Remote Sensing Coastal Change
The Dynamic Sands of North Core Banks
Hurricanes and Constant Change on Cape Lookout National Seashore
Fire plus Flood equals Beach
A new study combines decades of coastal satellite imagery with hydrologic and oceanographic data to look at how changes on land affect coastlines in Big Sur, California
Eyes in the Sky
How Satellite Imagery Transforms Shoreline Monitoring From “Data-Poor” to “Data-Rich”
We use remote-sensing technologies—such as aerial photography, satellite imagery, structure-from-motion (SfM) photogrammetry, and lidar (laser-based surveying)—to measure coastal change along U.S. shorelines.
Why measure coastal change?
Quantifying coastal change is essential for calculating trends in erosion, evaluating processes that shape coastal landscapes, and predicting how the coast will respond to future storms and sea-level rise, all critical for U.S. coastal communities.
Rapid developments have occurred in remote-sensing technologies during the 21st century. With our collaborators in and beyond the Department of the Interior, we seek to apply these technologies in innovative ways to advance understanding of coastal systems and their hazards.
Using photogrammetry to map natural hazards
USGS and partners collect extensive photographic data using airplanes and drones in coastal settings vulnerable to hazards such as landslides, coastal erosion, and powerful storms. Photos are processed with a digital technique called structure-from-motion photogrammetry to make accurate orthomosaics of the coastal landscape. Comparing the data with previously documented conditions provide “before” and “after” perspectives of the effects of those coastal hazards. These data often have immediate or time-sensitive relevance to public health and safety.
Tracking shoreline change from space
For decades, the USGS has monitored shoreline change in the United States by measuring and recording information at a study site (on the ground), using LIDAR (light detection and ranging) or GPS (Global Positioning System) surveys to meticulously collect data on beaches from coast to coast. These surveys are precise but costly, requiring lots of travel, hours of work, and expensive equipment. Using these methods to regularly monitor the 95,471 miles of coastline in the U.S. has been a monumental and impractical task.
That’s why USGS scientists are increasingly using Earth-observing satellites as their “eyes in the sky,” collecting and analyzing satellite imagery to study coastal change.
What traditionally was a labor- and time-intensive endeavor has been transformed by the high quality and quantity of data provided by satellite remote-sensing techniques. For example, global-scale studies of the world’s coastlines have been completed for a fraction of the cost of many labor-intensive field studies.
Seafloor mapping technology
To accurately measure seafloor change at millimeter-scale resolutions—in order to, say, monitor the growth and recovery of coral reefs—USGS scientists developed the “Structure-from-Motion Quantitative Underwater Imaging Device with 5 Cameras” system, or SQUID-5. With its five-camera array, SQUID-5 enables researchers to collect high-resolution images in shallow-water environments, which can be used to create complex three-dimensional seafloor maps with unprecedented accuracy and geolocation.
SQUID-5 is the product of a cross-center partnership. Ocean Engineer Gerry Hatcher of the Pacific Coastal and Marine Science Center (PCMSC) and Dave Zawada of the St. Petersburg Coastal and Marine Science Center (SPCMSC) are two of the lead scientists behind the creation of SQUID-5. The Remote Sensing Coastal Change team at PCMSC engineered the device, and the Processes Impacting Seafloor Change and Ecosystem Services team (PISCES) at SPCMSC is tasked with its deployment and data collection.
Using video imagery to study coastal change
USGS researchers analyze the imagery and video collected from camera installations known as CoastCams in order to remotely sense a range of processes, which include shoreline position, sandbar migration, rip-channel formation, wave run-up on the beach, alongshore current, and nearshore bathymetry.
USGS plans to install additional CoastCam systems at other U.S. locations. The knowledge gained will improve computer-derived simulations of coastal flooding and shoreline change that communities can use to plan for sea-level rise, changing storm patterns, and other threats to beaches.
Marconi Beach, Massachusetts
Nuvuk (Point Barrow), Alaska
Unalakleet, Alaska
Santa Cruz, California
Sunset State Beach, California
Tyndall Air Force Base, Florida
We are using video imagery, scanned aerial photographs, digital images collected from fixed-wing aircraft, and digital images collected from multi-rotor UAS to study coastal processes.
Data associated with this project
DUNEX topographic, bathymetric, and supporting GPS data collected in Pea Island National Wildlife Refuge, North Carolina 2020-2021 (ver. 1.1, May 2024) DUNEX topographic, bathymetric, and supporting GPS data collected in Pea Island National Wildlife Refuge, North Carolina 2020-2021 (ver. 1.1, May 2024)
Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09 Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09
Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09 Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09
Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation
Aerial Imagery of the North Carolina Coast: 2019-11-26 Aerial Imagery of the North Carolina Coast: 2019-11-26
Aerial Imagery of the North Carolina Coast: 2019-10-11 Aerial Imagery of the North Carolina Coast: 2019-10-11
Point clouds, bathymetric maps, and orthoimagery generated from overlapping lakebed images acquired with the SQUID-5 system near Dollar Point, Lake Tahoe, CA, March 2021 Point clouds, bathymetric maps, and orthoimagery generated from overlapping lakebed images acquired with the SQUID-5 system near Dollar Point, Lake Tahoe, CA, March 2021
Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian
Overlapping lakebed images and associated GNSS locations acquired near Dollar Point, Lake Tahoe, CA, March 2021 Overlapping lakebed images and associated GNSS locations acquired near Dollar Point, Lake Tahoe, CA, March 2021
Structure from motion products associated with UAS flights in Sandwich, Massachusetts between January 2016 - September 2017 Structure from motion products associated with UAS flights in Sandwich, Massachusetts between January 2016 - September 2017
Aerial Photogrammetry Data and Products of the North Carolina coast: 2018-10-06 to 2018-10-08, post-Hurricane Florence Aerial Photogrammetry Data and Products of the North Carolina coast: 2018-10-06 to 2018-10-08, post-Hurricane Florence
Below are multimedia items associated with this project.
Tracking Coastal Change with Photogrammetry
Monitoring coastal changes is important for the millions of people that live along coasts in the United States, particularly as climate change hastens coastal erosion by raising sea levels and fueling powerful storms. The USGS uses remote-sensing technologies—such as aerial photography, satellite imagery, structure-from-motion photogrammetry, and lidar (laser-based surveying)—to measure coastal...
Publications associated with this project
The future of coastal monitoring through satellite remote sensing The future of coastal monitoring through satellite remote sensing
Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs
Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event
Human-in-the-Loop segmentation of earth surface imagery Human-in-the-Loop segmentation of earth surface imagery
Modeling of barrier breaching during Hurricanes Sandy and Matthew Modeling of barrier breaching during Hurricanes Sandy and Matthew
Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement
Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation
A survey of storm-induced seaward-transport features observed during the 2019 and 2020 hurricane seasons A survey of storm-induced seaward-transport features observed during the 2019 and 2020 hurricane seasons
Littoral sediment from rivers: Patterns, rates and processes of river mouth morphodynamics Littoral sediment from rivers: Patterns, rates and processes of river mouth morphodynamics
Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide
Community for data integration 2018 funded project report Community for data integration 2018 funded project report
Accurate bathymetric maps from underwater digital imagery without ground control Accurate bathymetric maps from underwater digital imagery without ground control
Below are news stories associated with this project.
We use remote-sensing technologies—such as aerial photography, satellite imagery, structure-from-motion (SfM) photogrammetry, and lidar (laser-based surveying)—to measure coastal change along U.S. shorelines.
Why measure coastal change?
Quantifying coastal change is essential for calculating trends in erosion, evaluating processes that shape coastal landscapes, and predicting how the coast will respond to future storms and sea-level rise, all critical for U.S. coastal communities.
Rapid developments have occurred in remote-sensing technologies during the 21st century. With our collaborators in and beyond the Department of the Interior, we seek to apply these technologies in innovative ways to advance understanding of coastal systems and their hazards.
Using photogrammetry to map natural hazards
USGS and partners collect extensive photographic data using airplanes and drones in coastal settings vulnerable to hazards such as landslides, coastal erosion, and powerful storms. Photos are processed with a digital technique called structure-from-motion photogrammetry to make accurate orthomosaics of the coastal landscape. Comparing the data with previously documented conditions provide “before” and “after” perspectives of the effects of those coastal hazards. These data often have immediate or time-sensitive relevance to public health and safety.
Tracking shoreline change from space
For decades, the USGS has monitored shoreline change in the United States by measuring and recording information at a study site (on the ground), using LIDAR (light detection and ranging) or GPS (Global Positioning System) surveys to meticulously collect data on beaches from coast to coast. These surveys are precise but costly, requiring lots of travel, hours of work, and expensive equipment. Using these methods to regularly monitor the 95,471 miles of coastline in the U.S. has been a monumental and impractical task.
That’s why USGS scientists are increasingly using Earth-observing satellites as their “eyes in the sky,” collecting and analyzing satellite imagery to study coastal change.
What traditionally was a labor- and time-intensive endeavor has been transformed by the high quality and quantity of data provided by satellite remote-sensing techniques. For example, global-scale studies of the world’s coastlines have been completed for a fraction of the cost of many labor-intensive field studies.
Seafloor mapping technology
To accurately measure seafloor change at millimeter-scale resolutions—in order to, say, monitor the growth and recovery of coral reefs—USGS scientists developed the “Structure-from-Motion Quantitative Underwater Imaging Device with 5 Cameras” system, or SQUID-5. With its five-camera array, SQUID-5 enables researchers to collect high-resolution images in shallow-water environments, which can be used to create complex three-dimensional seafloor maps with unprecedented accuracy and geolocation.
SQUID-5 is the product of a cross-center partnership. Ocean Engineer Gerry Hatcher of the Pacific Coastal and Marine Science Center (PCMSC) and Dave Zawada of the St. Petersburg Coastal and Marine Science Center (SPCMSC) are two of the lead scientists behind the creation of SQUID-5. The Remote Sensing Coastal Change team at PCMSC engineered the device, and the Processes Impacting Seafloor Change and Ecosystem Services team (PISCES) at SPCMSC is tasked with its deployment and data collection.
Using video imagery to study coastal change
USGS researchers analyze the imagery and video collected from camera installations known as CoastCams in order to remotely sense a range of processes, which include shoreline position, sandbar migration, rip-channel formation, wave run-up on the beach, alongshore current, and nearshore bathymetry.
USGS plans to install additional CoastCam systems at other U.S. locations. The knowledge gained will improve computer-derived simulations of coastal flooding and shoreline change that communities can use to plan for sea-level rise, changing storm patterns, and other threats to beaches.
Marconi Beach, Massachusetts
Nuvuk (Point Barrow), Alaska
Unalakleet, Alaska
Santa Cruz, California
Sunset State Beach, California
Tyndall Air Force Base, Florida
We are using video imagery, scanned aerial photographs, digital images collected from fixed-wing aircraft, and digital images collected from multi-rotor UAS to study coastal processes.
Data associated with this project
DUNEX topographic, bathymetric, and supporting GPS data collected in Pea Island National Wildlife Refuge, North Carolina 2020-2021 (ver. 1.1, May 2024) DUNEX topographic, bathymetric, and supporting GPS data collected in Pea Island National Wildlife Refuge, North Carolina 2020-2021 (ver. 1.1, May 2024)
Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09 Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09
Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09 Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09
Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation
Aerial Imagery of the North Carolina Coast: 2019-11-26 Aerial Imagery of the North Carolina Coast: 2019-11-26
Aerial Imagery of the North Carolina Coast: 2019-10-11 Aerial Imagery of the North Carolina Coast: 2019-10-11
Point clouds, bathymetric maps, and orthoimagery generated from overlapping lakebed images acquired with the SQUID-5 system near Dollar Point, Lake Tahoe, CA, March 2021 Point clouds, bathymetric maps, and orthoimagery generated from overlapping lakebed images acquired with the SQUID-5 system near Dollar Point, Lake Tahoe, CA, March 2021
Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian
Overlapping lakebed images and associated GNSS locations acquired near Dollar Point, Lake Tahoe, CA, March 2021 Overlapping lakebed images and associated GNSS locations acquired near Dollar Point, Lake Tahoe, CA, March 2021
Structure from motion products associated with UAS flights in Sandwich, Massachusetts between January 2016 - September 2017 Structure from motion products associated with UAS flights in Sandwich, Massachusetts between January 2016 - September 2017
Aerial Photogrammetry Data and Products of the North Carolina coast: 2018-10-06 to 2018-10-08, post-Hurricane Florence Aerial Photogrammetry Data and Products of the North Carolina coast: 2018-10-06 to 2018-10-08, post-Hurricane Florence
Below are multimedia items associated with this project.
Tracking Coastal Change with Photogrammetry
Monitoring coastal changes is important for the millions of people that live along coasts in the United States, particularly as climate change hastens coastal erosion by raising sea levels and fueling powerful storms. The USGS uses remote-sensing technologies—such as aerial photography, satellite imagery, structure-from-motion photogrammetry, and lidar (laser-based surveying)—to measure coastal...
Publications associated with this project
The future of coastal monitoring through satellite remote sensing The future of coastal monitoring through satellite remote sensing
Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs
Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event
Human-in-the-Loop segmentation of earth surface imagery Human-in-the-Loop segmentation of earth surface imagery
Modeling of barrier breaching during Hurricanes Sandy and Matthew Modeling of barrier breaching during Hurricanes Sandy and Matthew
Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement
Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation
A survey of storm-induced seaward-transport features observed during the 2019 and 2020 hurricane seasons A survey of storm-induced seaward-transport features observed during the 2019 and 2020 hurricane seasons
Littoral sediment from rivers: Patterns, rates and processes of river mouth morphodynamics Littoral sediment from rivers: Patterns, rates and processes of river mouth morphodynamics
Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide Cliff Feature Delineation Tool and Baseline Builder version 1.0 user guide
Community for data integration 2018 funded project report Community for data integration 2018 funded project report
Accurate bathymetric maps from underwater digital imagery without ground control Accurate bathymetric maps from underwater digital imagery without ground control
Below are news stories associated with this project.