Terrain Analysis Slope ( Landslide susceptibility) Aspect ( Solar insolation, vegetation) Catchment or dispersal area ( Runoff volume, soil drainage) Flow.

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)

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

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)

1 2 3

4 5 6

7 8 9

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

Slope Definitions (cont.)

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

1 2 3

4 5 6

7 8 9

Terrain Analysis• Indices (e.g., TPI/BPI, rugosity)• 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)

Dawn Wright

Emily Lundblad*, Emily Larkin^, Ron RinehartDept. of Geosciences, Oregon State University

Josh Murphy, Lori Cary-Kothera, Kyle DraganovNOAA Coastal Services Center

Benthic Terrain Modeler

GIS Training for Marine Resource Management

Monterey, CAPhoto by

Maps courtesy of National Park of American Samoa

Artwork by Jayne Doucette, Woods Hole Oceanographic Institution

By former OrSt grad student Emily Larkin

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

OrSt & USFEarliest Multibeam Surveys

By OrSt grad student Emily Lundblad

Completed by NOAA CRED

By OrSt grad student Kyle Hogrefe

Benthic Habitat Pilot Area, DMWR

Fagatele Bay National Marine Sanctuary, 2001 bathy

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

(after Weiss 2001)

Algorithm compares each cell’s elevation to the mean elevation of the surrounding cells in an annulus or ring.

bpi<scalefactor> = int((bathy - focalmean(bathy, annulus, irad, orad)) + .5)

Bathymetric Position Index

-3m-

|---2---| |---------4-------|

resolution = 3 m

irad = 2 cells (6 m)

orad = 4 cells (12 m)

scalefactor = resolution * orad = 36 m

• Negative bpi = depression

• Positive bpi = crest

• Zero bpi = constant slope or flat

(1) Crests

(2) Depressions

A surficial characteristic of the seafloor based on a bathymetric position index value range at a broad scale & slope values.

(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

Broadscale Zones from BPI

1. Narrow depression 8. Open slopes

2. Local depression on flat 9. Local crest in depression

3. Lateral midslope depression 10. Local crest on flat

4. Depression on crest 11. Lateral midslope crest

5. Broad depression with an open bottom 12. Narrow crest

6. Broad flat 13. Steep slope

7. Shelf

A surficial characteristic of the seafloor based on a BPI value range at a combined fine scale & broad scale, slope & depth

Finescale Structures from BPI

BPI Zone and

Structure

Classification

Flowchart

Emily Lundblad, OrSt M.S. Thesis

Structure Classification Decision Tree

Emily Lundblad, OrSt M.S. Thesis

Emily Lundblad, OrSt M.S. Thesis

Fish Abundance & BPI

Courtesy of Pat Iampietro, CSU-MB, ESRI UC 2003

2005 HURL Sub & ROV surveys

Ka‘imikai-o-Kanaloa Pisces IV or V

RCV-150

Rugosity• Measure of how rough or bumpy a surface is, how convoluted and complex• Ratio of surface area to planar area

Graphics courtesy of Jeff Jenness, Jenness Enterprises, and Pat Iampietro, CSU-MB

Surface area based onelevations of 8 neighbors

3D view of grid on the left Center pts of 9 cells connectedTo make 8 triangles

Portions of 8 triangles overlapping center cellused for surface area

Emily Lundblad, OrSt M.S. Thesis

Fine BPI+

+Broad BPI

Slope

Step One Step Two Step Three

Bathymetry

BenthicTerrain

Step Four

ClassificationDictionary

BTM Methodology

Classification Wizard

Help Pages

Standardization Over Multiple Areas

Classification Dictionary

Classification Dictionary

Classification Dictionary

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

Animated Terrain Flyovers

Dr. K, OSU and Aileen Buckley, ESRIDr. K, OSU and Aileen Buckley, ESRI

Our Tools Portal …dusk.geo.orst.edu/djl/samoa/tools.html

Image courtesy of FBNMS

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

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., 2006. A benthic terrain classification scheme for American Samoa, Marine Geodesy, 26(2). 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.

Gateway to the LiteratureWright, 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.