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Peter Chirico; Lawrence Handley
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. ASTERís 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.
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.
U.S. Department of Agriculture - Natural Resources Conservation Service (NRCS) Department of the Interior - U.S. Geological Survey Department of Commerce - National Oceanic and Atmospheric Administration (NOAA) Environmental Protection Agency (EPA) Smithsonian Institution - National Museum of Natural History (NMNH)
Abbott, Isabella A.
Dawson, E. Y.
Wynne, Michael J.
To date, most of the collection is herbarium mounts which follow standard herbarium protocols for mounting and storing and labeling, i.e. labels have the following info: 1. Geographic area of collection (i.e. name of island, county, state) 2. Binomial, including author(s) 3. Where collected, including latitude, and longitude if possible 4. Depth, substratum type, etc., including how collected (SCUBA, dredge, submersible, etc.) 5. Specific ecological information 6. Collector; date of collection 7. Collector's field number for specimen or collection 8. Person who identified the specimen
Mounts are dried and fixed to herbarium paper and stored in museum cabinets with insect prevention chemicals inside.
Our continued work in the field will begin to include some preserved specimens and these will also follow the Smithsonian protocols.
Ishman, S. E.; Edwards, L. E.; Willard, D. A.
All submerged aquatic vegetation in the test areas will be mapped using GPS units.
A second task is to examine the relationship between seagrass and environmental factors over a time-scale of years and decades by using sediment cores from two known and well-documented areas of seagrass die-off, Barnes Key and Rabbit Key. These cores preserve a record of the substrate, salinity, nutrient supply, and the fauna and flora present at a site. By examining the link between environmental factors and seagrass over an extended period, we can test hypotheses about the causes of seagrass die-off. These hypotheses include 1) changes in salinity; 2) changes in light availability; 3) changes in water temperature; 4) nutrient availability; 5) disease; and 6) increases in atmospheric dust (aerosols). These data will be compiled with the analysis of historical aerial photos and remote sensing data (task 1) to determine 1) if any distinctive patterns in the sequence of events can be detected in the cores, the aerial photos, and or the remote sensing data, and 2) in hindsight, did the aerial photographs and/or remote sensing data pick up some signal to the beginning of the stressors that could be used as a predictive tool to monitor seagrass beds for future problems.
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 remote sensing data acquired for this area will be used to test different platforms, to determine detection limits, and to isolate distinct signals for different types of vegetation. Once ground-truthing and remote sensing data analysis is completed, archived remote sensing data can then be used to examine the sequences of events leading up to the die-offs. Sediment cores provide a long-term perspective on changes in nutrient geochemistry, substrate, water chemistry (salinity, temperature, oxygen), and changes in the biota.
We will determine the ideal sensor bands and spatial resolution for detecting subaquatic vegetation using the different platforms and sensor types. The proposed work consists of the acquisition of remote sensing imagery, aerial photographic interpretation, imagery classification, ground truth data collection and the comparison and evaluation of results.
Data from the selected remote sensing systems will be acquired for a specific time period in FY 2003. Preferably, all imagery will be collected on the same date/time to ensure environmental variables are constant for all images and will be closely correlated with field mapping. If this is not possible, due to tasking and weather limitations, the best possible combination of imagery and image dates will be acquired. These data sets include Landsat 7 ETM+, ASTER, Quickbird, Natural color, 1:12,000 scale aerial photography, historical photography and, if available, a test image of HYPERION hyperspectral data.
Digital image processing will be performed on the Landsat, ASTER, and Quickbird imagery using portable spectrometers and field observation data. Several processing and enhancement methods will be applied including, conversion from sensor radiance to absolute reflectance prior to processing. Images will be created based on spectral responses of seagrass, water and other environmental materials as well as statistical significance tests, where applicable. The goal of the image processing is to develop the best possible band combination and processing technique for each sensor for the purpose of mapping seagrass habitat in Florida Bay. Once processed, image classes, which correspond to those used in the aerial photography interpretation step, will be developed for each sensor used. Ancillary information will be compiled for the study area that will include the bathymetry, tidal range, water temperature, and salinity. These data layers will assist in the processing and evaluation of the satellite.
The aerial photography mapping protocol consists of stereoscopic photo interpretation, cartographic transfer, and digitization of seagrass habitat in accordance with previously used seagrass classification systems. Other important aspects of the protocol include the development of a classification system, validation with ground control data, quality control, and peer review. The information derived from the photography will subsequently be transferred using a Zoom Transfer Scope onto a stable medium overlaying USGS 1:12,000 scale quadrangle basemaps. Habitat classification done through the aerial photography interpretation will be stored as a geographic information system (GIS) data layer to be used for remote sensing correlation analysis. Historical photography will also be analyzed to evaluate spatial size and location related to historical die-off events. We will begin gathering these historical photographs in FY03, but interpretation will probably take place in FY04.
The resultant image classifications of each sensor system will be correlated against both the independent ground truth data and the aerial photography interpretation habitat classes for an assessment of accuracy. The different methodologies and image processing techniques will be evaluated based on their correlation with the habitat maps and ground truth data. The procedures will be documented and explained to show the comparison of sensor systems, band combinations, spatial resolution and correlation with ground truth.
Field mapping in the study area will start in FY 2003. Three to six test sites (at scales ranging from 1m up to a maximum of 90 m2) will be selected within the study area that represent different environments, habitats and water depths typical of the area. These sites will be selected based on preliminary field observations and examinations of historical and current aerial photographs.
Investigations at each test site will include the following: 1) All submerged aquatic vegetation (SAV) within the test areas will be mapped using GPS units. The perimeter of the grass beds and any associated barren sections will be measured and documented. Over the course of this project these boundaries will serve as a baseline to determine any expansion or contraction of vegetation that may occur. 2) The SAV within the test area will be identified to species level and a permanent reference collection of samples established. After the initial identification and sampling process is complete, the plant biomass density will be measured and documented by using a grid system and counting plant stems and/ or shoots within a random sampling of the grid layout. 3) Portable spectrometer readings will be made over areas of different types of SAV to determine the spectral signatures associated with each type. These data will be critical in interpreting the remote sensing data. 4) Water quality measurements will be taken including; salinity, temperature, turbidity, chlorophyll content, PH, dissolved oxygen, ammonia, nitrates, nitrites, etc. at each test site. 5) Water depth, sediment depth, and substrate type will be documented at each measured site. 6) Selected test sites will be monitored on a regular basis (4-8 week intervals) in order to establish seasonal variability within the submerged plant communities. The increases and decreases in abundance of various groups of SAV and the phytoplankton densities within the water column can then be established.
In FY 2003 we will process two cores collected in June 2001 from Barnes Key and Rabbit Key. Sampling and analyses of cores will follow methods established by the Florida Bay Ecosystem History Project. Cores will be dated using 210-Pb geochronology. Faunal and floral assemblage analyses will be conducted on 2-cm segments of the cores from Barnes and Rabbit Key; these data will be interpreted using a large dataset on the biology and ecology of modern Florida Bay fauna and flora. The presence of seagrass-indicator species (primarily molluscs in FY 2003) will be quantified in down-core samples.
U.S. Department of the Interior, U.S. Geological Survey, Center for
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