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

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)

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

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 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

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

Maps courtesy of National Park of American Samoa

Artwork by Jayne Doucette, Woods Hole Oceanographic Institution

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

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 1991 - 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

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

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 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

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-------| 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-

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

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

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 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

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

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

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, 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., 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.

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): 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.