TY - JOUR
T1 - Measuring change at Earth's surface
T2 - On-demand vertical and three-dimensional topographic differencing implemented in OpenTopography
AU - Scott, Chelsea
AU - Phan, Minh
AU - Nandigam, Viswanath
AU - Crosby, Christopher
AU - Arrowsmith, J. Ramon
N1 - Funding Information:
C. Scott was supported by U.S. National Science Foundation Postdoctoral Fellowship 1625221 and by the School of Earth and Space Exploration at Arizona State University. M. Phan, V. Nandigam, C. Crosby, and J R. Arrowsmith acknowledge grants 1948997, 1948994, and 1948857 from the U.S. National Science Foundation. We thank Steve Delong, Mike Oskin, Andrea Ham-pel (editor), and three anonymous reviewers for constructive comments on the manuscript. We thank Professor Nick Hedley at Simon Fraser University, Canada, for sharing his experience about using the topographic differencing tool in his class.
Publisher Copyright:
© 2021 The Authors. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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U2 - 10.1130/GES02259.1
DO - 10.1130/GES02259.1
M3 - Article
AN - SCOPUS:85112842540
SN - 1553-040X
VL - 17
SP - 1318
EP - 1332
JO - Geosphere
JF - Geosphere
IS - 4
ER -