Seagrass species: are they spectrally distinct?

Fyfe, S. K.; Dekker, Arnold G.

doi:10.1109/igarss.2001.978147

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Reflectance spectra of seagrass; R rs 0 À ð Þ represents subsurface remote sensing reflectance; R b rs represents the bottom reflectance. Figure 6(b) also shows that the regions of excellent separation between different LAI could be found around 555, 650, 675 and 700 nm, where the absorption troughs and reflectance peaks were related to seagrass photosynthetic and accessory pigments (Fyfe and Dekker 2001). Large variations in chlorophyll content result in relatively small variations in leaf absorptance.…”

Section: Lai and The Spectral Responsementioning

confidence: 93%

Analysis of seagrass reflectivity by using a water column correction algorithm

Yang

Cao

et al. 2010

International Journal of Remote Sensing

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Seagrass in optically shallow water can generate optical signals that can be tracked remotely. Unfortunately the signals from the bottom are relatively weak and can be affected by the water column when concentrations of suspended particles, chlorophyll and coloured dissolved organic matter are high. An optical model simulating the propagation of light for retrieving the bottom reflectance was developed. Implementation of the method was found to be effective for improving the accuracy of coastal habitat maps, and essential for deriving empirical relationships between remotely sensed data and interesting features in the marine environment. The appropriate wavebands for seagrass mapping, which generally lay between 500 and 630 nm and 680 and 710 nm, were obtained by means of full visual inspection and analysis of the correct spectra. Additionally, a strong relationship between the reflectance value at 715 nm and Leaf Area Index was found, with a correlation coefficient of 0.99.

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Section: Lai and The Spectral Responsementioning

confidence: 93%

Analysis of seagrass reflectivity by using a water column correction algorithm

Yang

Cao

et al. 2010

International Journal of Remote Sensing

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“…In coastal waters, spectral scattering and absorption by phytoplankton, suspended organic and inorganic matter, and dissolved organic substances may further restrict the light passing to and being reflected up from the benthos (Fyfe and Dekker 2001). The spectral discrimination between aquatic plant species concentrates on their pigment-related spectral features within the visible wavelengths, where light penetrates the water column and can be reflected back to the sensor (Fyfe 2003).…”

Section: Hyperspectral Band Selectionmentioning

confidence: 99%

“…They also made a comparison of different classification methods. Efforts also have been made for identifying seagrass species from hyperspectral data (Fyfe and Dekker 2001). Water-quality parameters are found to be supportive of the presence of seagrasses based on the bio-optical models from the research conducted by Biber et al (2008).…”

Section: Introductionmentioning

confidence: 99%

“…A hyperspectral sensor system can generate hundreds of images ofthe same area in very narrow bands (10-20 nm) over a broad spectrum (1-14 /zm), which allows characterization, identification, and classification of land covers with high accuracy and robustness. The use of hyperspectral imagery is particularly useful for distinguishing biological and geological subclasses that would not be distinguishable in multispectral data, for example, among different phytoplankton classes or species (Hoepffner and Sathyendranath 1993;Fyfe and Dekker 2001).…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Approach toward Detection of Seagrass Patches from Hyperspectral Imagery

Liu

Sukcharoenpong

et al. 2012

Marine Geodesy

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Changes in the coverage of seagrass populations are considered to be a key indicator of the health and biodiversity of coastal ecosystems. The overall extent of seagrass meadows is declining worldwide, primarily due to human-induced disturbances. In Tampa Bay, Florida, a nearly 35% loss of seagrass coverage occurred from the 1950s to the 2000s. This decline was primarily due to the effects of human population growth. To examine closely the continuing declining trend of this major indicator ofthe health of coastal ecosystems, a systematic approach for extracting seagrass patches using EO-1 Hyperion hyperspectral imagery has been developed. In our previous work, a method based on Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) was developed and successfully applied to military object recognition using hyperspectral and multispectral imagery. It showed great potential in target detection of hyperspectral imagery. In this work, it is extend and applied in seagrass extraction.This study includes (a) dimensionality reduction of the hyperspectral data, (b) seagrass extraction using LEGION and four other methods, and (c) analysis and evaluation ofthe results in an experiment involving two test sites at Tampa Bay, Florida. The results demonstrated that the methodology has the potential to provide timely seagrass coverage information for coastal zone management at greatly increased efficiency.

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“…The subsurface irradiance reflectance can be estimated [31]. R water (0 − , λ) can be further expressed as:…”

Section: The Improved Optically Shallow Water Modelmentioning

confidence: 99%

The Empirical Models to Correct Water Column Effects for Optically Shallow Water

Yang¹

2016

Empirical Modeling and Its Applications

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Seagrass as one of the blue carbon sinks plays an important role in environment, and it can be tracked remotely in the optically shallow water. Usually the signals of seagrass are weak which can be confused with the water column. The chapter will offer a model to simulate the propagation of light. The model can be used to improve the accuracy of seagrass mapping. Based on the in situ data, we found that the appropriate wavebands for seagrass mapping generally lie between 500-630 nm and 680-710 nm as well. In addition, a strong relationship between the reflectance value at 715 nm and LAI was found with a correlation coefficient of 0.99. The chapter provided an improved algorithm to retrieve bottom reflectance and map the bottom types. That would be meaningful for management and preservation of coastal marine resources.

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Seagrass species: are they spectrally distinct?

Cited by 8 publications

References 9 publications

Analysis of seagrass reflectivity by using a water column correction algorithm

Analysis of seagrass reflectivity by using a water column correction algorithm

A Systematic Approach toward Detection of Seagrass Patches from Hyperspectral Imagery

The Empirical Models to Correct Water Column Effects for Optically Shallow Water

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