging task for a variety of plant species. In this investigation, leaf N concentration and corresponding laboratory hyperspectral data were examined for two species of mangrove (Avicennia germinans, Rhizophora mangle) representing a variety of conditions (healthy, poor condition, dwarf) of a degraded mangrove forest located in the Mexican Pacific. This is the first time leaf nitrogen content has been examined using close range hyperspectral remote sensing of a degraded mangrove forest. Simple comparisons between individual wavebands and N concentrations were examined, as well as two models employed to predict N concentrations based on multiple wavebands. For one model, an Artificial Neural Network (ANN) was developed based on known N absorption bands. For comparative purposes, a second model, based on the well-known Stepwise Multiple Linear Regression (SMLR) approach, was employed using the entire dataset. For both models, the input data included continuum removed reflectance, band depth at the centre of the absorption feature (BNC), and log (1/BNC). Weak to moderate correlations were found between N concentration and single band spectral responses. The results also indicate that ANNs were more predictive for N concentration than was SMLR, and had consistently higher r2 values. The highest r2 value (0.91) was observed in the prediction of black mangrove (A. germinans) leaf N concentration using the BNC transformation. It is thus suggested that artificial neural networks could be used in a complementary manner with other techniques to assess mangrove health, thereby improving environmental monitoring in coastal wetlands, which is of prime importance to local communities. In addition, it is recommended that the BNC transformation be used on the input for such N concentration prediction models.
Keywords: hyperspectral remote sensing; mangrove; nitrogen; Mexican Pacific; artificial neural networks hyperspectral remote sensing; mangrove; nitrogen; Mexican Pacific; artificial neural networks
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, C.; Kovacs, J.M.; Wachowiak, M.P.; Flores-Verdugo, F. Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations. Remote Sens. 2013, 5, 891-908.

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