James A. Goodman

Classification of benthic composition in a coral reef environment using spectral unmixing

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 NASAs 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.

Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe Bay, Hawaii

Hyperspectral instruments provide the spectral detail necessary for extracting multiple layers of information from inherently complex coastal environments. We evaluate the performance of a semi-analytical optimization model for deriving bathymetry, benthic reflectance, and water optical properties using hyperspectral AVIRIS imagery of Kaneohe Bay, Hawaii. We examine the relative impacts on model performance using two different atmospheric correction algorithms and two different methods for reducing the effects of sunglint. We also examine the impact of varying view and illumination geometry, changing the default bottom reflectance, and using a kernel processing scheme to normalize water properties over small areas. Results indicate robust model performance for most model formulations, with the most significant impact on model output being generated by differences in the atmospheric and deglint algorithms used for preprocessing.

Accelerating an Imaging Spectroscopy Algorithm for Submerged Marine Environments Using Graphics Processing Units

Remote sensing is utilized across a wide array of disciplines, including resource management, disaster relief planning, environmental assessment, and climate change impact analysis. The data volume and processing requirements associated with remote sensing are rapidly expanding as a result of the increasing number of satellite and airborne sensors, greater data accessibility, and expanded utilization of data intensive technologies such as imaging spectroscopy. However, due to the limited ability of current computing systems to gracefully scale with application requirements, particularly in the desktop level market, large amounts of data are currently underutilized or never explored. Computing limitations thus constrain our ability to efficiently and accurately address key science questions using remote sensing. The current evolution in general purpose computing on Graphics Processing Units (GPUs), an emerging technology that is redefining the field of high performance computing, facilitates significantly improved computing capabilities for current and future image analysis needs. We demonstrate the advantages of this technology by accelerating an imaging spectroscopy algorithm for submerged marine habitats using GPU computing. Results indicate that considerable improvement in performance can be achieved using a single GPU on a standard desktop computer. This technology has enormous potential for continued growth exploiting high performance computing, and provides the foundation for significantly enhanced remote sensing capabilities.

Sensitivity Analysis of a Hyperspectral Inversion Model for Remote Sensing of Shallow Coastal Ecosystems

This paper presents a sensitivity analysis of a semi-analytical inversion model for hyperspectral remote sensing of shallow coral ecosystems. Results consistently demonstrated that the estimates of water optical parameters, bottom albedo and bathymetry are most sensitive to values assigned for two of the fixed parameters: the spectral slope of the absorption coefficient for gelbstoff; and the spectral power coefficient for calculating the backscattering coefficient. This suggests that inversion model performance can be enhanced by incorporating improved estimates for these two fixed parameters, either through more explicit physical equations or through location-specific empirical relationships.

Hyperspectral projection of a coral reef scene using the NIST hyperspectral image projector

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.

Parallel Implementation of an Inversion Model for Hyperspectral Remote Sensing

This paper describes the implementation of a semi-analytical inversion model within a parallel processing framework. The greater processing speed obtained with this parallel implementation is demonstrated. A reduction of 97% in the execution time is achieved. This approach enables real time processing capabilities and more complex analysis to simultaneously classify water properties, bathymetry and benthic composition associated with coral reefs and other shallow coastal subsurface environments.

Fusion of hyperspectral imagery and bathymetry information for inversion of bioptical models

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.

Underwater unmixing and water optical properties retrieval using HyCIAT

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.

Development of a field test environment for the validation of coastal remote sensing algorithms: Enrique Reef, Puerto Rico

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.

Subsurface unmixing with application to underwater classification

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.