A Method to Analyze the Potential of Optical Remote Sensing for Benthic Habitat Mapping

Abstract

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos' natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae. OPEN ACCESSRemote Sens. 2015, 7 13158

Spectral Reflectance of Palauan Reef-Building Coral with Different Symbionts in Response to Elevated Temperature

“…The similarity of the reflectance spectra from distinct host species and symbionts further supports past remote sensing studies employing inversion of a representative endmember (where an optical model is applied to field reflectance data) to identify coral and benthic constituents [1][2][3][4]71,72]. The relationship between integrated reflectance and symbiont concentration may prove useful to assess or monitor reef condition and the potential for bleaching.…”

Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier

“…Figure 4 shows the classification accuracy (i.e., error matrix diagonals-see Tables S1-S6 in Supplementary Materials) of each benthic class for the six depth-specific BSP-classifiers. The general trend of this figure is a decrease in classification accuracy as the depth increases, which is expected as the R rs between benthic classes become less distinct with increasing depth [27]. Up to 6 m, the classifiers generally have high classification accuracy (>90%) for Seagrass, Turf Algae, Calcareous Algae and all coral classes.…”

“…Here, Calcareous Algae refers to crustose coralline algae. Note that (1) Green algae is not included in the classifier because it is unlikely to dominate an 8 × 8 m pixel and can be optically inseparable from seagrass at depth as noted by Garcia et al [27] (2) Soft coral has also excluded due its spectral similarity with Brown Coral [28] and (3) reflectance spectra for Rubble was not available and thus not included, though it is ecologically important in a coral reef ecosystem. Hochberg et al [28] describes the spectral features present in Figure 2.…”

“…Furthermore, the use of assemblage classes may enable the inclusion of a Green Macroalgae class. This study excluded Green Macroalgae because its reflectance spectrum is spectrally inseparable with that of Seagrass at depth [27]. A Green Macroalgae assemblage class on the other hand will be more optically distinct from Seagrass due to the addition of other spectral features from Turf Algae and Live Coral.…”

Identification and Mapping of Marine Submerged Aquatic Vegetation in Shallow Coastal Waters with WorldView-2 Satellite Data

“…There are numerous studies that have used highresolution satellite images for identifying and mapping coastal habitats such as coral reefs (Benfield et al, 2007), sea grass meadows (Guimarães et al, 2011), mangroves (Heenkenda et al, 2014;Ibrahim et al, 2015), macroalgae (Garcia et al, 2015;Tin, O'Leary, and Fotedar, 2015), and freshwater/ salt marsh (Carle, Wang, and Sasser, 2014). However, these studies mostly utilized sensors with fewer than four spectral bands in the visible domain, which limited the detailed classification of vegetation (Feilhauer et al, 2013).…”