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Generalization for The National Map with emphasis on the National Hydrography Dataset

Generalization for The National Map with emphasis on the National Hydrography Dataset. Lawrence Stanislawski, Science Applications International Corporation (SAIC) Michael Starbuck, U.S. Geological Survey Michael Finn, U.S. Geological Survey E. Lynn Usery, U.S. Geological Survey

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Generalization for The National Map with emphasis on the National Hydrography Dataset

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  1. Generalization for The National Map with emphasis onthe National Hydrography Dataset Lawrence Stanislawski, Science Applications International Corporation (SAIC) Michael Starbuck, U.S. Geological Survey Michael Finn, U.S. Geological Survey E. Lynn Usery, U.S. Geological Survey Pat Turley, U.S. Geological Survey Josh Wilkerson, U.S. Geological Survey 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  2. Overview of The National Map Project objectives Generalization Strategy National Hydrography Dataset – Flowline network Geometric characteristics of hydrographic network Test Data Results 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  3. Consistent framework for geographic knowledge needed by the • Nation • Provides public access to high-quality, geospatial data and • information from multiple partners to help support decision- • making by resource managers and the public. • Product of a consortium of Federal, State, and local partners • who provide geospatial data to enhance America's ability to • access, integrate, and apply geospatial data at global, national, • and local scales. • Includes eight primary data themes: • Orthoimagery, • Elevation, • Hydrography, • Transportation, • Structures, • Boundaries, • Geographic names, • Land use/land cover, • Most themes are populated, and content will continue to be • added and updated The National Map 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  4. Created from many data sources and Web Map Services (WMS) • owned by many organizations. • Data sources are electronically stored at various scales and • resolutions. Consequently, when data are extracted from several WMS and horizontally integrated in geospatial data applications, the differences between data sources become apparent. • The ability to render one or more scales of The National Map data into an appropriate, or functionally equivalent, representation at a user-specified scale could substantially enhance analysis capabilities of these data. • Research Topic: development of automated generalization approach that renders a functionally equivalent dataset at a user-specified scale, focusing on the National Hydrography Dataset (NHD) layer of The National Map. The National Map 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  5. National Hydrography Dataset • Vector data layer of The National Map representing surface waters of the United States. • Includes a set of surface water reaches • Reach: significant segment of surface water having similar hydrologic characteristics, such as a stretch of river between two confluences, a lake, or a pond. • A unique address, called a reach code, is assigned to each reach, which enables linking of ancillary data to specific features and locations on the NHD. Reach code from Lower Mississippi subbasin 08010100000413 region-subregion-accounting unit-subbasin-reach number 08010100000413 The National Map National Hydrography Dataset (NHD) 08010100000696 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  6. National Hydrography Dataset • Divided and distributed at watershed basin and subbasin boundaries. • Stored in an ArcGIS geographic database (geodatabase) model. • Three levels of detail (resolutions) • Medium (1:100,000-scale source) • only complete layer • High (1:24,000-scale source) • three-quarters complete • Local (1:12,000 or larger source) The National Map National Hydrography Dataset (NHD) Subregions along northern shore of Gulf of Mexico 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  7. Develop a generalization strategy that can be implemented on subsets of the NHD. • Strategy should produce a dataset in the NHD model format that maintains: • feature definitions, • reach delineations, • feature relationships, and • flow connections between remaining generalized features. • Extracted dataset should function with NHD applications • less detail • faster processing speed • Extracted level of detail: • user-specified, or • defined by display scale Objectives 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  8. Base data: user specified, or highest resolution that covers desired area. • Feature pruning – removal of feature that are too small for desired output scale. • select a subset of network features • select a subset of area features • remove point features associated with pruned line or area features • Feature simplification • removal of vertices • aggregation, amalgamation, merging, etc. • Focus so far has been on network pruning Generalization Approach 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  9. Drainage network: flow-oriented digital lines that represent the connectivity and flow between surface water features. • Network features types: • Stream/river • Canal/ditch • Pipeline • Connector • Artificial path • Connector represents a path where surface flow is known to exist but was not included in the source material. • Artificial path represents a flow path through an areal water feature that is connected to the drainage network. Areal features that may include artificial paths are: • area of complex channel, canal/ditch, estuary, ice mass, lake/pond, playa, reservoir, stream/river, swamp/marsh, and wash National Hydrography Dataset (NHD) Flowline Network 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  10. Linear Canal/Ditch Areal Lake/Pond Linear Streams Areal Stream/River Linear Connector Areal Lake/Pond NHD Features Artificial Paths

  11. National Hydrography Dataset (NHD) Flowline Network 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  12. Quantities used to compare geometric characteristics of a stream drainage network: Catchment area – surface runoff in this area flows into the associated network segment. Upstream arbolate sum – sum of the lengths of all upstream network features that flow into a point of a network. Drainage proportion – proportion of the arbolate sum at a point on the network to the total length of the drainage network. Upstream drainage area – sum of all upstream catchment areas up to a point in the network. Upstream drainage density – ratio of the arbolate sum to the upstream drainage area at a point in the network. Basin drainage density – the ratio of length of all network features to the sum of all catchment areas in the network. The sum of all catchment areas in the network may also be referred to as the basin area. Number of confluence-to-confluence segments – the number of segments between network feature confluences (intersections) that exist in the network. Stream order – method of numbering network segments within a drainage system, in which the smallest unbranched mapped tributary is called first order. Segments where two or more first order tributaries intersect becomes a second order segment and so on (Horton 1945). Bifurcation ratio – the ratio of the number of segments of any order to the number of segments of the next highest order. Basin bifurcation ratio – the average of the bifurcation ratios of each stream order in the drainage network. Methods Drainage network characteristics 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  13. Network drainage characteristics summarized by NHD subbasin • Arc Macro Language Programs • Python geoprocessing scripts • Rapid method that uses Thiessen polygons to the estimate catchment area for each network drainage feature was developed for these computations Methods 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  14. Flowline Network Pruning • Extract the most prominent network features. • Eliminate network features having an upstream drainage area that is less than a threshold. • Features that drain larger areas are more likely to be maintained than features that drain smaller areas. • Minimizes the effect of disparate data collection. • Produces a uniform distribution of network features. Methods • Iteratively increase upstream drainage area threshold until desired output drainage density is achieved. • Requires scale-dependency for ease of use. 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  15. Variation in density of mapped drainage features along 7.5-minute quadrangle boundaries for medium resolution NHD. Background colors delineate subbasins. Methods Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  16. Modeled relationship between • Elevation-derived drainage density • Minimum drainage area for stream formation • Elevation data extracted from the National Elevation Dataset (NED, USGS 2005) and resampled to a 30 meter cell size. • Approximate stream channels were derived from the elevation data through commonly used methods. • Varied minimum drainage area for stream formation on the flow accumulation grid between 0.135 km2 (150 cells) to 90 km2 (100,000 cells) • Completed for two study areas. Methods Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  17. Gasconade-Osage subregion (1029) falls in the Interior Plains and Interior Highlands physiographic divisions. Methods 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  18. Methods Colorado Headwaters and Gunnison subregions (1401 and 1402) fall in Intermontane Plateaus and Rocky Mountain System physiographic divisions. 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  19. Determined that an approximate linear relationship should exist between the square root of map scale and drainage density derived from mapped network features. • Approximate drainage densities determined for the two study areas at four scales of hydrographic vector data: • 1:24,000, NHD • 1:100,000, NHD • 1:500,000, river Reach File version 1 (RF1) • 1:2,000,000, DLG data Methods 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  20. Results Drainage Network Characteristics Subbasin drainage density (km/km2) for medium resolution NHD 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  21. Sample Dataset from Gasconade-Osage subregion: • Pomme De Terre (PDT) subbasin (NHD 10290107). • Medium level of drainage density difference between the high and medium resolution NHD subbasins for the PDT (difference about 0.6421 km/km2). Results Drainage Network Characteristics Relative magnitude of drainage density differences between the high resolution layer and the medium resolution layer of the region. Darker red indicates a higher difference in density. 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  22. Results Drainage Network Characteristics Pomme De Terre Subbasin 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  23. Results Drainage Network Characteristics Pomme De Terre Subbasin 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  24. Divergent to convergent network segments increment stream order when one or both segments receive additional flow. Results Drainage Network Characteristics Pomme De Terre Subbasin 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  25. Results Drainage Network Characteristics Pomme De Terre Subbasin Graduated symbols of arbolate sum values by network reach for Pomme De Terre subbasin. High resolution (green), Medium resolution (purple) 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  26. High Res. Results: Drainage Network Characteristics Pomme De Terre Subbasin Frequencies of catchment drainage densities Medium Res. 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  27. High Catchment area and upstream drainage density vs. arbolate sum Results: Drainage Network Characteristics Pomme De Terre Subbasin Medium 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  28. Thiessen-estimated catchments derived for hydrographic network features do not precisely follow the ridgelines of surface models. Results: Drainage Network Characteristics Pomme De Terre Subbasin 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  29. Gasconade-Osage subregion. Relationship between elevation-derived drainage density and minimum drainage area for stream formation. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  30. Gasconade-Osage subregion. Linear relationship predicting elevation-derived drainage density from the inverse of the square root of minimum drainage area for stream formation. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  31. Estimation of drainage density from desired output scale for generalization. Gasconade-Osage subregion. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  32. Colorado Headwaters and Gunnison subregions Relationship between elevation-derived drainage density and minimum drainage area for stream formation. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  33. Colorado Headwaters and Gunnison subregions Linear relationship predicting elevation-derived drainage density from the inverse of the square root of minimum drainage area for stream formation. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  34. Estimation of drainage density from desired output scale for generalization. Colorado Headwaters and Gunnison subregions Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  35. Pomme De Terre network generalization • Tested process to remove less significant network features. • Goal: Prune high resolution network to resemble medium resolution network (drainage density 0.942 km/km2) • Prune using minimum upstream drainage area. • Iteratively increase upstream drainage area threshold until desired output basin drainage density is achieved. Results: Pruned high resolution to minimum upstream drainage area of 2.158 km2 to yield 0.939 km/km2 basin drainage density. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  36. Pomme De Terre network generalization Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  37. Pomme De Terre network generalization Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  38. Pomme De Terre network generalization Results: Flowline Network Pruning Graduated symbols of arbolate sum values by network reach for Pomme De Terre subbasin. High resolution (green), Medium resolution (purple), Generalized (pink). 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  39. Pomme De Terre network generalization Medium resolution (orange) over generalized (pink) Results: Flowline Network Pruning Generalized (pink) over medium resolution (orange) 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  40. Pomme De Terre network generalization Generalized (pink) over high resolution (green) Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  41. To summary

  42. Pomme De Terre network generalization Results: Flowline Network Pruning Drainage density distribution of reach catchments for Pomme De Terre subbasin of high-resolution NHD network generalized to 0.942 km/km2 basin drainage density. Maximum frequency at 1.32 km/km2 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  43. Pomme De Terre network generalization Results: Flowline Network Pruning Catchment area and upstream drainage density relationships with arbolate sum for high-resolution Pomme De Terre subbasin generalized to 0.942 km/sq km basin drainage density 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  44. Pomme De Terre network generalization Summary of stream length by stream order for high resolution, medium resolution, and generalized high resolution Pomme De Terre subbasin. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  45. Pomme De Terre network generalization Summary of stream frequency by stream order for high resolution, medium resolution, and generalized high resolution Pomme De Terre subbasin. Results: Flowline Network Pruning 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  46. Identified and automated computations for quantities that characterize an NHD flowline network. Reach and basin statistics, along with visual inspections assist analysis and comparison of the network drainage. Identified a linear relationship to estimate watershed drainage density of elevation-derived streams from the minimum upstream drainage area for stream formation. Identifed a linear relationship to estimate drainage density appropriate for a user-specified map scales, to be used for network pruning. Developed and tested a network pruning strategy that provides a user- specified network drainage density based on upstream drainage area, which also maintains reaches and network connectivity. Network pruning process furnishes a network that is structurally similar to a target dataset. However, when compared to the target dataset, a larger number of shorter, confluence-to-confluence segments and reaches were maintained in the generalized network because that was a pre-existing condition of the source dataset. Summary 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  47. Upstream drainage area quickly rises to overwhelming sizes. We may need to implement a numeric scaling technique to the pruning strategy. Additional work is required to tailor the pruning strategy to be affected by terrain or topographic variations. Next steps in developing an automated generalization include defining a pruning strategy for area features and rules for feature simplification, and also implementing a line simplification process. Aside from enhancing our understanding of the relationship between terrain and surface hydrography, it is possible that similar automated pruning strategies can be developed for other vector layers of The National Map. Summary 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  48. Gold, C. M. and D. Thibault. 2001. Map Generalization by Skeleton Retraction, In Proceedings, International Cartographic Association, Beijing,China, pp.2072-2081. Horn, R.C., L. McKay, and, S.A. Hanson. 1994. " History of the U.S. EPA's River Reach File: A National Hydrographic Database Available for ArcInfo Applications ," in Proceedings of the Fourteenth Annual ESRI User Conference, Environmental Systems Research Institute, Redlands, CA. Horton, R.E. 1945. Erosional development of streams and their drainage basins: Hydrophysical approach to quantitative morphology. Geological Society of America Bulletin, 56, 275-370. Maidment, D. R. 1996. GIS and hydrologic modeling – an assessment of progress. Third International Conference on GIS and Environmental Modeling. January 22-26, 1996. Sante Fe, New Mexico. Northcott, W. 2002. Lecture notes to Biosystems Engineering 481: Agricultural and Small Watershed Hydrology. College of Engineering, Michigan State University. Pidwirny, M. 2004. Stream Morphometry. Accessed June 9, 2005, at URL: http://www.physicalgeography.net/fundamentals/10ab.html Sanchez, R.R. 2002. GIS-Based Upland Erosion Modeling, Geovisualization, and Grid Size Effects on Erosion Simulations with CASC2D-SED. Ph.D. Dissertation, Civil Engineering Dept., Colorado State University. References 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  49. Smith, P.N.H, and D.R. Maidment. 1995. Hydrologic Data Development System. Report 95-1 Center for Research in Water Resources. University of Texas at Austin. Tarbotom, D. G. and D.P. Ames. 2001. Advances in the mapping of flow networks from digital elevation data. World Water and Environmental Resources Congress, May 20-24,2001, Orlando, Florida. U.S. Geological Survey. 2005. National Elevation Dataset. Accessed June 7, 2005, at URL: http://ned.usgs.gov/. U.S. Geological Survey. 2004. Physiographic divisions of conterminous U.S. Accessed June 14, 2005, at URL: http://aa179.cr.usgs.gov/metadata/wrdmeta/physio.htm. U.S. Geological Survey. 2003. Streams and Waterbodies of the United States. Accessed June 14, 2005, at URL: http://nationalatlas.gov/atlasftp.html. U.S. Geological Survey. 2000. The National Hydrography Dataset: Concepts and Contents. Accessed June 8, 2005, at URL http://nhd.usgs.gov/chapter1/index.html. U.S. Geological Survey. 1955. Map Publication Scales. Book 1, Part B, Chapter 1 of the Geological Survey Topographic Instructions. March 1955. 13pp. Verdin, K. L. 1997. A system for topologically coding global drainage basins and stream networks. 1997 ESRI International GIS User Conference Proceedings. References 2005 ESRI International User Conference, July 25-29. San Diego, CA.

  50. Questions?Generalization for The National Map with emphasis onthe National Hydrography Dataset Lawrence Stanislawski, lstan@usgs.gov Michael Starbuck, U.S. Geological Survey Michael Finn, U.S. Geological Survey E. Lynn Usery, U.S. Geological Survey Pat Turley, U.S. Geological Survey Josh Wilkerson, U.S. Geological Survey 2005 ESRI International User Conference, July 25-29. San Diego, CA.

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