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Most remote-sensing techniques rely on imagery taken at a specific time and place. What if researchers could crowd-source this data from volunteers taking cell phone-photographs at the beach?

Monitoring and characterizing coastal change requires detailed and repeated observations over the course of months to decades. As climate change accelerates sea-level rise and contributes to more frequent and intense landscape disturbances such as wildfire and flooding, methods to efficiently map and monitor landscapes with high accuracy are needed to better understand how these processes will affect coastal areas, now and in the future. 

Remote-sensing techniques such as analyzing and interpreting satellite imagery; collecting aerial imagery with planes or drones to create orthomosaics; and using airborne light detection and ranging (LIDAR) are widely used by scientists to study coastal change. While these tools are immensely powerful and always evolving, they can be time-intensive, limited to small geographies, or lack spatial and/or temporal resolution.  

Most remote-sensing techniques rely on imagery taken at a specific time and place. What if researchers could crowd-source this data from volunteers taking cell phone-photographs at the beach? 

A new study from USGS and Washington Sea Grant collaborators outlines a method to monitor coastal change using structure-from-motion (SfM) imagery created from crowd-sourced photographs taken from beaches or nearshore waters. The study focuses on coastal cliffs and bluffs—structurally complex environments that are often more challenging to monitor using other remote-sensing methods.

 Examples of photoset dense point clouds using 3D, 4D, and Fixed-Floating alignment approaches
Examples of photoset dense point clouds using different alignment approaches outlined in the study.

“Remote-sensing technology is improving constantly and allows us to study coastal change processes in a variety of different environments. But with coastal bluffs and cliffs, there are still lots of unanswered questions about processes of erosion,” said Phillipe Wernette, USGS Research Geologist and lead author of the study. “Finding data that captures those processes has traditionally been difficult to get. Utilizing a way to crowd-source SfM imagery, combined with LIDAR data, gets us closer to the frequency of data collection we need to better understand these processes.” 

Building on other citizen science coastal monitoring initiatives such as CoastSnap, this low-cost approach to landscape monitoring represents an opportunity for researchers and managers to engage stakeholders in citizen-science projects with a low barrier to entry. Because the method does not rely on having precise camera location or rotation information, it is possible to utilize a variety of crowd-sourced images, including some combination of images and sets of images acquired with camera phones. 

“Perhaps the biggest pitfall with this approach is that it still requires a researcher with SfM software to oversee and process the crowd-sourced photographic data,” said Wernette. “Someone could adapt this method and build an app that collects photos from a community of users and processes them with machine learning, for instance. The hope is that other interested parties take this concept and build on it.” 

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