1. Extent and Mask Extent of original data Extent of analysis area Mask – areas of interest Remember all rasters are rectangles.

2. Examining the Pattern of Land Surface Characteristics Describing and Quantifying Landscape Patterns Why? Examine cause and effect Detect changes Develop relationships

3. Landscape Metrics Landscape Composition Landscape Configuration

4. Quantifying Landscape Pattern Important Issues to Consider –1 st need a clear a priori statement of the objectives and/or hypothesized pattern changes. –Classification scheme is critical. –Scale (resolution and extent) must be defined. –How should you identify a “patch”. –Most metrics are correlated. –What constitutes a significant change?

5. How should you identify a “patch”? Four-neighbor vs. eight-neighbor rule 4-way (no diagonals): 13 patches 8-way (with diagonals): 11 patches A Patch is also a Region in ESRI Grid.

6. RegionGroup Region Group Function will assign a unique value to all regions. You can use 4 or 8 neighbor cells to define a region. Default = 4. You can define connection “Within” the current zones or “Cross” zones if you specify values to exclude. I always “Link” the original zone values to the new regions.

7. Landscape Metrics Composition –Fraction or proportion (p i ) occupied by type i. –Richness (s): number of types present. –Relative Richness (R): R = (s / s max ) * 100. S max = Potential number of types, hard to determine. –Diversity (H): –Dominance (D):

8. Landscape Metrics Configuration: Patch-based Metrics –Patch-based measurements: number, size, perimeter (i.e. edge), shape, and density –Perimeter/Area Ratio – indication of shape –Connectivity (RS i ) RS i = LC i / (p i * TotalArea) where LC i = size of the largest patch in type i Fragmentation index for type i = 1 - RS i –Fractals Used as a measure of patch size.

9. Landscape Metrics Configuration: Contagion –Probabilities of adjacency (q i,j ): where n i = # of cells in type i n i,j = # of cells when type i is adjacent to type j –Contagion (C): where P ij = probability that two randomly chosen adjacent cells belong to types i and j.

10. Diversity The concept of diversity has two components: –Variety or Richness (# of species or cover types) –Relative Abundance s (Richness) addresses the first H (Diversity) and D (Dominance) indices addresses the second –H is also called an Evenness Index Example: 2 sites, both with 10 types –Site A: Type 1 = 90%, Types 2-10 = 10% combined –Site B: All types are equal 10% –Which is more diverse?

11. Kepner et al., EPA-NERL/ESD http://www.epa.gov/nerl1/land-sci/san-pedro.htm

12. Change in Land Cover Extent

14. Landscape Change Statistics Courtesy Bill Kepner, US-EPA

15. Other Software If you are interested in landscape analysis there are other software produces available. FRAGSTAT is on open source software package that is heavily used in landscape analysis. It is designed to compute a wide variety of landscape metrics for categorical map patterns. –http://www.umass.edu/landeco/research/fragstats/fragstats. html USGS Landscape Analysis Tool Portal –http://rmgsc.cr.usgs.gov/latp/index.shtml

16. The Resolution must be Defined – Small features will “drop-out” as resolution is decreased which occurs when: Vector data – increase in minimum mapping unit (MMU) size Raster data – increase in cell size

17. Factors That Must be Considered Classification Scheme is Critical – Resolution of attributes The more “detailed the classification the more complicated (rich/diverse) the landscape.

18. The Extent of the analysis area affects the measurement of landscape features. The larger the extent the greater the probability that rare types will be present. A smaller extent will give more weight to dominate features. Bigger is usually “better”, BUT it is Important that your analysis area extent matches the portion of the landscape you want to study.

19. Factors to consider when determining the optimal extent and resolution: 1. O’Neill’s rules: a. Resolution should be 2 to 5 times smaller than the spatial features being analyzed. b. Extent should be 2 to 5 times larger than the largest patches. 2. Estimate the “zone of influence” of the phenomenon being studied. 3. Important to stay within portion of the landscape being studied. Potential stratifications: climatic, elevation, land ownership, land use 4. Minimum resolution may be control by measurement technique 5. When in doubt conduct a study on the affect of scale and extent.

20. Vector to Raster Conversion Points and Lines are relatively easy compared to polygons. – With points and lines you always change dimensions The basic problem is how to assign values to a uniform set of cells from a non- uniform set of polygons.

21. Centroid Method Using this method, each cell is assigned the value of the feature that passes through the center of the cell. This method can be used for any feature type, but is particularly useful in coding continuous data, such as elevation, temperature or density. Assign a cell values based on samples or predictions based on the cell center location. Ex: Used in interpolation.

22. Predominant Type Method (majority weighting) The value of the feature that fills the majority of the cell is assigned to the location. This method is good for discrete or noncontinuous data such as land cover, vegetation, or soils, where the boundaries of the objects can be defined and their associated value assigned to the cell when it occupies the majority of the cell.

23. Most Important Type Method (Unequal Weighting) Each cell is assigned the value associated with the features that have been specified as more important to the study. For example, you may want to retain the identity of locations or zones that contain endangered plant species, even if the endangered plants don’t fill the majority of the grid cell.

24. Percentage Breakdown Method In this method, a cell is assigned several values, one per feature, according to the percent each feature occupies within the cell. This is a difficult and costly method to implement at data entry. However, it can be especially useful for statistical data. Typically used for “large” cells – i.e. polygons

25. Polygon to Raster Tool Cell_Center Maximum_Area –Assignment will be based on the largest single feature. Maximum_Combine (Majority) –Assignment will be based on type with the largest combined area. Priority Field –Defined the field that will used to determine preference.

26. Organ Pipe NM

27. Landscape Metrics Affect of Resolution (grain size) on Landscape Representation Based on Organ Pipe Cactus National Monument Vegetation Resolution# of Type # of Patches Average Patch Size (ha) 1 x 1 m 19 158 106.6 5 x 5 m 19 158 106.6 10 x 10 m 19 158 106.6 20 x 20 m 19 156 108.0 50 x 50 m 19 172 97.0 100 x 100 m 19 247 68.4 250 x 250 m 17 104 162.8 500 x 500 m 15 60 274.2 1000 x 1000 11 28 625.0 2000 x 2000 m 9 13 1,230.8 3000 x 3000 m 4 5 3,060.0