20121002 15375400 1.0 FGDC CSDGM Metadata FALSE Canopy_SWRanches 0.000 002 839991.727500 881785.317469 616817.381564 629417.647374 1 Projected GCS_North_American_1983 NAD_1983_StatePlane_Florida_East_FIPS_0901_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/10.0'><WKT>PROJCS["NAD_1983_StatePlane_Florida_East_FIPS_0901_Feet",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",656166.6666666665],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-81.0],PARAMETER["Scale_Factor",0.9999411764705882],PARAMETER["Latitude_Of_Origin",24.33333333333333],UNIT["Foot_US",0.3048006096012192],AUTHORITY["EPSG",2236]]</WKT><XOrigin>-17791300</XOrigin><YOrigin>-41645400</YOrigin><XYScale>137249422.94257641</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.0032808333333333335</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>2236</WKID></ProjectedCoordinateSystem> Linear Unit: Foot_US (0.304801) 20121010 15432100 20121010 15432100 Victoria Morrow Broward County Planning and Redevelopment Division GIS Manager 20121010 ArcGIS Metadata 1.0 Canopy_SWRanches vector digital data This dataset is a collection of 35 individual feature classes, composed of 31 cities and 4 unincorporated areas within Broward County. Tree canopy coverage is a valuable attribute for understanding the amount and location of tree canopy in the study area. In this case the study area is urban Broward County which includes 31 cities and unincorporated areas. Prospective uses for this information include assessment of tree canopy trends since the previous county-wide study which was done using 1996 aerial photography as the basis for mapping. It will also be used to develop targets for future tree canopy by the various municipalities who are trying to develop achievable goals; and to evaluate achievable canopy coverage for various zoning and land use categories. It is expected to provide some of the technical basis for the County’s Urban Forest Management Plan which is currently under development as well as the basis for carbon sequestration efforts which are expected to part of future sustain ability efforts. Broward County Environmental Protection and Growth Management Department Victoria Morrow Broward County Planning and Redevelopment Division GIS Manager Eric Myers Environmental Protection and Growth Management Department Deputy Director Broward County Broward 2010 urban canopy tree geoscientificInformation environment climatologyMeteorologyAtmosphere ISO 19115 Topic Categories canopy broward Acknowledgement of the Broward County Environmental Protection and Growth Management Department would be appreciated for any products derived from this data. ESRI ArcGIS 10.0.5.4400 -80.203807 -80.166406 26.214594 26.327893 1 -80.440191 -80.312721 26.064196 26.028981 Vector data quality controlled in house using randomly generated sampling points throughout the project. 2012-06-01T00:00:00 Corey Smolik Scheda Ecological Associates Mapping and Design Team Leader An Object Based Image Analysis (OBIA)was performed by Photo Science Inc. to support Scheda Ecological Associates' task to map Broward County Tree Canopy. Four hundred fifty-six color infrared (CIR) images were supplied as the base data and processed individually within eCognition. Photo Science then acquired LiDAR imagery, from digital coast (http://www.csc.noaa.gov/digitalcoast/), for the exact geospatial extent of the supplied imagery. The LiDAR was processed for Bare Earth which was subtracted with the LiDAR digital surface model (DSM or elevation first return). This subtraction product is referred to as a normailzed digital surface model (nDSM), which approximates true height of features found on the ground. This set of imagery (CIR and nDSM) and a water boundary file found at http://gis.broward.org/GISData.htm, provided the start of the OBIA work within the eCognition software environment. The input imagery was then further processed to provide a 5x5 median filter of the nDSM and a surface calculation of slope (Zevenbergen, Thorne (ERDAS)) based on the median filter of the nDSM. The segmentation routine used to process the imagery proceeded as follows. The thematic vector layer, water from Broward GIS, was burned in at the chessboard level, thus creating rasterized representations of the vector layer in image space. These water objects were flagged based on the classification from the vector layer and classified as water with object space. Water objects were then ignored from further processing. An edge ratio split of the median nDSM was performed to separate tall from unclassified objects. Tall objects were then segmented into smaller quadtree segments and then merged with like objects based on a multi-resolution segmentation merge which was based aerial imagery and slope. Thus allowing objects to be divided by a threshold of normalized difference Vegetation Index (NDVI) response, where low NDVI response objects are then classified as building and then building objects are then merged together. The remaining tall objects are classified as potential forest patches with spectral dependencies and merged. Likewise the remaining unclassified objects are merged. Buildings were then further refined to remove small objects that were smaller than a minimum mapping unit MMU (100 sq. ft.) and had a neighbor water object, and then buildings were merged again. Remaining small objects of potential forest patches were then removed based on the MMU. Potential forest patches were then reclassified to a Canopy class, to remove the NDVI and brightness dependencies associated with the potential forest patch class, which allows for more objects to become canopy. A pixel based smoothing routine, a 5x5 median kernel, was applied to each canopy object through a parent process object routine which processes each object iteratively. This pixel based smoothing routine allows for the customary roundness of canopy features. Another remove features based on MMU and relative border to water was performed. Then a single pixel coating was applied to each canopy object to mimic the imagery due to the LiDAR being of a different resolution than the imagery collect, which tended to slightly under-estimate the canopy at the edge of each object. Finally an additional remove objects less than the MMU was performed to ensure no straggling objects of less than the MMU remained. 2012-01-03T00:00:00 Nathaniel Morton Photo Science, Inc. Remote Sensing Specialist Vector output of the Object Based Image Analysis was manually inspected and cleaned. Corey Smolik Scheda Ecological Associates Mapping and Design Team Leader Vector data was evaluated by Broward County using randomly generated assessment points 2012-07-02T00:00:00 Victoria Morrow Broward County Planning and Redevelopment Division GIS Manager 0 Canopy_SWRanches Feature Class 0 FID FID OID 4 0 0 Internal feature number. ESRI Sequential unique whole numbers that are automatically generated. Acres ACRES Double 19 18 8 Shape_Area Area of feature in internal units squared. ESRI Positive real numbers that are automatically generated. SHAPE_AREA Double 19 0 0 Shape Feature geometry. ESRI Coordinates defining the features. Shape Geometry 0 0 0 SHAPE_LEN SHAPE_LEN Double 19 0 0 Shapefile 0.000 dataset EPSG 7.4.1 Simple FALSE 0 FALSE FALSE