2018 - 2020 USGS Lidar: Florida Peninsular FDEM

Dates of collection for Alachua County

Dates of collection for Nassau County

Dates of collection for Collier County

Date of collection for Key Biscayne (Miami-Dade County)

Dates of collection for Brevard County

Dates of collection for Duval County

Dates of collection for Clay County

Dates of collection for Columbia County

Dates of collection for DeSoto County

Dates of collection for Dixie County

Dates of collection for Glades County

Dates of collection for Hardee County

Dates of collection for Hendry County

Dates of collection for Highlands County

Dates of collection for Okeechobee County

Dates of collection for St. Johns County

Dates of collection for Orange County

Dates of collection for Martin County

Dates of collection for Sarasota County

Dates of collection for Manatee County

Dates of collection for Madison County

Dates of collection for Suwannee County

Dates of collection for Union County

Dates of collection for Baker County

Dates of collection for Bradford County

Dates of collection for Charlotte County

Dates of collection Gilchrist County

Dates of collection for Indian River County

Dates of collection for St. Lucie County

Dates of collection for Hamilton County

Dates of collection for Palm Beach County

Dates of collection for Taylor County

Date of collection for Lafayette County

Dates of collection for Polk County

Dates of collection for Osceola County

Create custom data files by choosing data area, product type, map projection, file format, datum, etc. A new metadata will be produced to reflect your request using this record as a base. Change to an orthometric vertical datum is one of the many options.

Bulk download of data files in LAZ format, Florida State Plane North/East/West NAD83(2011) coordinates and orthometric heights in feet.

Link to the reports, breaklines, metadata, spatial metadata, vertical accuracy information, and shapefiles.

Link to the Alachua County lidar report.

The Data Access Viewer (DAV) allows a user to search for and download elevation, imagery, and land cover data for the coastal U.S. and its territories. The data, hosted by the NOAA Office for Coastal Management, can be customized and requested for free download through a checkout interface. An email provides a link to the customized data, while the original data set is available through a link within the viewer.

Link to the Baker County lidar report.

Link to the Bradford County lidar report.

Link to the Brevard County lidar report.

Link to the Charlotte County lidar report.

Link to the Clay County lidar report.

Link to the Collier County lidar report.

Link to the Columbia County lidar report.

Link to the DeSoto County lidar report.

Link to the Dixie County lidar report.

Link to the Duval County lidar report.

Link to the Gilchrist County lidar report.

Link to the Glades County lidar report.

Link to the Hamilton County lidar report.

Link to the Hardee County lidar report.

Link to the Hendry County lidar report.

Link to the Highlands County lidar report.

Link to the Indian River County lidar report.

Link to the Key Biscayne lidar report.

Link to the Lafayette County lidar report.

Link to the Madison County lidar report.

Link to the Manatee County lidar report.

Link to the Martin County lidar report.

Link to the Nassau County lidar report.

Link to the Okeechobee County lidar report.

Link to the Orange County lidar report.

Link to the Osceola County lidar report.

Link to the Palm Beach County lidar report.

Link to the Polk County lidar report.

Link to the Sarasota County lidar report.

Link to the St. Johns County lidar report.

Link to the St. Lucie County lidar report.

Link to the Suwannee County lidar report.

Link to the Taylor County lidar report.

Link to the Union County lidar report.

Link to the USGS Project Report that provides information about the project, vertical accuracy results, and the point classes and sensors used.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Entwine Point Tile (EPT) is a simple and flexible octree-based storage format for point cloud data. The data is organized in such a way that the data can be reasonably streamed over the internet, pulling only the points you need. EPT files can be queried to return a subset of the points that give you a representation of the area. As you zoom further in, you are requesting higher and higher densities. A dataset in EPT will contain a lot of files, however, the ept.json file describes all the rest. The EPT file can be used in Potree and QGIS to view the point cloud.

Link to view the point cloud, using the Entwine Point Tile (EPT) format, in the 3D Potree viewer.

This data source was used (along with airborne GPS/IMU data) to georeference the LiDAR point cloud data.

This site provides all the EPT links to the 34 out of the 35 datasets that make up this project. At the time of processing, Collier County was not yet available by EPT.

Raw Data and Boresight Processing: The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used Quantum Spatial, Inc. proprietary and commercial software to calculate initial boresight adjustment angles based on sample areas selected in the lift. These areas cover calibration flight lines collected in the lift, cross tie, and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technicians then analyzed the results and made any necessary additional adjustment until it was acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps matched for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was less than or equal to 7 cm RMSEz within individual swaths and less than or equal to 10 cm RMSEz or within swath overlap (between adjacent swaths). 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed checkpoints after the z correction to ensure the requirement of NVA = 19.6 cm 95% Confidence Level (Required Accuracy) was met.

LAS Point Classification: The point classification was performed as described below. The bare earth surface was manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface was finalized, it was then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) LiDAR data inside of the Lake Pond and Double Line Drain hydro-flattened breaklines were then classified to Water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 1 meter was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) points were reclassified to the correct classification after the automated classification was completed. All data were manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. Global Mapper was used as a final check of the bare earth dataset. GeoCue was then used to create the deliverable industry-standard LAS files for both the All Point Cloud Data and the Bare Earth. Quantum Spatial, Inc. proprietary software was used to perform final statistical analysis of the classes in the LAS files, on a per tile level to verify final classification metrics and full LAS header information.

The NOAA Office for Coastal Management (OCM) downloaded data sets from this USGS site:

ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/Elevation/LPC/Projects/

The data sets downloaded were:

FL_Peninsular_FDEM_2018_D19_DRRA/FL_Peninsular_FDEM_Collier_2018/ Number of files: 530

FL_Peninsular_FDEM_2018_D19_DRRA/FL_Peninsular_FDEM_KeyBiscayne_2020/ Number of files: 14

FL_Peninsular_FDEM_2018_D19_DRRA/FL_Peninsular_FDEM_Nassau_2018/ Number of files: 763

These files were processed to the Data Access Viewer (DAV) and https. Additional areas will be downloaded, processed, and added to this data set as they are made available from USGS.

The data were in FL State Plane East (NAD83 2011), US survey feet coordinates and NAVD88 (Geoid12B) elevations in feet. The data were classified as: 1 - Unclassified, 2 - Ground, 6 - Buildings, 7 - Low Noise, 9 - Water, 17 - Bridge Decks, 18 - High Noise, 20 - Ignored Ground. OCM processed all classifications of points to the Digital Coast Data Access Viewer (DAV). Classes available on the DAV are: 1, 2, 6, 7, 9, 17, 18, 20.

OCM performed the following processing on the data for Digital Coast storage and provisioning purposes:

1. Internal OCM scripts were run to check the number of points by classification and by flight ID and the gps, elevation, and intensity ranges.

2. Internal OCM scripts were run on the laz files to:

a. Convert from orthometric (NAVD88) elevations to ellipsoid elevations using the Geoid12B model

b. Convert the Collier, Key Biscayne, and Nassau laz files from FL State Plane East Zone 0901 (NAD83 2011), US survey foot coordinates to geographic coordinates

c. Convert from vertical units of feet to meters

d. Filter points outside the elevation range of -100 to 3000 feet

e. Filter points outside the geographic range of the bounding coordinates of -89, 24, -79, 31 degrees

f. Assign the geokeys, sort the data by gps time and zip the data to database and to https.

With the release of the remainder of the counties in EPT format, the on prem versions of Nassau County and Key Biscayne were removed from the database and the USGS EPT versions were leveraged instead. Collier County was not available as EPT at this time and so the data for it was retained on prem. The dataset was however, converted from ellipsoid elevations to NAVD88 (GEOID12B) elevations in order to be in the same vertical datum as the leveraged USGS EPT county versions.

Original point clouds in LAS/LAZ format were restructured as Entwine Point Tiles and stored on Amazon Web Services. The data were re-projected horizontally to WGS84 web mercator (EPSG 3857) and the vertical units were converted to meters (NAVD88 Geoid12B).

The NOAA Office for Coastal Management (OCM) created references to the Entwine Point Tile (EPT) files that were ingested into the NOAA Digital Coast Data Access Viewer (DAV). No changes were made to the data. At the time of processing, Collier County was not yet available via EPT. The DAV will access the point cloud as it resides on Amazon Web Services (AWS) under the usgs-lidar-public container.

These are the AWS URLs being accessed:

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Alachua_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Baker_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Bradford_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Brevard_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Charlotte_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Clay_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Columbia_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Desoto_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Dixie_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Duval_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Gilchrist_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Glades_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Hamilton_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Hardee_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Hendry_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Highlands_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_IndianRiver_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_KeyBiscayne_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Lafayette_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Madison_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Manatee_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Martin_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Nassau_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Okeechobee_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Orange_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Osceola_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_PalmBeach_2019/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Polk_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Sarasota_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_StJohns_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_StLucie_2020/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Suwannee_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Taylor_2018/ept.json

https://s3-us-west-2.amazonaws.com/usgs-lidar-public/FL_Peninsular_FDEM_Union_2018/ept.json