20210526
11434300
1.0
FGDC CSDGM Metadata
FALSE
MD_SAVHabitat_2015To2019_Final_with_Counties_wm
22.704
-8623265.179800
-8354438.885200
4423313.174500
4825654.127500
1
002
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
WGS_1984_Web_Mercator_Auxiliary_Sphere
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20210526
12063000
20210526
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24000
24000
FGDC
Virginia Institute of Marine Science
SAV Data Administrator
804/684-7088
Rt. 1208 Greate Rd.
Gloucester Point
VA
32061
US
savadmin@vims.edu
20210526
ArcGIS Metadata
1.0
http://www.vims.edu/bio/sav
22.704
File Geodatabase Feature Class
MD_SAVHabitat_2015To2019_Final_with_Counties_wm
2020-12-02
Virginia Institute of Marine Science
vector digital data
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The 2019 Chesapeake Bay SAV Coverage was mapped from digital multispectral imagery with a 25cm GSD to assess water quality in the Bay. WorldView 2 satellite imagery acquired from Digital Globe through the NGA NextView program was used to augment the aerial imagery for the Belmont Bay portion of the Potomac River. Each area of SAV was interpreted from the rectified imagry and classified into one of four density classes by the percentage of cover. The SAV beds were entered into an SDE GIS fetaure class using the quality control procedures documented below. The dataset contains all SAV areas that were identified from the areas flown. Some areas that are presumed to contain no SAV were not flown. Some small beds, particularly along narrow tributaries may not have been distinguishable on the aerial photography.</SPAN></P><P><SPAN>This layer combines SAV presence from 2015 through 2019 with Maryland County boundaries to create a five-year SAV Habitat layer.</SPAN></P></DIV></DIV></DIV>
The annual SAV aerial photographic monitoring program provides a comprehensive and accurate measure of change in SAV relative abundance that has been used to link improving water quality to increases in bay living resources. This layer shows the Maryland location of the SAV foot print over a 5 year period (2015 - 2019) combined with Maryland County Boundary information.
SAV Ecology, Monitoring, & Restoration Program, Virginia Institute of Marine Science
Virginia Institute of Marine Science
SAV Data Administrator
804/684-7088
Rt. 1208, Greate RD
Gloucester Point
VA
23062
US
savadmin@vims.edu
Chesapeake Bay
Chincoteague Bay
U.S. Postal State Abbreviations
VA
DE
MD
U.S. Department of Commerce, 1987, Codes for the identification of the States, the District of Columbia and the outlying areas of The United States, and associated areas (Federal Information Processing Standard 5-2):Washington ,D. C., National Institute of Standards and Technology.
51
10
24
U.S. Department of Commerce, 1977, Countries, dependencies, areas of special sovereignty, and their principal administrative divisions (Federal Information Processing Standard 10-3):Washington, D.C., National Institute of Standards and Technology.
US
vegetative surface cover
submerged aquatic vegetation
SAV
vegetative surface cover
DE
submerged aquatic vegetation
MD
SAV
US
Chesapeake Bay
Chincoteague Bay
51
10
24
Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.1.9270
-77.588763
-74.999616
36.653889
39.756832
ground condition
2019-05-29
2019-09-22
1
-77.464109
-75.049201
39.722195
36.886608
The GIS data has been cross-checked against data tables and ground observations.
The dataset contains all SAV areas that were identified from the areas flown. Some areas that are presumed to contain no SAV were not flown. Some small beds, particularly along narrow tributaries may not have been distinguishable on the aerial photography. Turbid conditions and poor weather conditions in 2019 prevented us from mapping the northern portion of the Delmarva Peninsula Coastal Bays zone, including Chincoteague, Sinepuxent, Isle of Wight, and Assawoman bays, representing 3,641 ha of SAV in 2017 (8,996 ac, 55% of the zone total).
The density attribute for all beds has been reviewed by an experienced photo-interpreter for correctness and consistency with analysis for prior years.
Horizontal positional accuracy is based upon orthorectification of scanned aerial photography using USGS digital orthothophoto quadrangles for horizontal control and USGS 30m DEM data as vertical control. Horizontal RMSE for individual flightlines was held to less than four meters.
2019 SAV Aerial multispectral digital imagery Digital images 5/29/2019 - 9/22/2019
24000
2019 SAV Aerial imagery
2019 SAV Aerial imagery
2019-09-22
Airphotographics Inc
document
New 2004 Chesapeake Bay Program Segments adjusted to include land areas
New 2004 Chesapeake Bay Program Segments adjusted to include land areas
New 2004 Chesapeake Bay Program Segments adjusted to include land areas
2008-10-06
Virginia Institute of Marine Science
map
WorldView 2 satellite imagery acquired from Digital Globe through the NGA NextView program,
2019 Auxiliary Imagery
2019 Auxiliary Imagery
State boundaries adjusted to include shore areas
State boundaries adjusted to include shore areas
State boundaries adjusted to include shore areas
2008-10-06
Virginia Instute of Marine Science
map
2019 SAV Orthophoto Mosaics Datasets 5/29/2019 - 9/22/2019
24000
2019 SAV Orthophoto Mosaics
2019 SAV Orthophoto Mosaics
2019-09-22
Virginia Institute of Marine Science
document
Digital imagery is georectified and orthographically corrected to produce a seamless series of aerial mosaics following the standard operating procedures (SOP). ERDAS IMAGINE Photogrammetry image processing software is used to orthographically correct the individual flight lines using a bundle block solution. Camera lens calibration data is matched to the image location of fiducial points to define the interior camera model. Control points from USGS DOQQ images, National Agriculture Imagery Program (NAIP), Virginia Base Mapping Program (VBMP), MD Six Inch, and ESRI World imagery provide the exterior control, which is enhanced by a large number of image-matching tie points produced automatically by the software when IMU data were not available. The exterior and interior models are combined with a 10-meter resolution digital elevation model (DEM) from the USGS National Elevation Dataset (NED) to produce an orthophoto for each aerial photograph. The orthophotographs area combined into mosaic datasets for each flightline which are then combined into a single Bay-wide mosaic dataset.
2020-04-28
The state boundaries were overlayed on the dataset by VIMS staff, dividing any beds that fell into multiple states.
2020-12-02
The bay segments were overlayed on the dataset by VIMS staff, dividing any beds that fell into multiple segments.
2020-12-02
The SAV beds are interpreted on-screen from the orthophoto mosaics using ESRI ArcGIS software. The identification and delineation of SAV beds by photo interpretation utilizes all available information including: knowledge of aquatic grass signatures on film, distribution of SAV in 2018 from aerial imagery, 2018 ground survey information, and aerial site surveys. National Agricultural Imagery Program (NAIP) aerial imagery, as well as WorldView 2 and 3 satellite imagery acquired from Digital Globe through the NGA NextView program, WorldView 4 imagery purchased from Apollo Mapping, and PlanetScope satellite imagery from Planet Labs was used to augment the aerial imagery in portions of the Bay and tributaries where imagery had not been acquired because of highly turbid water, weather, and security restrictions during the acquisition phase. In addition to delineating SAV bed boundaries, an estimate of SAV density within each bed was made by visually comparing each bed to an enlarged crown density scale similar to those developed for estimating crown cover of forest trees from aerial photography (Paine, 1981). Bed density was categorized into one of four classes based on a subjective comparison with the density scale. These were: 1, very sparse (<10% coverage); 2, sparse (10-40%); 3, moderate (40-70%); or 4, dense (70-100%). Either the entire bed or subsections within the bed were assigned a bed density number (1 to 4) corresponding to the above density classes. Some beds were subsectioned to delineate variations of SAV density. Additionally, each distinct SAV bed or bed subsection was assigned an identifying one or two letter designation unique to its map. Coupled with the appropriate SAV quadrangle number and year of photography, these letter designations uniquely identify each SAV bed in the database.
2020-12-02
2622
MD_SAVHabitat_2015To2019_Final_with_Counties_wm
VIMS
VIMS - SAV Program
Feature Class
2622
OBJECTID
FID
OID
4
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
ST
U.S. State which contains the SAV bed
US State Abbreviations
MD
Maryland
State Abbreviations
DC
Washington DC
State Abbreviations
DE
Delaware
State Abbreviations
VA
Virginia
State Abbreviations
ST
String
2
0
0
SHAPE
Feature geometry.
ESRI
Coordinates defining the features.
Shape
Geometry
0
0
0
SAVHabitat
SAVHabitat
String
50
0
0
YrRange
Five Year Range
String
50
0
0
COUNTY
COUNTY
String
80
0
0
DISTRICT
DISTRICT
Integer
4
0
0
COUNTY_FIP
COUNTY_FIP
Integer
4
0
0
COUNTYNUM
COUNTYNUM
Integer
4
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
Shape_Area
Shape_Area
Double
8
0
0
Area of feature in internal units squared.
Esri
Positive real numbers that are automatically generated.
dataset
EPSG
8.8(9.3.1.2)
Simple
FALSE
2622
TRUE
FALSE