Subsurface unmixing for benthic habitat mapping using hyperspectral imagery and lidar-derived bathymetry

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.

[1]  Ellsworth F. LeDrew,et al.  Measuring and modeling water column effects on hyperspectral reflectance in a coral reef environment , 2002 .

[2]  Miguel Velez-Reyes,et al.  Solving adundance estimation in hyperspectral unmixing as a least distance problem , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[3]  D. Jupp,et al.  Mapping coral reef benthic substrates using hyperspectral space-borne images and spectral libraries , 2006 .

[4]  John D. Hedley,et al.  A remote sensing method for resolving depth and subpixel composition of aquatic benthos , 2003 .

[5]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. , 1999, Applied optics.

[6]  Joong Yong Park,et al.  Use of SHOALS bottom reflectance images to constrain the inversion of a hyperspectral radiative transfer model , 2004, SPIE Defense + Commercial Sensing.

[7]  Antonio J. Plaza,et al.  Spatial Preprocessing for Endmember Extraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Miguel Vélez-Reyes,et al.  Subsurface unmixing with application to underwater classification , 2007, SPIE Remote Sensing.

[9]  Jon Sellars,et al.  A Sensor Fusion Approach to Coastal Mapping , 2005 .

[10]  Robert A. Leathers,et al.  Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: A comparative spectral classification approach , 2003 .

[11]  Kazuo Nadaoka,et al.  Simultaneous estimation of benthic fractional cover and shallow water bathymetry in coral reef areas from high-resolution satellite images , 2012 .

[12]  Trijntje Valerie Downes,et al.  Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. , 2005, Applied optics.

[13]  André Morel,et al.  Diffuse reflectance of oceanic shallow waters: influence of water depth and bottom albedo , 1994 .

[14]  Carmen C. Zayas-Santiago.  Landscape structure in two reefs in La Parguera and the distribution of the Lytechinus variegatus , 2011 .

[15]  Chris Roelfsema,et al.  Mapping seagrass species, cover and biomass in shallow waters : An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia) , 2008 .

[16]  Lorenzo Bruzzone,et al.  Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Mark Lee,et al.  Fusion of hyperspectral and bathymetric laser data in Kaneohe Bay, Hawaii , 2003, SPIE Defense + Commercial Sensing.

[18]  Wojciech M. Klonowski,et al.  Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments , 2011 .

[19]  Sunil Narumalani,et al.  Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach , 2007 .

[20]  James A. Goodman,et al.  An update on SeaBED: a TesBED for validating subsurface aquatic hyperspectral remote sensing algorithms , 2008, Remote Sensing.

[21]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[22]  Shawn Hunt,et al.  Ground truth data collection for unmixing algorithm evaluation , 2008, SPIE Defense + Commercial Sensing.

[23]  Mark Lee,et al.  BENTHIC MAPPING OF COASTAL WATERS USING DATA FUSION OF HYPERSPECTRAL IMAGERY AND AIRBORNE LASER BATHYMETRY , 2003 .

[24]  C. Mobley,et al.  Hyperspectral remote sensing for shallow waters. I. A semianalytical model. , 1998, Applied optics.

[25]  S. Ustin,et al.  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. , 2008, Applied optics.

[26]  James A. Goodman,et al.  Development of a field test environment for the validation of coastal remote sensing algorithms: Enrique Reef, Puerto Rico , 2006, SPIE Remote Sensing.

[27]  Stuart R. Phinn,et al.  Efficient radiative transfer model inversion for remote sensing applications , 2009 .

[28]  M. P. Lesser,et al.  Bathymetry, water optical properties, and benthic classification of coral reefs using hyperspectral remote sensing imagery , 2007, Coral Reefs.

[29]  Wojciech M. Klonowski,et al.  Shallow water substrate mapping using hyperspectral remote sensing , 2011 .

[30]  William Philpot,et al.  Using SHOALS LIDAR system to detect bottom material change , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[31]  Maria C. Torres-Madronero,et al.  Underwater unmixing and water optical properties retrieval using HyCIAT , 2009, Optical Engineering + Applications.

[32]  James A. Goodman,et al.  Classification of benthic composition in a coral reef environment using spectral unmixing , 2007 .

[33]  Miguel Velez-Reyes,et al.  A probabilistic sensitivity analysis of water-leaving radiance to water constituents in coastal shallow waters , 2003, SPIE Optics + Photonics.

[34]  Vinod Ramnath,et al.  Active-passive data fusion algorithms for seafloor imaging and classification from CZMIL data , 2010, Defense + Commercial Sensing.