An Open-Source Workflow for Point Cloud based Geomorphic Change Detection and Sediment Budget Analysis
Point cloud-based methods to enhance the spatial resolution and precision of change detection workflows.
Remote sensing-based topographic datasets are widely used to detect surface changes in landscape features, such as from riverbank erosion or flood sediment deposition. Change detection methods use elevation products derived from 3D datasets that are increasingly collected as point clouds from LiDAR and Structure-from-Motion (SfM) photogrammetry. However, many popular tools do not fully utilize the 3D point cloud data for geomorphic change detection as previous methods were developed for use with older raster-based (2.5D) products and these data provide an easier mechanism for summarizing results. Our project seeks to bridge this gap, by developing an open-source workflow that integrates existing point cloud change detection tools with automated processes for summarizing and analyzing change results. The goal is to offer functionality similar to raster-based toolsets that takes advantage of the precision and structure of point cloud datasets. With these tools, we expect change detection to be more 1) accessible, 2) practical, and 3) intuitive for end users while contributing to reproducibility and accuracy of results. Our approach supports CDI principles by promoting open, scalable, and collaborative tools for better decision-making.
Point cloud-based methods to enhance the spatial resolution and precision of change detection workflows.
Remote sensing-based topographic datasets are widely used to detect surface changes in landscape features, such as from riverbank erosion or flood sediment deposition. Change detection methods use elevation products derived from 3D datasets that are increasingly collected as point clouds from LiDAR and Structure-from-Motion (SfM) photogrammetry. However, many popular tools do not fully utilize the 3D point cloud data for geomorphic change detection as previous methods were developed for use with older raster-based (2.5D) products and these data provide an easier mechanism for summarizing results. Our project seeks to bridge this gap, by developing an open-source workflow that integrates existing point cloud change detection tools with automated processes for summarizing and analyzing change results. The goal is to offer functionality similar to raster-based toolsets that takes advantage of the precision and structure of point cloud datasets. With these tools, we expect change detection to be more 1) accessible, 2) practical, and 3) intuitive for end users while contributing to reproducibility and accuracy of results. Our approach supports CDI principles by promoting open, scalable, and collaborative tools for better decision-making.