"Cole, K. L., Cannella, J., Coats, L., Arundel, S.T., Mead, J. and Fisher, J. 2008. “The biogeographic histories of Pinus monophylla and Pinus edulis over the last 50,000 years.” Journal of Biogeography 35:257-269.
Samantha T Arundel, Ph.D.
Biography
Sam is a Research Geographer with the Center of Excellence for Geospatial Information Science. She received PhD and MA degrees in Geography from Arizona State University and a BS in Geography from Northern Arizona University in Flagstaff, where she became an assistant professor in 2000 and then associate professor in 2004. Since joining the USGS in 2009, Sam has devoted her career to automating terrain mapping and analysis. Presently, her research focuses on AI methods to map natural features for the nation.
Education
BS Geography, Northern Arizona University, 1990
MA Geography, Arizona State University, 1994
PhD Geography, Arizona State University, 2000
Professional Experience
Assistant Professor of Geography, Northern Arizona University, 2000 - 2004
Associate Professor of Geography, Northern Arizona University, 2004 - 2009
Geographer, Applied Research, U.S. Geological Survey, 2009 – 2015
Research Geographer, Center of Excellence for Geographic Information Science, 2015 - present
Affiliations
American Society for Photogrammetrical Engineering and Remote Sensing - Member
Cartography and Geographic Information Society - Board Member
Association of American Geographers - Member
Non-USGS Partners
Wenwen Li, Arizona State University
Jarlath O'Neil-Dunne, University of Vermont
Gaurav Sinha, Ohio University
Other USGS Publications
Sinha G., Arundel, S.T., Stewart, K., Mark, D., Hahmann, T., Romero, B., Sorokine, A., Usery, E. L., and McKenzie, G. 2018. A Reference Landform Ontology for Automated Delineation of Depression Landforms from DEMs. In: Fogliaroni P., Ballatore A., Clementini E. (eds) Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017). COSIT 2017 2017. Lecture Notes in Geoinformation and Cartography. Springer, Champagne.
Arundel, S.T. and Sinha, G. 2018. Validating GEOBIA based terrain segmentation and classification for automated delineation of cognitively salient landforms. In: Fogliaroni P., Ballatore A., Clementini E. (eds) Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017). COSIT 2017 2017. Lecture Notes in Geoinformation and Cartography. Springer, Champagne.
Arundel, S. T., Bulen, A. N., Adkins, K. F., Brown, R. E., Lowe, A. J., Mantey, K. S., and Phillips, L. A. 2017. Assimilation of the National Elevation Dataset and Launch of the 3D Elevation Program through the USGS Spatial Data Infrastructure. International Journal of Cartography, 1-22. ttp://dx.doi.org/10.1080/23729333.2017.1288533
Arundel, S.T., Thiem, P.T. and Constance, E.W. 2017. Automated Extraction of Hydrographically-Corrected Contours for the Conterminous United States: The U.S. Geological Survey US Topo Product. Cartography and Geographic Information Science, 1-25. http://dx.doi.org/10.1080/15230406.2016.1230027
Arundel, S.T. 2016. Pairing semantics and object-based image analysis for national terrain mapping - a first-case scenario of cirques. In: GEOBIA 2016 : Solutions and Synergies., 14 September 2016 - 16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC). https://doi.org/10.3990/2.431
Gesch, D.B., Evans, G.A., Oimoen, M.J. and Arundel, S.T. In Press. “The National Elevation Dataset.” DEM Users Manual, 3rd Edition (D.F. Maune, editor), American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland.
S.T. Arundel, Phillips, L.A., Lowe, A.J., Bobinmyer, J., Mantey, K.S., Dunn, C.A., Constance, E.W. and Usery, E.L. 2015. Preparing The National Map for the 3D Elevation Program – products, process and research, Cartography and Geographic Information Science, 42:sup1, 40-53.
Arundel, S.T., Phillips, L.A., Lowe, A. J., Bobinmyer, J., Mantey, K.S., Dunn, C. A., Constance, E. W. and Usery, E. L. 2016. Research and Development for The National Map – Preparing for the 3D Elevation Program. Cartography and Geographic Information Science.
Stanislawski, L.V., Doumbouya, A.T., Miller-Corbett, C.D., Buttenfield, B.P. and Arundel, S.T. 2012. Scaling stream densities for hydrologic generalizations. Proceedings of the Seventh International Conference on Geographic Information Science, Columbus, OH, http://www.giscience.org/past/2012/proceedings/abstracts/giscience2012_p....
Science and Products
Year Published: 2021
Spatial data reduction through element -of-interest (EOI) extraction
Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven...
Arundel, Samantha; Usery, E. LynnAttribution: Core Science Systems, National Geospatial Program
Year Published: 2020
Improving the positional and vertical accuracy of named summits above 13,000 ft in the United States
The National Map (TNM) portal provides public access to U.S. Geological Survey (USGS) high-resolution topographic datasets, and maps from the Historical Topographic Map Collection (HTMC). Elevation values shown on HTMC maps were obtained from ground spot elevation measurements, as compared to today’s elevation measurements derived from more...
Arundel, Samantha; Sinha, Gaurav; Chan, ArthurAttribution: Core Science Systems
Year Published: 2020
GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning
Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of...
Arundel, Samantha; Li, Wenwen; Wang, SizheAttribution: Core Science Systems
Year Published: 2020
Automated location correction and spot height generation for named summits in the coterminous United States
Spot elevations published on historical U.S. Geological Survey topographic maps were established as needed to enhance information imparted by the quadrangle’s contours. In addition to other features, labels were routinely placed on mountain summits. While some elevations were established through field survey triangulation, many were computed...
Arundel, Samantha; Sinha, GauravAttribution: Core Science Systems
Year Published: 2019
A spatio-contextual probabilistic model for extracting linear features in hilly terrain from high-resolution DEM data
This paper introduces our research in developing a probabilistic model to extract linear terrain features from high resolution DEM (Digital Elevation Model) data. The proposed model takes full advantage of spatio-contextual information to characterize terrain changes. It first derives a quantifiable measure of spatio-contextual patterns of linear...
Zhou, Xiran; Li, Wenwen; Arundel, SamanthaAttribution: Core Science Systems
Year Published: 2019
The effect of resolution on terrain feature extraction
Recent increase in the production of high-resolution digital elevation models (DEMs) from lidar data has led to interest in their use for terrain mapping. Although the impact of different resolutions has been studied relative to terrain characteristics like roughness, slope and curvature, its relationship to the extraction of terrain features...
Arundel, Samantha; Li, Wenwen; Zhou, XiranAttribution: Core Science Systems
Year Published: 2019
Deep convolutional neural networks for map-type classification
Maps are an important medium that enable people to comprehensively understand the configuration of cultural activities and natural elements over different times and places. Although a massive number of maps are available in the digital era, how to effectively and accurately locate and access the desired map on the Internet remains a challenge...
Zhou, Xiran; Li, Wenwen; Arundel, Samantha; Liu, JunAttribution: Core Science Systems
Year Published: 2019
Validating the use of object-based image analysis to map commonly-recognized landform features in the United States
The U.S. Geological Survey (USGS) National Geospatial Program (NGP) seeks to i) create semantically-accessible terrain features from the pixel-based 3D Elevation Program (3DEP) data, and ii) enhance the usability of the USGS Geographic Names Information System (GNIS) by associating boundaries with GNIS features whose spatial representation is...
Arundel, Samantha; Sinha, GauravAttribution: Core Science Systems
Year Published: 2018
The National Elevation Dataset
The National Elevation Dataset (NED) is a primary elevation data product that has been produced and distributed by the U.S. Geological Survey (USGS). Since its inception, the USGS has compiled and published topographic information in many forms, and the NED is a significant development in this long line of products that describe the land surface....
Gesch, Dean B.; Evans, Gayla A.; Oimoen, Michael J.; Arundel, SamanthaAttribution: Earth Resources Observation and Science (EROS) Center, Earth Resources Observation and Science Center
View Citation
Gesch, D.B., Evans, G.A., Oimoen, M.J., and Arundel, S.T., 2018, The National Elevation Dataset, in Maune, D., and Nayegandhi, A., eds., Digital Elevation Model Technologies and Applications: The DEM Users Manual, 3rd Edition: Bethesda, Maryland, American Society for Photogrammetry and Remote Sensing, p. 83-110.
Year Published: 2017
A reference landform ontology for automated delineation of depression landforms from DEMs
Abstract The landform reference ontology (LFRO) is being developed to formalize ontological distinctions underlying naïve geographic cognition and reasoning about landforms. The LFRO taxonomy is currently based only on form-based distinctions. In this significantly revised version, several new categories have been added to explicate ontological...
Sinha, Gaurav; Arundel, Samantha; Hahmann, Torsten; Usery, E. Lynn; Stewart, Kathleen C.; Mark, DavidAttribution: Core Science Systems
Year Published: 2017
The National Map seamless digital elevation model specifications
This specification documents the requirements and standards used to produce the seamless elevation layers for The National Map of the United States. Seamless elevation data are available for the conterminous United States, Hawaii, Alaska, and the U.S. territories, in three different resolutions—1/3-arc-second, 1-arc-second, and 2-arc-second. These...
Archuleta, Christy-Ann M.; Constance, Eric W.; Arundel, Samantha T.; Lowe, Amanda J.; Mantey, Kimberly S.; Phillips, Lori A.View Citation
Archuleta, C.M., Constance, E.W., Arundel, S.T., Lowe, A.J., Mantey, K.S., and Phillips, L.A., 2017, The National Map seamless digital elevation model specifications: U.S. Geological Survey Techniques and Methods, book 11, chap. B9, 39 p., https://doi.org/10.3133/tm11B9.
Year Published: 2015
1-Meter Digital Elevation Model specification
In January 2015, the U.S. Geological Survey National Geospatial Technical Operations Center began producing the 1-Meter Digital Elevation Model data product. This new product was developed to provide high resolution bare-earth digital elevation models from light detection and ranging (lidar) elevation data and other elevation data collected over...
Arundel, Samantha T.; Archuleta, Christy-Ann M.; Phillips, Lori A.; Roche, Brittany L.; Constance, Eric W.View Citation
Arundel, S.T., Archuleta, C.M., Phillips, L.A., Roche, B.L., and Constance, E.W., 2015, 1-meter digital elevation model specification: U.S. Geological Survey Techniques and Methods, book 11, chap. B7, 25 p. with appendixes, http://dx.doi.org/10.3133/tm11B7.
Pre-USGS Publications
Cole, K.L. and Arundel, S.T. 2007. “Modeling the climatic requirements for Southwestern plant species.” Proceedings of the Twenty-First Annual Pacific Climate Workshop, Technical Report 79 (ed. by S.W. Starratt, P. Cornelius and J.G. Joelson Jr.), pp. 31-39. Interagency Ecological Program for the San Francisco Estuary, State of California, Sacramento, California.
Cole, K.L., Ironside, K., Arundel, S.T., Duffy, P. and Shaw, J. 2007. “Modeling future plant distributions on the Colorado Plateau: an example using Pinus edulis.” The Colorado Plateau III: integrating research and resources management for effective conservation (ed. by C. van Riper III and M. Sogge), pp. 319-330. University of Arizona Press, Tucson.
Cole, K.L., Ironside, K., Arundel, S.T., Duffy, P. and Shaw, J. 2007. “Projecting the Future Distribution of Pinyon Pine.” White Paper for the Arizona Governor.
Cole, K.L. and Arundel, S.T. 2007.” Modeling the climatic requirements for Southwestern plant species.” Proceedings of the Twenty-First Annual Pacific Climate Workshop, Technical Report 79 (ed. by S.W. Starratt, P. Cornelius and J.G. Joelson Jr.), pp. 31-39. Interagency Ecological Program for the San Francisco Estuary, State of California, Sacramento, California.
Arundel, S. T. 2005. “Using Spatial Models to Establish Climatic Limiters of Plant Species’ Distributions.” Ecological Modelling 162:159-181.
Arundel, S.T. and Cole, K.L. 2004. “The Need for More Precise, Accurate, and Complete Plant Distribution Data, and Its Importance in Reconstructing Past Climates,” Annual Meeting of the American Geophysical Union Proceedings, San Francisco, December.
Cole, K.L., Ferguson, G., Cannella, J., Spellenburg, R., Saunders, A., Arundel, S.T. & Riser, J. 2003. “Digital rangemaps for North American one and two-needled pinyon pines (Pinus monophylla, P. edulis, and fallax and californiarum-types).”
Arundel, S.T. 2002. “Modeling Plant Species’ Climatic Limiters to Analyze Macrofossils in Sonoran Desert Packrat Middens.” Quaternary Research, 58(2):112-121."