Mapping of benthic habitats from hyperspectral imagery can be achieved by integrating bio-optical models with common techniques for hyperspectral image processing, such as spectral unmixing. Several algorithms have been described in the literature to compensate or remove the effects of the water column and extract information about the benthic habitat characteristics utilizing only measured hyperspectral imagery as input. More recently, the increasing availability of lidar-derived bathymetry information offers the possibility to incorporate this data into existing algorithms, thereby reducing the number of unknowns in the problem, for the improved retrieval of benthic habitat properties. This study demonstrates how bathymetry information improves the mapping of benthic habitats using two algorithms that combine bio-optical models with linear spectral unmixing. Hyperspectral data, both simulated and measured, in-situ spectral data, and lidar-derived bathymetry data are used for the analysis. The simulated data is used to study the capabilities of the selected algorithm to improve estimates of benthic habitat composition by combining bathymetry data with the hyperspectral imagery. Hyperspectral images captured over Emique in Puerto Rico using an AISA Eagle sensor is used to further test the algorithms using real data. Results from analyzing this imagery demonstrate increased agreement between algorithm output and existing habitat maps and ground truth when bathymetry data is used jointly with hyperspectral imagery.
Remote sensing of shallow submerged marine ecosystems presents a challenging environment for information extraction
algorithms, where physically based solutions commonly require complex, computationally intensive algorithms. The
inherent variations in water depth, water properties, and surface waves all impact the measured remote sensing signal,
and the strong absorption of light in water also limits the effective range of wavelengths available for analysis. An
algorithm has been developed to address this multifaceted problem. The algorithm uses a two-stage inverse semianalytical
optimization model and spectral unmixing scheme to derive water column properties, water depth and habitat
composition from imaging spectroscopy data. In addition to testing and validation studies, work on this algorithm has
included improving its efficiency using the computing power of graphical processor units (GPUs). This improvement
provides accelerated execution of the algorithm, and by leveraging more robust optimization routines, also facilitates
increased accuracy in algorithm output. Initial results from implementing the algorithm on a single GPU using a
conservative optimization strategy indicate substantial improvement in performance can be achieved using this
technology. We present an overview of the algorithm, provide example output, discuss the GPU parallelization
approach, and illustrate the performance achievements that have been obtained using GPU technology.
Bioptical models are used jointly with hyperspectral imaging in inversion procedures for mapping of benthic habitats.
Several algorithms have been described in the literature to remove the effects of the water column and extract
information about the sea bottom that only take into consideration the measured hyperspectral image. However the
availability of LIDAR derived bathymetry information opens the possibility of using this information for improved
retrieval of the bottom properties. We present in this paper a study using simulated and hyperspectral imagery on the
improvement in benthic habitat mapping that can be achieved by fusing bathymetry and hyperspectral imagery.
Simulation results show that it is possible to obtain accurate bottom abundance estimates 5-10 meters beyond what can
be obtained with hyperspectral imaging alone in clear waters. With real data we demonstrate increase in accuracy with
respect to ground truth.
Unmixing of the seabed for benthic habitat mapping in shallow coastal waters is a difficult problem due to the
confounding effects of space variant bathymetry and water optical properties, which result in signatures for the same
habitat classes to look different at the water surface across the image. This paper discusses different approaches to
modify the linear unmixing approach to account for variable water optical properties and bathymetry and their
implementation in the Hyperspectral Coastal Image Analysis Toolbox (HyCIAT). This toolbox allows the processing of
hyperspectral imagery of shallow coastal areas to estimate water column optical properties, bathymetry, and perform
unmixing for bottom composition. HyCIAT has been developed as part of the UPRM Hyperspectral Solutionware
project to develop software tools for hyperspectral image processing. The tool has been developed under the
MATLABTM environment and it includes a series of algorithms developed by UPRM researches under a graphical user
interface that facilitates its use by the remote sensing community. The paper describes algorithms implemented in the
toolbox, gives an overview of the graphical user interface, and presents results of its applications to AVIRIS and AISA
hyperspectral imagery collected over Kaneohe Bay in Hawaii and over Southwestern Puerto Rico, respectively.
Improving the understanding of the optical scene components associated with coral reef imagery will advance the ability
to map and monitor coral reefs using remote sensing. One tool that can aid in understanding the components in these
scenes is the NIST Hyperspectral Image Projector (HIP). In this paper a hyperspectral scene is reformatted for projection
using the HIP by first unmixing image spectra into endmembers. The abundance images representing each of the
endmembers are then projected using the NIST HIP and collected by a hyperspectral imager. Since the scene is from a
digital source, it can be used repeatedly without concern for changing measurement conditions. This work represents
one of the first steps in developing scene projection capabilities that can be used for sensor characterization, algorithm
testing or to have optical components changed independently in order to better understand the overall effects on the total
observed scene.
SeaBED is an integrated data set and testing infrastructure for researchers to validate subsurface aquatic remote sensing
algorithms. The purpose behind developing SeaBED is to collect multiple levels of image, field, and laboratory data with
which to validate physical models, inversion algorithms, feature extraction tools and classification methods for
subsurface aquatic sensing using hyperspectral imaging. The focus of this testbed facility is a field site located on
Enrique Reef in southwestern Puerto Rico. This field site, which includes a heterogeneous mixture of both coral reef and
seagrass habitats, offers a well defined system for evaluating analysis techniques under natural environmental
conditions. Data produced from the field site currently includes airborne, satellite, and field-level hyperspectral and
multispectral images, in situ spectral signatures, and water bio-optical properties. This data provides a valuable
combination of sensing imagery and fully characterized ground truth information for developing and validating remote
sensing algorithms. A major accomplishment for SeaBED was the collection of high-resolution hyperspectral imagery
and associated ground truth of the near shore reefs and coastal ecosystems in southwestern Puerto Rico in 2007. The
mission included 1740 km2 of imagery acquired at 4 m spatial resolution, with 110 km2 enhanced coverage of four
science areas at 1, 2, 4 and 8 m spatial resolutions to facilitate investigation of spatial scaling issues and the testing of
subsurface unmixing algorithms. We present an overview of SeaBED and also describe particulars of the 2007 data
collection campaign, including both image acquisition and field data collection efforts.
Remote sensing is being applied with increasing success in the evaluation and management of coral ecosystems. We demonstrate a successful application of hyperspectral image analysis of the benthic composition in Kaneohe Bay, Hawaii using data acquired from NASA's Airborne Visible Infrared Imaging Spectrometer. We employ a multi-level approach, combining a semi-analytical inversion model with linear spectral unmixing, to extract information on the coral, algae and sand composition of each pixel. The unmixing model is based on the spectral characteristics of the dominant species and substrate types in Kaneohe Bay, and uses an optimization routine to mathematically invert the relationship of how each component spectrally interacts and mixes. The functional result is the ability to quantitatively classify individual pixel composition according to the percent contribution from each of three main reef components. Output compares favorably with available field measurements and habitat information for Kaneohe Bay, and the overall analysis illustrates the capacity to simultaneously derive information on water properties, bathymetry and habitat composition from hyperspectral remote sensing data. Further, the resulting spatial analysis capacity contributes an improved capability for monitoring coral ecosystems and an important basis for resource management decisions.
Hyperspectral remote sensing is an increasingly important tool for evaluating the complex spatial dynamics associated
with estuarine and nearshore benthic habitats. Hyperspectral remote sensing is being utilized to retrieve information
about coastal environments, such as coastal optical water properties and constituents, benthic habitat composition, and
bathymetry. Essentially, the spectral detail offered by hyperspectral instruments facilitates significant improvements in
the capacity to differentiate and classify benthic habitats. A design tradeoff in the design of existing and proposed
hyperspectral spaceborne platforms is that high spectral resolution comes with a price of low spatial resolution when
compared to existing multispectral spaceborne sensors. The expectation is that the high spectral resolution will
compensate for the reduction in spatial resolution by providing information to retrieve some of the lost spatial detail as
well as other pieces of information not possible to retrieve using multispectral sensors. This paper reviews different
approaches to unmixing of hyperspectral imagery over benthic habitats. Two specific methods that combine water
optical properties retrieval with linear unmixing are then described and compared with a standard approach to linear
unmixing over land as applied to benthic habitat unmixing. Results show that water column correction is necessary for
accurate mapping and that, by removing the water column, we obtain significant improvement in retrieval of bottom
fractional coverage for algae, sand and reef endmembers.
Benthic habitats are the different bottom environments as defined by distinct physical, geochemical, and biological
characteristics. Hyperspectral remote sensing has great potential to map and monitor the complex dynamics associated
with estuarine and nearshore benthic habitats. However, utilizing hyperspectral unmixing to map these areas requires
compensating for variable bathymetry and water optical properties. In this paper, we compare two methods to unmix
hyperspectral imagery in estuarine and nearshore benthic habitats. The first method is a two-stage method where
bathymetry and optical properties are first estimated using Lee's inversion model and linear unmixing is then performed
using variable endmembers derived from propagating bottom spectral signatures to the surface using the estimated
bathymetry and optical properties. In the second approach, a nonlinear optimization approach is used to simulatenously
retrieve abundances, optical properties, and bathymetry. Preliminary results are presented using AVIRIS data from
Kaneohe Bay, Hawaii. SHOALS data from the area is used to evaluate the accuracy of the retrieved bathymetry and
comparisons between abundance estimates for sand, algae and coral are performed. These results show the potential of
the nonlinear approach to provide better estimates of bottom coverage but at a significantly higher computational price.
The experimental work also points to the need for a well characterized site to use for unmixing algorithms testing and
validation.
Remote sensing is increasingly being used as a tool to quantitatively assess the location, distribution and relative health of coral reefs and other shallow aquatic ecosystems. As the use of this technology continues to grow and the analysis products become more sophisticated, there is an increasing need for comprehensive ground truth data as a means to assess the algorithms being developed. The University of Puerto Rico at Mayaguez (UPRM), one of the core partners in the NSF sponsored Center for Subsurface Sensing and Imaging Systems (CenSSIS), is addressing this need through the development of a fully-characterized field test environment on Enrique Reef in southwestern Puerto Rico. This reef area contains a mixture of benthic habitats, including areas of seagrass, sand, algae and coral, and a range of water depths, from a shallow reef flat to a steeply sloping forereef. The objective behind the test environment is to collect multiple levels of image, field and laboratory data with which to validate physical models, inversion algorithms, feature extraction tools and classification methods for subsurface aquatic sensing. Data collected from Enrique Reef currently includes airborne, satellite and field-level hyperspectral and multispectral images, in situ spectral signatures, water bio-optical properties and information on habitat composition and benthic cover. We present a summary of the latest results from Enrique Reef, discuss our concept of an open testbed for the remote sensing community and solicit other users to utilize the data and participate in ongoing system development.
Benthic habitats are the different bottom environments as defined by distinct physical, geochemical, and biological
characteristics. Remote sensing is increasingly being used to map and monitor the complex dynamics associated with
estuarine and nearshore benthic habitats. Advantages of remote sensing technology include both the qualitative benefits
derived from a visual overview, and more importantly, the quantitative abilities for systematic assessment and
monitoring. Advancements in instrument capabilities and analysis methods are continuing to expand the accuracy and
level of effectiveness of the resulting data products. Hyperspectral sensors in particular are rapidly emerging as a more
complete solution, especially for the analysis of subsurface shallow aquatic systems. The spectral detail offered by
hyperspectral instruments facilitates significant improvements in the capacity to differentiate and classify benthic
habitats. This paper reviews two techniques for mapping shallow coastal ecosystems that both combine the retrieval of
water optical properties with a linear unmixing model to obtain classifications of the seafloor. Example output using
AVIRIS hyperspectral imagery of Kaneohe Bay, Hawaii is employed to demonstrate the application potential of the two
approaches and compare their respective results.
Hyperspectral imagery is a powerful sensing technology for quantitative monitoring of coastal environments. In this paper, we present an algorithm to retrieve water optical properties, bathymetry, and bottom albedo using nonlinear optimization techniques. The proposed method combines the Lee semianalytical model, which relates the quantities of interest to the measured remote sensing reflectance, with a modified version of the Goodman linear mixing model for analysis of the bottom albedo. The estimation problem is posed as a nonlinear least squares problem, where the fractional abundances of the mixing model are linear and the optical properties and bathymetry are nonlinear. A simple
two-stage Gauss-Seidel optimization algorithm is employed to compute the estimates and take full advantage of the problem structure. We use both simulated and AVIRIS hyperspectral imagery to compare the combined modeling approach with the Lee approach for retrieval of optical properties and bathymetry and with the unmixing approach of
Goodman for determining bottom fractional composition. Results show that the proposed retrieval approach generally produces improved estimates of water optical properties, bathymetry and bottom composition but at a significantly higher computational cost. Results also indicate that although the approach is limited in its capacity to resolve bottom composition as a function of increasing depth and water turbity, it retains a robust capability for estimating water optical properties, even in unfavorable conditions for retrieving bathymetry and bottom albedo.
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