Evaluating Generalizations of Hydrography in Differing Terrains for The National Map of the United States

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1 Evaluating Generalizations of Hydrography in Differing Terrains for The National Map of the United States Cynthia Brewer, Pennsylvania State University Barbara Buttenfield, University of Colorado Boulder E. Lynn Usery, CEGIS, U.S. Geological Survey Research Assistants: Chelsea Hanchett and Paulo Raposo, Penn State Mamata Akella, PSU ESRI Chris Anderson-Tarver and Jochen Wendel, CU-Boulder TNM data prep: NHD programming: Much advice: Tom Hale, USGS Larry Stanislawski, USGS Charlie Frye, ESRI

2 Scope Multiscale topographic mapping Generalization of National Hydrography Dataset (1:24,000 High Resolution NHD) Multiscale topographic map design For USGS web delivery portal The National Map (TNM) Scale range 1:20,000 1:200,000 (1:5K 1:2M planned) Why focus on hydrography? Highly sensitive to scale and landscape differences Commonly displayed on most base maps

3 Objectives Work toward full automation Minimize anticipated times for manual editing Use COTS software or USGS available code Maximize efficiency Make no more generalized data versions than necessary Level of detail (LoD) data Preserve important hydrographic/cartographic differences Terrain differences flat, hilly, mountainous Next: climate impacts humid, dry

Landscape Types Subbasin Sample Humid Flat: Upper Suwannee, FL-GA Hilly: Pomme de Terre, MO Mountainous: South Branch Potomac, W V Dry Flat: Lower Beaver, UT Hilly: Lower

4 Landscape Types Subbasin Sample Humid Flat: Upper Suwannee, FL-GA Hilly: Pomme de Terre, MO Mountainous: South Branch Potomac, W V Dry Flat: Lower Beaver, UT Hilly: Lower Prairie Dog Town Fork Red, TX Mountainous: Piceance-Yellow, CO Urban St. Louis, MO; Atlanta, GA Four in Iowa: North and South Raccoon, Middle Des Moines, and Lake Red Rock

5 Contextual Evaluating the solutions Map series across range of scales Critique by domain experts (hydrologists, cartographers) Validation Compare against 100K Medium Resolution NHD Metric Summary statistics on retained geometry Channel length, network local density Catchment areas, upstream drainage

6 Building an LoD 3 case studies A. Isolate a single, continuous centerline Intersect NHD Flowlines with NHD Areas Dissolve on GNIS name Simplify B. Identify NHD Flowlines by local density Prune differentially Simplify, concatenate C. Isolate local textures from NHD Areas / Waterbodies Analyze, aggregate, simplify or smooth May require treating feature codes individually

7 Case Study A: Isolate a single continuous centerline Pomme de Terre River, Missouri humid - hilly We are almost there

8 Pomme de Terre, MO NHD 24K humid - hilly 3. Prune and simplify flowlines 1. Select and simplify waterbodies and areas 2. Primary and secondary centerlines

9 Draft result MO 50K LoD mapped at 100K High resolution (24K NHD) Derived 50K LoD

10 Validation Medium resolution (100K NHD) Derived 50K LoD

11 Missouri series with data from The National Map 24K map ArcGIS to JPEG to screen capture

12 Missouri, 50K map with 24K hydro Missouri 50K map with 24K hydro

13 Missouri, 50K map with 50K LoD Missouri 50K map with 50K LoD hydro Visual evaluation of hydro in map context

14 Missouri 50K LoD and original 24K hydro

15 Case Study B: Identify NHD Flowlines by local density South Branch Potomac River, West Virginia humid - mountainous

16 What is meant by local density

S. Br. Potomac, WV NHD 24K humid - mountainous 1. Prune differentially, merge, build diffs_file 3. Isolate non-centerline flowlines Pruning criterion: 4.

17 S. Br. Potomac, WV NHD 24K humid - mountainous 1. Prune differentially, merge, build diffs_file 3. Isolate non-centerline flowlines Pruning criterion: 4. Simplify 2. Intersect NHD flowlines with NHD areas / waterbodies to delineate centerlines Summed remaining stream channel length Summed original catchment area

18 1. Prune differentially, merge, build diffs_file

19 Draft result WV 50K LoD mapped At 100K: With original 24K hydro With derived 50K LoD

20 Draft result WV 50K LoD mapped At 100K: With original 100K hydro With derived 50K LoD

21 West Virginia series from TNM 24K West Virginia series with data from The National Map 24K map

22 West Virginia, 80K West Virginia 80K

23 West Virginia, 200K West Virginia 200K

24 Case Study C: Isolate local textures from NHD Areas and Waterbodies Upper Suwannee River, Florida-Georgia humid - flat

25 Upper Suwannee, FL-GA NHD 24K humid - flat 1. Swamp / Marsh 2. Lakes / Ponds

26 1. Swamp/ Marsh

27 1. Swamp/ Marsh

28 Florida-Georgia, 80K map with 24K hydro Florida-Georgia, 80K map with 24K hydro

29 Florida-Georgia, 80K map with 50K LoD Florida-Georgia, 80K map with 50K LoD hydro

30 The National Map: eight themes elevation land use / land cover boundaries transportation structures hydrography geographic names orthoimagery nationalmap.gov C. Brewer et al. New Viewer announced for December ICC2009, November 2009

31 Practical considerations in design development No custom edits on TNM data (no clean up) Simple geometric symbols, so no missing fonts or pictures on export Regular fonts for export and file sharing (vs. USGS look with Souvenir and Univers) Lots of group layers to easily turn off categories while evaluating appearance All rasters and layers with transparency at bottom of TOC so export retains editable vectors and type No over/under passing on bridges and ramps (user may query this detail using GeoPDF click out to Google)

32 Color contrasts Red roads vs brown contours use gray contours

33 Color contrasts Red roads vs brown contours use gray contours All colors lighter than black labels few halos (only blue hydro labels and green reservation labels use halos)

34 Color contrasts Red roads vs brown contours use gray contours All colors lighter than black labels few halos (only blue hydro labels and green reservation labels use halos) This is not a road map do not use whole contrast range on road categories

35 Color contrasts Red roads vs brown contours use gray contours All colors lighter than black labels few halos (only blue hydro labels and green reservation labels use halos) This is not a road map do not use whole contrast range on road categories Contrasting outlines on point symbols separate from each other and background

36 Color contrasts Red roads vs brown contours use gray contours All colors lighter than black labels few halos (only blue hydro labels and green reservation labels use halos) This is not a road map do not use whole contrast range on road categories Contrasting outlines on point symbols separate from each other and background Leave contrast available for update and overlay of operational information magenta could be used for additions if no magenta symbols

37 Systematic color categories for symbols Human themes: points: emergency hospitals schools transportation built-up areas Rd Or Pu admin reserves hillshade Br Gy Yl YG Cy Gn Natural themes: wooded areas forest reserves parks boundaries Bu hydrography

38 Systematic color categories for symbols Human themes: points: emergency hospitals schools transportation built-up areas Rd Or Pu admin reserves hillshade Br Gy Yl YG Cy Gn Natural themes: wooded areas forest reserves parks boundaries Bu hydrography

39 24K 100K Multiscale durability: good point overlaps

layer, classed differently (not symbolized) More level-of-detail (LoD)

40 Hydrography Tapered by symbolizing upstream drainage area (stream order not useful) Intermittent and perennial symbolized Label with a paired layer, classed differently (not symbolized) More level-of-detail (LoD) databases in development (if 50K LoD covers , does 200K LoD cover ?)

41 Representing stream hierarchy Class upstream drainage area attribute and represent by width and color

Stream tapering Width (pts) RGB lighter 0.50 100,180,200 0.

42 Stream tapering Width (pts) RGB lighter ,180, ,180,200 med blue ,165, ,165,200 darker ,150, ,150,200

43 Multicolor hillshade with transparency

44 Additional work Generalization sequences for additional landscapes: settlement patterns (rural vs. urbanized) and coastal Tailor generalization algorithms, parameters, sequences to landscape types Decide the smallest number of tailored solutions that will apply to any generalization situation, nationally. Confirm they are not interchangeable through testing Test topo designs in multiple formats (ArcGIS, GeoPDF, cached tiles for web services, print) and multiple resolutions (print, 96 ppi desktop, 130+ ppi laptop)

45 Funding acknowledgements USGS Center of Excellence for Geospatial Information Science (CEGIS) Funded through Department of Interior CESU program, January 2007 to present Resources Project resources: ScaleMaster.org Lynn s Center: cegis.usgs.gov Cindy s website: babs Meridian Lab: greenwich.colorado.edu

Place Still Matters: Generalizing the NHD by Local Terrain and Climate

Place Still Matters: Generalizing the NHD by Local Terrain and Climate Barbara Buttenfield, Geography, CU-Boulder Cynthia Brewer, Geography, Penn State E. Lynn Usery, USGS-CEGIS Research Assistants: Chris