20210628 15293500 1.0 SEAGRASS2020 002 381037.688716 695000.008297 858557.042113 1396253.039225 1 Projected GCS_North_American_1983_HARN Linear Unit: Foot_US (0.304801) NAD_1983_HARN_StatePlane_Florida_West_FIPS_0902_Feet <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.6'><WKT>PROJCS[&quot;NAD_1983_HARN_StatePlane_Florida_West_FIPS_0902_Feet&quot;,GEOGCS[&quot;GCS_North_American_1983_HARN&quot;,DATUM[&quot;D_North_American_1983_HARN&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;False_Easting&quot;,656166.6666666665],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,-82.0],PARAMETER[&quot;Scale_Factor&quot;,0.9999411764705882],PARAMETER[&quot;Latitude_Of_Origin&quot;,24.33333333333333],UNIT[&quot;Foot_US&quot;,0.3048006096012192],AUTHORITY[&quot;EPSG&quot;,2882]]</WKT><XOrigin>-17791300</XOrigin><YOrigin>-41645400</YOrigin><XYScale>3048.0060960121928</XYScale><ZOrigin>0</ZOrigin><ZScale>1</ZScale><MOrigin>0</MOrigin><MScale>1</MScale><XYTolerance>0.0032808333333333331</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>2882</WKID><LatestWKID>2882</LatestWKID></ProjectedCoordinateSystem> 20210726 11244300 20210726 11244300 Chris J. Anastasiou, PhD Southwest Florida Water Management District Chief Scientist 352-796-7211 2379 Broad Street Brooksville Florida 34604 20210726 ArcGIS Metadata 1.0 23906 SEAGRASS2020 2021-05-28 NV5 Geospatial, Nathaniel Morton, GISP, Remote Sensing Technical Expert NV5 Powered by Quantum Spatial 10033 MLK Street N, Suite 200 St. Petersburg, Florida 33716 vector digital data The Southwest Florida Water Management District (SWFWMD) is responsible for the protection and management of natural systems and water quality within its geographic boundaries. One component of evaluating the ecological health of coastal ecosystems is to map and monitor submerged aquatic vegetation (SAV) communities. This project is a continuation of previous efforts to determine the status and trends (if any) of SAV resources in the coastal waters of the District. The results of this effort will be used to determine the performance of ongoing efforts to improve water quality and quantity in coastal waters of the District.The dataset serves as documentation of the seagrass habitat within St. Joseph Sound and Clearwater Harbor, Tampa Bay, Sarasota Bay, Lemon Bay and Charlotte Harbor as it existed in January 2020. Seagrass data were photointerpreted from 2020 1:24,000 scale natural color aerial photography and classified using the SWFWMD modified FLUCCS classification system. Horizontal accuracy of meets NMAS for 1:12,000 scale maps. The minimum mapping unit for classification was 0.25 acres. Creation of a 2020 seagrass map from 1:24,000 scale natural color aerial photography that is consistent with and aligned to the previous 2018 seagrass map. This data was produced for the SWFWMD's "2020 Districtwide Seagrass Mapping" project. NV5 Geospatial Chris J. Anastasiou, PhD Southwest Florida Water Management District Chief Scientist 352-796-7211 2379 Broad Street Brooksville Florida 34604 Southwest Florida Water Management District Sarasota Bay Charlotte Harbor St. Joseph's Sound and Clearwater Harbor Tampa Bay Lemon Bay map 2020 seagrass coastal sav FLUCCS habitat benthic submerged vegetation map 2020 seagrass coastal Southwest Florida Water Management District sav FLUCCS Sarasota Bay habitat Charlotte Harbor St. Joseph's Sound and Clearwater Harbor Tampa Bay benthic Lemon Bay submerged vegetation Translation of files to formats other than those described here is the sole responsibility of individuals downloading the data. The positional accuracy of this data meets USGS NMAS for 1:12000 scale maps. This data has a minimum classification accuracy of 90% for all categories. Mapping conventions applied to the delineation and classification of seagrass categories were: 1) Outer boundaries of beds are more important than internal structure (patchiness, shapes of sand patches within beds). 2) Outer boundaries of beds are more important than density categorization within beds. 3)Minimum Mapping Unit (MMU) is 0.25 acres. It is more important to include small isolated patches than similar sized patches that were a part of a large matrix. Care should be taken in mapping small areas of seagrass when only a small amount of seagrass is present, e.g. around a spoil island. When deciding whether an area with patches of seagrass was one polygon of patchy seagrass or individual seagrass polygons, apply the guideline of MMU of 0.25 acres. If an area has a few patches under 0.25 acres err on the side of including these rather than excluding them. Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.0.8321 Extent contains the resource 2021-05-28 -82.853954 -81.879462 26.693195 28.174795 Interpreted from 1:24,000 natural color aerial photography flown in Winter 2020. Although the imagery was collected under near cloud free conditions there were some clouds as well as turbidity issues in and around some of the passes. Data were compiled using Esri ArcGIS in a manual digitizing environment. 1 -82.853954 -81.879462 28.174795 26.693195 Mapping and GIS Section Southwest Florida Water Management District (352) 796-7211 2379 Broad Street (U.S. 41 South) Brooksville Florida 34604-6899 The data are being provided on an 'as is' basis. The District specifically disclaims any warranty, expressed or implied, including, but not limited to, the implied warranties or merchantability and fitness for a particular use. The entire risk as to quality and performance is with the user. In no event will the District or its staff be liable for any direct, indirect, incidental, special, consequential, or other damages, including loss of profit, arising out of the use of these data even if the District has been advised of the possibility of such damages. All data are intended for resource management use. Feature Class map was assessed against imagery at greater than 90% map was assessed against imagery at greater than 90% Evaluated and reviewed by GIS applications. NV5 Powered by Quantum Spatial performed an accuracy assessment on the data to compare in-field classification to photointerpreted map classification. Random sample points of polygons coded were generated and were located in the field with a Global Positioning System (GPS). A 90% accuracy rating for seagrass categories based on the randomly selected seagrass sample points was required. Evaluated and reviewed by GIS applications. No official spec document followed. 2021-05-28 Evaluated via GIS applications. 1 The positional accuracy of this data meets USGS NMAS for 1:12000 scale maps. Horizontal accuracy of the data corresponds to the positional accuracy of the aerotriangulated photography that was controlled by GPS survey control points. Digital linework was reviewed over the 2020 seagrass data and positional accuracy checked by navigating to features using coordinates selected from aerotriangulated photography for comparison to linework data collected representing those features. Evaluated and reviewed by GIS applications. No official spec document followed. 2021-05-28 Evaluated via GIS applications. 1 Photointerpretation for the project was accomplished using manual digitizing techniques. Photointerpreters utilized previously collected field data and in-process collection of field data along with other available collateral data to assist in their interpretation. Throughout the photointerpretation process, positional accuracy of seagrass boundaries was maintained according to the photographic signatures apparent on the photography. The quality of the 2020 images was good apart from a few areas of turbidity which made it difficult to discern the seagrass beds (especially the edges of the beds). The quality of the reflown imagery in Tampa Bay, Sarasota, Charlotte Harbor and Anclote (intracoastal waterways and passes) was very good. Existing 2020 seagrass polygons that could not be confirmed from the 2020 imagery were field verified and the seagrass boundaries remained unchanged if seagrass was confirmed in the field. Many seagrass boundaries could be confirmed using the 'histogram stretch' on the imagery within ArcGIS. The standard deviation of '2' was most effective in enhancing seagrass beds that were otherwise inconspicuous on the un-stretched imagery. Seagrass, macroalge and drift algae frequently coexist and the edges of the seagrass beds can be indistinct due to the dominant dark algae signatures present on the aerials. Extra fieldwork was performed in these areas to help with the delineation of the seagrass extent. The sparse Halodule beds were difficult to interpret due to the subtle signature changes therefore slight elevation differences along with the field data aided the interpretation. Identification of oysters in the field assisted in differentiation of their signature in this and were classified independently using the 6540 code. In some locations the boundaries of the darker algal signatures were used to assist in differentiation of seagrass polygons. The following coding and classification system was applied during photointerpretation: FLUCCS_Code - FLUCCS_Descrip 5400 -Water; 6510 - Tidal Flat; 6540 - Oyster Bar; 7210 – Submerged Other Than Seagrass; 9113 - Patchy Seagrass; 9116 – Continuous Seagrass; 9121 - Attached Macroalgae; 9122 – Composite Colonized Algae; 0 – Land, Classifications. The following classification conventions were used to ensure consistency of delineation: Open water are delineated using code of 5400. All permanently flooded, open water, non-vegetated areas within the study area to the brackish seaward limits. Marine was separated from Bays and Estuaries (5400) by dividing lines extending across the mouths of inlets that join the two classification types. Tidal Flats areas are delineated using code of 6510. Tidal Flats are non-vegetated areas of sand or mud. Intertidal, non-vegetated, unconsolidated sediments located in estuarine/low energy environments, habitats capable of supporting seagrass if able to establish. Submerged Other Than Seagrass (7210) are used for sandy bottoms or non-seagrass SAV benthic bottoms less than 6 feet deep which are currently void of seagrass but with the potential for future seagrass recruitment. Continuous, dense seagrass beds are delineated using code of 9116. These areas exhibit a continuous and uniform signature. Small, (less than one-quarter acre) sandy bottom features may be interspersed within the bed, but these areas are not dominant. Areas that appear as continuous beds of seagrass communities, regardless of species composition, will be mapped. Patchy seagrass beds are delineated using a code of 9113. This category appears as singular, isolated patches of seagrass or extensive areas of patch strands mixed with open bottom. Typically, these areas appear as rounded clumps, or as elongated strands mixed with sand. Attached Macroalgae are delineated using code of 9121. Only pure stands of green algae, Caulerpa prolifera and Sargassum, are mapped. All polygons mapped as algae (9121) are field verified during the mapping process. When Caulerpa prolifera or Sargassum co-exists with seagrass beds, only the grass is mapped where beds of attached algae may be distinguishable from seagrass. Where feasible, they should be mapped and field verified if >10% cover. Composite Colonized Algae are delineated using code of 9122. Co-dominant matrix of sparse macroalge that may exist with hard bottom, sponges, etc. without a presence of seagrass. While performing the photointerpretation for this project, the following guidelines were used: A. The outer boundaries of beds are more important than internal structure (patchiness, shapes of sand patches within) of beds. B. The outer boundaries of beds are more important than density categorization within beds. C. The minimum mapping unit is .25 acres. It is more important to include small isolated patches than similar sized patches that are part of a larger seagrass complex. Care is taken in mapping small areas of seagrass when only a small amount of seagrass is present, e.g., around a spoil island. Under this guideline, small isolated (i.e. Stand-alone) patches of seagrass will be mapped wherever visible on the photography. Within patchy (9113) seagrass beds each .25 acre "patch" is not mapped individually because the entire bed is the mapped object. Quality Assurance and Quality Control NV5 Geospatial, Inc. has established, documented, implemented and maintains a thorough quality control system NV5 Geospatial ‘s Quality Control techniques are designed to ensure products/materials provided by NV5 Geospatial are subject to adequate control of quality to ensure customer satisfaction. The techniques are designed to prevent discrepancies from occurring and are enforced with positive corrective action when necessary. Quality control reviews were ongoing throughout the project. The 2020 Seagrass geodatabase was compared to the 2018 Seagrass dataset so that the differences between the datasets could be analyzed and was examined for adherence to mapping conventions, the PI Key, spatial and classification accuracy, and photointerpretation consistency. Below is a description of the quality control steps that were used for the SWFWMD’s 2020 “Districtwide Seagrass Mapping” project. Upon receipt of the 2020 aerial photography, NV5 Geospatial’s Quality Assurance Manager and the designated QA Photointerpreter systematically reviewed seagrass delineations and classifications interpreted from the aerial photography. Using Esri ArcGIS software, the QA Manager reviewed the seagrass delineations interpreted on the aerial photographs. The original photointerpreter completed the required revisions and resubmitted the data to NV5 Geospatial’s Project Manager. The data was reassessed by the quality assurance manager prior to delivery. Data quality was monitored by a combination of manual and automated analyses. A set of automated quality control programs were used to check for errors, such as missing polygon label points, label points with no attributes, invalid attribute codes, missing attributes within the info tables, and two adjacent polygons with the same attributes. The current photointerpretation key contains examples of all FLUCCS classification types delineated for the study and includes both textural descriptions of the classification types along with several aerial photo clips clearly delineated to show users the seagrass signatures on the photos. Final Geodatabase at the conclusion of the photointerpretation processes for building topology and validating the dataset were completed. Unique identifiers (record numbers) for resulting polygons were assigned automatically by the system according to normal ArcGIS conventions. A 2020 seagrass Geodatabase of the entire SWFWMD Tarpon Springs to Boca Grande Seagrass Mapping project was delivered by electronic transfer. 2021-05-28 Nathaniel Morton, GISP NV5 Powered by Quantum Spatial Remote Sensing Technical Expert 727-576-9500 10033 MLK Street N, Suite 200 St. Petersburg Florida 33716 20220103 SEAGRASS2020 SDE Geodatabase feature class ESRI Geodatabase Feature Class 23906 FLUCCSDESC Class codes from the Florida Land Use, Cover and Forms Classification System (FLUCCS), which was originally compiled by Florida Department of Transportation, State Topographic Bureau. FDOT Class codes from the Florida Land Use, Cover and Forms Classification System (FLUCCS), which was originally compiled by Florida Department of Transportation, State Topographic Bureau. FDOT FLUCCSDESC String 80 0 0 FLUCCSCODE Class codes from the Florida Land Use, Cover and Forms Classification System (FLUCCS), which was originally compiled by Florida Department of Transportation, State Topographic Bureau. FDOT Class codes from the Florida Land Use, Cover and Forms Classification System (FLUCCS), which was originally compiled by Florida Department of Transportation, State Topographic Bureau. FDOT FLUCCSCODE Integer 4 10 0 SHAPE Feature geometry. ESRI Coordinates defining the features. 20190226 20190226 SHAPE Geometry 4 0 0 SHAPE.STArea() Area of feature in internal units squared. ESRI Positive real numbers that are automatically generated. 20190226 20190226 SHAPE.STArea() Double 0 0 0 DATESTAMP The date the feature was last edited or updated. SWFWMD Date of final delivery DATESTAMP Date 8 0 0 OBJECTID Internal feature number. 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