@article {3460,
	title = {Statewide USGS 3DEP Lidar Topographic Differencing Applied to Indiana, USA},
	journal = {Remote Sensing},
	volume = {14},
	year = {2022},
	pages = {847},
	abstract = {Differencing multi-temporal topographic data (radar, lidar, or photogrammetrically derived point clouds or digital elevation models\&mdash;DEMs) measures landscape change, with broad applications for scientific research, hazard management, industry, and urban planning. The United States Geological Survey\&rsquo;s 3D Elevation Program (3DEP) is an ambitious effort to collect light detection and ranging (lidar) topography over the United States\&rsquo; 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\&ndash;2013 and 2016\&ndash;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.},
	issn = {2072-4292},
	doi = {10.3390/rs14040847},
	url = {https://www.mdpi.com/2072-4292/14/4/847},
	author = {Scott, Chelsea and Beckley, Matthew and Phan, Minh and Zawacki, Emily and Crosby, Christopher and Nandigam, Viswanath and Arrowsmith, Ramon}
}
@article {3410,
	title = {Measuring change at Earth{\textquoteright}s surface: On-demand vertical and three- dimensional topographic differencing implemented in OpenTopography},
	journal = {Geosphere},
	year = {2021},
	month = {May},
	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 topo-graphic 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{\textendash}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 individ-ual data sets and hinders the community{\textquoteright}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.},
	issn = {1553-040X},
	doi = {10.1130/GES02259.1},
	url = {https://doi.org/10.1130/GES02259.1},
	author = {Scott, Chelsea and Phan, Minh and Nandigam, Viswanath and Crosby, Christopher and Arrowsmith, J. Ramon}
}
@inbook {3302,
	title = {Chapter 11 - Zero to a trillion: Advancing Earth surface process studies with open access to high-resolution topography},
	booktitle = {Remote Sensing of Geomorphology},
	series = {Developments in Earth Surface Processes},
	volume = {23},
	year = {2020},
	pages = {317 - 338},
	publisher = {Elsevier},
	organization = {Elsevier},
	abstract = {High-resolution topography (HRT) is a powerful observational tool for studying the Earth{\textquoteright}s surface, vegetation, and urban landscapes, with broad scientific, engineering, and education-based applications. Submeter resolution imaging is possible when collected with laser and photogrammetric techniques using the ground, air, and space-based platforms. Open access to these data and a cyberinfrastructure platform that enables users to discover, manage, share, and process then increases the impact of investments in data collection and catalyzes scientific discovery. Furthermore, open and online access to data enables broad interdisciplinary use of HRT across academia and in communities such as education, public agencies, and the commercial sector. OpenTopography, supported by the US National Science Foundation, aims to democratize access to Earth science-oriented, HRT data and processing tools. We utilize cyberinfrastructure, including large-scale data management, high-performance computing, and service-oriented architectures to provide efficient web-based visualization and access to large, HRT datasets. OT colocates data with processing tools to enable users to quickly access custom data and derived products for their application, with the ultimate goal of making these powerful data easier to use. OT{\textquoteright}s rapidly growing data holdings currently include 283 lidar and photogrammetric, point cloud datasets (>1.2 trillion points) covering 236,364km2. As a testament to OT{\textquoteright}s success, more than 86,000 users have processed over 5 trillion lidar points. This use has resulted in more than 290 peer-reviewed publications across numerous academic domains including Earth science, geography, computer science, and ecology.},
	keywords = {cyberinfrastructure, Geoinformatics, Geomorphology, Surface processes, topography},
	issn = {0928-2025},
	doi = {https://doi.org/10.1016/B978-0-444-64177-9.00011-4},
	url = {http://www.sciencedirect.com/science/article/pii/B9780444641779000114},
	author = {Christopher J. Crosby and J. Ram{\'o}n Arrowsmith and Viswanath Nandigam},
	editor = {Paolo Tarolli and Simon M. Mudd}
}
@article {3303,
	title = {The GAGE Data and Field Response to the 2019 Ridgecrest Earthquake Sequence},
	journal = {Seismological Research Letters},
	year = {2020},
	month = {03},
	abstract = {The July 2019 Ridgecrest sequence was observed in exquisite detail by the National Science Foundation{\textquoteright}s (NSF) Geodetic Facility for the Advancement of Geoscience (GAGE) Network of the Americas (NOTA), which has a dense array of continuously observing Global Navigation Satellite System (GNSS) stations and subarrays of strain and seismic borehole networks in southern California. Two hundred and eighteen GNSS and 10 borehole NOTA stations within 250~km of the epicentral area recorded the sequence. Special downloads of high-rate data from sites within a specified radius of each earthquake were initiated by the GAGE Facility for the time period of 1.5 days before and 1.5 days after each event to ensure transient deformation was captured at a high-temporal resolution. Rapid field deployments of temporary GNSS stations were carried out by UNAVCO in support of NSF-funded investigators and U.S. Geological Survey activities. The data recorded by the permanent network are available from the GAGE Facility{\textquoteright}s Data Center at UNAVCO, data recorded at the temporary campaign sites will also be made available on completion of data collection. The OpenTopography project, of which UNAVCO is a partner, released a preliminary pre-event digital surface model of the area covering the Ridgecrest earthquake sequence to support the ongoing imaging efforts to measure the deformation from these events. In this article, we document the significant amount of detailed, open-access geodetic data available from GAGE to study this sequence and advance our understanding of earthquake processes, the geodynamics of the California eastern shear zone, and our capacity to respond to damaging earthquakes for research.},
	issn = {0895-0695},
	doi = {10.1785/0220190283},
	url = {https://doi.org/10.1785/0220190283},
	author = {Mattioli, Glen S. and Phillips, David A. and Hodgkinson, Kathleen M. and Walls, Chris and Mencin, David J. and Bartel, Beth A. and Charlevoix, Donna J. and Crosby, Chris and Gottlieb, Michael J. and Henderson, Brent and Johnson, Wade and Maggert, David and Mann, Doerte and Meertens, Charles M. and Normandeau, Jim and Pettit, Joe and Puskas, Christine M. and Rowan, Linda and Sievers, Charles and Zaino, Annie}
}
@article {3399,
	title = {Measuring success for a future vision: Defining impact in science gateways/virtual research environments},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {n/a},
	year = {2020},
	abstract = {Summary Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations.},
	keywords = {futuristic vision, measuring impact, science gateways, success metrics, virtual research environments},
	doi = {https://doi.org/10.1002/cpe.6099},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6099},
	author = {Calyam, Prasad and Wilkins-Diehr, Nancy and Miller, Mark and Brookes, Emre H. and Arora, Ritu and Chourasia, Amit and Jennewein, Douglas M. and Nandigam, Viswanath and Drew LaMar, M. and Cleveland, Sean B. and Newman, Greg and Wang, Shaowen and Zaslavsky, Ilya and Cianfrocco, Michael A. and Ellett, Kevin and Tarboton, David and Jeffery, Keith G. and Zhao, Zhiming and Gonz{\'a}lez-Aranda, Juan and Perri, Mark J. and Tucker, Greg and Candela, Leonardo and Kiss, Tamas and Gesing, Sandra}
}
@article {3372,
	title = {OpenAltimetry-rapid analysis and visualization of Spaceborne altimeter data},
	journal = {Earth Science Informatics},
	year = {2020},
	pages = {1{\textendash}10},
	author = {Khalsa, Siri Jodha S and Borsa, Adrian and Nandigam, Viswanath and Phan, Minh and Lin, Kai and Crosby, Christopher and Fricker, Helen and Baru, Chaitan and Lopez, Luis}
}
@article {3340,
	title = {Leading Progress in Digital Terrain Analysis and Modeling},
	journal = {ISPRS International Journal of Geo-Information},
	volume = {8},
	year = {2019},
	pages = {372},
	abstract = {Digital Terrain analysis (DTA) and modeling has been a flourishing interdisciplinary field for decades, with applications in hydrology, geomorphology, soil science, engineering projects and computer sciences. Currently, DTA is characterized by a proliferation of multispectral data from new sensors and platforms, driven by regional and national governments, commercial businesses, and scientific groups, with a general trend towards data with higher spatial, spectral or temporal resolutions. Deriving meaningful and interpretable products from such a large pool of data sources sets new challenges. The articles included in this special issue (SI) focuses on terrain analysis applications that advance the fields of hydrology, geomorphology, soil science, geographic information software (GIS), and computer science. They showcase challenging examples of DTA tackling different subjects or different point of views on the same subject.},
	issn = {2220-9964},
	doi = {10.3390/ijgi8090372},
	url = {https://www.mdpi.com/2220-9964/8/9/372},
	author = {Sofia, Giulia and Eltner, Anette and Nikolopoulos, Efthymios and Crosby, Christopher}
}
@article {3329,
	title = {Measuring Success: How Science Gateways Define Impact},
	journal = {Proceedings of Gateways 2019},
	year = {2019},
	month = {Sep},
	url = {http://par.nsf.gov/biblio/10116256},
	author = {Wilkins-Diehr, Nancy and Miller, Mark and Brookes, Emre H and Arora, Ritu and Chourasia, Amit and Calyam, Prasad and Jennewein, Douglas M and Nandigam, Viswanath and LaMar, M Drew and Cleveland, Sean B and et al.}
}
@conference {3242,
	title = {Advancing Analysis of High Resolution Topography Using Distributed HPC Resources in OpenTopography},
	booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact},
	year = {2017},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {Data, HPC, LIDAR, science gateway, Software, topography},
	isbn = {978-1-4503-5272-7},
	doi = {10.1145/3093338.3093345},
	url = {http://doi.acm.org/10.1145/3093338.3093345},
	author = {Nandigam, Viswanath and Crosby, Christopher and Arrowsmith, Ramon and Phan, Minh and Lin, Kai and Gross, Benjamin}
}
@conference {3225,
	title = {Scaling GIS Analysis Tasks from the Desktop to the Cloud Utilizing Contemporary Distributed Computing and Data Management Approaches: A Case Study of Project-based Learning and Cyberinfrastructure Concepts},
	booktitle = {Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale},
	year = {2016},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {CyberGIS, GDAL, GRASS, Makeflow, Work Queue},
	isbn = {978-1-4503-4755-6},
	doi = {10.1145/2949550.2949573},
	url = {http://doi.acm.org/10.1145/2949550.2949573},
	author = {Swetnam, T. L. and Pelletier, J. D. and Rasmussen, C. and Callahan, N. R. and Merchant, N. and Lyons, E. and Rynge, M. and Liu, Y. and Nandigam, V. and Crosby, C.}
}
@proceedings {3087,
	title = {Leveraging XSEDE HPC resources to address computational challenges with high-resolution topography data},
	year = {2014},
	address = {Atlanta, GA},
	url = {http://dl.acm.org/citation.cfm?doid=2616498.2616564},
	author = {Choonhan Youn and Viswanath Nandigam and Minh Phan and David Tarboton and Nancy Wilkins-Diehr and Chaitan Baru and Christopher J Crosby and Anand Padmanabhan and Shaowen Wang}
}
@article {3089,
	title = {Seamless Synthetic Aperture Radar Archive for Interferometry Analysis},
	journal = {ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
	volume = {XL-1},
	year = {2014},
	pages = {65{\textendash}72},
	doi = {10.5194/isprsarchives-XL-1-65-2014},
	url = {http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1/65/2014/},
	author = {Baker, S. and Chaitan Baru and Bryson, G. and Buechler, B. and Christopher J Crosby and Fielding, E. and Meertens, C. and Nicoll, J. and Choonhan Youn}
}
@article {3090,
	title = {CyberGIS software: a synthetic review and integration roadmap},
	journal = {International Journal of Geographical Information Science},
	volume = {27},
	year = {2013},
	pages = {2122-2145},
	doi = {10.1080/13658816.2013.776049},
	url = {http://dx.doi.org/10.1080/13658816.2013.776049},
	author = {Shaowen Wang and Luc Anselin and Budhendra Bhaduri and Christopher J Crosby and Michael F. Goodchild and Yan Liu and Timothy L. Nyerges}
}
@inbook {3091,
	title = {Online access and processing of LiDAR topography data},
	booktitle = {Geoinformatics},
	year = {2011},
	pages = {251{\textendash}265},
	publisher = {Cambridge University Press},
	organization = {Cambridge University Press},
	isbn = {9780511976308},
	url = {http://dx.doi.org/10.1017/CBO9780511976308.017},
	author = {Christopher J Crosby and Ram{\'o}n J Arrowsmith and Viswanath Nandigam and Chaitan Baru},
	editor = {G. Randy Keller and Chaitan Baru}
}
@conference {3093,
	title = {OpenTopography: A Services Oriented Architecture for Community Access to LIDAR Topography},
	booktitle = {Proceedings of the 2Nd International Conference on Computing for Geospatial Research \& Applications},
	year = {2011},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {cyberinfrastructure, LIDAR, SOA},
	isbn = {978-1-4503-0681-2},
	doi = {10.1145/1999320.1999327},
	url = {http://doi.acm.org/10.1145/1999320.1999327},
	author = {Sriram Krishnan and Christopher J Crosby and Viswanath Nandigam and Phan, Minh and Cowart, Charles and Chaitan Baru and Ram{\'o}n J Arrowsmith}
}
@inbook {3095,
	title = {Database Design for High-Resolution LIDAR Topography Data},
	booktitle = {Scientific and Statistical Database Management},
	series = {Lecture Notes in Computer Science},
	volume = {6187},
	year = {2010},
	pages = {151-159},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	isbn = {978-3-642-13817-1},
	doi = {10.1007/978-3-642-13818-8_12},
	url = {http://dx.doi.org/10.1007/978-3-642-13818-8_12},
	author = {Viswanath Nandigam and Chaitan Baru and Christopher J Crosby}
}
@inbook {3097,
	title = {A Three Tier Architecture for LiDAR Interpolation and Analysis},
	booktitle = {Computational Science {\textendash} ICCS 2006},
	series = {Lecture Notes in Computer Science},
	volume = {3993},
	year = {2006},
	pages = {920-927},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	isbn = {978-3-540-34383-7},
	doi = {10.1007/11758532_123},
	url = {http://dx.doi.org/10.1007/11758532_123},
	author = {Jaeger-Frank, Efrat and Christopher J Crosby and Memon, Ashraf and Viswanath Nandigam and Ram{\'o}n J Arrowsmith and Conner, Jeffery and Ilkay Altintas and Chaitan Baru},
	editor = {Alexandrov, VassilN. and van Albada, GeertDick and Sloot, PeterM.A. and Dongarra, Jack}
}
