How much benthic information can be retrieved with hyperspectral sensor from the optically complex coastal waters?

Abstract

The Baltic Sea represents an optically complex case 2 water type, where high concentrations of water column constituents limit acquisition of benthic information. Different preprocessing steps were applied to the hyperspectral compact airborne spectrographic imager (CASI) image to extract as much useful benthic information as possible. Atmospheric correction, minimum noise fraction transform, and sun-glint correction were performed to acquire water surface reflectance data. Additionally, a water column removal procedure was applied to acquire bottom reflectance data. Although retrieved CASI water surface reflectance spectra generally matched the magnitudes and shapes of in situ measured spectra, then the applied water column correction algorithm did not yield accurate bottom reflectance spectra. Therefore, both benthic habitat and bathymetry maps were retrieved from the CASI sea surface image data set. An image-based supervised classification method produced a good quality benthic habitat map from the shallow Pakri study area (depth < 3.0 m) with an overall accuracy of 80%. A site-specific algorithm was developed for the bathymetry retrieval utilizing a green-yellow CASI band ratio. Validation of the bathymetry map for depths shallower than 4.0 m revealed an R 2 value of 0.88 and a root-mean-square error of 0.32 m. The assessment of the benthic substrate detectability limits in the Baltic Sea revealed that, at the wavelength of deepest light penetration (near 570 nm), the depth restriction for CASI benthic substrate detection was 7.6 m for sand, 5.0 m for green macroalgae, 3.0 m for higher order vegetation, and 3.1 m for brown macroalgae. The depth limit to which bathymetric mapping is practical in our study site was estimated to be around 3.5 to 4.0 m. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Benefit of the Potential Future Hyperspectral Satellite Sensor (BIODIVERSITY) for Improving the Determination of Water Column and Seabed Features in Coastal Zones

“…The results in this work are remarkable particularly when compared with a recent study that estimated the detectability limits for seabed estimation for the CASI hyperspectral sensor in the complex waters of the Baltic sea [10]. Vahtmäe et al observed that the detectability threshold for the brightest seabed composed of sand was 7.5 m, 5 m for brighter benthic flora of green macroalgae, and 3 m for darker benthic flora composed of higher plants and brown macroalgae.…”

“…Despite multiple limitations, the hyperspectral remote sensing has developed as a potentially promising technology to study marine and freshwater benthic maps as well as bathymetry [10]. The detectable information is restricted within a maximum depth depending on the sensor characteristics and environmental factors.…”

“…Depth estimation from remote sensing data depends on the physical changes in the spectral radiance that is reflected from the seabed which is correlated with the water depth variation. However, beyond a threshold depth, the increase in depth affecting the benthic reflectance is too small to be resolved by the sensor and inversion models [10], [43].…”

“…Studies using remote sensing performed to date in the optically shallow waters have generally attempted to map the spatial distribution of single benthic habitats or habitats with a single dominant species or multiple heterogeneous habitats (seagrasses, macroalgae, or coral reefs). Studies have also been conducted on the temporal dynamics of the benthic habitats which aid in the understanding of variations in environmental conditions [10]. Bathymetry estimation is one of the research topics where remote sensing applications have been most frequently used.…”

“…Bathymetry estimation is one of the research topics where remote sensing applications have been most frequently used. Thus, the remote sensing technique plays an important and expanding role in ocean applications [10].…”