Advanced GIS for UCCE - Analysis

1. Advanced GIS for UCCE - Analysis August 22, 2007 Maggi Kelly Department of Environmental Science, Policy, and Management Karin Tuxen-Bettman GIIF http://giif.cnr.berkeley.edu

2. This Afternoon�s Outline Overview of specific GIS analysis Spatial statistics Landscape ecology Hydrologic modeling and watershed delineation Examples of spatial analysis in natural resource science and ecology Overview of land cover datasets Other software for integrated statistical analysis Spatial analysis and statistics tools in ArcGIS 9.2 Computer exercises: Choose from 1 or more applications, including: Map & measure polygonal clusters and patterns Measure point patterns and distributions Hydrologic modeling and watershed delineation using the Model Builder Using Google Earth for 3D visualization

3. What are Spatial Statistics? Spatial statistics are not traditional statistics about things that happen to have spatial component� Spatial statistics take space into account, e.g. distance. Two types: Descriptive � characterizes pattern How are points distributed? What is the pattern? Where are the clusters? Quantitative � quantifies/measures pattern (e.g. pattern, relationships, trends) How clustered/dispersed is the data? What are the relationships with other data?

4. What is Landscape Ecology? Spatial pattern is linked to ecological process i.e. Turner, Forman and Godron, etc. A landscape is made of Structure Patch, corridor, mosaic Size, shape, spatial configuration Function Population dynamics, nutrient cycling, competition, succession, physical processes Change Anthropogenic change Natural change

5. Hydrology concerns the movement of water across a surface, the flow of water through a drainage system What is Hydrologic Modeling & Watershed Delineation?

6. Methods for performing GIS analysis Ask your question, Collect your data, Choose a GIS analysis method, Calculate the statistic(s) and/or metrics, Interpret the statistics, and Test significance.

7. Land Cover Datasets Multi-source Land Cover Dataset (2002, 2006) Source: CDF (http://frap.cdf.ca.gov/data/frapgisdata/select.asp) Spatial resolution: 100 meter (2002), 30 m (2006) Landfire dataset (2005) Source: USGS (http://www.landfire.gov/products_overview.php) Spatial resolution: 30 m Coastal-Change Analysis Project (2002) �coastal counties only! Source: NOAA (http://csc.noaa.gov/crs/lca/pacificcoast.html) Spatial resolution: 30 m National Land Cover Dataset Source: USGS (http://edcftp.cr.usgs.gov/pub/data/landcover/states/) Spatial resolution: 30 m CalGAP (1986) Source: UCSB CalGAP Project (http://www.biogeog.ucsb.edu/projects/gap/gap_data_state.html) Spatial resolution: 4 ha MMU CalVeg77 (1977) (http://frap.cdf.ca.gov/data/frapgisdata/select.asp) Wieslander Vegetation Type Mapping Project (1920s) (http://vtm.berkeley.edu)

8. Measuring Geographic Distributions(e.g. How are the points distributed?) Mean Median Central feature

9. Spatial Statistics

10. Spatial Pattern Analysis Pattern of point distribution Nearest neighbor index Ripley�s K Theissen polygons, or Voronoi diagrams Semi-variogram Quadrat analysis Pattern of point and polygon values Continuous data: gradients and localized variability Moran�s I Getis-Ord General G Kriging Discrete/categorical data Landscape pattern metrics Join count

11. PATTERN OF POINT DISTRIBUTION:Neighborhood Operations What is close to me? Methods Straight-line distance (Euclidean distance) Spider diagram Distance of cost over network Cost over a surface Buffers Variable distance buffers Filters Local, Focal and Zonal functions Distance to/from features Theissen polygons, or Voronoi diagrams

12. PATTERN OF POINT DISTRIBUTION:Nearest Neighbor Index Calculates the average distance between points Significance is tested with Z-score Types Inter-centroid distance Boundary-boundary distance

13. PATTERN OF POINT DISTRIBUTION:Ripley�s K Function Counts the # of features within defined distances Measures spatial arrangement (clustered, uniform, random) Uses multiple simulations to create arandom distribution envelope Detect the scale of those patterns,e.g. what is the cluster size? Assumes: Stationary: No trends in the data Isotropy: No directional detection (although it is possible to modify the K function to detect anisotropy. Regular study area (rarely encountered)

14. PATTERN OF POINT DISTRIBUTION:Ripley�s K function

15. Spatial Autocorrelation Spatial autocorrelation measures the level of interdependence between the variables, the nature and strength of the interdependence Can be either positive or negative Positive spatial autocorrelation has all similar values appearing together, while negative spatial autocorrelation has dissimilar values appearing in close association (less common) Measured by: Semivariograms Moran�s I Geary�s C

16. PATTERN OF POINT DISTRIBUTION:Semivariograms Range: the average distance within which the variable remains spatial autocorrelated ? the extent of spatial trends, distance beyond which sampling is random Sill: the maximum variance of the sample data Nugget: measurement errors or smaller variations within the minimum sampling distance ? the noise in the data

17. PATTERN OF POINT DISTRIBUTION:Semivariograms

18. PATTERN OF POINT DISTRIBUTION:Semivariograms

19. PATTERN OF POINT & POLYGON VALUES:Moran�s I Shows similarity of neighboring features Provides a single statistics summarizing pattern For continuous data Spatial covariation/total variation Ranges from �1 to 1 Positive = positive spatial autocorrelation, negative represents negative autocorrelation. 0 = no spatial autocorrelation (random).

20. PATTERN OF POINT & POLYGON VALUES:Getis-Ord Gi and General G Hot-spot analysis, showing concentration of high or low values Indicates whether high or low values are clustered Uses a neighborhood based on a distance you specify Applies a weight to those within the distance that have similar values

21. Other Software for Statistical Analysis Fragstats http://www.umass.edu/landeco/research/fragstats/fragstats.html ArcGIS Geostatistical Analyst http://www.esri.com/geostatisticalanalyst/ GEODA Great for categorical (and other!) pattern analysis FREE: https://www.geoda.uiuc.edu/ VARIOWIN Great for semi-variograms FREE: http://www-sst.unil.ch/research/variowin/ R FREE: http://www.r-project.org/ S+ spatial statistics module NOT FREE: http://www.insightful.com/products/spatial/ SAS NOT FREE: http://www.sas.com/technologies/analytics/statistics/

22. PATTERN OF POINT & POLYGON VALUES:Landscape Pattern Metrics Landscape Ecology uses �pattern metrics� to quantify structure Size Patch size Shape Elongated, circular, amount of edge Spatial configuration Measuring patterns in the mosaic (patch metrics) Clustered, dispersed Dominance, linkages, isolation, proximity� Fragmentation, isolation, connectivity

23. ArcGrid enabled Fragstats

24. Landscape Metrics:ONE metric per site (�landscape�)

25. Class Metrics:ONE metric per class in the map

26. Patch Metrics:ONE metric per patch (�landscape�)

27. Problems with Pattern Metrics There has been much scrutiny of these techniques, and criticism, including� Metrics are highly redundant Metrics are very sensitive to inputs and to scale Conceptual flaws in landscape pattern analysis Unwarranted relationships between pattern and process Quantifying pattern without considering process Ecological irrelevance of landscape indices Two recent papers discuss these issues and more: Wu, J. 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecology 19: 125-138. Li, H., and J. Wu. 2004. Use and misuse of landscape metrics. Landscape Ecology 19: 389-399.

28. Definitions Drainage system: Area upon which water falls, and the network through which it travels to an outlet Drainage basin: Area that drains water to a common outlet This area is normally defined as the total area flowing to a given outlet, or pour point. Other common terms for a drainage basin are watershed, basin, catchment, or contributing area. Outlet, or pour point: Point at which water flows out of an area Usually the lowest point along the boundary of the drainage basin Drainage divide or watershed boundary: The boundary between two basins

29. Definitions Network Outlet Stream channels Junction, or node: Intersection of two stream channels Interior links: Sections of a stream channel connecting two successive junctions, or a junction Exterior links: Outermost branches of the tree, (i.e., they have no tributaries).

30. Hydrologic Analysis

31. Flow Direction The output of this request is an integer Grid whose values range from 1 to 255. The values for each direction from the center are: For example, if the direction of steepest drop was to the left of the current processing cell, its flow direction would be coded as 16.

32. Flow Accumulation Flow Accumulation creates a grid of accumulated flow to each cell, by accumulating the weight for all cells that flow into each downslope cell. Hydrography is usually created with a threshold of accumulated cell values.

33. Hydrology Tools in ArcToolbox Watersheds & basins Snap Pour Point Stream to Feature:simplify vs. non-simplify Stream Order

34. Data for Hydrological GIS Elevation: SF Bay Area Regional Database (BARD) 30m and some 10m DEMs: http://bard.usgs.gov SF Bay NGA 2m DEM: see GIIF California: 90m DEM: see GIIF National Elevation Dataset (NED) 30m DEM: http://ned.usgs.gov North America: 1,000m DEM (ESRI): see GIIF Global: 1km GTOPO30 (USGS): http://edcdaac.usgs.gov/gtopo30/gtopo30.html Stream gage data (daily and real-time): USGS National Water Information Systems (NWIS) Watersheds, water districts, rivers: Calif. Spatial Information Library (CaSIL): http://gis.ca.gov U.S. National Hydrography Dataset (NHD): http://nhd.usgs.gov/

35. Elevation Data