Next Article in Journal
Estimating Winter Annual Biomass in the Sonoran and Mojave Deserts with Satellite- and Ground-Based Observations
Previous Article in Journal
Use of Satellite Radar Bistatic Measurements for Crop Monitoring: A Simulation Study on Corn Fields
Article Menu

Export Article

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

Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations

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
View Full-Text   |   Download PDF [3031 KB, uploaded 19 June 2014]   |  

Abstract

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. View Full-Text
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 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top