Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks

ABSTRACT

Automated generalization software must accommodate multi-scale representations of hydrographic networks across a variety of geographic landscapes, because scale-related hydrography differences are known to vary in different physical conditions. While generalization algorithms have been tailored to specific regions and landscape conditions by several researchers in recent years, the selection and characterization of regional conditions have not been formally defined nor statistically validated. This paper undertakes a systematic classification of landscape types in the conterminous United States to spatially subset the country into workable units, in preparation for systematic tailoring of generalization workflows that preserve hydrographic characteristics. The classification is based upon elevation, standard deviation of elevation, slope, runoff, drainage and bedrock density, soil and bedrock permeability, area of inland surface water, infiltration-excess of overland flow, and a base flow index. A seven class solution shows low misclassification rates except in areas of high landscape diversity such as the Appalachians, Rocky Mountains, and Western coastal regions.

RÉSUMÉ

Les logiciels de généralisation automatique doivent adapter la représentation multiple des réseaux hydrographiques à la variété des paysages géographiques puisque l’on sait que les différences hydrographiques à différentes échelles varient en fonction des conditions physiques. Bien que ces dernières années les chercheurs aient adapté les algorithmes de généralisation à des régions et paysages spécifiques, la sélection et la caractérisation des conditions régionales n’ont pas été formellement définies ni validées de façon statistique. Ce papier entreprend une classification systématique des types de paysages des Etats Unis continentaux afin de segmenter le pays en unité de traitement en préparation de l’application d’un processus de généralisation adapté et systématique qui préserverait les caractéristiques hydrographiques. La classification est basée sur l’altitude, son écart type, la pente, le ruissellement, la densité du drainage et du substrat rocheux, la perméabilité du sol et du substrat rocheux, la superficie des eaux de surface intérieures, l’écoulement par dépassement de la capacité d'infiltration et l’indice de débit. Une classification répartie en sept classes montre un faible taux de mauvaise classification à l’exception de régions à forte diversité paysagère telles que les Appalaches, les Montagnes Rocheuses et les régions de la côte ouest.

KEYWORDS:

• Cartographic generalization
• National Hydrography Dataset
• physiographic regimes

Acknowledgements

The authors thank Chris Anderson-Tarver and Jochen Wendel of the University of Colorado-Boulder for assistance with generalization modeling and data processing, and Jessica Janssen, Jeremy Koontz, and Peter Vaziri of the USGS for programming and data processing assistance. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Lawrence V. Stanislawski is a Research Cartographer for the U.S. Geological Survey Center of Excellence for Geospatial Information Science in Rolla, Missouri. His principle research interests involve geospatial data accuracy, generalization, multi-scale representation, hydrographic feature extraction from remotely sensed data, and high-performance computing.

Michael P. Finn is a recently retired Research Cartographer from the USGS Center of Excellence for Geospatial Information Science. Mike worked as a Computer and IT Specialist, and a Research Cartographer with the US Geological Survey for 18 years prior to his retirement. He also has 10 years of experience with the US Air Force and 7 years with the Defense Mapping Agency. His research interests are in high-performance computing of geospatial data, spatial coordinate systems, map projections; environmental modeling, data integration and generalization.

Barbara P. Buttenfield is Professor of Geography and Director of the Meridian La at the University of Colorado in Boulder. She is also a Research Faculty Affiliate with USGS Center for Excellence in GIScience, and leads the Data Harmonization project for the CU Grand Challenge “Earth Lab” initiative. She publishes research on cartographic generalization, multi-scale geospatial database design and data integration, and visualization of data and uncertainty in environmental models.

ORCID

Lawrence V. Stanislawski http://orcid.org/0000-0002-9437-0576

Michael P. Finn http://orcid.org/0000-0003-0415-2194

Barbara P. Buttenfield http://orcid.org/0000-0001-5961-5809

Additional information

Funding

Barbara Buttenfield's work was partially supported by the Grand Challenge Initiative ‘Earth Lab’ funded by the University of Colorado. http://www.colorado.edu/grandchallenges/.