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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. With TikTok emerging as one of the most popular social mediaplatforms, there is significant potential for science communicators tocapitalize on this success and to share their science with a broad, engagedaudience. While videos of chemistry and physics experiments are prominentamong educational science content on TikTok, videos related to thegeosciences are comparatively lacking, as is an analysis of what types ofgeoscience videos perform well on TikTok. To increase the visibility of thegeosciences and geophysics on TikTok and to determine best strategies forgeoscience communication on the app, we created a TikTok account called“Terra Explore” (@TerraExplore). The Terra Explore account is a jointeffort between science communication specialists at UNAVCO, IRIS(Incorporated Research Institutions for Seismology), and OpenTopography. Weproduced 48 educational geoscience videos over a 4-month period betweenOctober 2021 and February 2022. We evaluated the performance of each videobased on its reach, engagement, and average view duration to determine thequalities of a successful video. Our video topics primarily focused onseismology, earthquakes, topography, lidar (light detection and ranging),and GPS (Global Positioning System), in alignment with our organizationalmissions. Over this time period, our videos garnered over 2 million totalviews, and our account gained over 12 000 followers. The videos thatreceived the most views received nearly all (∼ 97 %) oftheir views from the For You page, TikTok's algorithmic recommendation feed. Wefound that short videos (< 30 s) had a high average view duration,but longer videos (> 60 s) had the highest engagement rates.Lecture-style videos that were approximately 60 s in length had moresuccess in both reach and engagement. Our videos that received the highestnumber of views featured content that was related to a recent newsworthyevent (e.g., an earthquake) or that explained location-based geology of arecognizable area. Our results highlight the algorithm-driven nature ofTikTok, which results in a low barrier to entry and success for new sciencecommunication creators. 

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.