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GIS Analysis Models for Marine Data

This tutorial explores different GIS analysis models for marine data, including binary, ranking, rating, and weighted rating models. It also demonstrates how to extend a landslide susceptibility model to incorporate additional factors and assess risk.

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GIS Analysis Models for Marine Data

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  1. Arc Marine Data Model Exercise Exercise and data at dusk.geo.orst.edu/djl/arcgis/ArcMarine_Tutorial/ What to turn in: Screen snapshot of what your ArcMap session looks like at the end of Section 4 (including dynseg referencing) Answers to 2 simple questions at end of Section 4 (which cruise? which vehicle?) Can put all of the above in a single MS-Word document, labeled with your NAME please! Due in Dropbox, April 28th, 6:00 p.m.

  2. Midterm on Wednesday

  3. Midterm • May start off with multiple choice • Bulk will be short answer/short essay • Lecture PPTs and your notes • Will not include • Labs or Arc Marine exercise • Journal articles

  4. Major concepts • Representations • Object vs Field • Model, data model, analysis model • Data models • UML terminology, basic procedure from • Reality --> conceptual --> logical --> physical • Customized ArcGIS data models • For enterprise GIS • Analysis Models • Binary, ranking, rating, weighted rating

  5. Major concepts - cont. • Geodatabase • what it is, why it’s important • Topology, Spatial Analysis • what they are, why they are important, how they relate • Web GIS from guest lectures • Object orientation • Identity, inheritance, encapsulation

  6. GIS Analysis Models

  7. GIS Analysis ModelGraphical modeling framework tied to actual GIS functionsFunctions, Data, Numerical Models, Tools, etc.

  8. ArcGIS 9 Model Builder

  9. ArcGIS 9 Model Builder

  10. From Designing Gdbs - Ch 7Arc Hydro & HEC-RASHydrologic Engineering Centers River Analysis System See also “Demo 2” from Apr 12 lecture

  11. Arc Marine & Model Builder From Brett Lord-Castillo, M.S. thesis, and Lord-Castillo et al., Transactions in GIS, in review, 2009

  12. Arc Marine & Model Builder Models to automatically extract environmental data layers for spatio-temporal analysis Model: Get-SST AML to Modeler conversion at ArcGIS 9.x From Marine Data Model Technical Workshop, 2005 ESRI UC, Halpin et al.

  13. The Anatomy of a GIS Analysis ModelBerry, Chs. 24-26 • compare several GIS models to illustrate different analysis modeling approaches • compare varying levels of results from these models • GIS is only as good as its data • GIS is only as good the expression of its data

  14. “It’s All Downhill from Here” • the case for landslide susceptibility • terrain steepness (high slope/low slope) • soil type (unstable/stable) • vegetation cover (bare/abundant)

  15. BINARY model:codes cells 1 for susceptible0 for unsusceptiblemultiplicative: cells must meet all 3 criteria

  16. BINARY model:multiplies maps for Y/N solutionRANKING model:adds maps for a range of solutions

  17. RATING model:averages maps for an even greater range of solutionsscale of 1 to 9 (most) for each condition

  18. RATING model:for example one cell might be9 in SL layer, 3 in SO, 3 in CO(9 + 3 + 3) / 3 = 5 or moderate susc.

  19. Weighted Rating Model • suppose SL is considered to be 5 times more important than SO or CO? • so one cell might be:9 * 5 in SL layer, 3 in SO, 3 in CO • ((9*5)+ 3 + 3) / 3 = 17 • fairly high susc.

  20. 4 Models for Landslide Susceptibility:Banana Bread to Fruit Cake! • BINARY • 1 for SL, 0 for SO, 0 for CO • 1 * 0 * 0 = 0 NO susceptibility • RANKING • 1 for SL, 0 for SO, 0 for CO • 1 + 0 + 0 = 1 LOW susceptibility • RATING • 9 for SL, 3 for SO, 3 for CO • (9 + 3 + 3) / 3 = 5 MODERATE susceptibility • WEIGHTED RATING • 9 for SL, 3 for SO, 3 for CO • ((9*5) + 3 + 3) / 3 = 17 HIGH susceptibility

  21. Banana Bread to Fruitcake • data input to the models - constant • logic of models or conceptual fabric of process - different • rating models most “robust” • continuum of responses/answers • foothold to extend model even further • from critical to contributing factors

  22. Extension of Landslide Model to Risk:Consider Proximity to Features That we Really Care About

  23. Extending a GIS Model ( cont. ) • Risk • variable width road buffers as a function of SLOPE • buffer widens in steep areas • Extending hazard to risk • weighted roads based on slopes • weight roads based on traffic volume, emergency routes, etc. • buildings: commercial, residential, etc. • economic value of threatened features, potential resource loss

  24. Additional Factors • in addition to or instead of SL, SO, CO other critical factors may be considered: • physical: bedrock type, depth to faulting • disturbance: construction areas, gophers? • environmental: storm frequency, rainfall patterns • seasonal: freezing and thawing cycles in spring • historical: past earthquake events

  25. Water depth Sediment depth Substrate type Sediment type Exposure Rugosity/BPI Slope/Aspect Water chemistry Water temperature Voids/caverns (size & depth) Vegetation Biotic interactions Anthropogenic factors Benthic Habitat Example:Parameters Important to Benthic Species What can we measure directly, interpret, or derive? Deidre Sullivan, MATE Center, Monterey, CA

  26. Bathymetric grid created from multibeam x,y,z data Monterey Bay data courtesy of MATE Center and Cal-State Monterey Bay

  27. Slope grid derived from bathymetry

  28. Aspect grid derived from bathymetry

  29. Rugosity grid derived from bathymetry using the Benthic Terrain Modeler Measure of surface area to planar area

  30. 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

  31. Bathymetric Position Index (BPI)derived from bathymetry using the Benthic Terrain Modelerdusk.geo.orst.edu/djl/samoa/tools.html

  32. (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

  33. Substrate type interpreted from Backscatter or Side Scan Sonar images

  34. Building a Suitability Model • What do we know about the species’ habitat requirements? • Can we describe these habitat requirements using GIS data? • Do we have enough information? Is it at the right scale? • Does the model work?

  35. Validate the model & Benthic Terrain Modeler Bathymetric Position Index BPI

  36. Using Standard Deviation to Classify Values 1 2 3 68% 95% 99%

  37. Binary Model(Multiplication) 1 0 0 1 = * = * Areas that satisfy both criteria Rugosity greater than 1.2 SD BPI greater than 1.5 SD

  38. Ranking Model(Addition) 1 0 0 0 2 1 = + 1 = + Ranking because it develops an ordinal scale of increasing suitability Rugosity is greater than 1.2 SD BPI greater than 1.5 SD

  39. Rating Model Uses a consistent scale with more than two states to characterize the habitat (simple average) + = 1 0 0 1 1 2 Rugosity is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 BPI is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 Rating because it develops a relative rating based on the simple average of the factors

  40. Uses a consistent scale with more than two states to characterized the habitat, however it is a weighted average Weighted Rating Model ( ) + = * 5 1 0 0 1 1 2 Rugosity is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 BPI is divided into 4 classes by SD then reclassified to values of 1, 2, 3, 4 Weighted rating develops a relative ranking with the most critical factors given more weight

  41. How do they compare? Ranking Binary Rating Wt. Rating

  42. Model Validation

  43. “Mapematics” • Rating models considered most “mapematical” • how were weighting factors decided? • guess-timates? • derived from predictive statistical technique? • need right set of maps/data over a large area • based on an experiment in the field? • lots of time, funding, energy • Review literature for existing mathematical model and make them “mapematical” (i.e., use them!)

  44. GEO 580 ExamplePredicting presence of the sensitive lichen Usnea longissima in managed landscapesDylan Keon GEO 580 project

  45. Gateway to the Literature • Joerin, F., Using GIS and outranking multicriteria analysis for land-use suitability assessment, Int. J. Geog. Inf. Sci., 15 (2), 153-174, 2001. • Jankowski, P., and T. Nyerges, GIS-supported collaborative decision making: Results of an experiment, Annals AAG, 91 (1), 48-70, 2001. • Chau, K.T. et al., Landslide hazard for Hong Kong using landslide inventory and GIS, Computers & Geosciences, 30: 429-443, 204.

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