Geodatabase development for coastal studies conducted by the Naval Research Laboratory (NRL) is essential to support the exploitation of hyperspectral imagery (HSI). NRL has found that the remote sensing and mapping science community benefits from coastal classifications that group coastal types based on similar features. Selected features in project geodatabases relate to significant biological and physical forces that shape the coast. The project geodatabases help researchers understand factors that are necessary for imagery post processing, especially those features having a high degree of temporal and spatial variability. NRL project geodatabases include a hierarchy of environmental factors that extend from shallow water bottom types and beach composition to inland soil and vegetation characteristics. These geodatabases developed by NRL allow researchers to compare features among coast types. The project geodatabases may also be used to enhance littoral data archives that are sparse. This paper highlights geodatabase development for recent remote sensing experiments in barrier island, coral, and mangrove coast types

Bachmann, Charles M

Fry, John C

Fusina, Robert A

Gross, Carl

Jones, Christopher A

Lee, Krista

Li, Rong-Rong

Montes, Marcos J

Nichols, C Reid

Parrish, Christopher

Sellars, Jon

White, Stephen A

English

University of New Hampshire

University of New Hampshire
University of New Hampshire Scholars' Repository
Center for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping
7-2010
Geodatabase Development to Support
Hyperspectral Imagery Exploitation
Robert A. Fusina
U.S. Naval Research Laboratory
John C. Fry
Marine Information Resources Corporation Email First Middle Last
C Reid Nichols
Marine Information Resources Corporation
Charles M. Bachmann
U.S. Naval Research Laboratory
Rong-Rong Li
U.S. Naval Research Laboratory
See next page for additional authors
Follow this and additional works at: https://scholars.unh.edu/ccom
Part of the Oceanography and Atmospheric Sciences and Meteorology Commons
This Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire
Scholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New
Hampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.
Recommended Citation
Fusina, Robert A.; Fry, John C.; Nichols, C Reid; Bachmann, Charles M.; Li, Rong-Rong; Sellars, Jon; Parrish, Christopher; Montes,
Marcos J.; Gross, Carl; White, Stephen A.; Lee, Krista; and Jones, Christopher A., "Geodatabase Development to Support
Hyperspectral Imagery Exploitation" (2010). IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 785.
https://scholars.unh.edu/ccom/785
Authors
Robert A. Fusina, John C. Fry, C Reid Nichols, Charles M. Bachmann, Rong-Rong Li, Jon Sellars,
Christopher Parrish, Marcos J. Montes, Carl Gross, Stephen A. White, Krista Lee, and Christopher A. Jones
This conference proceeding is available at University of New Hampshire Scholars' Repository: https://scholars.unh.edu/ccom/785
GEODATABASE DEVELOPMENT TO SUPPORT HYPERSPECTRAL IMAGERY 
EXPLOITATION
*Robert A. Fusina °John C. Fry °C. Reid Nichols     *Charles M. Bachmann     *Rong-Rong Li
†Jon Sellars     †Chris Parrish     *Marcos J. Montes     *Carl Gross
    †Stephen A. White     ‡Krista Lee     ‡Christopher A. Jones 
*Naval Research Laboratory, Remote Sensing Division, Coastal Science and  Interpretation Section, 
Code 7232, 4555 Overlook Ave., SW, Washington, D.C. 20375; bachmann@nrl.navy.mil 
°Marine Information Resources Corporation, 12337 Pans Spring Court 
Ellicott City, Maryland 21042 
†NOAA/NOS, National Geodetic Survey, Remote Sensing Division, 1315 East-West Hwy 
Silver Spring, MD 20910 
‡Naval Post-Graduate School, Remote Sensing Center, Physics Department, 833 Dyer Road 
Naval Postgraduate School, Monterey, CA 93943
ABSTRACT
Geodatabase development for coastal studies 
conducted by the Naval Research Laboratory (NRL) is 
essential to support the exploitation of hyperspectral 
imagery (HSI).  NRL has found that the remote 
sensing and mapping science community benefits from 
coastal classifications that group coastal types based 
on similar features. Selected features in project 
geodatabases relate to significant biological and 
physical forces that shape the coast. The project 
geodatabases help researchers understand factors that 
are necessary for imagery post processing, especially 
those features having a high degree of temporal and 
spatial variability. NRL project geodatabases include a 
hierarchy of environmental factors that extend from 
shallow water bottom types and beach composition to 
inland soil and vegetation characteristics. These 
geodatabases developed by NRL allow researchers to 
compare features among coast types.  The project 
geodatabases may also be used to enhance littoral data 
archives that are sparse. This paper highlights 
geodatabase development for recent remote sensing 
experiments in barrier island, coral, and mangrove 
coast types.   
Index Terms—geodatabase, coast type, hyperspectral 
imagery, ground control, geospatial analysis 
1. INTRODUCTION 
The NRL routinely investigates coastal regions, a 
dynamic environment that numerous researchers have 
tried to partition into descriptive groups [1], [2], [3].  
This paper will summarize how data were archived 
during remote sensing campaigns conducted in littoral 
areas surrounding the Virginia Coast Reserve (VCR), 
Marine Corps Base Hawaii (MCB-HI), and Shoalwater 
Bay Training Area (SWBTA) in Queensland, 
Australia.  The first investigation used NRL’s 
Compact Airborne Spectral Imager (CASI) 
(www.itres.com), a visible and near infra-red (VNIR) 
hyperspectral sensor, to study the barrier island coast 
and lagoons found along Cape Charles, Virginia [4], 
[5], [6], [7].  The second study occurred during 
January and February 2009 and involved analysis of 
the coralline coast surrounding Kaneohe and 
Waimanalo Bays in Hawaii. A third experiment was 
conducted during May 2009 along the extensive 
mangrove coast surrounding Shoalwater Bay in 
Queensland, Australia.  Remote sensing campaigns in 
Hawaii and Australia both used HyMapTM, a VNIR 
4224978-1-4244-9566-5/10/$26.00 ©2010 IEEE IGARSS 2010
and short-wave infra-red (SWIR) hperspectral sensor 
(www.hyvista.com), to acquire HSI over the study 
area.
All three investigations included a 
comprehensive calibration and validation component 
where scientists collected in-water optical data, 
meteorological data, geotechnical information and 
substrate reflectance data, and canopy and leaf level 
spectral reflectance.  Calibration for bathymetry 
consisted of both in-water spectral reflectance profiles 
measured with an Analytical Spectral Devices (ASD) 
spectrometer as a function of depth for various bottom 
types with validation data provided by small boat 
soundings and beach surveys with post-processed 
kinematic GPS in the foreshore. Other beach 
properties measured along transects consisted of grain 
size profiles, substrate moisture, bearing strength, 
shear, as well as the in situ spectral reflectance and 
GPS location of each position along beach transects.
Vegetation spectral libraries were also developed 
including both canopy and leaf level spectral 
reflectance to retrieve vegetation coverage maps.  
2. PROJECT GEODATABASES 
       The geodatabase combines spatial data collected 
from the coast in a data repository that supports 
imagery analysis and remote sensing research. For the 
VCR, MCB-HI, and SWBTA remote sensing 
campaigns, NRL developed project geodatabases 
representing a barrier island, coral, and mangrove 
coast. The coastal geodatabases have a common look 
and feel and support the objective determination of the 
dominant influences contributing to the characteristics 
of a particular coast. They are also described in data 
reports produced following remote sensing campaigns 
[8], [9], and [10].  
Once imagery post processing was complete, all 
HSI was stored in the geodatabase along with other 
forms of digital products (raster) such as nautical 
charts and historical images. Table 1 provides a list of 
data provided in the geodatabase.
Table 1. Selected geodatabase contents. 
Data Description Type Purpose
Coastal Spectra Text Spectral Matching 
Dynamic Deflection 
Modulus 
Text Imagery Processing 
Geodetic Control Vecto
r
Imagery Processing 
Imagery Raster Mapping
METOC Time Series 
(e.g., winds, humidity, 
visibility, water levels)  
Text Atmospheric 
Correction and 
Imagery Processing 
Photographs Raster Imagery Processing 
Sediment Cores Text Imagery Processing 
Soundings Text Imagery Processing 
Underwater Spectra Text Spectral Matching 
Data used for atmospheric correction, calibration, and 
validation of imagery (ascii text) forms the basis for a 
hierarchical coastal classification system. Data 
includes information ranging from grain size 
distributions (microscale) to prevailing winds 
(synoptic scale). Considerable background information 
(vector) provides the location of man-made objects, 
the extent of vegetation, and important ground control. 
Data extracted from the geodatabase supports the 
development of look up tables and models that are key 
to developing statistical relationships needed to 
produce bathymetric retrievals, bearing strength maps, 
and other coastal products. 
NRL coastal remote sensing campaigns rely on a 
single-user geodatabase for individual GIS work in a 
desktop environment. The ArcGIS Desktop facilitates 
the analysis of observed data elements and parameters 
across investigations and coast types [11]. ESRI’s 
ArcCatalog is used to store geographical information, 
while tabular data are stored as Microsoft® Excel 
spreadsheets divided into similar attribute data folders 
in Microsoft® Explorer. Study data are viewed using 
ArcMap.  The geodatabase supports tasks such as 
atmospheric correction since information on 
atmospheric gases, winds, and waves are accessed to 
remove spectral atmospheric transmission and 
scattered path radiance from radiance data collected by 
hyperspectral imaging [12].  In addition, the 
geodatabase includes all ground-based or laboratory-
based spectral libraries to identify key features in the 
imagery. In ArcMap and ArcGIS Explorer attribute 
data are accessed by linking to spreadsheets or 
document files. Linking to spreadsheets containing the 
attribute data allows one to view actual instrument 
measurements as well as graphs pertaining to the data. 
By utilizing the identify tool in ArcMap, links to 
attribute data spreadsheets, photographs, web sites, 
and text files can be accessed by simply clicking the 
hyperlink icon. ArcGIS Explorer also allows for access 
to important archives that are web-enabled such as 
weather station observations.  One can link to website 
attribute data by clicking the shapefile and navigating 
to the website in the pop-up window.  Project attribute 
4225
data such as spreadsheets of bearing strength of beach 
sediments and the corresponding photographs are 
accessed through relative file pathname links in the 
contents section of the GIS. 
     For those researchers not having an ArcGIS 
Desktop license, NRL provides collaborators with a 
geodatabase that was built using ArcGIS Explorer 900, 
a free-ware version of ArcGIS Desktop.  The freeware 
version has allowed non-ArcGIS specialists to be able 
to easily view quicklook format images of HSI; view 
and obtain data from GPS ground truth points; and use 
the attribute data for those points to develop mapping 
products with the use of HSI and ENVI.  The 
combined data in the geodatabase was essential in the 
production of shallow water bathymetric charts, 
vegetation layers, and bearing strength maps.  This 
work extends the work of Francis Shepard’s 
classification system [1] to support the remote sensing 
and mapping science community. 
3. RESULTS 
The ArcGIS file geodatabase benefits NRL 
researchers for various structural, performance and 
data management reasons.  It has few size limitations, 
allows the storage of vector, raster, and text products, 
is easily migrated to collaborators, and allows updates 
to spatial indexes.  Geodatabase size is provided in 
Table 2.
Table 2.  NRL geodatabases contain considerable 
information that supports imagery analysis and map making. 
Size, GB NRL Remote 
Sensing Campaign Raster Vector Text
Total  
Size
VCR 704 2.2 0.8 707GB 
MCB-HI 222 0.8 0.2 223GB 
SWBTA 293 2.5 0.5 296GB 
The geodatabase contains rapidly accessible 
information. Ground control information (e.g., Ground 
and bathymetric surveys) supports geo-rectification 
and the tide synchronization of imagery collections.  
Meteorological data (e.g., wind speed, relative 
humidity, ozone, and aerosol data) supports 
atmospheric correction.  Oceanographic information 
(e.g., water levels fluctuations and soundings) supports 
the retrieval of bathymetry from HSI. Geotechnical 
data, e.g., grain size distributions, are especially useful 
to support engineering studies. Figure 1 displays a 
typical geotechnical product describing the beach 
along a particular transect during the remote sensing 
campaign in Australia.
Figure 1. Comparison of grain size distributions along a 
transect at the SWBTA.  
Grain size distribution data in addition to other forms 
of geotechnical data help to characterize coastal types 
and these data are used to develop bearing strength 
maps. Figure 2 presents a bearing strength map which 
was created from geotechnical data and HSI.  
Figure 2. Bearing strength map created from geotechnical 
data collection at freshwater beach in the SWBTA. 
The project geodatabases were provided to 
collaborators from University of Virginia for the VCR, 
resource managers from Marine Corps Installations for 
MCB-HI, and environmental managers from the 
Australian Defence Force and imagery analysts from 
the Naval Oceanographic Office for SWBTA.
4. CONCLUSIONS 
The format of NRL project geodatabases are leading 
to a hierarchical coastal classifaction system. The 
project geodatabases help establish rules for key data 
layers and feature classes common to specific coast 
4226
types. They are contributing to new algorithms and 
models which can be used to build HSI-based bearing 
strength map, shallow water bathymetric charts, and 
other coastal products for representative coast types.   
5. REFERENCES 
[1] Shepard, Francis P. 1973. Submarine Geology. New York: 
Harper & Row, 517 p. 
[2] Inman, D. L., C. E. Nordstrom, 1971. Tectonic and 
Morphologic Classification of Coasts, Journal of Geology, 79(1), 
pp. 1-21.
[3] Wright, L.D., 1985, River deltas, in Davis, Jr., R. A., ed., 
Coastal sedimentary environments (2nd ed.): New York, NY, 
Springer Verlag, p. 1-76. 
[4] Bachmann, C. M., M. J. Montes, R. A. Fusina, C. Parrish, J. 
Sellars, A. Weidemann, W. Goode, V. Hill, R. Zimmerman, C. R. 
Nichols, P. Woodward, K. McIlhany, D. Korwan, M. Crawford, J. 
Monty, B. Truitt, A. Schwarzschild, 2008b. “Very Shallow Water 
Bathymetry Retrieval from Hyperspectral Imagery at the Virginia 
Coast Reserve (VCR’07) Multi-Sensor Campaign,” Proc. 
IGARSS’08, Boston, MA, July 2008. 
[5] Bachmann, C.M., M.J. Montes, R.A. Fusina, C. Parrish, J. 
Sellars, A. Weidemann, W. Goode, C.R. Nichols, P. 
Woodward, K. McIlhany, V. Hill, R. Zimmerman, D. Korwan, B. 
Truitt, and A. Schwarzschild, Bathymetry Retrieval from 
Hyperspectral Imagery in the Very Shallow Water Limit: A Case 
Study from the 2007 Virginia Coast Reserve (VCR'07) Multi-
Sensor Campaign, 2010, Marine Geodesy, Volume 33, Issue 1, 
pages 53 – 75. 
[6] Bachmann, C. M., C. R. Nichols, M. J. Montes, R.-R. Li, P. 
Woodward, R. A. Fusina, W. Chen, M. Crawford, V. Mishra, W. 
Kim, J. Monty, K. McIlhany, K. Kessler, D. Korwan, D. Miller, E. 
Bennert, G. Smith, D. Gillis, J. Sellars, C. Parrish, A. 
Schwarzschild, B. Truitt, 2008a. “Remote Sensing Retrieval of 
Substrate Bearing Strength from Hyperspectral Imagery at the 
Virginia Coast Reserve (VCR’07) Multi-Sensor Campaign,” Proc. 
IGARSS’08, Boston, MA, July 2008.  
[7]Bachmann, C. M., C. R. Nichols, M. Montes, R. Li, P. 
Woodward, R. A. Fusina, W. Chen, V. Mishra, W. Kim, J. Monty, 
K. McIlhany, K. Kessler, D. Korwan, D. Miller, E. Bennert, G. 
Smith, D. Gillis, J. Sellers, C. Parrish, A. Schwarzschild, B. Truitt, 
“Retrieval of Substrate Bearing Strength from Hyperspectral 
Imagery During the Virginia Coast Reserve (VCR ’07) Multi-
Sensor Campaign,” Marine Geodesy, in press. 
[8] VCR Data Report 
[9] HI-HARES Data Report 
[10] SWBTA Data Report 
[11] Environmental Systems Research Institute, Geodatabase-Data 
Storage and Management for GIS. Available online. URL: 
http://www.esri.com/software/arcgis/geodatabase/index.html. 
Accessed December 3, 2009. 
[12] Montes, M.J., B.-C. Gao, and C. O. Davis, 2001, A new 
algorithm for atmospheric correction of hyperspectral remote 
sensing data, in Geo-Spatial Image and Data Exploitation II,
William E. Roper, Editor, Proceedings of the SPIE, Vol. 4383, 23-
30.
4227


Geodatabase Development to Support Hyperspectral Imagery Exploitation

UNH Scholars' Repository

University of New HampshireUniversity of New Hampshire Scholars' RepositoryCenter for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping7-2010Geodatabase Development to SupportHyperspectral Imagery ExploitationRobert A. FusinaU.S. Naval Research LaboratoryJohn C. FryMarine Information Resources Corporation Email First Middle LastC Reid NicholsMarine Information Resources CorporationCharles M. BachmannU.S. Naval Research LaboratoryRong-Rong LiU.S. Naval Research LaboratorySee next page for additional authorsFollow this and additional works at: https://scholars.unh.edu/ccomPart of the Oceanography and Atmospheric Sciences and Meteorology CommonsThis Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New HampshireScholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of NewHampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.Recommended CitationFusina, Robert A.; Fry, John C.; Nichols, C Reid; Bachmann, Charles M.; Li, Rong-Rong; Sellars, Jon; Parrish, Christopher; Montes,Marcos J.; Gross, Carl; White, Stephen A.; Lee, Krista; and Jones, Christopher A., "Geodatabase Development to SupportHyperspectral Imagery Exploitation" (2010). IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 785.https://scholars.unh.edu/ccom/785AuthorsRobert A. Fusina, John C. Fry, C Reid Nichols, Charles M. Bachmann, Rong-Rong Li, Jon Sellars,Christopher Parrish, Marcos J. Montes, Carl Gross, Stephen A. White, Krista Lee, and Christopher A. JonesThis conference proceeding is available at University of New Hampshire Scholars' Repository: https://scholars.unh.edu/ccom/785GEODATABASE DEVELOPMENT TO SUPPORT HYPERSPECTRAL IMAGERY EXPLOITATION*Robert A. Fusina °John C. Fry °C. Reid Nichols     *Charles M. Bachmann     *Rong-Rong Li†Jon Sellars     †Chris Parrish     *Marcos J. Montes     *Carl Gross    †Stephen A. White     ‡Krista Lee     ‡Christopher A. Jones *Naval Research Laboratory, Remote Sensing Division, Coastal Science and  Interpretation Section, Code 7232, 4555 Overlook Ave., SW, Washington, D.C. 20375; bachmann@nrl.navy.mil °Marine Information Resources Corporation, 12337 Pans Spring Court Ellicott City, Maryland 21042 †NOAA/NOS, National Geodetic Survey, Remote Sensing Division, 1315 East-West Hwy Silver Spring, MD 20910 ‡Naval Post-Graduate School, Remote Sensing Center, Physics Department, 833 Dyer Road Naval Postgraduate School, Monterey, CA 93943ABSTRACTGeodatabase development for coastal studies conducted by the Naval Research Laboratory (NRL) is essential to support the exploitation of hyperspectral imagery (HSI).  NRL has found that the remote sensing and mapping science community benefits from coastal classifications that group coastal types based on similar features. Selected features in project geodatabases relate to significant biological and physical forces that shape the coast. The project geodatabases help researchers understand factors that are necessary for imagery post processing, especially those features having a high degree of temporal and spatial variability. NRL project geodatabases include a hierarchy of environmental factors that extend from shallow water bottom types and beach composition to inland soil and vegetation characteristics. These geodatabases developed by NRL allow researchers to compare features among coast types.  The project geodatabases may also be used to enhance littoral data archives that are sparse. This paper highlights geodatabase development for recent remote sensing experiments in barrier island, coral, and mangrove coast types.   Index Terms—geodatabase, coast type, hyperspectral imagery, ground control, geospatial analysis 1. INTRODUCTION The NRL routinely investigates coastal regions, a dynamic environment that numerous researchers have tried to partition into descriptive groups [1], [2], [3].  This paper will summarize how data were archived during remote sensing campaigns conducted in littoral areas surrounding the Virginia Coast Reserve (VCR), Marine Corps Base Hawaii (MCB-HI), and Shoalwater Bay Training Area (SWBTA) in Queensland, Australia.  The first investigation used NRL’s Compact Airborne Spectral Imager (CASI) (www.itres.com), a visible and near infra-red (VNIR) hyperspectral sensor, to study the barrier island coast and lagoons found along Cape Charles, Virginia [4], [5], [6], [7].  The second study occurred during January and February 2009 and involved analysis of the coralline coast surrounding Kaneohe and Waimanalo Bays in Hawaii. A third experiment was conducted during May 2009 along the extensive mangrove coast surrounding Shoalwater Bay in Queensland, Australia.  Remote sensing campaigns in Hawaii and Australia both used HyMapTM, a VNIR 4224978-1-4244-9566-5/10/$26.00 ©2010 IEEE IGARSS 2010and short-wave infra-red (SWIR) hperspectral sensor (www.hyvista.com), to acquire HSI over the study area.All three investigations included a comprehensive calibration and validation component where scientists collected in-water optical data, meteorological data, geotechnical information and substrate reflectance data, and canopy and leaf level spectral reflectance.  Calibration for bathymetry consisted of both in-water spectral reflectance profiles measured with an Analytical Spectral Devices (ASD) spectrometer as a function of depth for various bottom types with validation data provided by small boat soundings and beach surveys with post-processed kinematic GPS in the foreshore. Other beach properties measured along transects consisted of grain size profiles, substrate moisture, bearing strength, shear, as well as the in situ spectral reflectance and GPS location of each position along beach transects.Vegetation spectral libraries were also developed including both canopy and leaf level spectral reflectance to retrieve vegetation coverage maps.  2. PROJECT GEODATABASES        The geodatabase combines spatial data collected from the coast in a data repository that supports imagery analysis and remote sensing research. For the VCR, MCB-HI, and SWBTA remote sensing campaigns, NRL developed project geodatabases representing a barrier island, coral, and mangrove coast. The coastal geodatabases have a common look and feel and support the objective determination of the dominant influences contributing to the characteristics of a particular coast. They are also described in data reports produced following remote sensing campaigns [8], [9], and [10].  Once imagery post processing was complete, all HSI was stored in the geodatabase along with other forms of digital products (raster) such as nautical charts and historical images. Table 1 provides a list of data provided in the geodatabase.Table 1. Selected geodatabase contents. Data Description Type PurposeCoastal Spectra Text Spectral Matching Dynamic Deflection Modulus Text Imagery Processing Geodetic Control VectorImagery Processing Imagery Raster MappingMETOC Time Series (e.g., winds, humidity, visibility, water levels)  Text Atmospheric Correction and Imagery Processing Photographs Raster Imagery Processing Sediment Cores Text Imagery Processing Soundings Text Imagery Processing Underwater Spectra Text Spectral Matching Data used for atmospheric correction, calibration, and validation of imagery (ascii text) forms the basis for a hierarchical coastal classification system. Data includes information ranging from grain size distributions (microscale) to prevailing winds (synoptic scale). Considerable background information (vector) provides the location of man-made objects, the extent of vegetation, and important ground control. Data extracted from the geodatabase supports the development of look up tables and models that are key to developing statistical relationships needed to produce bathymetric retrievals, bearing strength maps, and other coastal products. NRL coastal remote sensing campaigns rely on a single-user geodatabase for individual GIS work in a desktop environment. The ArcGIS Desktop facilitates the analysis of observed data elements and parameters across investigations and coast types [11]. ESRI’s ArcCatalog is used to store geographical information, while tabular data are stored as Microsoft® Excel spreadsheets divided into similar attribute data folders in Microsoft® Explorer. Study data are viewed using ArcMap.  The geodatabase supports tasks such as atmospheric correction since information on atmospheric gases, winds, and waves are accessed to remove spectral atmospheric transmission and scattered path radiance from radiance data collected by hyperspectral imaging [12].  In addition, the geodatabase includes all ground-based or laboratory-based spectral libraries to identify key features in the imagery. In ArcMap and ArcGIS Explorer attribute data are accessed by linking to spreadsheets or document files. Linking to spreadsheets containing the attribute data allows one to view actual instrument measurements as well as graphs pertaining to the data. By utilizing the identify tool in ArcMap, links to attribute data spreadsheets, photographs, web sites, and text files can be accessed by simply clicking the hyperlink icon. ArcGIS Explorer also allows for access to important archives that are web-enabled such as weather station observations.  One can link to website attribute data by clicking the shapefile and navigating to the website in the pop-up window.  Project attribute 4225data such as spreadsheets of bearing strength of beach sediments and the corresponding photographs are accessed through relative file pathname links in the contents section of the GIS.      For those researchers not having an ArcGIS Desktop license, NRL provides collaborators with a geodatabase that was built using ArcGIS Explorer 900, a free-ware version of ArcGIS Desktop.  The freeware version has allowed non-ArcGIS specialists to be able to easily view quicklook format images of HSI; view and obtain data from GPS ground truth points; and use the attribute data for those points to develop mapping products with the use of HSI and ENVI.  The combined data in the geodatabase was essential in the production of shallow water bathymetric charts, vegetation layers, and bearing strength maps.  This work extends the work of Francis Shepard’s classification system [1] to support the remote sensing and mapping science community. 3. RESULTS The ArcGIS file geodatabase benefits NRL researchers for various structural, performance and data management reasons.  It has few size limitations, allows the storage of vector, raster, and text products, is easily migrated to collaborators, and allows updates to spatial indexes.  Geodatabase size is provided in Table 2.Table 2.  NRL geodatabases contain considerable information that supports imagery analysis and map making. Size, GB NRL Remote Sensing Campaign Raster Vector TextTotal  SizeVCR 704 2.2 0.8 707GB MCB-HI 222 0.8 0.2 223GB SWBTA 293 2.5 0.5 296GB The geodatabase contains rapidly accessible information. Ground control information (e.g., Ground and bathymetric surveys) supports geo-rectification and the tide synchronization of imagery collections.  Meteorological data (e.g., wind speed, relative humidity, ozone, and aerosol data) supports atmospheric correction.  Oceanographic information (e.g., water levels fluctuations and soundings) supports the retrieval of bathymetry from HSI. Geotechnical data, e.g., grain size distributions, are especially useful to support engineering studies. Figure 1 displays a typical geotechnical product describing the beach along a particular transect during the remote sensing campaign in Australia.Figure 1. Comparison of grain size distributions along a transect at the SWBTA.  Grain size distribution data in addition to other forms of geotechnical data help to characterize coastal types and these data are used to develop bearing strength maps. Figure 2 presents a bearing strength map which was created from geotechnical data and HSI.  Figure 2. Bearing strength map created from geotechnical data collection at freshwater beach in the SWBTA. The project geodatabases were provided to collaborators from University of Virginia for the VCR, resource managers from Marine Corps Installations for MCB-HI, and environmental managers from the Australian Defence Force and imagery analysts from the Naval Oceanographic Office for SWBTA.4. CONCLUSIONS The format of NRL project geodatabases are leading to a hierarchical coastal classifaction system. The project geodatabases help establish rules for key data layers and feature classes common to specific coast 4226types. They are contributing to new algorithms and models which can be used to build HSI-based bearing strength map, shallow water bathymetric charts, and other coastal products for representative coast types.   5. REFERENCES [1] Shepard, Francis P. 1973. Submarine Geology. New York: Harper & Row, 517 p. [2] Inman, D. L., C. E. Nordstrom, 1971. Tectonic and Morphologic Classification of Coasts, Journal of Geology, 79(1), pp. 1-21.[3] Wright, L.D., 1985, River deltas, in Davis, Jr., R. A., ed., Coastal sedimentary environments (2nd ed.): New York, NY, Springer Verlag, p. 1-76. [4] Bachmann, C. M., M. J. Montes, R. A. Fusina, C. Parrish, J. Sellars, A. Weidemann, W. Goode, V. Hill, R. Zimmerman, C. R. Nichols, P. Woodward, K. McIlhany, D. Korwan, M. Crawford, J. Monty, B. Truitt, A. Schwarzschild, 2008b. “Very Shallow Water Bathymetry Retrieval from Hyperspectral Imagery at the Virginia Coast Reserve (VCR’07) Multi-Sensor Campaign,” Proc. IGARSS’08, Boston, MA, July 2008. [5] Bachmann, C.M., M.J. Montes, R.A. Fusina, C. Parrish, J. Sellars, A. Weidemann, W. Goode, C.R. Nichols, P. Woodward, K. McIlhany, V. Hill, R. Zimmerman, D. Korwan, B. Truitt, and A. Schwarzschild, Bathymetry Retrieval from Hyperspectral Imagery in the Very Shallow Water Limit: A Case Study from the 2007 Virginia Coast Reserve (VCR'07) Multi-Sensor Campaign, 2010, Marine Geodesy, Volume 33, Issue 1, pages 53 – 75. [6] Bachmann, C. M., C. R. Nichols, M. J. Montes, R.-R. Li, P. Woodward, R. A. Fusina, W. Chen, M. Crawford, V. Mishra, W. Kim, J. Monty, K. McIlhany, K. Kessler, D. Korwan, D. Miller, E. Bennert, G. Smith, D. Gillis, J. Sellars, C. Parrish, A. Schwarzschild, B. Truitt, 2008a. “Remote Sensing Retrieval of Substrate Bearing Strength from Hyperspectral Imagery at the Virginia Coast Reserve (VCR’07) Multi-Sensor Campaign,” Proc. IGARSS’08, Boston, MA, July 2008.  [7]Bachmann, C. M., C. R. Nichols, M. Montes, R. Li, P. Woodward, R. A. Fusina, W. Chen, V. Mishra, W. Kim, J. Monty, K. McIlhany, K. Kessler, D. Korwan, D. Miller, E. Bennert, G. Smith, D. Gillis, J. Sellers, C. Parrish, A. Schwarzschild, B. Truitt, “Retrieval of Substrate Bearing Strength from Hyperspectral Imagery During the Virginia Coast Reserve (VCR ’07) Multi-Sensor Campaign,” Marine Geodesy, in press. [8] VCR Data Report [9] HI-HARES Data Report [10] SWBTA Data Report [11] Environmental Systems Research Institute, Geodatabase-Data Storage and Management for GIS. Available online. URL: http://www.esri.com/software/arcgis/geodatabase/index.html. Accessed December 3, 2009. [12] Montes, M.J., B.-C. Gao, and C. O. Davis, 2001, A new algorithm for atmospheric correction of hyperspectral remote sensing data, in Geo-Spatial Image and Data Exploitation II,William E. Roper, Editor, Proceedings of the SPIE, Vol. 4383, 23-30.4227

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Geodatabase Development to Support Hyperspectral Imagery Exploitation

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