1. GIS and Landscapes Lisa A. Schulte Forest Ecology and Management Topography Soils Climate Vegetation Distribution yx1x2x3yx1x2x3 = f(c, s, t) Model Probability of

2. What are geographical/spatial data?

3.  Any data that can be mapped  Have x- and y-coordinate

4. Types of spatial data?

5.  Topographic maps  Hydrographic maps  Political/Administrative/Property boundaries  Road networks  Remote Sensing (aerial photography, satellite)  Data on people: census data, land use, marketing surveys  Data on natural resources: climate, geology, hydrology, soil, natural hazards, biological activity  Data on utilities

6. Why use GIS?

7. Limitations of a map:  2-D representation of 3-D  Limited to a single scale  Snapshot in time  Difficult to manipulate data GIS Overcomes These!!!

8. What is a GIS?  A set of computer tools for collecting, storing, retrieving, transforming, and displaying spatial data from the real world (Burrough and McDonnell 1998).  Many functions = may parts. Computer Screen Printer Scanner Digitizing Table CD FTP CD Network

9. Core parts of a GIS: 1)User interface/GIS tools Responsible for capturing, storing, retrieving, displaying, customizing, and sharing data 2)Spatial Database Responsible for storing and querying data Computer Screen Printer Scanner Digitizing Table CD FTP CD Network Spatial Database

10. How do we represent the real world digitally? Physical reality Real world model Data model Database Maps/ reports From: Bernhardsen 1999 Actual phenomena: -Properties -Connections Entity: -Type -Attributes -Relationships Object: -Type -Attributes -Relationships -Geometry -Quality Object: -Type -Attributes -Relationships -Geometry -Quality

11. Spatial Data Components Spatial Data Attribute Component Geometric Component Categorical Point Line Area (polygon/cell) QualitativeQuantitative Ordinal Interval Ratio

12. Spatial Data Components Spatial Data Attribute Component Geometric Component Categorical Point Line Area (polygon/cell) QualitativeQuantitative Ordinal Interval Ratio

13. Geometric Representation Point: 0-D object that specifies geometric location specified through a set of coordinates. Line segment (vector): 1-D object that is a direct line between 2 endpoints. String: a sequence of line segments. Polygon: 2-D object bounded by at least 3 1-D line segments. Raster cell/pixel: 2-D that represents an element of regular tesselation of a surface.

14. Vector Data Model

15. Raster Data Model

17. TIN Data Model

18. TIN Raster Data Model

19. Vector vs. Raster  Very important choice!  Advantages of vector: Good representation of entity data models Space efficient storage of data Topology can be described explicitly and be easily manipulated Efficient query operation  Advantages of raster: Simple data structure Efficient representation of highly variable data Mathematical modeling easier because all entities have simple, regular shape

20. Georeferencing:  Matching up spatial database with earth coordinate system  Coordinate systems Latitude/Longitude – distortion near poles Universal Transverse Mercator – divide globe up into strips – good for large datasets State Plane – each state has own – most accurate for at this scale

21. How do we represent the real world digitally?  Selecting applicable scale  Through simplification!  Two basic components associated with spatial data: 1.Geometric component Data Model 2.Attribute component Classification

22. Who produces spatial data?

23.  National agencies (USGS, USFS, NOAA, DNR)  Military organizations  Remote sensing companies (aerial photography, satellite)  Utility companies  Climatologists, geologists, hydrologists, ecologists, geographers, oceanographers, etc.  Grad students!

24. Data Acquisition: Computer Screen Printer Scanner Digitizing Table CD FTP CD Network

25. Data Acquisition:  Field surveys  Digitizing Trace lines on map Labor intensive  Scanning Scan map Edit data  Remote sensing  Deriving from existing GIS data layers  Downloading

26. Web Sources of GIS Data:  USGS Remotely sensed, DEMs, Soils, Hydrographies http://www.usgs.gov  NOAA - National Climatic Data Center Climate http://www.ncdc.noaa.gov/ol/about/ncdcnoaa.html  US Census Bureau Demographic http://www.census.gov/geo/tigerline/tl_1998.html  Wisconsin State Cartographer’s Office – Wisconsin Land Information Clearinghouse Various http://wisclinc.state.wi.us/

27. GIS software: Computer Screen Printer Scanner Digitizing Table CD FTP CD Network

28. GIS software:  Arc/Info ESRI (http://www.esri.com/)  ArcView ESRI (http://www.esri.com/)  IDRISI Clark Labs (http://www.clarklabs.org/)  GRASS Baylor University (http://www.baylor.edu/~grass/)  Imagine ERDAS (http://www.erdas.com/products/product.html)

29. GIS functionality  Spatial queries Site analysis Trend analysis Pattern analysis  Spatial overlay  Spatial modeling  Network operations  Interpolation  Digital terrain analysis  Statistical analysis

31. Who uses spatial data?

32.  Agriculture  Archaeology  Demographers  Environmental scientists and managers  Epidemiology and health scientists  Emergency services  Land planners  Marketing agencies  Naviation  Real estate  Tourism  Utilities

33. Uncertainty… From: Lunetta et al. 1991

34. Spatial data in landscape ecology… From: Bernhardsen 1999 Resolution? Data model? Attribute representation? Trustworthiness?

35. Nine factors to consider when embarking on spatial analysis with GIS: 1.Real world phenomena simple/complex? 2.Data used to describe real world phenomena detailed/generalized? 3.What data types are used to describe the phenomena? 4.Can phenomena be represented in a database exactly/vaguely? 5.Do database entities represent discrete/continuous real world entities? 6.Were the attributes of database entities obtained by complete enumeration or by sampling? 7.Will the database be used for descriptive/administrative/analytical purposes? 8.Will the database be used to make inferences about the real world? 9.Is the process under consideration static/dynamic? (Burroughs and MacDonnell 1998)

36. References  Bernhardtsen, T. 1999. Geographic information systems: an introduction, 2 nd edition. John Wiley and Sons, New York, New York, USA.  Burrough, P. A., and R. A. McDonnell. 1998. Principles of geographic information systems. Oxford University Press, Inc., New York, New York, USA.  Johnston, C.A. 1998. Geographic information systems in ecology. Blackwell Science, Oxford, UK.  Lunetta, R.S., R.G. Congalton, L.K. Fenstermaker, J.R. Jensen, K.C. McGwire, and L.R. Tinney. 1991. Remote sensing and geographic information system data integration: error sources and research issues. Photogrammetric Engineering and Remote Sensing 57:677-687.