- Bureau of Land Management
- California Coastal Conservancy
- California Ocean Protection Council
- Cayuga County, New York
- Chatham County, Georgia
- City of Baltimore, Maryland
- Coastal Georgia Regional Development Center
- Delaware Coastal Program
- Delaware Department of Natural Resources and Environmental Control
- Environmental Protection Agency
- Federal Emergency Management Agency
- Florida Department of Environmental Protection
- Florida Division of Emergency Management
- Florida Fish and Wildlife Conservation Commission
- Harris County Flood Control District, Texas
- Hawaii Office of Planning
- Maryland Department of Natural Resources
- Mississippi Department of Environmental Quality
- NASA Wallops Flight Facility
- NOAA Coastal Services Center
- NOAA National Geodetic Survey
- NOAA Office of Ocean and Coastal Resource Management
- National Park Service
- New Jersey Department of Environmental Protection
- Northwest Florida Water Management District
- Oregon Department of Forestry
- Oregon Department of Geology and Mineral Industries
- Oregon Parks and Recreation Department
- Pearl River County, Mississippi
- Puget Sound Lidar Consortium
- Scripps Institute of Oceanography
- South Carolina Department of Natural Resources
- Southwest Florida Water Management District
- St. Johns County, Florida
- St. Johns River Water Management District
- Texas Water Development Board
- U.S. Army Corps of Engineers
- USACE Jacksonville District
- USACE National Coastal Mapping Program
- USACE St. Louis District
- USDA Natural Resources Conservation Service
- U.S. Geological Survey
- USGS Coastal and Marine Geology Program
- University of Connecticut
- University of Texas
- Volusia County, Florida
Many different partners and groups, and several Center-led data projects, have contributed to the lidar data collection housed and distributed by the NOAA Coastal Services Center. The data span more than a decade and were collected using several different sensors. The collection includes data from topographic and bathymetric lidar sensors.
Data are available for all of the coastal states and range from shoreline strips to full county coverage. The products have been delivered to the Center in various formats, projections, datums, and units. Once received, the data are reviewed, checked for errors, and standardized in a single format, projection, and datum.
- Area of Coverage: Partial or full coastal counties
- Date(s) Available: 1997 to present (vary by location)
- Format: Points in ASCII X,Y,Z, LAS, or LAZ; digital elevation models (DEMs) in floating point grid, GeoTIFF, and ASCII Grid; and contours in shapefile and AutoCad exchange formats
- Resolution: Point density is 0.1 to 8 pts/meter2
- Accuracy: Elevations at 95 percent confidence typically better than 30 centimeters (cm)
- What are you using the lidar for?
Let us know at email@example.com.
- Data are received in various formats, datums, and projections and are processed to a common format, datum, and projection for data storage and provisioning purposes. Errors are reviewed and, when possible, removed. Some data are further processed to generate bare-earth products, but the level of processing varies with each data set.
- Distribution system generates a variety of elevation products from the mass point data including contours, points (of varying return number and classification), intensity images, and full DEMs. These products can be requested for unique, user-defined areas, projections, datums, units, and formats.
- Data can be selected by a user-defined area of interest
- Data are available in multiple projections and datums, and the following output formats:
- ASCII X, Y, Z Pts.
- Floating Pt. Grid
- 8-bit Tiff
- 32-bit Float Tiff
- Shapefile Contour
- DXF Contour
- LAS 1.x
- ASCII Grid
Notes and Limitations:Coverage varies from location to location—some areas feature multiple years of data and other coastal areas feature no coverage. Users should not use this data for critical applications without a full awareness of the limitations. The data is provided nearly as received. The Center does review the data but makes no guarantees on reported vertical or horizontal accuracies.
Early data were flown at lower resolutions (0.25 pts/meter) and are typically not available with bare earth classification. More recent data can exceed 8 pts/meter and have bare earth surface classifications. Accuracies (95 percent confidence) range from 10 to 40 cm in open terrain; the specifics of each data set are provided via metadata and fact sheets.
Stories from the Field
Planning for Sea Level Rise Adaptation at the Site Scale in New Jersey
Data from the Sea Level Rise and Coastal Flooding Impacts Viewer was used to assess vulnerability and provide recommendations at three publicly accessible waterfront recreation areas.
Building Resilient Communities Using a Beachfront Vulnerability Index in South Carolina
The Beachfront Vulnerability Index helps communities become more resilient to storm surge and erosion.
Understanding Vulnerability to Sea Level Rise in Southeast Florida
Data from the Sea Level Rise and Coastal Flooding Impacts Viewer helped in the development of policies and programs to address sea level rise.
Assessing Land-Based Threats to Coral Reef Habitats in Laolao Bay, CNMI
OpenNSPECT and the Habitat Priority Planner were used to assess land based threats and prioritize restoration areas.
Modeling Sediment Yield in Hawaii
N-SPECT was used to estimate the amount of sediment transported to Pelekane Bay.
Assessing Beach and Dune Susceptibility in Coastal New Jersey
The Beach-Dune Susceptibility Assessment uses lidar and aerial imagery data to develop a vulnerability rating.
Using Lidar to Plan for Sea Level Rise in Oregon
Coastal lidar and aerial photography made it possible to create a diked-land vulnerability inventory to help Oregon prepare for sea-level rise.
Using Geospatial Techniques to Plan for Climate Change Impacts on Coastal Habitats in South Carolina
Partners used the Sea Level Affecting Marshes Model and Habitat Priority Planner to identify priority lands to conserve as sea level rises.
Using Lidar to Determine Bluff Recession Rates for Lake Erie
Lidar data replaced existing field-based methods used to measure bluff recession rates.
Mapping Flood Forecasts for Better Flood Planning in Texas Communities
High-resolution elevation data were used to create inundation maps for various flood levels at river forecast locations.
Visualizing Sea Level Rise to Engage Municipal Government Officials in Coastal South Carolina
Maps created using lidar-derived elevation data engage stakeholders and illustrate future impacts of tidal flooding caused by sea level rise.
Partnering to Map Oceans and Coasts for Multiple Needs in North Carolina
High-resolution digital aerial imagery and topographic lidar support multiple applications throughout NOAA.
Adapting to Sea Level Rise in Miami-Dade County, Florida
County departments identify specific actions to adapt to climate change through the Roadmap for Adapting to Coastal Risk training.
Visualizing the Impacts of Sea Level Rise in Delaware
Lidar data were used to generate maps showing the possible impacts of inundation using three different sea level rise scenarios.
Improving Inundation Prediction and Visualization Capabilities for the New England Coast
Coastal flood Web-mapping applications help coastal managers in New England visualize and enhance awareness of coastal flooding.
Modeling Future Development for Eastern North Carolina
C-CAP and lidar data were used to model potential future growth and land cover change.
Developing Consistent Methods for Mapping Sea Level Rise in Southeast Florida
Four counties joined forces with other agencies and organizations to agree upon consistent methods for mapping sea level rise to better prepare for sea level rise-related issues.
Assessing Hazard Vulnerability and Resilience in Coastal Communities of the Delaware Bay
New Jersey is engaging communities in hazard mitigation planning through the use of vulnerability assessment tools.
Identifying Areas for Water Quality Improvement Projects in Oahu, Hawaii
The Habitat Priority Planner and N-SPECT were used with lidar and land cover data to identify sites that affect water quality in the Ko’olaupoko region.
Identifying Areas Vulnerable to Sea Level Rise in Georgia
The Sea Level Rise and Coastal Flooding Impacts Viewer helped a barrier island community develop an adaptation plan to prepare for and adapt to sea level rise.
Coastal Elevation List Server
Provides subscribers with an easy way to stay in touch with the community of practitioners providing and applying high-resolution elevation data in coastal areas.
Mapping Inundation Uncertainty
Answers questions about primary errors in elevation data and outlines a new procedure that quantifies mapping errors.
Lidar Data Collected in Marshes: Its Error and Application for Sea Level Rise Modeling
Describes techniques to correct for lidar errors in marshes and how lidar is used in a common marsh migration model.
Introduction to Lidar
Provides an introduction to the concepts of lidar data with application examples for the Pacific region
Working with Lidar in ArcGIS 10 Tutorial
Provides guidance on working with lidar using ArcToolbox tools in ArcGIS 10
Lidar Provisioning Guidance for the Digital Coast Data Access Viewer
Provides guidance on the choices available when provisioning customized lidar data sets
Conservation Applications of Lidar
Lists webinars, tools, trainings, and other resources from the University of Minnesota Water Resources Center