The Nature Conservancy Florida Chapter, Natrx Inc. (2024). Biscayne Bay Shoreline Parcels: Nature-Based Solution Assessments and Carbon Estimations [gis dataset]. University of Miami Institute for Data Science and Computing. last updated: 0001-01-01 (update frequency: Never). This dataset provides parcel-level assessments of nature-based solution NBS) opportunities along the shorelines of Biscayne Bay, Florida, spanning from North Miami Beach to the upper Florida Keys. For each parcel, the dataset includes shoreline change analysis SCA) rates derived from NAIP multispectral imagery using Marsh Edge from Image Processing MEIP) with Area Based Analysis ABA , NBS scenario recommendations (mangrove planting, mangrove with sand infill, sill-only, or seawall habitat enhancement), implementation cost estimates, and carbon estimation metrics. NBS scenarios were selected using a decision tree based on urban/non-urban classification, erosion rates, and nearshore depth profiles. Carbon estimates cover soil organic carbon preservation (sill scenarios) and total carbon stock (mangrove scenarios). The dataset supports coastal resilience planning and climate adaptation decision-making for government agencies, planners, and conservation practitioners in the Miami-Dade region and is designed to be transferable to other coastal areas. Key words: mangroves, shoreline change analysis, erosion, coastal typology, blue carbon, Miami-Dade, sea level rise, carbon sequestration, living shorelines, flood resilience, Biscayne Bay Open Geospatial Consortium Standards: dynamic vector tiles Analytical Functions: gdsc_get_attributes(tablename=mdc_2024_biscayne_nbs_parcels,lat=25.718,lng=-80.279,identifier=parcelnumb,normal='') ETL documentation

ETL for mdc_2024_biscayne_nbs_parcels

######### # GDSC ETL is performed in two steps: shell script to ETL data into postgres # and then additional transformations with SQL in postGIS (if needed). #### ######### # Step 1 - bash script (pseudo code) #### # create directory structure and move into it mkdir -p /data/mdc_2024_biscayne_nbs_parcels/{download,etl} && cd /data/mdc_2024_biscayne_nbs_parcels # set update flag based on last update and update frequency do_update=0 if date() < last_update + update_frequency else do_update = 1 # load into postGIS with: ogr2ogr -lco GEOMETRY_NAME=geom -f PostgreSQL PG:"" download/mdc_2024_biscayne_nbs_parcels.gpkg -nlt multipolygon -nln mdc_2024_biscayne_nbs_parcels ######### # Step 2 - SQL script (pseudo code): #### # remove duplicate points and make geometries valid: UPDATE mdc_2024_biscayne_nbs_parcels SET geom=ST_MakeValid(ST_RemoveRepeatedPoints(geom)); # add local geometry column and reproject existing geometries into local EPSG: SELECT AddGeometryColumn ( 'mdc_2024_biscayne_nbs_parcels', 'geom_local', 26917, 'multipolygon', 2 ); UPDATE mdc_2024_biscayne_nbs_parcels SET geom_local=ST_MakeValid(ST_RemoveRepeatedPoints(ST_Transform(ST_Multi(geom),26917))); CREATE INDEX mdc_2024_biscayne_nbs_parcels_geom_local_idx ON mdc_2024_biscayne_nbs_parcels USING GIST (geom_local); NOTIFY pgrst, 'reload schema';