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Monitoring Sub-aquatic Vegetation Through Remote Sensing: a Pilot Study in Florida Bay

Entry ID: USGS_SOFIA_monitor_sav_rs_fb_04

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Summary

Abstract: This pilot study will focus on Florida Bay, a region that suffered the loss of 40,000 ha of turtle grass in a die-off event that began in 1987, and a small, localized die-off in 1999. These events were well documented and provide a baseline for testing methods of monitoring grass beds remotely. Remote sensing data, including aerial photos and satellite imagery data, and data extracted from ... sediment cores will be used to examine the long-term sequences of events leading up to seagrass die-off events. The objectives of this pilot study are to develop a methodology for monitoring spatial and temporal changes in sub-aquatic vegetation using remote sensing, satellite imagery, and aerial photography, and to analyze potential causes of seagrass die-off using geographic, geologic and biologic tools. The ultimate goal is to develop a method for forecasting potential sea-grass die-offs and to determine if remediation efforts would be cost-effective. Florida Bay is selected for the pilot study because the thorough documentation of the 1987-1988 die-off event provides a baseline for examining data preceding and succeeding the event. In addition, a small well studied die-off occurred in 1999-2000 at Barnes Key in Florida Bay. A 10-15 km2 portion of Florida Bay that encompasses areas affected by the 1987 and 1999 die-offs will be analyzed for this pilot study. Current remotely sensed data, aerial photos and satellite images from this area will be used to test different platforms, determine detection limits, and to attempt to isolate distinct signals for different types of vegetation. When ground-truthing is completed, archived remotely sensed data and/or aerial photographs can then be used to examine the sequences of events leading up to the die-offs. The remotely sensed data can be compared and compiled with the data collected by seagrass biologists in 1987 and 1999, and to sediment core data collected at the sites of seagrass die-off. Sediment cores provide a long-term perspective on changes in nutrient geochemistry, substrate, water chemistry (salinity, temperature, oxygen), and changes in the biota. The geologic, biologic and remotely sensed data will be integrated and analyzed to determine the patterns of change and sequences of events that occur in healthy seagrass beds and in beds undergoing a die-off. Several remote sensor types will be compared in this study to determine the ideal sensor bands and spatial resolution necessary to detect and monitor the health of seagrass beds. The sensors to be tested include Landsat 7 (30m multi-spectral spatial resolution), ASTER (15 and 30m multi-spectral), Quickbird (2.5m multi-spectral and <1m panchromatic), and large-scale aerial photography (anticipated spatial resolution .25m with visible and near-infrared bands). Imagery with bands in the blue wavelength may help to penetrate water and infrared or near-infrared bands are predicted to perform better for resolving vegetation. It is theorized that through a combination of blue, and infrared bands and higher spatial resolution it will be possible to map the extent of seagrass beds. Although Landsat ETM+ 7 has several bands in desirable wavelengths, this sensor is predicted to be too course of a dataset to resolve individual seagrass beds. Landsat ETM+ may be used to develop an index of chlorophyll values that may be translated into a measure of seagrass health. ASTERs multiple infrared bands and increased spatial resolution may be successful in distinguishing between the types of vegetation, but these bands are not designed for water penetration. Higher spatial resolution platforms are predicted to have better mapping capabilities. The Quickbird sensor can provide 2.5m spatial resolution with multi-spectral capability. The multi-spectral bands include a blue band for water penetration and a near-infrared band for vegetation detection. Finally, aerial photography flown at low altitude represents the highest spatial resolution (.25m) and can be collected in visible and near-infrared to allow processing of blue and infrared bands. A combination of sensor types to maximize both spatial resolution and spectral signatures may provide the best solution for mapping and monitoring seagrass beds.

Seagrass beds are essential components of any marine ecosystem because they provide feeding grounds, nurseries, and habitats for many forms of marine life, including commercially valuable species; they are important foraging grounds for migratory birds; and they anchor sediments and impede resuspension and coastal erosion during storms. This valuable natural resource has been suffering die-offs around the world in recent years, yet the causes of these die-offs are undetermined. The purpose of this project is to use a number of tools - geographic, geologic, and biologic - to investigate the causes of seagrass die-offs and to develop methods that can be used to monitor the health of seagrass meadows. If we understand the causes of the die-offs and can easily monitor the health of seagrass beds, then resource managers have a tool for forecasting areas of potential die-offs. By integrating remotely sensed data, biological data and core data the long-term (decadalscale) sequences of events leading up to die-off events can be examined. These data can be contrasted to normal seasonal changes that occur in healthy grass beds to establish criteria for identifying areas that may be on the threshold of experiencing a decline. This provides a very powerful predictive tool for resource managers. By examining the causes of die-off and the natural patterns of change in seagrass meadows over biologically significant periods of time we can determine the components of change that may be related to anthropogenic activities versus natural cycles of change. This information would allow resource managers to make informed decisions about the cost-effectiveness of and mechanisms for remediation, if an area of decline was identified via the predictive tool. Once the predictive tools and potential remediation tools have been developed in this pilot study, in well-studied seagrass meadows, the tools can be applied to threatened coastal ecosystems around the country and worldwide.

Related URL

Link: GET DATA

Description: The database may be downloaded from the SOFIA website

Multimedia Sample

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Description: Approximate area of study developed from USGS ortho-photomap (1972) showing core locations at Barnes (Bar) and Rabbit Keys (RK).

Geographic Coverage

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Spatial coordinates

N: 25.0

S: 24.9

E: -80.75

W: -80.87

Data Set Citation

Dataset Originator/Creator: G. Lynn Wingard Peter Chirico; Lawrence Handley

Dataset Title: Monitoring Sub-aquatic Vegetation Through Remote Sensing: a Pilot Study in Florida Bay

Dataset Release Date: 2006-04

Data Presentation Form: database

Online Resource: http://sofia.usgs.gov/projects/monitor_sav/

Temporal Coverage

Start Date: 2002-10-01

Stop Date: 2004-09-30

Location Keywords

CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA

CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA > FLORIDA

Science Keywords

BIOSPHERE > TERRESTRIAL ECOSYSTEMS > WETLANDS

BIOSPHERE > VEGETATION > VEGETATION SPECIES

ISO Topic Category

BIOTA

ENVIRONMENT

IMAGERY/BASE MAPS/EARTH COVER

INLAND WATERS

Platform

LANDSAT-7

QUICKBIRD > DigitalGlobe's QuickBird

Instrument

ASTER > Advanced Spaceborne Thermal Emission and Reflection Radiometer

ETM+ > Enhanced Thematic Mapper Plus

HYPERION > HYPERSPECTRAL IMAGER

Project

USGS_SOFIA > South Florida Information Access

Quality The field data contained in this database have not been reviewed for publication and therefore may contain inconsistencies or errors. The field measurements (such as salinity and temperature) were made on an variety of instruments over the years. Project personnel have made every attempt to calibrate and standardize the instruments and check the data, however, the field data should be considered preliminary. Also taxonomic names may not represent the most up to date usage, but are internally consistent.

Access Constraints None

Use Constraints Any data are subject to change and are not citeable until reviewed and approved for official publication.

Ancillary Keywords

Monroe County

Everglades National Park

Data Set Progress

COMPLETE

Originating Center

Data Center

SOUTH FLORIDA INFORMATION ACCESS, FLORIDA INTEGRATED SCIENCE CENTER, U.S. GEOLOGICAL SURVEY, U.S. DEPARTMENT OF THE INTERIOR

Data Center URL: http://sofia.usgs.gov/

Data Center Personnel
Name: HEATHER S. HENKEL Phone: 727.803.8747 ext. 3028 Fax: 727.803.2032 Email: hhenkel at usgs.gov Contact Address: USGS-FISC-St. Petersburg 600 Fourth St. South City: St. Petersburg Province or State: FL Postal Code: 33701 Country: USA

Distribution

Distribution Size: 4.8

Distribution Format: MS Access

Fees: None

Personnel

G. LYNN BREWSTER-WINGARD

Role: TECHNICAL CONTACT

Phone: 703-648-5352

Fax: 703-648-6953

Email: lwingard at usgs.gov

Contact Address:

USGS

926A National Center

City: Reston

Province or State: VA

Postal Code: 20192

Country: USA

ALICIA ALEMAN

Role: DIF AUTHOR

Email: alicia.m.aleman at nasa.gov

Contact Address:

Goddard Space Flight Center

Code 610.2

City: Greenbelt

Province or State: MD

Postal Code: 20771

Country: USA

Publications/References

Brewster-Wingard, G. Lynn, Ishman, S. E.; Edwards, L. E.; Willard, D. A., 1996, Preliminary Report on the Distribution of Modern Fauna and Flora at Selected Sites in North-central and North-eastern Florida Bay,
USGS Open-File Report, 96-0732, Reston, VA, U.S. Geological Survey.
http://pubs.usgs.gov/pdf/of/ofr96732.html

Creation and Review Dates

DIF Creation Date: 2007-02-20

Last DIF Revision Date: 2009-02-24

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