OR How will GIS and RS help in Salmon Modeling OR Why is Miles Talking about Fish?

1. Spatial and Physical Models Related to Processes across the LandscapeMiles Logsdonmlog@u.washington.edu OR How will GIS and RS help in Salmon Modeling OR Why is Miles Talking about Fish?

2. “Our” agenda • What is GIS? • What is the difference between a Spatial model and a Spatial Explicit Model • What is a theoretical basis for the application of GIS and spatial data analysis in modeling? • What model “methods” or “tools” directly apply to Landscape processes?

3. Questions For Miles: • How can these central landscape features be described and linked to a fish-habitat model? - with a lot of work • What are the 2 (or 3 or 4) biggest sources of uncertainty in making predictions about how Spatial Data Analysis affects salmon - me • What 2 (or 3 or 4) alternative scenarios of current or future conditions would you suggest should be explored to make our model predictions about the effects of habitat change on salmon more robust to uncertainties? – full funding of my research. See final slide for more complete responses

4. My agenda • Show you pretty pictures • Talk about “stuff” I enjoy • Justify Spatial analysis as a field of study Spatial Information Technologies GIS - GPS – Remote Sensing http://boto.ocean.washington.edu/oc_gis_rs

5. Spatial Information Technologies • Geographic Information Systems – GIS • Global Positioning System – GPS • Remote Sensing and Image Processing - RS Technologies to help answer: • What is “here”? … give a position • What is “next” to “this”? … given some description • Where are all of the “???” … detecting or finding • What is the spatial pattern of “???” • When “X” occurs here, does “Y” also occur?

6. GIS Geographic Information System GIS - A system of hardware, software, data, people, organizations and institutional arrangements for collecting, storing, analyzing, and disseminating information about areas of the earth. (Dueker and Kjerne 1989, pp. 7-8) • GIS - The organized activity by which people • Measure aspects of geographic phenomena and processes; • Represent these measurements, usually in a computer database; • Operate upon these representations; and • Transform these representations. (Adapted from Chrisman, 1997) A KEY POINT: Geo-referenced Data

7. RS: Remote Sensing • Remote Sensing is a technology for sampling radiation and force fields to acquire and interpret geospatial data to develop information about features, objects, and classes on Earth's land surface, oceans, and atmosphere (and, where applicable, on the exterior's of other bodies in the solar system). • Remote Sensing is detecting and measuring of electromagnetic energy (usually photons) emanating from distant objects made of various materials, so that we can identify and categorize these object by class or type, substance, and spatial distribution.

8. Suggested Reading • Chrisman, Nicholas, 1997, “Exploring Geographic Information Systems”, John Wiley & Sons, • Burrough, P. A., 1986, “Principles of Geographical Information Systems for Land Resources Assessment”, Monographs on Soil and Resources Servey #12, Oxford Science Publications • Miller, Roberta Balstad, 1996, "Information Technology for Public Policy", in GIS and Environmental Modeling: Progress and Research Issues, editors, Michael F. Goodchild, Louis T. Steyaert, Bradley O. Parks, Carol Johnston, David Maidment, Michael Crane, and Sandi Glendinning, GIS World Books. • Goodchild, Michael F., "The Spatial Data Infrastructure of Environmental Modeling", in GIS and Environmental Modeling: (see above). • Faber, G. Brenda, William W. Wallace, Raymond M. P. Miller, "Collaborative Modeling for Environmental Decision Making", proceedings of the GIS'96 Tenth Annual Symposium on Geographic Information Systems, Vancouver, B.C., March 1996.

9. The larger context (Chrisman, 1997)

10. Integrated System Model System Models Process Models Data Models PRISM MM5 DHSVM POM CRYSTAL UrbanSim Slope Satiability Land Cover Change Evapotranporation INTEGRATION .ETC Population Growth Water Supply & Demand ……. ….. Population Land Cover Water usage Stream Flow Salinity land ownership ……. …….. Soil Texture Geology Elevation Stream Network Temperature Rainfall

11. Integrated System Model Integrated System Model Integrated System Model PRISM PRISM PRISM System Models System Models System Models Process Models Process Models Process Models Data Models Data Models Data Models INTEGRATION elevation wind landcover elevation wind landcover elevation wind landcover TIME – Understanding?

12. Land Use Energy Balance Soils Temperature Precipitation Vegetation Biophysical Data Layers

13. POINT 0 0 0 0 1 0 0 0 0 5 5 3 AREA 1 3 3 1 1 2 LINE 1 0 0 0 1 0 0 0 1 Concept of Spatial Objects • POINTS • LINES • AREA Raster Data Encoding Vector Data Encoding

14. VECTOR Data Model

15. 1 3 2 2 2 3 15 10 1 4 12 5 11 Data Relationships are invariant to translation and rotation Vector - Topology Descriptive Spatial Object VAR1 VAR2 1 2 3 x1,y1 x2,y2 x3,y3 1 2 3 Fnode Tnode x1y1, x2y2 VAR1 VAR2 1 2 1 2 xxyy, xxyy 2 3 xxyy,xxyy 1 2 1 VAR1 VAR2 1 2 10, 11, 12, 15 10, ……. 1 2

16. RASTER Data Model

17. Raster TopologyMap Algebra • In a raster GIS, cartographic modeling • is also named Map Algebra. • Mathematical combinations of raster layers • several types of functions: • Local functions – do not consult the 8 neighbors • Focal functions – function on the “kernel” of neighboring cells • Zonal functions – function on cells that test true in a different layer • Global functions – based upon the distribution of “all” cells • Functions can be applied to one or multiple layers

18. Focal Sum (sum all values in a neighborhood) 2 0 1 1 2 3 0 4 2 1 1 2 2 3 3 2 (3x3) 12 13 = 17 19 • Focal Mean (moving average all values in a neighborhood) 2 0 1 1 2 3 0 4 4 2 2 3 1 1 3 2 1.8 1.3 1.5 1.5 2.2 2.0 1.8 1.8 2.2 2.0 2.2 2.3 2.0 2.2 2.3 2.5 (3x3) = Focal Function: Examples

19. Digital Elevation Model – Raster Data Model Thanks to David Maidment: http://www.ce.utexas.edu/prof/maidment

20. D8 – Determine the Direction of flow

21. Assign a value to indicate the direction of flow. Then for each cell determine the number of cell “upstream” ” Set a threshold for the minimum value of flow accumulation which defines a stream

23. Data Modeling Issues for hydrology • Spatial and temporal scale • Irrigation • Diversions • Impoundment • Urban water use • Other urbanization effects

24. Temporal Averaging:Example: 1-month rainfall • Evaporation and discharge modeled as functions of soil moisture content • How to handle long-interval (1-month) RF? • Constant (drizzle) or One Big Event • Drizzle: ET too high, Discharge too low • Big Event: ET too low, Discharge too high

25. Urbanization Effects • Water Use: How much outdoor use? • Waster Water: How disposed? • Urban Hydrology Reduced infiltration Concentration of water Reduced ET

26. Satellite Remote Sensing June 27, 2001

27. Remote Sensingin brief Thanks to Robin Weeks

28. The “PIXEL”

29. Ground Truth

30. Classified Product

31. Urban I (10-30% developed) Urban II (30-60%) Urban III (> 60%) Short Grass Tall grass Crop/mixed Irrigated Crop Mixed Woodland Bog or Marsh Evergreen Shrub Coniferous I Coniferous II Coniferous III Coniferous IV Deciduous Broadleaf Non-forested (Altered-unknown) Non-forested (Altered-shrub) Ice cap / Glacier Water DOES PATTERN MATTER? Evaluating the Impact of Landscape Pattern on Watershed Hydrology Prism ‘98 Classified Landcover Snoqualmie Drainage Basin

32. 4 landscapes with different patterns Classified “real” Random Same composition Smooth Patchy 12% more Forests Patches 12% less Forests Patches

33. Patchy – 1998 more water Random - 1998 Smooth – 1998 Less water A 12% change in the forest composition, impacts the total accumulated flow to a greater degree then does a change in the pattern of the landscape with the same composition. Accumulated Sum Difference (1990 – 1991): The Difference in the total amount of water flowing past the mouth of the basin between the “real” landscape (1998 classified) and the “simulated”pattern – Random, Patchy, and Smooth

34. Change in Landcover Through an Increase in Impervious Surfaces 1991 LANDCOVER CHANGE 1998 Maplewood Creek – an Urban Watershed

35. 140 cfs (91) cfs (98) cfs (Hist) 120 100 80 60 40 20 0 0 20 40 60 80 100 Maplewood Creek, of the Lower Cedar River 1998 1991 Historical Discharge (cfs) Assuming the same rainfall record we experienced between 1989 – 1991, The amount of Discharge at peak flow increased ~67% over historical conditions, and ~11% between 1991 - 1998 Recurrence interval (years)

36. SUMMARY POINTS Spatial Data Analysis The accurate description of data related to a process operating in space, the exploration of patterns and relationships in such data, and the search for explanation of such patterns and relationships Spatial Analysis vs. Spatial Data Analysis Spatial Analysis = what is here, and where are all the X’s ??? Spatial Data Analysis = observation data for a process operating in space and methods are used to describe or explain the behavior, and/or relationship with other phenomena.

37. Questions For Miles: • How can these central landscape features be described and linked to a fish-habitat model? – spatially explicit definition of objects and processes that are consistent with spatially reference data models • What are the 2 (or 3 or 4) biggest sources of uncertainty in making predictions about how Spatial Data Analysis affects salmon – data definition and/or data resolution • What 2 (or 3 or 4) alternative scenarios of current or future conditions would you suggest should be explored to make our model predictions about the effects of habitat change on salmon more robust to uncertainties? – Does Pattern Matter? Does a change in configuration of landcover produce a change in function of the landscape for a give process.