Submerged aquatic vegetation (SAV) CRSSA image classification of the Barnegat Bay - Little Egg Harbor estuary, New Jersey: 2009

Metadata:


Identification_Information:
Citation:
Citation_Information:
Originator: Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University
Publication_Date: 20110511
Title:
Submerged aquatic vegetation (SAV) CRSSA image classification of the Barnegat Bay - Little Egg Harbor estuary, New Jersey: 2009
Geospatial_Data_Presentation_Form: vector digital data
Series_Information:
Series_Name: Submerged aquatic vegetation (SAV) mapping of Barnegat Bay and Little Egg Harbor, New Jersey ; CRSSA distributes GIS data representing SAV in Barnegat Bay: 1968, 1979, 1985-87, 1996-99, 2003, 2009
Issue_Identification: Yr 2009
Publication_Information:
Publication_Place: New Brunswick, NJ
Publisher: CRSSA - Rutgers University
Other_Citation_Details:
Project Principal Investigator: Richard G. Lathrop, Director, CRSSA; Co-Investigator: Scott M. Haag, Institute of Marine and Coastal Sciences, Rutgers University, CRSSA; Acknowledgements: This project was funded by the Barnegat Bay Partnership and we thank the partnership for their support. We express our sincere appreciation for the assistance of Gina Petruzelli, Greg Sakowicz, Chris Huch and the staff at Rutgers University Marine Field Station in the collection of the field reference data. Many thanks to Mike Kennish for his useful insights concerning the health of seagrass in Barnegat Bay - Little Egg harbor. We also acknowledge the helpful comments of an outsider reviewer in improving this report.
Online_Linkage: http://crssa.rutgers.edu/projects/coastal/sav/
Online_Linkage: http://crssa.rutgers.edu/projects/coastal
Online_Linkage: http://crssa.rutgers.edu/
Larger_Work_Citation:
Citation_Information:
Originator: Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University
Title:
CRSSA Coastal Studies: Barnegat Bay
Online_Linkage: http://crssa.rutgers.edu/
Description:
Abstract:
This is a GIS data layer representing submerged aquatic vegetation (SAV) of the Barnegat Bay - Little Egg Harbor estuary, 2009, developed by classifying high resolution airborne digital camera imagery. Included are the submerged aquatic vegetation densities mapped into three classes throughout the study area and their respective area and perimeter for each polygon. The three classes of SAV are: 1) Dense (80% - 100% cover), 2) Moderate (40% - 80% cover), and 3) Sparse (10% - 40% cover). For full documentation, please refer to the technical report, 'Assessment of Seagrass Status in the Barnegat Bay - Little Egg Harbor Estuary System: 2003 and 2009 (Rutgers University, Lathrop and Haag, 2010), which is listed in the cross reference section of this metadata document.
Purpose:
The purpose of this study was to map the areal extent and density of submerged aquatic vegetation within the Barnegat Bay and Little Egg Harbor, New Jersey as part of ongoing monitoring for the Barnegat Bay Partnership. Our goal was to develop a methodology that was comparatively objective in delineating bed boundaries and characterizing seagrass density, was cost-effective and easily repeatable for future monitoring purposes. We applied this conceptual framework to the mapping and spatial analysis of seagrass beds and the broader benthic environment in Barnegat Bay-Little Egg Harbor estuary in New Jersey, USA.

SAV is a key indicator of the health of the Barnegat Bay estuary, and is under constant stress from a number of sources. These stresses cause changes in seagrass bed characteristics which in turn make frequent monitoring of this habitat type necessary.
Supplemental_Information:
PLEASE READ ALL METADATA DOCUMENTATION AND THE TECHNICAL REPORT (web link available in the cross reference section of this metadata document):

Lathrop, R.G. and S. Haag. 2010. Assessment of Seagrass Status in the Barnegat Bay - Little Egg Harbor Estuary System: 2003 and 2009. Rutgers University, Grant F. Walton Center for Remote Sensing and Spatial Analysis, and the Institute for Marine and Coastal Sciences, New Brunswick, NJ.


ALSO IMPORTANT:

1. Please refer to the accuracy assessment of the 2009 SAV image object classification. The results are available in the published technical report (Lathrop and Haag, 2010, p.14, 15), and are also in this metadata document.

2. The vector SAV polygons appear as vectorized raster data due to the image object classification technique (eCognitionT software) utilized for the 2009 mapping.

3. Due to differences in adjacent image quality in the study area (16 individual image tiles), there are some instances where the SAV classification changes or halts at an image edge. These instances are reported in the following locations (UTM NAD83 Zone 18 X,Y coordinates): 1) 574800, 4406377; 2) 572201, 4401409; 3) 563998, 4381235; 4) 561867, 4378599; 5) 561990, 4378601; 6) 562440, 4378599 .

4. Other SAV GIS data available on the CRSSA web are SAV for the 1968, 1979, 1985-87, 1996-99, and 2003 time periods. Please follow the cross reference links in the metadata.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 20110511
Currentness_Reference:
publication date
Status:
Progress: Complete
Maintenance_and_Update_Frequency: Unknown
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -74.284991
East_Bounding_Coordinate: -74.049059
North_Bounding_Coordinate: 40.056674
South_Bounding_Coordinate: 39.545495
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: biota
Theme_Keyword: oceans
Theme:
Theme_Keyword_Thesaurus: none
Theme_Keyword: submerged aquatic vegetation
Theme_Keyword: benthic
Theme_Keyword: seagrass
Theme_Keyword: coastal
Theme_Keyword: remote sensing
Theme_Keyword: image classification
Theme_Keyword: CRSSA
Theme_Keyword: Rutgers University
Theme_Keyword: Barnegat Bay National Estuary Program
Place:
Place_Keyword_Thesaurus: none
Place_Keyword: Barnegat Bay
Place_Keyword: Little Egg Harbor
Place_Keyword: Ocean County
Place_Keyword: New Jersey
Place_Keyword: Barnegat Bay watershed
Temporal:
Temporal_Keyword_Thesaurus: none
Temporal_Keyword: 2009
Access_Constraints: None
Use_Constraints:
These data as presented provide a regional picture of SAV distribution and are not intended for site level permit applications or litigation purposes. These maps and data, alone, are not sufficient to determine the presence or absence of SAV. Conclusive evidence concerning the presence or absence of SAV requires site inspections, preferably at several points in time during the SAV growing season.

Data are supplied 'as-is' without warranty of any kind. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, Rutgers University cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data. Rutgers University makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

Any maps, publications, reports or any other type of document produced as a result of utilizing this data will credit the original map author(s) as listed as well as the Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University.
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Richard G. Lathrop
Contact_Organization: Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA)
Contact_Position: Director
Contact_Address:
Address_Type: mailing and physical address
Address:
School of Environmental and Biological Sciences
Address:
14 College Farm Road
Address:
Rutgers, The State University of New Jersey
Address:
14 College Farm Road
City: New Brunswick
State_or_Province: NJ
Postal_Code: 08901-8551
Country: USA
Contact_Voice_Telephone: 732-932-1582
Contact_Facsimile_Telephone: 732-932-2587
Contact_Electronic_Mail_Address: lathrop@crssa.rutgers.edu
Hours_of_Service: M-F 8.30am - 4.30pm EST USA
Data_Set_Credit:
Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University
Security_Information:
Security_Classification: Unclassified
Native_Data_Set_Environment:
Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 3; ESRI ArcCatalog 9.3.1.1850
Cross_Reference:
Citation_Information:
Title:
Submerged Aquatic Vegetation (SAV) WEB SITE at CRSSA
Edition: current
Online_Linkage: http://crssa.rutgers.edu/projects/coastal/sav/
Online_Linkage: http://crssa.rutgers.edu/projects/coastal/
Online_Linkage: http://crssa.rutgers.edu/
Cross_Reference:
Citation_Information:
Title:
Barnegat Bay Partnership (formerly known as the Barnegat Bay National Estuary Program)
Online_Linkage: http://bbp.ocean.edu/
Cross_Reference:
Citation_Information:
Originator: Grant F. Walton Center for Remote Sensing and Spatial Analysis, School of Environmental and Biological Sciences, Rutgers University
Originator: Institute of Marine and Coastal Sciences, School of Biological and Environmental Sciences, Rutgers University
Publication_Date: 20100903
Title:
Technical Report: Assessment of Seagrass Status in the Barnegat Bay - Little Egg Harbor Estuary: 2003 and 2009
Geospatial_Data_Presentation_Form: document
Online_Linkage: http://crssa.rutgers.edu/projects/coastal/sav/
Online_Linkage: http://crssa.rutgers.edu/data/reports.shtml
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Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
(TEXT EXTRACTED FROM THE TECHNICAL REPORT)

An accuracy assessment was conducted using the 124 validation sites for both a presence absence and categorical values (sparse, moderate or dense) (Table 3 & Table 4). In addition, an un-weighted Kappa statistic was used to normalize the influence of categories that cover a disproportionate area. A high level of agreement was obtained between the mapped and in situ data with an overall accuracy of 87% and a Kappa value of 73%. For the 4 class seagrass density map (Table 4), an overall accuracy of 70% and a Kappa statistic of 47% was obtained, representing a moderate level of agreement. NOAA protocols (Finkbeiner et al., 2001) suggest that overall thematic accuracy should be greater than 85% and a kappa of > 0.5. The seagrass presence/absence map meets these criteria, while the 4 class seagrass density map is slightly below. Table 3 suggests that most of the errors of omission (i.e., producer's accuracy) and commission (i.e., user's accuracy) for the presence/absence seagrass map are similar. Table 4 suggests that the 4 class map does not consistently differentiate between moderate and dense seagrass habitat. While every effort was undertaken to reduce the spatial error in relating the field reference data to the imagery (i.e., 1-2 meters for imagery georegistration and 2-5 meters for geolocating the field reference data collection location), this positional error coupled with the fine scale patchiness of some seagrass beds can result in a disagreement between the reference data and the mapping.

The procedure to select the validation sites (random vs. targeted) can drive which error (omission and commission vs. categorical) is better constrained. In the 2009 survey, we opted to more randomly sample across the entire estuary to provide a better estimate on the total errors of omission and commission of seagrass presence/absence. Consequently, the sample sizes within the individual seagrass density categories (i.e., spare, moderate and dense) were limited. Future work should attempt to collect a larger number of samples in known seagrass habitat to provide more information on the accuracy of the categorical nature of the GIS maps (if this knowledge is deemed important).

The 2009 imagery collection was a challenge due to meteorological events (cloud cover), which caused two separate imaging attempts before good aerial photography could be obtained. Some areas of high turbidity were found in LEH and southern Barnegat Bay. On further analysis of historical Landsat satellite imagery, it was noted that these areas routinely experience higher turbidity events than other parts of the BB-LEH estuarine system. In future image missions to monitor seagrass in BB-LEH particular attention should be paid to the eastern ICW near the Route 72 Bridge (-74 15 W 39 42 N) to determine if water clarity is sufficient to discern features on the bottom of the Little Egg Harbor and Southern Barnegat Bay (SBB) estuary as this is the area that was observed to have the highest frequency of turbidity events.


Table 3. Presence / absence accuracy assessment matrix for the 2009 seagrass survey.
PLEASE REFER TO TECH REPORT LINKED FROM CROSS REFERENCE (PAGE 15 OF REPORT)

Table 4. Class accuracy assessment matrix for the 2009 seagrass survey.
PLEASE REFER TO TECH REPORT LINKED FROM CROSS REFERENCE (PAGE 15 OF REPORT)
Completeness_Report:
Due to differences in adjacent image quality in the study area (16 individual image tiles), there are some instances where the SAV classification changes or halts at an image edge. These instances are reported in the following locations (UTM NAD83 Zone 18 X,Y coordinates): 1) 574800, 4406377; 2) 572201, 4401409; 3) 563998, 4381235; 4) 561867, 4378599; 5) 561990, 4378601; 6) 562440, 4378599 .
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
Horizontal positional accuracy at root mean square error of +/- 1 to 2 meters.
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: Air Photographics, Inc.
Title:
Aerial photography collection for the Barnegat Bay SAV project, 2009
Geospatial_Data_Presentation_Form: remote-sensing image
Online_Linkage: -
Type_of_Source_Media: CD-ROM
Source_Time_Period_of_Content:
Time_Period_Information:
Multiple_Dates/Times:
Single_Date/Time:
Calendar_Date: 20090628
Single_Date/Time:
Calendar_Date: 20090707
Single_Date/Time:
Calendar_Date: 20090804
Source_Currentness_Reference:
ground condition
Process_Step:
Process_Description:
Methods

(TEXT EXTRACTED FROM THE TECHNICAL REPORT)

Current (2009) extent of seagrass across the BB-LEH system

The first objective of this project was to quantify the location of seagrass across the BBLEH estuary system for the 2009 growing season. To accomplish this aerial photography dataset was collected, processed into image objects (polygons), and classified to create a GIS dataset showing the location of seagrass across the BB-LEH. The following methods sections describe the steps used to create the output GIS dataset. In addition an accuracy assessment was undertaken to determine how well this GIS dataset maps seagrass across the BB-LEH.

a). Aerial Photography Collection

An aerial photography mission was undertaking during the summer of 2009. Film aerial photography was collected on June 28, July 7, and August 4, 2009, using a Navajo HS airplane equipped with a Leica RC30 camera, lens # 13234, focal length 152.720 mm, and a variable exposure time of 260-420 milli-seconds. Two types of film were used; a grey scale AGFA 80 and color film AGFA 100. The same plane and camera was used for all three imaging missions. The plane flew at an altitude ~ 3,658 m and speed of 180 km hr-1 per hour. The plane flew three survey lines, two in the southern estuary due to bay width and one in the northern estuary for both the June 28 and July 7 aerial flyover, the August 4 date was only flown to collect imagery in the northern part of the study area. Two passes were made per day, the first to collect black and white photography and the second to collect color photography. The resultant film was then processed and scanned through a high resolution scanner resulting in a digital image with 18,278 by 18,292 pixels in a scale of 1 to 2,000. These scans were ortho-rectified and projected into Universal Transverse Mercator (Zone 18 North, North American Datum 1983, GRS Spheroid of 1980) with a horizontal positional accuracy at root mean square error of 1- 2 meters. The resulting geo tiffs were mosaicked into 15 larger blocks for later analysis.

b). In situ data collection

A number of in situ sites were visited to collect reference information to enable the interpretation of the aerial photography (Figure 1 & Appendix I). Reference sites were selected to match a subset of the in situ references sites selected during the 2003 Lathrop study (Lathrop et al. 2006). Reference sites were not selected in a random probabilistic manner, but rather targeted transects across the study area n = 167. In addition, 15 sample sites were selected for a late season review (October of 2009) for areas of uncertainty in the imagery. An additional 120 sample points were collected in June 2009 as part of an ongoing research project (Kennish 2009 unpublished data). These data points were also included in the study as field reference sites, although their collection used a different technique than the data points used in this study. A second in situ n = 124 dataset was collected to provide a validation dataset which was selected using a stratified random sampling design to focus on shallow water habitats mimicking the depth distribution of seagrass within the BB-LEH estuary. These points were distributed to match the depth distribution on the 2003 seagrass survey. To accomplish this, 2003 seagrass presence absence data from (Lathrop et al 2006) was intersected with the NOAA Nautical Charts Depth information (Charts 12324: edition 25, 1990 and 12316: edition 25, 1992 from Lathrop et al. (2001). For each 0.3048 meter depth (1 foot) category a number of field sites were randomly chosen to match the percentage of area of all seagrass habitat at that depth. This matched the random seagrass sites depth histogram to the depth histogram of the presence/absence seagrass data from 2003. These points were distributed to match the probability of finding seagrass at a specific depth. This validation dataset was not used in the image mapping and classification process but kept as an independent data set to compare with the wall-to-wall GIS map to create an error matrix, a producer's and user's accuracy assessment, and a Kappa statistic. As a secondary step after the accuracy assessment was completed the validation dataset was used to clean up the final GIS dataset.

For all of the in situ data collected for this project (the reference dataset n = 167 and the validation dataset n=124), field collection was accomplished as follows. The field survey was conducted from the Rutgers University Marine Field Station (RUMFS) using a 20 foot maritime skiff. Navigation to field locations was accomplished with a Garmin 530s marine GPS/Sonar system. Upon arrival at the preselected field locations, the boat weighed anchor. Next, an L shaped 4 meter x 5 meter grid made of 1.905 cm pvc (figure. 3) was lowered over the side of the boat. A diver entered the water and affixed a GPS Magellan Mobile Mapper 6 (2-5 meter horizontal accuracy) to the outside L of the survey grid (marked in Figure 2). A compass reading was taken along the left-hand axis of the sampling grid. The compass reading and the GPS position allowed precise placement of the sampling grid on the benthos to a higher level of accuracy than the boatbased GPS unit. The diver then visited grid 1 through 8 and recorded information on SAV presence / absence (yes no), percent cover of seagrass species (R. maritima and Z. marina) (0 to 100 in 10% increments), and percent coverage macroalgae (0 to 100 in 10% increments). This data was verbally relayed to the boat captain who recorded the data on write-in-the-rain paper. Upon completion of field data collection, the GPS unit was removed and the sampling grid returned to the boat. Field sheets were then signed, dated, and entered into a digital database. The precise location of each sampling grid was determined using MatlabT and simple geometry using the GPS location in UTM coordinates and the compass bearing. A correction for magnetic declination (difference between the North Pole and the magnetic North Pole) was calculated using NOAA website (http://www.ngdc.noaa.gov/geomagmodels/Declination.jsp) for July 15th, 2009, 39.9745 N 74.1514 W magnetic declination equals 12 degrees and 47 minutes.

c) Image pre-processing

An important step in image classification is the clumping of similar pixels into image objects for classification. To accomplish that task, each image collected in 2009 was filtered using the aggregate command available in Arc GridT for a 2x2 grid window selecting the median cell value. This was done to remove areas of local light scatter from wave tops, Langmuir circulation lines, and to reduce the size of the imagery for processing. The median was selected over the mean to avoid skewing from light scattering which can cause areas of high image reflectance (white capping) and shadows. The rectified mosaicked color photography was then imported into eCognition(TM) to support image segmentation and classification. eCognition(TM) is an image analysis software package that segments raster data in an unsupervised method minimizing the intra-polygon (image object) variance while maximizing inter-polygon (image object) variance. The user can control the weight of each imagery band by changing a coefficient between 0 and 1 (0 no input for that band 1 full input) by band and a unit less scale parameter which determines the average image object area. As the scale parameter increases greater spectral heterogeneity is allowed increasing the average size of the image objects. Multiple scale image objects can be created by running a multiple resolution segmentation procedure. Two-scale parameters were used for each image mosaic layer 1); a small scale parameter between 10-15; 2) a large-scale parameter 50-70 (Figure 3). The smaller scale parameter resulted in image objects with a mean size of .073 ha, mode of .045 ha, 25 percentile of .02 ha, and the 75 percentile at .09. This scale parameter was selected to meet the target minimum mapping unit of .05 ha (500 m^2). The minimum mapping unit defines the smallest feature delineated in the map or the amount of detail a map contains. The band coefficients used were 1 for blue, 0.7 for green, and 0.5 for red. The coefficients were selected by trial and error by the operator to maximize the difference between seagrass and other benthic habitats.

d). Image object classification

A manual classification where each image object was visually interpreted and assigned to one of four classes of seagrass density (high 100-80% percent cover, medium < 80%- 40% cover, sparse > 40% and <= 10% cover, and no seagrass <10% - 0%). The field reference data was used to inform the interpretation. The larger scale image objects (scale parameter 50-70) were first manually classified using eCognitionT. The large image object classifications were then forced down into the smaller image objects (scale parameter of 10-15) based on the nested polygon structure. Smaller image objects on edge areas and internal to the larger image objects were then manually reclassified when necessary. This method sped up the manual classification effort allowing large contiguous areas of seagrass to be classified quickly while also allowing precise classification on seagrass edge and gap areas (Figure 4 from Lathrop et al. 2006). The reference data also contained information on seagrass species and macroalgae percent cover these categories were not mapped as part of the manual classification. To create the final GIS dataset and accuracy assessment dataset the finer-scale image objects were exported to Environmental Research Institute ESRIT shapefile format. To determine how well the image objects described seagrass presence/absence and density across the BB-LEH an accuracy assessment was undertaken. To accomplish this the classified image objects were compared to the validation dataset within a GIS to create an accuracy assessment matrix, error of omission and commission, overall accuracy assessment, and a Kappa coefficient. This is similar to the methods employed by Lathrop et al. (2006). The Kappa coefficient is a measure of agreement between two categorical datasets correcting for the random chance that categories will agree. These measures of accuracy were completed to determine how accurately seagrass vs. all other habitats were mapped, and to determine how well the maps reflected the density of seagrass habitat based on the in situ data.
Process_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Richard G. Lathrop
Contact_Organization: Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA)
Contact_Voice_Telephone: 732 932-1582
Contact_Facsimile_Telephone: 732-932-2587
Contact_Electronic_Mail_Address: lathrop@crssa.rutgers.edu
Hours_of_Service: M-F 8.30am - 4.30pm EST USA
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Vector
Point_and_Vector_Object_Information:
SDTS_Terms_Description:
SDTS_Point_and_Vector_Object_Type: G-polygon
Point_and_Vector_Object_Count: 1920
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Grid_Coordinate_System:
Grid_Coordinate_System_Name: Universal Transverse Mercator
Universal_Transverse_Mercator:
UTM_Zone_Number: 18
Transverse_Mercator:
Scale_Factor_at_Central_Meridian: 0.999600
Longitude_of_Central_Meridian: -75.000000
Latitude_of_Projection_Origin: 0.000000
False_Easting: 500000.000000
False_Northing: 0.000000
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: coordinate pair
Coordinate_Representation:
Abscissa_Resolution: 0.000000
Ordinate_Resolution: 0.000000
Planar_Distance_Units: meters
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: Geodetic Reference System 80
Semi-major_Axis: 6378137.000000
Denominator_of_Flattening_Ratio: 298.257222
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Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: bbleh_sav09
Entity_Type_Definition:
submerged aquatic vegetation (SAV) image classification
Attribute:
Attribute_Label: FID
Attribute_Definition:
Internal feature number.
Attribute_Definition_Source:
ESRI
Attribute_Domain_Values:
Unrepresentable_Domain:
Sequential unique whole numbers that are automatically generated.
Attribute:
Attribute_Label: Shape
Attribute_Definition:
Feature geometry.
Attribute_Definition_Source:
ESRI
Attribute_Domain_Values:
Unrepresentable_Domain:
Coordinates defining the features.
Attribute:
Attribute_Label: OBJECTID
Attribute:
Attribute_Label: CLASSNUM
Attribute_Definition:
Numeric code for SAV class
Attribute_Definition_Source:
CRSSA
Attribute:
Attribute_Label: CLASSDESC
Attribute_Definition:
SAV class description
Attribute_Definition_Source:
CRSSA
Attribute:
Attribute_Label: Shape_Leng
Attribute_Definition:
Length of feature (meters)
Attribute_Definition_Source:
ESRI
Attribute:
Attribute_Label: Shape_Area
Attribute_Definition:
Area of feature in internal units squared (sq. meters)
Attribute_Definition_Source:
ESRI
Attribute_Domain_Values:
Unrepresentable_Domain:
Positive real numbers that are automatically generated.
Overview_Description:
Entity_and_Attribute_Overview:
These data as presented provide a regional picture of SAV distribution and are not intended for site level permit applications or litigation purposes. These maps and data, alone, are not sufficient to determine the presence or absence of SAV. Conclusive evidence concerning the presence or absence of SAV requires site inspections, preferably at several points in time during the SAV growing season.
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University
Contact_Address:
Contact_Voice_Telephone: 732-932-1582
Contact_Facsimile_Telephone: 732-932-2587
Hours_of_Service: M-F 8.30am - 4.30pm EST USA
Resource_Description: Downloadable data
Distribution_Liability:
These data as presented provide a regional picture of SAV distribution and are not intended for site level permit applications or litigation purposes. These maps and data, alone, are not sufficient to determine the presence or absence of SAV. Conclusive evidence concerning the presence or absence of SAV requires site inspections, preferably at several points in time during the SAV growing season.

Data are supplied 'as-is' without warranty of any kind. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, Rutgers University cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data. Rutgers University makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

Any maps, publications, reports or any other type of document produced as a result of utilizing this data will credit the original map author(s) as listed as well as the Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Transfer_Size: 33.957
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Metadata_Reference_Information:
Metadata_Date: 20110512
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA)
Contact_Person: John A. Bognar
Contact_Position: GIS Coordinator
Contact_Address:
Address_Type: mailing and physical address
Address:
School of Environmental and Biological Sciences
Address:
Rutgers, The State University of New Jersey
Address:
14 College Farm Road
City: New Brunswick
State_or_Province: New Jersey
Postal_Code: 08901-8551
Country: USA
Contact_Voice_Telephone: 732-932-1582
Contact_Facsimile_Telephone: 732-932-2587
Contact_Electronic_Mail_Address: johnb@crssa.rutgers.edu
Hours_of_Service: M-F 8.30am - 4.30pm EST USA
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Time_Convention: local time
Metadata_Security_Information:
Metadata_Security_Classification: Unclassified
Metadata_Extensions:
Online_Linkage: http://www.esri.com/metadata/esriprof80.html
Profile_Name: ESRI Metadata Profile
Metadata_Extensions:
Online_Linkage: http://www.esri.com/metadata/esriprof80.html
Profile_Name: ESRI Metadata Profile
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