Terrain Attributes Aid Soil Mapping on Low-Relief Indiana

Transcription

1 Terrain Attributes Aid Soil Mapping on Low-Relief Indiana Landscapes Zamir Libohova 12 1,2 Edwin Winzeler 1 Phillip R. Owens 1 1 Agronomy, Purdue University, West Lafayette, IN 2 USDA/NRCS, Indianapolis, IN

2 Other Cooperators USDA-NRCS, Soil Survey Program, Indiana Travis Neely State Soil Scientist; Kevin Norwood MLRA Leader; Norm Stephens Soil Scientist; John Allen Soil Scientist. USDA-NRCS Field Office Howard County Cary Smith District Conservationist; Calvin Hartman District Conservationist Technician. USDA-ARS, National Soil Erosion Laboratory (Carbon Pool Stability) Diane Stott Research Scientist

3 Rationale and Background Soil map units are the lowest category of the national soil classification system that t provides spatial soil data with discrete boundaries and unique values; Soil series as part of the soil map unit is: the most homogeneous classes in the system of taxonomy; however, it uses ranges for most of the soil characteristics without providing a spatial characterization of these ranges in terms of their spatial distribution or occurrence on the landscape and/or catchment scale. While, ranges in soil properties are useful for characterizing soil heterogeneity and diversity, certain soil properties can be represented spatially on a continuum basis within soil map units when used in conjunction with other spatial data;

4 Rationale and Background Traditional Soil Map SSURGO with discrete boundaries and unique values for each polygon Continuum Fuzzy Soil Map based on terrain attributes

5 Northern Indiana Landscape Loess over till/outwash; Low relief; Corn and Soybean; Tile drained. Southern Indiana Landscape Loess over sandstone/shale; High relief; Forest and pasture; Not tile drained.

6 WeC2- Wellston 6-12% slope Catchment GID3-Gilpin Gil i 12-18% 18% slope Detailed Field Soil Survey Soil Pits SSURGO Transect Southern Indiana Landscape

7 WeC2- Wellston 6-12% slope Southern Indiana Landscape

8 SSURGO - Soil Information Wellston Gilpin Wellston cm 46 cm 140 cm 81 cm 81 cm 140 cm Soil Surface Minimum Solum Thickness Maximum Solum Thickness Predicted Water Table 91 Ranges for Solum Thickness

9 SSURGO - Soil Information 69 Gilpin Wellston Zanesville Deuchars Apalona Solum Thickness (cm) O l i R f l thi k d i l ( ) Overlapping Ranges for solum thickness and unique values (mean) for each polygon based on the Traditional Soil Map SSURGO

10 Predicted Soil Moisture Temporal Distribution A, AE and E Horizons Bt and Btx Horizons BC, C and Cr Horizons Precipitation Minimum range for horizon thickness Maximum range for horizon thickness

11 Loess Thickness Field Soil Survey

12 Field Soil Survey A p B t A p B t 2Bt x 2Bt x 3Bt C r C r 3Bt Soil Surface A p Surface Bt x Surface Predicted Water Table Lithic/paralithic Surface Paleosol Surface R/C r 3Bt

13 Materials and Methods

14 SSURGO - Soil Information 152 cm 102 cm Fincasle Brookston Cyclone/ Kokomo Brookston Fincasle 102 cm 152 cm 178 cm 91 cm 91 cm Soil Surface Minimum Depth to Dense Till Maximum Depth to Dense Till Predicted Water Table Ranges for Depth to Dense Till

15 SSURGO - Soil Information Thickness (cm) Depth to (cm) Soil Series Loess Argillic Epipedon Dense Till Carbonates Texture Fincastle SiCL Brookston SiCL Pewamo SiCL/CL/SiC/C Blount SiCL/CL/SiC/C Crosby SiCL/SiL/CL/SiC Kokomo SiCL/CL Depth to Carbonates a 200 a a a cm a a 40 0 Fincastle Brookston Pewamo Blount Crosby Kokomo Soil Series

16 Field Soil Survey 75 cm Fincasle 60 cm Brookston Cyclone/ Kokomo Brookston 60 cm Fincasle 125 cm 135 cm 75 cm 125 cm 78 cm 78 cm Soil Surface Depth to Dense Till Predicted Water Table Ranges for Depth to Dense Till

17 Traditional Soil Survey Tools Slope Survey Soil Property Data Collection Source: Stereo scoping Source: Source:

18 Legend counties DEM_5M Value High: Kilometers Low : : Northern Indiana Landscape

19 Legend counties DEM_5M Value High : Low : soilmu_a_in067 1: Kilometers Northern Indiana Landscape

20 Legend DEM_5M Value counties High : : Kilometers Low : Southern Indiana Landscape

21 Legend DEM_5M Value counties High : Low : : Kilometers Southern Indiana Landscape

22 Legend DEM_5M Value counties High : Low : : soilmu_a_in037 Southern Indiana Landscape Kilometers

23 Legend DEM_5M Value counties High : Low : : soilmu_a_in037 Southern Indiana Landscape Kilometers

24 Soil Development Water Soil Properties Soil-Landscape Southern Indiana Landscape Northern Indiana Landscape

25 Study Area Soils in Howard County Over 9300 soil polygons 18 series 3 major parent material regions 5 soils cover 80% of the land in Howard County

26 Soils in Howard County Fincastle/Brookston Region Blount/Pewamo Region Crosby/Brookston Region Are there relationships between these 5 soils and terrain attributes? p Can we use those relationships to generate continuum soil property maps (raster based)?

27 Soil Information Materials and Methods SSURGO Soil Map 1:20,000 Field observations Digital Elevation Model DEM Indiana DEM 1.5 m resolution Terrain analysis SAGA (System for Automated Geoscientific Analysis) software Topographical wetness index calculated with variations in Holmgren exponent values (Holmgren, 1994) Slope, altitude above channel network, other terrain attributes. Fuzzy logic inference Soil Landscape Interface Model (SoLIM V 1.5) A-Xing Zhu, Xun Shi, James E. Burt, Fei Du. Carbon and Nitrogen Total C and N by dry combustion

28 Topographic Wetness Index TWI TWI is a measure of the potential for water to accumulate in certain landscape positions: Where: a = the upslope area in m 2, per unit contour length, contributing flow to a pixel, and b = slope angle acting on a cell measured in radians (Quinn et al., 1995);

29 Procedures Establish soil-landscape relationships (data mining); Derive terrain attributes from DEM: Topographic Wetness Index (TWI); Altitude Above Channel; Curvature; Slope; Elevation; Soil parent material zone. Use these relationships to Create Rule Based Cases with SoLIM; Compare to field data. Materials and Methods

30 Soil Frequency Distributions vs. Terrain Attributes Terrain attribute: Altitude above channel network Terrain attribute: Curvature Potentially useful Probably not useful Brookston Fincastle Brookston Fincastle We observe the separation between peaks of the histograms Frequen ncy ABCN Frequen ncy Curvature

31 Soil Frequency Distribution vs. TWI Terrain attribute: Wetness Index TWI = 10 TWI = 13 Fincastle Brookston Frequency Potentially Very useful! Wetness index

32 Shaded Relief Elevation Model, 242 to 248 meters Wetness Index, 8 to 20 Slope, 0 to 4% SSURGO Miles Brookston Fincastle

33 Hardened Map Brookston Fincastle 97% 5%

34 Brookston/Fincastle Difference SSURGO SoLIM Mapp Brookston according to SSURGO but Fincastle according to SoLIM Fincastle according to SSURGO but Brookston according to SoLIM km

35 Terrain-Soil Matching for Brookston Fuzzy membership values (from 0 to 100%) 2% 100% km

36 Terrain-Soil Matching for Fincastle Fuzzy membership values (from 0 to 100%) 97% 5% km

37 Create Property Map with SoLIM To estimate the soil property p SoLIM uses: We already have S k ij the fuzzy membership value used to make the hardened soil map. D ij : the estimated soil property value at (i, j); S k ij: the fuzzy membership value for kth soil at (i, j); D k : the representative property value for kth soil. So we only need to specify D k, the representative values of the property of interest for each soil In this case, let s assign values to carbonate depth for Fincastle and Brookston in the east section of the county. Fincastle: 100 cm (low range of OSD) Brookston: 170 cm (high range of OSD)

38 Predicted depth to carbonates 100 to 170 cm km

39 Validation and Accuracy Assessment Randomly sample a number of points (~300) to develop baseline data for SoLim and check current soil series; Create a map of predicted soil properties and compare the field values with the predicted; Create confidence Create confidence intervals for predicted properties.

40 Validation and Accuracy Assessment Field Measured Loess Thickness vs. TASM/SoLIM and SSURGO SAMPLE_ID TASM/ SSURGO Field TASM/ SSURGO Field vs Field vs TASM vs SoLIM Measured SoLIM TASM SUURGO SSURGO EZ_111 Fincastle Fincastle EZ_115 Fincastle Brookston n Number of Matches Total Number Matches (%) Field vs TASM Field vs SSURGO TASM vs SSURGO % Loess Argillic Epipedon Dense Till Carbonate Texture Soil Property

41 Aerial Photography of Howard County

42 Total C by sample location vs. TWI Total C (%)

43 Total C by sample location vs. TWI Legend Total C(%) TWI <VALUE>

44 TWI vs. Total Soil Carbon Total C vs. Topographic Wetnes Index (TWI) Total C (% %) y = e 0.148x R 2 = TWI Total C (%) Legend TWI <VALUE> m

45 Advantages to fuzzy approach After collecting a number of random data points we can observe the difference between the predicted and actual values to document our error in terms of confidence intervals. Example statement: these predictions have been shown to be accurate within X cm 95% of the time in field trials. Each point in the soilscape is given a single estimated value rather than a range of values. We can publish formal terrain/soil relationships (histogram relationships) to help make our knowledge more accessible (less tacit). The process, though tedious, is less tedious than hand-drawing polygons Higher accuracy and consistency of soil mapping (we think!) Maps more compatible with other raster-based geospatial data

46 What is next? C stability on different landscape positions; Incubation of soil samples; Using stable isotope ratios 12 C/ 13 C to determine age of soil C at key landscape positions; Generating maps of spatial distribution of C stability; Nutrient Management Plans for P in collaboration with Brad Joern at Purdue.

47 Soil CO 2 Incubation Preliminary results

48 Soil CO 2 Incubation Preliminary results TWI Sample CO ug g -1 High TWI soil Low TWI 900 EZ_111 0 Day_3 Day_7 Day_10 Day_14 Time EZ_112 EZ_113 EZ_114 EZ_115 EZ_116 EZ_117 EZ_118 EZ_119 EZ_ EZ_1110-2

49 Thank You