Digital twins have long been associated with buildings, utilities, and transportation networks: complex assets with long lifecycles and enormous volumes of operational data. But as remote sensing advances, that same capability is opening up for natural systems: rivers that migrate, coastlines that erode, wetlands that expand and contract, and watersheds that respond differently to every storm, drought, or wildfire.
In this GoGeomatics article, NV5’s Sven Cowan explores how digital twin technology is being applied to natural environments, using tools like topobathymetric lidar, multibeam sonar, and satellite imagery to build living, continuously updated representations rather than relying on one-time snapshots. He also examines how AI is helping specialists interpret increasingly data-rich environmental models, so they can spend less time searching through data and more time acting on what it reveals, whether that’s tracking flood risk as a channel shifts, monitoring shoreline erosion along a coastline, measuring how a habitat restoration project is actually performing, or understanding how upstream changes ripple downstream through a watershed.