1. The Knowledge Discoverer Module in ArcSIE: A Software Tool for Soil Survey Update Xun Shi 1, Jessica Philippe 2, Robert Long 2, and Tom D’Avello 3 1 SIE LLC and Dartmouth College 2 USDA-NRCS, St. Johnsbury, VT 3USDA-NRCS, NSSC-GRU, Morgantown, WV

2. ArcSIE is a software package designed for NRCS soil scientists to use in their daily soil mapping practice. ArcSIE works as an extension of ArcMap. ArcSIE can be downloaded from www.arcsie.com Knowledge Discoverer (KD) is a module in ArcSIE for soil survey update.

3. www.arcsie.com

4. New types of data and new informationBetter data: high resolution and/or accuracy Reasons for soil survey update We have better environmental data now.

5. Reasons for soil survey update We have better environmental data now. We have better knowledge now. New or improved knowledgeBetter ways to express and represent the knowledge New soil types New information about soil properties New knowledge about optimal slope for the soil to occur New knowledge about the relationship between soil and wetness index

6. Spatial mismatchAttribute mismatch Reasons for soil survey update We have better environmental data now. We have better knowledge now. Inconsistency between different regions.

7. The Knowledge Discoverer Approach The approach is to discover, revise, and reuse the knowledge (soil-landscape model) implicitly represented by an existing soil map, during which it incorporates updated (better) knowledge and data.

8. A 3-step Process Extracting the knowledge (soil-landscape models) from the existing map.

9. 769770 771 772 771 772773 769770 771 772 771 772 770 771 772 770 771 772 770 771 772773 770 771 772 773774776 771 772 773 774775776778781 772 773 774775 776777779781783785 772 773 774775 777778779781784786788 771 772773774776778779782786789792796 769 770771772775778781785791795800804810 768769 771774778783789796803809814819824 768769771774778784791800808815822828833839 769770773777783791801811821829836842847853 770772775781789799811823834843850855860866 Extracting the knowledge (soil-landscape models) from the existing map Find out the frequencies of different values of an environmental factor within a map unit polygon. Use the frequencies to construct a math function characterizing the soil- factor relationship, represented by a curve.

10. Extracting the knowledge (soil-landscape models) from the existing map

11. Extracting the knowledge (soil-landscape models) from the existing map. Revising the extracted soil-landscape model. A 3-step Process

12. Revising the extracted soil-landscape model. Change the shape of the curve

13. Revising the extracted soil-landscape model. Integrate multiple curves

14. Revising the extracted soil-landscape model. Corresponding a curve to its polygon in the map

15. Revising the extracted soil-landscape model. Statistics of the raster cell values

16. Revising the extracted soil-landscape model. Export the revised curve into a rulebase

17. Environmental Factor List Rule Curve Editor Statistics Display Panel Vector Feature List Generated Rulebase User Interface of the Knowledge Discoverer

18. Extracting the knowledge (soil-landscape models) from the existing map. Revising the extracted soil-landscape model. Regenerating the soil map using the new model and new data. A 3-step Process

19. Regenerating the soil map using the new model and new data Use ArcSIE’s Inference Engine to generate the updated map with the new soil-landscape model and new data

20. The western section of the Orleans County, Vermont. An area the soil scientists were somewhat familiar with. Was mapped using the traditional method. Has recent LiDAR data available. Was further subset into just the lodgment till map units, for testing the Knowledge Discoverer on a catena. A Case Study

21. Original SSURGO polygon of Dixfield sandy loam, 8-15 percent slope Updated map One “typical” curve was selected to represent each map unit, and edited according to new knowledge/better data (in this case LiDAR derivatives) Knowledge Discoverer Soil Inference Engine

23. KD takes advantage of the knowledge in the existing map. KD takes advantage of new/better environmental data generated with modern technologies (e.g., LiDAR). KD incorporates new/better knowledge of current soil scientists. KD creates updated maps that meet the modern digital soil mapping standards. After all, these form the whole point of doing map update. Summary

24. This process is not starting from scratch and is not traditional soil mapping. It assumes that the SSURGO mapping is correct at some level (specifically, that the mapped parent materials are generally correct). The resulting updated map is a raster map, and a separate product from the SSURGO polygons. The results are not expected to fit within SSURGO constraints. It is recommended to undertake an MLRA-wide update project is by grouping map units into different catena. On the one hand, data and conceptual limitations make it difficult to update the entire area at once; on the other hand, updating just one map unit in a vacuum is not productive. Catena allows evaluating and adjusting a number of map units that are related to each other together. Some things to consider

25. For more information about the usage of Knowledge Discoverer, please download ArcSIE User’s Manual from www.arcsie.com.

26. Acknowledgement: The development of ArcSIE has been supported by contracts from USDA-NRCS since 2005. Thank you!