Search for: All records

Differencing multi-temporal topographic data (radar, lidar, or photogrammetrically derived point clouds or digital elevation models—DEMs) measures landscape change, with broad applications for scientific research, hazard management, industry, and urban planning. The United States Geological Survey’s 3D Elevation Program (3DEP) is an ambitious effort to collect light detection and ranging (lidar) topography over the United States’ lower 48 and Interferometric Synthetic Aperture Radar (IfSAR) in Alaska by 2023. The datasets collected through this program present an important opportunity to characterize topography and topographic change at regional and national scales. We present Indiana statewide topographic differencing results produced from the 2011–2013 and 2016–2020 lidar collections. We discuss the insights, challenges, and lessons learned from conducting large-scale differencing. Challenges include: (1) designing and implementing an automated differencing workflow over 94,000 km2 of high-resolution topography data, (2) ensuring sufficient computing resources, and (3) managing the analysis and visualization of the multiple terabytes of data. We highlight observations including infrastructure development, vegetation growth, and landscape change driven by agricultural practices, fluvial processes, and natural resource extraction. With 3DEP and the U.S. Interagency Elevation Inventory data, at least 37% of the Contiguous 48 U.S. states are already covered by repeat, openly available, high-resolution topography datasets, making topographic differencing possible. 

Abstract 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. 

We apply a deep learning model to segment and identify rock characteristics based on a Structure‐from‐Motion orthomap and digital elevation model of a rocky fault scarp in the Volcanic Tablelands, Eastern California, USA. By post‐processing the deep learning results, we build a semantic rock map and analyse the rock trait distributions. The resulting semantic map contains nearly 230 000 rocks with effective diameters ranging from 2 to 250 cm. Rock trait distributions provide a new perspective on rocky fault scarp development and extend past research on scarp geometry including slope, height and length. Heatmaps indicate rock size spatial distributions on the fault scarp and surrounding topographic flats. Median grain size changes perpendicular to the fault scarp trace, with the largest rocks on the downslope proximal to the scarp footwall. Correlation analyses illustrate the relationship between rock trait statistics and fault scarp geomorphology. Local fault scarp height correlates with median grain size (  = 0.6), the mean grain size of the largest rocks (  = 0.8) and the ratio of the number of small to large rocks (  = 0.4). The positive correlation (  = 0.8) between local fault scarp height and standard deviation of grain size suggests that rocks on a higher fault scarp are less well sorted. The correlation analysis between fault scarp height and rock orientation statistics supports a particle transportation model in which locally higher fault scarps have relatively more rocks with long axes parallel to fault scarp trace because rocks have a larger distance to roll and orient the long axes. Our work demonstrates a data‐driven approach to geomorphology based on rock trait distributions, promising a greater understanding of fault scarp formation and tectonic activity, as well as many other applications for which granulometry is an indicator of process.

 

Fault creep reduces seismic hazard and serves as a window into plate boundary processes; however, creep rates are typically constrained with sparse measurements. We use differential lidar topography (11–13 year time span) to measure a spatially dense surface deformation field along a 150 km section of the Central San Andreas and Calaveras faults. We use an optimized windowed‐iterative‐closest‐point approach to resolve independent creep rates every 400 m at 1–2 km apertures. Rates vary from <10 mm/year along the creeping fault ends to over 30 mm/year along much of the central 100 km of the fault. Creep rates are 3–8 mm/year higher than most rates from alignment arrays and creepmeters, likely due to the larger aperture of the topographic differencing. Creep is often focused along discrete fault traces, but strain is sometimes distributed in areas of complex fault geometry, such as Mustang Ridge. Our observations constrain shallow seismic moment accumulation and the location of the creeping fault trace.

 

Imaging tectonic creep along active faults is critical for measuring strain accumulation and ultimately understanding the physical processes that guide creep and the potential for seismicity. We image tectonic deformation along the central creeping section of the San Andreas Fault at the Dry Lake Valley paleoseismic site (36.468°N, 121.055°W) using three data sets with varying spatial and temporal scales: (1) an Interferometric Synthetic Aperture Radar (InSAR) velocity field with an ~100‐km footprint produced from Sentinel‐1 satellite imagery, (2) light detection and ranging (lidar) and structure‐from‐motion 3‐D topographic differencing that resolves a decade of deformation over a 1‐km aperture, and (3) surface fractures that formed over the 3‐ to 4‐m wide fault zone during a drought from late 2012 to 2014. The InSAR velocity map shows that shallow deformation is localized to the San Andreas Fault. We demonstrate a novel approach for differencing airborne lidar and structure‐from‐motion topography that facilitates resolving deformation along and adjacent to the San Andreas Fault. The 40‐m resolution topographic differencing resolves a 2.5 ± 0.2 cm/yr slip rate localized to the fault. The opening‐mode fractures accommodate cm/yr of fault slip. A 90% ± 30% of the 1‐km aperture deformation is accommodated over the several meter‐wide surface trace of the San Andreas Fault. The extension direction inferred from the opening‐mode fractures and topographic differencing is 40°–48° from the local trend of the San Andreas Fault. The localization of deformation likely reflects the well‐oriented and mature fault.

 

Observations of fault geometry and cumulative slip distribution serve as critical constraints on fault behavior over temporal scales ranging from a single earthquake to a fault’s complete history. The increasing availability of high-resolution topography (at least one observation per square meter) from air- and spaceborne platforms facilitates measuring geometric properties along faults over a range of spatial scales. However, manually mapping faults and measuring slip or scarp height is time-intensive, limiting the use of rich topography datasets. To substantially decrease the time required to analyze fault systems, we developed a novel approach for systematically mapping dip-slip faults and measuring scarp height. Our MATLAB algorithm detects fault scarps from topography by identifying regions of steep relief given length and slope parameters calibrated from a manually drawn fault map. We applied our algorithm to well-preserved normal faults in the Volcanic Tablelands of eastern California using four datasets: (1) structure-from-motion topography from a small uncrewed aerial system (sUAS; 20 cm resolution), (2) airborne laser scanning (25 cm), (3) Pléiades stereosatellite imagery (50 cm), and SRTM (30 m) topography. The algorithm and manually mapped fault trace architectures are consistent for primary faults, although can differ for secondary faults. On average, the scarp height profiles are asymmetric, suggesting fault lateral propagation and along-strike variations in the fault’s mechanical properties. We applied our algorithm to Arizona and Utah with a specific focus on the normal Hurricane fault where the algorithm mapped faults and other prominent topographic features well. This analysis demonstrates that the algorithm can be applied in a variety of geomorphic and tectonic settings.

 

Observations of surface deformation within 1–2 km of a surface rupture contain invaluable information about the coseismic behavior of the shallow crust. We investigate the oblique strike‐slip 2016 M7 Kumamoto, Japan, earthquake, which ruptured the Futagawa‐Hinagu Fault. We solve for variable fault slip in an inversion of differential lidar topography, satellite optical image correlation, and Interferometric Synthetic Aperture Radar (InSAR)‐derived surface displacements. The near‐fault differential lidar pose several challenges. The model fault geometry must follow the surface trace at the sub‐kilometer scale. Integration of displacement datasets with different sensitivities to the 3D deformation field and varying spatial distribution permits additional complexity in the inferred slip but introduces ambiguity that requires careful selection of the regularization. We infer a M_wearthquake. The maximum slip of 6.9 m occurred at 4.5‐km depth, suggesting an on‐fault slip deficit in the upper several kilometers of the crust that likely reflects distributed and inelastic deformation within the shallow fault zone.