Measuring change at Earth's surface; on-demand vertical and three-dimensional topographic differencing implemented in OpenTopography

• Published in: Geosphere (Boulder, Colo.) Vol. 17; no. 4; pp. 1318 - 1332
• Main Authors: Scott, Chelsea, Phan, Minh, Nandigam, Viswanath, Crosby, Christopher, Arrowsmith, J. Ramon
• Format: Journal Article
• Language: English
• Published: Geological Society of America 01.08.2021
• Subjects: algorithms; data bases; data processing; digital terrain models; errors; Geomorphology; landform evolution; landscapes; methods; OpenTopography; surface features; three-dimensional models; topography; uncertainty
• ISSN: 1553-040X; 1553-040X
• DOI: 10.1130/GES02259.1

Topographic differencing measures landscape change by comparing multitemporal high-resolution topography data sets. Here, we focused on two types of topographic differencing: (1) Vertical differencing is the subtraction of digital elevation models (DEMs) that span an event of interest. (2) Three-dimensional (3-D) differencing measures surface change by registering point clouds with a rigid deformation. We recently released topographic differencing in OpenTopography where users perform on-demand vertical and 3-D differencing via an online interface. OpenTopography is a U.S. National Science Foundation-funded facility that provides access to topographic data and processing tools. While topographic differencing has been applied in numerous research studies, the lack of standardization, particularly of 3-D differencing, requires the customization of processing for individual data sets and hinders the community's ability to efficiently perform differencing on the growing archive of topography data. Our paper focuses on streamlined techniques with which to efficiently difference data sets with varying spatial resolution and sensor type (i.e., optical vs. light detection and ranging [lidar]) and over variable landscapes. To optimize on-demand differencing, we considered algorithm choice and displacement resolution. The optimal resolution is controlled by point density, landscape characteristics (e.g., leaf-on vs. leaf-off), and data set quality. We provide processing options derived from metadata that allow users to produce optimal high-quality results, while experienced users can fine tune the parameters to suit their needs. We anticipate that the differencing tool will expand access to this state-of-the-art technology, will be a valuable educational tool, and will serve as a template for differencing the growing number of multitemporal topography data sets.