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Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations
- Source :
- Remote Sensing, Vol 5, Iss 2, Pp 891-908 (2013), Remote Sensing, Volume 5, Issue 2, Pages: 891-908
- Publication Year :
- 2013
- Publisher :
- MDPI AG, 2013.
-
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.
- Subjects :
- Mexican Pacific
010504 meteorology & atmospheric sciences
hyperspectral remote sensing
Science
0211 other engineering and technologies
chemistry.chemical_element
Wetland
Soil science
02 engineering and technology
01 natural sciences
nitrogen
Linear regression
14. Life underwater
Absorption (electromagnetic radiation)
Rhizophora mangle
021101 geological & geomatics engineering
0105 earth and related environmental sciences
geography
mangrove
geography.geographical_feature_category
biology
Ecology
Avicennia germinans
Hyperspectral imaging
15. Life on land
biology.organism_classification
Nitrogen
chemistry
General Earth and Planetary Sciences
Environmental science
Mangrove
artificial neural networks
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 5
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....2d5386d7dad43991b66007db3b0e6c4f