1. Comparison of the New Jersey Landscape Alison Burnett Mark June-Wells December 13, 2006

2. Questions How do multi-fractal, random, and a NJ map relate at the landscape scale? How do multi-fractal, random, and a NJ map relate at the landscape scale? In what landscape characteristics does the NJ map differ from the two random maps? In what landscape characteristics does the NJ map differ from the two random maps? What can class-level indices tell us about the spatial distribution of NJ state patches vs. random patch distribution. What can class-level indices tell us about the spatial distribution of NJ state patches vs. random patch distribution.

3. Hypothesis NJ State map will differ significantly from both random and multi-fractal random maps for the measured indices at the class and landscape levels. NJ State map will differ significantly from both random and multi-fractal random maps for the measured indices at the class and landscape levels.

4. Considerations Equal probability inputs for random map classes…Nature is not random. Equal probability inputs for random map classes…Nature is not random. Shape of NJ State vs. rectangle. Shape of NJ State vs. rectangle.

5. M&M Programs RULE RULE: RULE: Generate a series of random maps. Generate a series of random maps. Rows = 274; Columns = 148 Rows = 274; Columns = 148 34 classes – probability for each =.029 34 classes – probability for each =.029 Nearest Neighbor Rule 1 chosen Nearest Neighbor Rule 1 chosen No analysis…output as ASCII No analysis…output as ASCII Multifractal Map was created using 8 levels and an h=.5 Multifractal Map was created using 8 levels and an h=.5

6. M&M Programs Arc Converted all ascii files or NJ, Random, and Multifractal maps to grid files for analysis. Converted all ascii files or NJ, Random, and Multifractal maps to grid files for analysis. I love ARC!!!! I love ARC!!!!

7. M&M Programs Fragstats Fragstats: Fragstats: Conducted basic analysis at the class and landscape levels with a broad assemblage of indices. Conducted basic analysis at the class and landscape levels with a broad assemblage of indices. Neighbor Rule of 4 Neighbor Rule of 4 Parameters were chosen based on simplicity and efficacy of design. Parameters were chosen based on simplicity and efficacy of design.

8. Fragstats Parameters Landscape Edge Density Edge Density FRAC mean FRAC mean FRAC standard deviation FRAC standard deviation CONTIG mean CONTIG mean CONTIG std. dev. CONTIG std. dev. PROX mean PROX mean PROX std. dev. PROX std. dev. COHESION COHESION

9. Fragstats Parameters Class NP NP PD PD TE TE PAFRAC PAFRAC PROX weighted mean PROX weighted mean PROX standard deviation PROX standard deviation CLUMPY CLUMPY COHESION COHESION

10. M&M Programs ArcView ArcView3: Made maps for visual purposes

11. Results- Landscape Metrics Map TypeEdge Density NJ4.9 Random19538.6 Multi-fractal17821.8 NJ map significantly less than created maps NJ map significantly less than created maps Expected result for NJ Expected result for NJ Multi-fractal slightly less than random Multi-fractal slightly less than random

12. Results- Landscape Metrics Map Type Fractal Dimension meanstd. dev. NJ1.01930.0316 Random1.06260.2232 Multi-fractal1.07450.1912 Subsets of geometrical space within which they reside Subsets of geometrical space within which they reside Random/multi-fractal maps more complex Random/multi-fractal maps more complex

13. Results- Landscape Metrics Map Type Contig meanstd. dev. NJ0.12210.1659 Random0.01430.0445 Multi-fractal0.04030.0778 High=clumped; low=disperse High=clumped; low=disperse Expected that NJ by more clumped Expected that NJ by more clumped Random least clumped Random least clumped

14. Results- Landscape Metrics Map Type Proximity meanstd. dev. NJ23.83576.293 Random0.6950.209 Multi-fractal0.9910.474 Lower= more isolated Lower= more isolated Higher=more connected Higher=more connected Also expected from NJ map Also expected from NJ map

15. Results- Landscape Metrics Map TypeCohesion NJ95.1672 Random8.1095 Multi-fractal19.9875 NJ’s classes are very connected NJ’s classes are very connected Multi-fractals classes still more connected than random Multi-fractals classes still more connected than random

16. Results- Class Metrics Random and Multi-fractal General General Random and multi-fractal maps show similar values for each index across all patches Random and multi-fractal maps show similar values for each index across all patches Random maps, 34 patch types, all the same probability Random maps, 34 patch types, all the same probability As expected multi-fractal maps show higher clumpiness and cohesion than random maps As expected multi-fractal maps show higher clumpiness and cohesion than random maps Random map “total edge” values more contiguous throughout class types Random map “total edge” values more contiguous throughout class types “Patch density” same for both maps, slightly more evenly distributed for random “Patch density” same for both maps, slightly more evenly distributed for random “Number of patches” for random map almost all the same; more variation in multi-fractal “Number of patches” for random map almost all the same; more variation in multi-fractal

17. Results- Class Metrics NJ Map Patch Density significantly lower than created maps Patch Density significantly lower than created maps Greater variation in total edge as compared to created map Greater variation in total edge as compared to created map Proximity mean overall greater, showing more connectedness of classes Proximity mean overall greater, showing more connectedness of classes Fractal dimension NOT significantly different across maps Fractal dimension NOT significantly different across maps Patch cohesion variable “could be” highly correlated to patch type Patch cohesion variable “could be” highly correlated to patch type Moderately developed- 95.426 Moderately developed- 95.426 Estuarine marsh- 92.4115 Estuarine marsh- 92.4115 Cultivated- 94.5218 Cultivated- 94.5218 Golf courses- 7.933 Golf courses- 7.933

18. Conclusions NJ map is significantly different than both random and multi-fractal maps in class and landscape analysis NJ map is significantly different than both random and multi-fractal maps in class and landscape analysis Further, more rigorous investigation, would require producing a wider variety of map types and imputing real-life patch probabilities. Further, more rigorous investigation, would require producing a wider variety of map types and imputing real-life patch probabilities.

19. Acknowledgements GIS lab people who let us in on Saturday GIS lab people who let us in on Saturday Software companies Software companies Fragstats Fragstats Rule Rule Arc Arc ArcView3 ArcView3

20. Questions?