Automated Subpixel Photobathymetry and Water Quality Mapping

Abstract

New photobathymetry and water quality software is described here that utilizes subpixel analysis software (Subpixel Classifier) with an autonomous image calibration procedure and analytic retrieval algorithm to simultaneously retrieve and report bottom depth and the concentrations of suspended chlorophyll, suspended sediments, and colored dissolved organic carbon on a per-pixel basis from four-band multispectral image data. From the derived composition, the QSC2 (Quantitative Shoreline Characterization, Version 2.0) software also computes and reports water column visibility parameters (vertical and horizontal subsurface sighting ranges and turbidity, each at four wavelength band passes, plus Secchi depth as a scalar) as well as depth and turbidity confidence. QSC2 compensates for the effects of the atmosphere, sun and sky reflections from the water surface, subpixel contributions from exposed land, and variations in the bottom material properties. All information is derived automatically from the pixel data alone. The performance of the QSC2 software was demonstrated using a four-band Ikonos image of Plymouth, Massachusetts. Accuracies of the image-derived compositions, water clarity, and depths were assessed using field and laboratory measurements for eight representative lakes in the scene. The means of the differences of the field-measured and image-derived suspended chlorophyll and colored dissolved organic carbon concentrations for the eight lakes were 1.82 g/l and 4.34 mgC/l, respectively. The image-derived concentrations of suspended sediments were all below the threshold of detection for the field samples (5 mg/l), in agreement with the field data. The mean of the differences between field-measured and image-derived Secchi depths was 0.76 m. The mean depth difference was 0.57 m.

Hyperspectral Imagery in Fluvial Environments

“…A somewhat similar approach by Huguenin et al (2004) characterised depth, suspended chlorophyll, suspended sediments, and coloured Karaska et al (2004) to characterise the Neuse River, North Carolina, using the AVIRIS hyperspectral scanner. The authors used the technique to map variations in suspended chlorophyll, suspended minerals, coloured dissolved organic carbon, and turbidity.…”

Remote Sensing-Derived Bathymetry of Lake Poopó

“…One of these methods is very high resolution stereo imaging [10] but this method cannot be applied over flat and uniform surfaces of the lake bed. Photobathymetry [11,12] can be a solution for very shallow lakes. This method requires high resolution imaging and several assumptions about water turbidity and bottom reflectance.…”

MERIS Retrieval of Water Quality Components in the Turbid Albemarle-Pamlico Sound Estuary, USA

“…Karaska et al [15] mapped WQCs (Chl, TSM, CDOM and total attenuation) in the NRE using the photobathymetry and water quality software presented by Huguenin et al [16]. The software was based on a four-dimensional look-up-table (LUT) representing irradiance reflectance as a function of wavelength, depth, and concentrations of Chl, TSM and CDOM using the Morel and Prieur [17] model modified for shallow (finite) layers.…”

“…The software was based on a four-dimensional look-up-table (LUT) representing irradiance reflectance as a function of wavelength, depth, and concentrations of Chl, TSM and CDOM using the Morel and Prieur [17] model modified for shallow (finite) layers. Both [15] and [16] did not account for the important impacts of scattering phase function, illumination and observation geometry and variability of the specific absorption and backscattering coefficients. The results for the Advanced Visible-Infrared Imaging Spectrometer (AVIRIS) derived Chl using the above-mentioned approach for eight southeastern Massachusetts lakes was a R 2 = 0.89, while the relative errors varied from −64% to 207%; with a mean-normalized root-mean-squares error (NRMSE) of 74% [16].…”

“…Both [15] and [16] did not account for the important impacts of scattering phase function, illumination and observation geometry and variability of the specific absorption and backscattering coefficients. The results for the Advanced Visible-Infrared Imaging Spectrometer (AVIRIS) derived Chl using the above-mentioned approach for eight southeastern Massachusetts lakes was a R 2 = 0.89, while the relative errors varied from −64% to 207%; with a mean-normalized root-mean-squares error (NRMSE) of 74% [16]. Improved results were achieved by this approach for Chl retrieval along three NRE transects when the elapsed time t between the field data and the AVIRIS data collection was within one hour (R 2 = 0.67, 0.76 and 0.90); at t > 2 h results were not acceptable [15].…”