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Our Recent Research Efforts

Our Recent Research Efforts. Department of Geography, University of Wisconsin-Madison, Madison, Wisconsin, USA. A-Xing Zhu and James E. Burt. Outline. 1. Neighborhood Size Effects 2. Purposive Sampling. Impact of Neighborhood Size on Terrain Derivatives and Digital Soil Mapping.

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Our Recent Research Efforts

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  1. Our Recent Research Efforts Department of Geography, University of Wisconsin-Madison, Madison, Wisconsin, USA A-Xing Zhu and James E. Burt

  2. Outline 1. Neighborhood Size Effects 2. Purposive Sampling

  3. Impact of Neighborhood Size on Terrain Derivatives and Digital Soil Mapping A-Xing Zhu, James E. Burt, Michael Smith, Rongxun Wang, Jing Gao Department of Geography, University of Wisconsin-Madison, Madison, Wisconsin, USA

  4. Artificial Intelligence/Machine Learning Techniques Digital Soil Maps Soil-Environment Relationships f ( E ) Digital Terrain Analysis Cl, Pm, Og, Tp … Prediction through overlay (Inference) G.I.S./R.S. Techniques Digital Soil Mapping Using GIS/RS

  5. 10ft. Resolution, 10ft. NS 10ft. Resolution, 90ft. NS 10ft. Resolution, 180ft. NS 90ft. Resolution, 90ft. NS 90ft. Resolution, 180ft. NS • The values of these derivatives are computed over a neighborhood which seems to be impacted by: • DEM resolutions (Chang and Tsai, 1991; Thompson et al., 2001; Wilson et al., 2000) • Neighborhood size (NS) over which they are computed (Wood, 1996)

  6. The objective of this research is to investigate the effect of DEM resolution and NS on digital soil mapping • We seek the answers to the following three questions: Q1: How does the difference between field measured and computed slope gradient change with respect to DEM resolution and NS? Q2: What is the sensitivity of terrain derivatives to NS and resolution? Q3: What is the impact of NS on digital soil mapping? What impacts does resolution of DEM on the quality of digital soil map?

  7. 81 field slope measurements 43 field soil series observations (over a 75 hectare subsection with 4 different soil series) Two Study Sites Dane County, Wisconsin, USA

  8. Fitted Polynomial Surface Gridded Surface Method for computing terrain derivatives • First create a least squares regression polynomial surface over a user-specified neighborhood areas using (Wood, 1996; Shary et al., 2002; Schmidt et al., 2003): z = rx2 + ty2 + sxy + px + qy + u • Compute terrain derivatives (slope gradient, slope aspect, profile and contour curvature) by analyzing the polynomial (Florinsky, 1998). Software to do this: 3dMapper

  9. Experiment Design • Original Elevation data sources: • a 10ft. DEM from a 2000 air photography • a 30ft. DEM from a 1995 air photography • Coarsening the 10ft. DEM to 15ft., 20ft., 25ft., 30ft., 35ft. 40ft., 45ft., 50ft EMs using the nearest neighbor approach in ESRI Arc/INFO. • Coarsening the 30ft. DEM to 35ft. 40ft., 45ft., 50ft. DEMs using the nearest neighbor approach in ESRI Arc/INFO. • Values of terrain derivatives were computed for each DEM resolution over each neighborhood size, up to 300ft. with 5 ft increment

  10. Q 1: Difference in slope gradient by NS and resolution Observations: 1. The difference is smallest not at the finest neighborhood size, but rather at some larger size, about 100 feet in this case 2. Resolution does not seem to play a role

  11. Q 2: Sensitivity to NS (with resolution at 10 ft)

  12. Q 2: Relative change across NS (with resolution at 10 ft)

  13. Q 2: Sensitivity to DEM resolution (with NS around 150 ft)

  14. What does all of these say? Soil scientists do use a specific neighborhood size to measure slope gradient for soil investigation What they use is often different from what is computed using a 3x3 kernel. NS has much stronger impact on terrain derivatives at smaller neighborhood size The question is “so what?” Does this matter for digital soil mapping?

  15. Knowledge Acquisition GIS/RS Techniques (Environment Database) (Knowledgebase) Fuzzy Inference Engine Soil Series Map Experiment Design for impact on soil mapping • The SoLIM (Soil-Landscape Inference Model) approach (Zhu et al. 2001) is used to derive soil series maps:

  16. Knowledgebase Non-Terrain: Terrain derivatives: Constant Varying by NS and resolution Fuzzy Inference Engine … Versions of Soil Series Maps Experiment Design for impact on soil mapping Digital Soil Mapping Experiment: Environment Database Constant

  17. Thompson Farm Site Experiment Design for impact on soil mapping Evaluation of Soil Map Quality: Sampling Strategy: Transect sampling Quality Measure: Percent correctly mapped

  18. Q3: Impact on digital soil mapping - NS DEM Resolution 80 10 ft. 70 (%) Accuracy 60 15 ft. 50 30 ft. 40 30 20 10 Neighborhood Size 0 50 70 80 20 40 10 100 110 130 140 160 170 Observations: 1. Highest accuracy is neither at the smallest NS, nor at the largest NS, somewhere in the middle, about 100-110 feet similar to that of slope neighborhood size in this case 2. The difference in accuracy is quite large, about double in this case

  19. Q3: Impact on digital soil mapping - Role of DEM resolution Resolution 80 10 ft. (%) Accuracy 70 15 ft. 60 30 ft. 50 40 30 20 10 Neighborhood Size 0 70 50 80 20 40 10 110 130 100 140 160 170 Observations: 1. DEM resolution does not seem to play much a role when a variable neighborhood approach is taken, as long as the resolution is within the optimal NS. 2. Larger NS mutes the role of resolution even further.

  20. Summary of Findings 1. NS used by soil scientists are often different from that computed form the 3x3 kernel in ArcGIS 2. Impact of NS on terrain derivatives are much stronger at smaller NS. It is more important to choose a proper NS when NS is small. 3. The impact of this difference in neighborhood size on digital soil mapping can be amplified through the computed terrain derivatives, causing significant difference in the quality of the so-derived soil maps 4. DEM resolution does not have much an impact when neighborhood size is accounted for

  21. Implications • At least for digital soil mapping, choosing an appropriate • neighborhood size is more important than increasing the • resolution of DEM. • 2. Cost-effectiveness of acquiring high resolution DEM for digital • soil mapping needs re-examination. • 3. Common sense: scale or spatial extent or neighborhood size • should be tied to the scale of the process under concern, not • the resolution of the data at hand.

  22. Future Studies • What is the spatial extent (neighborhood size) soil scientists • use when conducting soil investigation? Does it change over • different landscape? How does it change among field soil • scientists? • What is the utility of high resolution DEM (particularly Lidar) • for digital soil mapping?

  23. Purposive Sampling for Digital Soil Mapping A-Xing Zhu1,Baolin Li2, Edward English1 Lin Yang2, Chengzhi Qin2, James E. Burt1, Chenghu Zhou2 1Department of Geography University of Wisconsin-Madison, USA 2 State Key Lab of Resources and Environmental Information System Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences, China

  24. Local Experts Large Point Set Typical Pedon Set Existing Soil Maps Knowledge Acquisition Artificial Neural Network Case-Based Reasoning Spatial Data Mining Relationships between Soil and Its Environment (Soil – Landscape Model) Environmental Conditions G.I.S./R.S. Predictive Soil Mapping: The Basis and Key Issue: S <= f ( E )

  25. Predictive Soil Mapping: The Problem: For areas where there are no soil experts and no available soil maps and no field observations how to obtain the needed soil-landscape model? Extensive fieldwork

  26. Predictive Soil Mapping: The challengeis that field observation is typically very costly, time consuming, and often ineffective Our objectiveis to develop a method to improve the efficiency of field sampling so that predictive soil is not only possible but also efficient for unmapped areas

  27. Combination of environmental conditions soil types How we do it? - Purposive Sampling Assumptions: The unique status of soil types is created under unique combinations of environmental conditions. The spatial locations of typical soil types can be approximated by the locations of unique combination of environmental conditions.

  28. GIS/RS Fuzzy Membership Map for Each Combination Environmental Conditions Fuzzy Classification - FCM Combination of environmental conditions Interpretation soil types Soil-Landscape Model Purposive Sampling Method: Sample the Center of the Combination

  29. Soil-Landscape Model Inference (prediction) Soil Spatial Distribution Environmental database Purposive Sampling For Predictive Mapping: S <= f ( E )

  30. 3500 meters 3200 meters Case Studies – The U.S. Case Study area: A glaciated plain in Wisconsin Soil scientist: John Campbell

  31. Class 9 Class 1 Class 7 Class 5 Class 4 Case Studies – The U.S. Case Memberships of Environmental Combinations

  32. Kidder McHenry Glacial Till St. Charles Virgil Mayville Sable Loess Cap Case Studies – The U.S. Case The catenary sequence of soils in the area

  33. Legend Legend 3,500 meters 3,500 meters Kidder Kidder McHenry McHenry St.Charles Mayville Virgil Virgil Sable Sable 3,200 meters 3,200 meters Case Studies – The U.S. Case Spatial distribution of soils over the area

  34. Case Studies – The U.S. Case Validation of Results A total of 50 field points* * Points used to develop the soil-landscape model were not used for validation

  35. Case Studies – The Chinese Case The study area is located in a small watershed in North-Eastern China Area: 60km2. Elevation: 276 ~ 363m Average slope: 2 °. Landuse: corn and wheat farming. Parent material: silt loam loess.

  36. Mollic Bori-Udic Cambosols Typic Hapli-Udic Isohumosols -1 Class 1 Class 3 Lithic Udi-Orthic Primosols Class 9 Class 4 Class 11 Typic Hapli-Udic Isohumosols -2 Class 12 Class13 Class 10 Class 8 Class 7 Class 6 Class 2，5 Typic Bori-Udic Cambosols Fibric Histic-typic Haplic-Stagnic Gleyosols Pachic Stagni-Udic Isohumosols Case Studies – The Chinese Case The catenary sequence of soil types

  37. Case Studies – The Chinese Case Soil type map Subgroup soil map of study area N Legend

  38. Case Studies – The Chinese Case Evaluation of soil map Validation points: 64 b. regular sampling a. Random sampling Overall accuracy: 72% Along the transect : 80% c. transecting sampling

  39. Summaries The approach does seem to allow us to improve the efficiency of developing soil-landscape model of reasonable quality. The approach seemed to work better in areas with strong relief However, the limitations of fuzzy classification techniques is the key challenge facing this approach

  40. Thank You!

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