1. Kyle Hogrefe continued
Arc Hydro, Arc Marine
Terrestrial
REA = rapid ecological assessment
drainage patterns to identify contiguous marine/terrestrial basins, impact on land use, freshwater inputs from affiliated catchments
Population density > pathogen and sediment loads in runoff
Arc Hydro terrain pre-processing tools  flow direction grid tags, flow accumulation, catchment, drainage areas
marinecoastalgis.net/kyle08

2. 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)

3. Slope and Aspect measured from an elevation or bathymetry raster 1 2 3
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

4. 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

5. 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

6. Slope Definitions (cont.)

7. Aspect tan (aspect) = b/c1 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

8. 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
*Emily Lundblad now with University of Hawaii and NOAA Coral Reef Ecosystem Division
^Emily Larkin now with NOAA Knauss Marine Policy Fellowship Program
GIS Training for Marine Resource Management
Monterey, June 13, 2005
Photo by

9. Maps courtesy of National Park of American Samoa

10. Artwork by Jayne Doucette, Woods Hole Oceanographic Institution

11. Eroded, subsided volcano
163 acres (0.25 square miles)
Eroded, subsided volcano
Rare tropical rainforest; parrot fish, damselfish and butterfly fish; giant clams, sea turtles, sharks
By former OrSt grad student Emily Larkin

12. FBNMS: Some Major Issues
Natural & human impacts
Crown-of-thorns invasion, hurricanes, bleaching
Illegal fishing, sewage outfall
In the late 1970s, a massive outbreak of crown-of-thorns starfish attacked the reef, destroying about 90 percent of the coral. The reef has also been battered by two major hurricanes (1990 and slight damage from Heta in 1994) and is recovering from a bleaching event (1993-’94). However, new growth is everywhere and the rich diversity of coral is being replenished. This natural cycle of growth and destruction is typical of a tropical ecosystem.
Photos courtesy of NOAA National
Marine Sanctuary System

13. OrSt & USF Earliest Multibeam Surveys
Fagatele Bay - FBNMS (Figure 4a), the smallest (0.65 km2), most remote, and least explored of the 13 NMS (1986) in United States waters, is a flooded caldera
Coconut Point - small peninsula extending into Pala Lagoon near the international airport - offshore from the international airport, outside the lagoon
Taema Bank - long, narrow submarine platform outside the mouth of Pago Pago harbor and is the southerly remnant of the sunken caldera that now forms the harbor
By OrSt grad student Emily Lundblad

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

15. Benthic Habitat Pilot Area, DMWR

16. Fagatele Bay National Marine Sanctuary, 2001 bathy

17. Bathymetric Position Index (from TPI, Jones et al
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)
Many physical and biological processes acting on the landscape are highly correlated with topographic position: in some cases a species’ habitat may be partially or wholly defined by the fact it is a hilltop, valley bottom, exposed ridge, flat plain, upper or lower slope, and so on
Similarly, these larger-scale features may be important habitat characteristics for some species in the marine environment
Thanks to Pat Iampietro, CSU-MB for conceptual introduction and assistance

18. 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
| |
| |
scalefactor = outer radius in map units * bathymetric data resolution
irad = inner radius of annulus in cells
orad = outer radius of annulus in cells
bathy = bathymetry grid
Negative bpi = depression
Positive bpi = crest
Zero bpi = constant slope or flat
-3m-

19. 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

20. 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 Open slopes
Local depression on flat Local crest in depression
Lateral midslope depression Local crest on flat
Depression on crest Lateral midslope crest
Broad depression with an open bottom 12. Narrow crest
Broad flat Steep slope
Shelf

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

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

23. Emily Lundblad, OrSt M.S. Thesis

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

25. 2005 HURL Sub & ROV surveys
Ka‘imikai-o-Kanaloa
Pisces IV or V
RCV-150

26. 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
For each cell in the grid, surface areas are based on triangle areas derived from eight triangles
Each triangle connects the center point of the central cell with the center points of two adjacent cells. These triangles are located in three-dimensional space, so that the area of the triangle represents the true surface area of the space bounded by the three points. 
The triangle area is adjusted so that it only represents the portion of the triangle that overlays the central cell.
The areas of the eight triangles are summed to produce the total surface area of that cell.
The surface ratio of the cell is calculated by dividing the surface area of the cell with the planimetric area of the cell.
Portions of 8 triangles
overlapping center cell
used for surface area
Graphics courtesy of Jeff Jenness, Jenness Enterprises, and Pat Iampietro, CSU-MB

27. reveals rugosity in association with complexity and depth.
dark blue areas have very high rugosity and yellow areas have low rugosity
NEED - common method for attributing visual data and derived grids
identifying the habitat types in segments, idea of where marine life is located. %algae, %coral
Emily Lundblad, OrSt M.S. Thesis

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

29. Classification Wizard

30. Help Pages

31. Standardization Over Multiple Areas

32. Classification Dictionary

33. Classification Dictionary

34. Classification Dictionary

35. 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

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

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

39. Other Resources GEO 580 web site - links GIS@OSU, “Data & Software”
Wilson and Gallant (ed.), Terrain Analysis
ESRI Virtual Campus library
campus.esri.com/campus/library

40. Gateway to the Literature
Guisan, A., Weiss, S.B., Weiss, A.D., GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143:
Jenness, J Grid Surface Areas: Surface Area and Ratios from Elevation Grids [Electronic manual]. Jenness Enterprises: ArcView® Extensions.
Jones, K., Bruce, et al., Assessing landscape conditions relative to water resources in the western United States: A strategic approach, Environmental Monitoring and Assessment, 64:
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),
Rinehart, R., D. Wright, E. Lundblad, E. Larkin, J. Murphy, and L. Cary- Kothera, ArcGIS 8.x Benthic Habitat Extension: Analysis in American Samoa. In Proceedings of the 24th Annual ESRI User Conference. San Diego, CA, August Paper
Weiss, Andy, Topographic Positions and Landforms Analysis (Conference Poster). ESRI International User Conference. San Diego, CA, July 9-13.

41. 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, 2009.
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):
Dunn, D.C. and Halpin, P.N., 2009, Rugosity-based regional modeling of hard-bottom habitat. Marine Ecology Progress Series, 377: doi: /meps07839
Borruso, G., Network density estimation: A GIS approach for analysing point patterns in a network space. Transactions in GIS, 12(3):