This is a first draft of slides for a presentation to a session on Predictive Ecosystem Mapping at Alberta Innovates.

1. Automated Predictive Mapping: Lessons Learned about the Process R. A. MacMillan LandMapper Environmental Solutions Inc.

3. Principals and Concepts Fundamental Principals Different Approaches to DSM

4. Fundamental Principals of DSM Pedotransfer functions (PTF) Bouma (1989): “ translating data we have into what we need ” Credit: Minasny & McBratney

5. Fundamental Principals of DSM Credit: Minasny & McBratney Principle 1: Do not predict something that is easier to measure or map than the predictor Effort

7. Predictive Mapping Concepts From: Dobos et al., 2006 JRC – EUR 22123

8. A Spatial Soil Inference System ( Lagacherie & McBratney, 2005) User interface User data DTM RS image X Existing Soil map Scorpan layers Soil observations Spatial Soil Information System DSM Function library Scorpan F. Pedotransfer F Class Content F. Allocation F. Predictor OUTPUT Function organiser

9. DSM Methods Translating Concepts into Results

10. Approaches to Producing Predictive Area-Class Maps

12. Concept of Fuzzy K-means Clustering Source: J . Balkovič & G . Čemanová Credit: Sobocká et al., 2003

13. Example of Application of Fuzzy K-means Unsupervised Classification From: Burrough et al., 2001, Landscsape Ecology Note similarity of unsupervised classes to conceptual classes

15. Supervised Classification Using Regression Trees From: Zhou et al., 2004 JZUS Note similarity of supervised rules and classes to typical soil-landform conceptual classes Note numeric estimate of likelihood of occurrence of classes

16. Supervised Classification Using Bayesian Analysis of Evidence From: Zhou et al., 2004 JZUS Note: ultimately this is just a way of establishing numerical measures of the likelihood of occurrence of each class to be predicted given the presence of a predictor class Note: the final, overall probability value is computed as a weighted average of the individual probabilities of each potential output class given each input class on n input maps

17. Supervised Classification Using Bayesian Analysis of Evidence/Classification Trees From: Zhou et al., 2004 JZUS

20. Knowledge-Based Classification In SoLIM From: Zhu,SoLIM Handbook

21. Knowledge-Based Classification In LandMapR From: MacMillan, 2005 Source: Steen and Coup é , 1997

22. PEM DSS Classification Using LandMapR Normal Mesic Moist Foot Slope Warm SW Slope Shallow Crest Organic Wetland Wet Toe Slope Cold Frosty Wet Permanent Lake

23. From: MacMillan, 2005 PEM from a knowledge-based approach can look like a normal PEM

24. Predictive Mapping 10 Lessons Learned from my BC PEM Mapping Experience

33. Lesson 8: Don’t Model What You Can Directly Map More Efficiently! Principle 1: Do not predict something that is easier to measure or map than to predict! So – if you can map it manually faster or better, do not hesitate to do so!

35. Lesson 10: Discontinuities are Important! From: Minar and Evans, 2008

36. Predictive Mapping Some lessons learned from my recent global soil mapping experiences

41. An Example of a Central Information Facility (GSIF)

42. An Example of a Central Information Facility (GSIF)

45. Thank You And Good Luck!