Terrain change time machine: creating LiDAR-like historical elevation data
This project leverages the USGS's photo archive and Structure from Motion algorithms to derive historical elevation and geomorphic data to catalyze a long-term landscape change analysis of a conservation area. We propose to create a best practices workflow and establish suitable accuracy metrics.
This project will establish best practices for developing historical data products from aerial photo archives using open-source Structure from Motion (SfM) methods. Detailed and accurate historical data are crucial to supporting conservation efforts and characterizing anthropogenic impacts in dynamic landscapes. The USGS Aerial Photo Single Frame archive’s many historic, publicly available aerial photographs can be leveraged to create LiDAR-like terrain data and high-resolution imagery from the past. What is needed to fully capitalize on this vast photo collection is an SfM toolkit establishing a workflow for consistent and optimal extraction of historical quantitative data. The foundational materials developed by this project will enable a broader group of consumers to harness and expand the potential applications of photo archives. This work supports CDI 2022 themes of enabling data connection, data readiness, and data comprehension and contributes to CDI Science Support Framework elements ‘Data & Information Assets’ and ‘Computational Tools & Services.’
Investigating geomorphic change using a structure from motion elevation model created from historical aerial imagery: A case study in northern Lake Michigan, USA
This project leverages the USGS's photo archive and Structure from Motion algorithms to derive historical elevation and geomorphic data to catalyze a long-term landscape change analysis of a conservation area. We propose to create a best practices workflow and establish suitable accuracy metrics.
This project will establish best practices for developing historical data products from aerial photo archives using open-source Structure from Motion (SfM) methods. Detailed and accurate historical data are crucial to supporting conservation efforts and characterizing anthropogenic impacts in dynamic landscapes. The USGS Aerial Photo Single Frame archive’s many historic, publicly available aerial photographs can be leveraged to create LiDAR-like terrain data and high-resolution imagery from the past. What is needed to fully capitalize on this vast photo collection is an SfM toolkit establishing a workflow for consistent and optimal extraction of historical quantitative data. The foundational materials developed by this project will enable a broader group of consumers to harness and expand the potential applications of photo archives. This work supports CDI 2022 themes of enabling data connection, data readiness, and data comprehension and contributes to CDI Science Support Framework elements ‘Data & Information Assets’ and ‘Computational Tools & Services.’