1 / 40

Terrain Analysis

Terrain Analysis. Slope ( Landslide susceptibility) Aspect ( Solar insolation, vegetation) Catchment or dispersal area ( Runoff volume, soil drainage) Flow path ( Distance of water flow to point) Profiles, fence diagrams Viewshed (visibility) Indices (e.g., TPI/BPI, rugosity). 1. 2. 3.

chanel
Download Presentation

Terrain Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Terrain Analysis • Slope (Landslide susceptibility) • Aspect (Solar insolation, vegetation) • Catchment or dispersal area (Runoff volume, soil drainage) • Flow path (Distance of water flow to point) • Profiles, fence diagrams • Viewshed (visibility) • Indices (e.g., TPI/BPI, rugosity)

  2. 1 2 3 4 5 6 7 8 9 Slope and Aspect • measured from an elevation or bathymetry raster • compare elevations of points in a 3x3 neighborhood • slope and aspect at one point estimated from its elevation and that of surrounding 8 points • number points row by row, from top left from 1 to 9

  3. 1 2 3 4 5 6 7 8 9 Typical Slope Calculation • b = (z3 + 2z6 + z9 - z1 - 2z4 - z7) / 8D • c = (z1 + 2z2 + z3 - z7 - 2z8 - z9) / 8D • b denotes slope in the x direction • c denotes slope in the y direction • D is the spacing of points (30 m) • find the slope that fits best to the 9 elevations • minimizes the total of squared differences between point elevation and the fitted slope • weighting four closer neighbors higher • tan (slope) = sqrt (b2 + c2)

  4. Slope Definitions • Slope defined as an angle • … or rise over horizontal run • … or rise over actual run • various methods • important to know how your favorite GIS calculates slope

  5. Slope Definitions (cont.)

  6. 1 2 3 4 5 6 7 8 9 Aspect • tan (aspect) = b/c • b denotes slope in the x direction • c denotes slope in the y direction • Angle between vertical and direction of steepest slope • Measured clockwise • add 180 to aspect if c is positive, 360 to aspect if c is negative and b is positive

  7. Benthic Terrain Modeler Dawn Wright Emily Lundblad*, Emily Larkin^, Ron Rinehart Dept. of Geosciences, Oregon State University Josh Murphy, Lori Cary-Kothera, Kyle Draganov NOAA Coastal Services Center GIS Training for Marine Resource Management Monterey, CA Photo by

  8. Maps courtesy of National Park of American Samoa

  9. Artwork by Jayne Doucette, Woods Hole Oceanographic Institution

  10. By former OrSt grad student Emily Larkin

  11. FBNMS: Some Major Issues • Natural & human impacts • Crown-of-thorns invasion, hurricanes, bleaching • Illegal fishing, sewage outfall Photos courtesy of NOAA National Marine Sanctuary System

  12. OrSt & USFEarliest Multibeam Surveys By OrSt grad student Emily Lundblad

  13. Completed by NOAA CRED By OrSt grad student Kyle Hogrefe

  14. Benthic Habitat Pilot Area, DMWR

  15. Fagatele Bay National Marine Sanctuary, 2001 bathy

  16. (after Weiss 2001) Bathymetric Position Index(from TPI, Jones et al., 2000; Weiss, 2001; Iampietro & Kvitek, 2002) Measure of where a point is in the overall land- or “seascape” Compares elevation of cell to mean elevation of neighborhood

  17. Bathymetric Position Index bpi<scalefactor>= int((bathy - focalmean(bathy, annulus, irad, orad)) + .5) Algorithm compares each cell’s elevation to the mean elevation of the surrounding cells in an annulus or ring. resolution = 3 m irad = 2 cells (6 m) orad = 4 cells (12 m) scalefactor = resolution * orad = 36 m |---2---| |---------4-------| • Negative bpi = depression • Positive bpi = crest • Zero bpi = constant slope or flat -3m-

  18. Broadscale Zones from BPI A surficial characteristic of the seafloor based on a bathymetric position index value range at a broad scale & slope values. • Crests • (2) Depressions (3) Flats (4) Slopes if (B-BPI >= 100) out_zones = 1 else if (B-BPI > -100 and B-BPI < 100 and slope <= gentle) out_zones = 3

  19. Finescale Structures from BPI A surficial characteristic of the seafloor based on a BPI value range at a combined fine scale & broad scale, slope & depth • Narrow depression 8. Open slopes • Local depression on flat 9. Local crest in depression • Lateral midslope depression 10. Local crest on flat • Depression on crest 11. Lateral midslope crest • Broad depression with an open bottom 12. Narrow crest • Broad flat 13. Steep slope • Shelf

  20. BPI Zone and Structure Classification Flowchart Emily Lundblad, OrSt M.S. Thesis

  21. Structure Classification Decision Tree Emily Lundblad, OrSt M.S. Thesis

  22. Emily Lundblad, OrSt M.S. Thesis

  23. Fish Abundance & BPI Courtesy of Pat Iampietro, CSU-MB, ESRI UC 2003

  24. 2005 HURL Sub & ROV surveys Ka‘imikai-o-Kanaloa Pisces IV or V RCV-150

  25. Rugosity • Measure of how rough or bumpy a surface is, how convoluted and complex • Ratio of surface area to planar area Surface area based on elevations of 8 neighbors 3D view of grid on the left Center pts of 9 cells connected To make 8 triangles Portions of 8 triangles overlapping center cell used for surface area Graphics courtesy of Jeff Jenness, Jenness Enterprises, and Pat Iampietro, CSU-MB

  26. Emily Lundblad, OrSt M.S. Thesis

  27. BTM Methodology Step Two Step Three Step Four Step One Slope Classification Dictionary Benthic Terrain + Bathymetry Fine BPI + Broad BPI

  28. Classification Wizard

  29. Help Pages

  30. Standardization Over Multiple Areas

  31. Classification Dictionary

  32. Classification Dictionary

  33. Classification Dictionary

  34. Use of Terrain Analysis Tools • Look at version # (e.g., v. 1.0, and all that that implies!) • Careful study of your own data • BPI scale factors • Fledermaus Viz and Profile Control helped in conjunction • Customized classification schemes • ArcGIS 9.x w/ latest Service Pack? • > 2.0 GHz processor, > 1 Gb disk space

  35. Animated Terrain Flyovers Dr. K, OSU and Aileen Buckley, ESRI

  36. Our Tools Portal …dusk.geo.orst.edu/djl/samoa/tools.html Image courtesy of FBNMS

  37. Other Resources • GEO 580 web site - links • GIS@OSU, “Data & Software” • www.geo.oregonstate.edu/ucgis/datasoft.html • Wilson and Gallant (ed.), Terrain Analysis • ESRI Virtual Campus library • campus.esri.com/campus/library

  38. Gateway to the Literature • Guisan, A., Weiss, S.B., Weiss, A.D., 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143: 107-122. • Jenness, J. 2003. Grid Surface Areas: Surface Area and Ratios from Elevation Grids [Electronic manual]. Jenness Enterprises: ArcView® Extensions. http://www.jennessent.com/arcview/arcview_extensions.htm • Jones, K., Bruce, et al., 2000. Assessing landscape conditions relative to water resources in the western United States: A strategic approach, Environmental Monitoring and Assessment, 64: 227-245. • Lundblad, E., Wright, D.J., Miller, J., Larkin, E.M., Rinehart, R., Battista, T., Anderson, S.M., Naar, D.F., and Donahue, B.T., A benthic terrain classification scheme for American Samoa, Marine Geodesy, 26(2), 2006. http://dusk.geo.orst.edu/mgd2006_preprint.pdf • Rinehart, R., D. Wright, E. Lundblad, E. Larkin, J. Murphy, and L. Cary-Kothera, 2004. ArcGIS 8.x Benthic Habitat Extension: Analysis in American Samoa. In Proceedings of the 24th Annual ESRI User Conference. San Diego, CA, August 9-13. Paper 1433. http://dusk.geo.orst.edu/esri04/p1433_ron.html • Weiss, Andy, 2001. Topographic Positions and Landforms Analysis (Conference Poster). ESRI International User Conference.San Diego, CA, July 9-13.

  39. Gateway to the Literature Wright, D.J. and Heyman, W.D., 2008. Marine and coastal GIS for geomorphology, habitat mapping, and marine reserves, Marine Geodesy, 31(4): 1-8. Sappington, J.M., Longshore, K.M., Thompson. D.B., 2007. Quantifying landscape ruggedness for animal habitat analysis: A case study using bighorn sheep in the Mojave Desert. J. of Wildlife Management, 71(5): 1419-1427. Dunn, D.C. and Halpin, P.N., 2009. Rugosity-based regional modeling of hard-bottom habitat. Marine Ecology Progress Series, 377: 1-11. doi:10.3354/meps07839 Borruso, G., 2008. Network density estimation: A GIS approach for analysing point patterns in a network space. Transactions in GIS, 12(3): 377-402.

More Related