DEPTH OF SOIL IN THE GOSS-GASCONADE-ROCK OUTCROP COMPLEX IN

CALLAWAY COUNTY, MISSOURI USING THE SOIL LAND INFERENCE MODEL (SoLIM)

A THESIS PRESENTED TO THE DEPARTMENT OF GEOLOGY AND GEOGRAPHY

IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE

By LYDIA VERBRUGGE

NORTHWEST MISSOURI STATE UNIVERSITY MARYVILLE, MISSOURI

April, 2006

DEPTH OF SOIL

Depth of Soil in the Goss-Gasconade-Rock outcrop complex in

Callaway County, Missouri Using the Soil Land Inference Model (SoLIM)

Lydia VerBrugge

Northwest Missouri State University

THESIS APPROVED

Thesis Advisor Date

Dean of Graduate School Date

Depth of Soil in the Goss-Gasconade-Rock Outcrop Complex in

Callaway County, Missouri Using the

Soil Land Inference Model (SoLIM)

Abstract

This study utilizes SoLIM (Soil Land Inference Model) to find the depth of soil in the

Goss-Gasconade-Rock outcrop complex in Callaway County, Missouri. The attributes

of the rugged terrain and the diverse vegetation within the soils complex are indicators of

soil depth. Shallow soils and deep soils are modeled using combinations of the

environmental indicators and fuzzy logic. Accuracy of the model is determined through

field verification. First, environmental information specific to the study area is obtained

through an interview with a soil scientist with local expertise in the soil-environmental

relationship. Then, two tacit points are designated using 3dMapper software to represent

the depth classes: shallow (0 to 20 inches), and deep (greater than 21 inches). These

points are used by the case-based reasoning (CBR) inference engine so environmental

variables such as slope, aspect, vegetation, landuse/landcover, curvature, and relative

position generate individual raster-based fuzzy membership maps of soil depth class.

During the process fuzzy membership maps are refined numerous times in order to

capture the soil scientist’s vision of the soil landscape. Hardened soil maps are created

from the integration of the fuzzy membership maps ultimately modeling the depth of soil.

Forty-two field sample points validated against the hardened soil map using SoLIM’s

error matrix find 52% accuracy of the model. Conclusively, data resolution, number of

iii

field sample points, and alteration of the fuzzy membership map for shallow soils on

northern aspects may increase accuracy of depth of soil modeled.

iv

TABLE OF CONTENTS

LIST OF FIGURES .......................................................................................................... vii

LIST OF TABLES ………………………………………………………………………ix

ACKNOWLEDGMENTS ……………………………………………………………….x

LIST OF ABBREVIATIONS …………………………………………………………..xi

CHAPTER 1: INTRODUCTION ………………………………………………………..1

1.1 Research Background ………………………………………………………4 1.2 Research Objectives …………………………………………………………..6 1.3 Study Area ……………………………………………………………………7

CHAPTER 2: LITERATURE REVIEW ………………………………………………...9

2.1 Soil Classification …………………………………………………………….9 2.2 Fuzzy Logic ………………………………………………………………...11

2.3 Fuzzy Logic in Soil Classification ……………………………….………….12 2.4 Soil Land Inference Model (SoLIM) ………………………………………..13

CHAPTER 3: METHODOLOGY …………………………………………………….16

3.1 Research Issues and Problems ………………………………………………16 3.2 Description of Data ………………………………………………………….17

3.3 Research Methodology ……………………………………………………..18 3.3.1 Spatial Data Collection …………………………………………...20 3.3.2 SoLIM Software Setup ………………………………………..….20 3.3.3 Knowledge Acquisition ………………………………………...…20 3.3.4 Construct GIS Database …………………………………………...23 3.3.5 Perform Soil Inference …………………………………………….26

CHAPTER 4: ANALYSIS RESULTS AND DISCUSSION ………………………….30

4.1 Parameters setting ………………………………………………………….30 4.2 Shallow soils fuzzy membership inference series ………………………….32 4.3 Deep soils fuzzy membership inference series ……………………………..37 4.4 Preliminary results ………………………….………………………………43 4.5 Refined results ………………………………………………………………44 4.6 Model verification …………………………………………………………..48 4.7 Discussion ………………………………………………………………..…53

v

CHAPTER 5: CONCLUSION …………………………………………………….…..54

5.1 Limitations ………………………………………………………………….54 5.2 Further improvement and future research …………………………………...57

REFERENCES...........................................................................................................…...59

vi

LIST OF FIGURES

Figure 1. Pedometrics (PM) ……………………………………………………………2

Figure 2. Extent of Goss-Gasconade-Rock outcrop complex in Callaway County, Missouri. ………………………………………………………………………….8

Figure 3. Traditional soil survey approach ……………………………………………10

Figure 4. SoLIM process ………………………………………………………………..19

Figure 5. SoLIM data directory structure ………………………………………………21

Figure 6. The soil-environment description of the Goss-Gasconade-Rock outcrop complex in Callaway County, Missouri …………………………………………24

Figure 7. Preparation of data layers for 3dMapper ……………………………………..27

Figure 8. The basic forms of membership function …………………………..………...27

Figure 9. Setting up tacit points and their width files ………………………………….29

Figure 10. Shallow soil fuzzy membership map from the initial inference …………….33

Figure 11. Shallow soil fuzzy membership map from the second inference ………….35

Figure 12. Shallow soil fuzzy membership map from the third inference …………….36

Figure 13. Deep soil fuzzy membership map from the initial inference ……………….39

Figure 14. Deep soil fuzzy membership map from the second inference ………………40

Figure 15. Deep soil fuzzy membership map from the third inference ……………….41

Figure 16. Deep soil fuzzy membership map from the fourth inference ……………….42

Figure 17. Hardened map based on the third inference of shallow soil fuzzy membership and the fourth inference of the deep soil fuzzy membership…………………….43

Figure 18. Shallow soil fuzzy membership map from the final inference …………….45

Figure 19. Deep soil fuzzy membership map from the final inference ……………….46

Figure 20. Final hardened map …………………………………………………………47

Figure 21. Field crew with tools ………………………………………………………..49

Figure 22. Field crew traversing the landscape ………………………………………..49

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Figure 23. Example of field plots ………………………………………………………51

Figure 24. Error matrix results ………………………………………………………….51

viii

LIST OF TABLES

Table 1. Data sources …………………………………………………………………18

Table 2. Soil depths in study area ………………………………………………………22

Table 3. Soil depths environmental variables modeled in study area …………………..22

Table 4. Necessary environmental variables to determine soil depth …………………..23

Table 5. Key to separate soil depths ……………………………………………………23

Table 6. DEM derivatives ………………………………………………………………26

Table 7. Shallow soil and deep soil tacit point values ………………………………….31

Table 8. Final shallow point and deep point curve types ……………………………….32

Table 9. The inference parameters for shallow soil fuzzy membership series …………33

Table 10. The inference parameters for deep soil fuzzy membership series ………….38

Table 11. The final inference parameters for soil fuzzy membership series …..……….45

Table 12. Field notes ……………………………………………………………………50

ix

ACKNOWLEDGEMENTS

Thesis Advisory Committee: Dr. Yi-Hwa Wu, Thesis Advisor Dr. Ming Hung, Committee Member Dr. Gregory Haddock, Committee Member

Allowing use of USDA-NRCS property such as GPS, field vehicles, tools, and software:

Roger Hansen, Missouri State Conservationist, USDA-NRCS Dennis Potter, Missouri State Soil Scientist, USDA-NRCS

Caryl Radatz, Soil Scientist, USDA-NRCS Expertise in local landscape for tacit points:

Caryl Radatz, Soil Scientist, USDA-NRCS Ralph Tucker, Soil Scientist, USDA-NRCS Dr. Fred Young, Soil Scientist, USDA-NRCS

Use of personal tools for field verification:

Dennis Potter, Missouri State Soil Scientist, USDA-NRCS Bill Pauls, Soil Scientist, USDA-NRCS

Clayton Lee, Soil Scientist, USDA-NRCS Teresa Gerber, Soil Scientist, USDA-NRCS Reggie Bennett, Wildlife Biologist, Missouri Department of Conservation

Field Crew: Caryl Radatz, Soil Scientist, USDA-NRCS Michael VerBrugge

Debbie Burgess, Cartographic Technician, USDA-NRCS Peter Kasprzak, Cartographic Technician, USDA-NRCS Alexis Gardner, Cartographic Aide, USDA-NRCS Katie Philbrick, Soil Scientist, USDA-NRCS Nathan Wood, Cartographic Technician, USDA-NRCS Patrick Short, Cartographic Aide, USDA-NRCS Jeanette Short, Earth Team Volunteer, USDA-NRCS

Software support and thesis recommendations: Dr. A-Xing Zhu, Professor of Geography, University of Wisconsin-Madison Dr. Jim Burt, Professor of Geography, University of Wisconsin-Madison Michael Smith, Michael Baker Corporation

Callaway County landowners:

Gary Scheal Bruce DeMurio

x

LIST OF ABBREVIATIONS

ASCII American Standard Code for Information Interchange

CBR Case-based reasoning DEM Digital Elevation Model DOQQ Digital Orthophoto Quarter Quadrangle GISc Geographic Information Science GPS Global Positioning System MLRA Major Land Resource Area MoRAP Missouri Resource Assessment Partnership MSDIS Missouri Spatial Data Information Service NAIP National Agriculture Imagery Program PM Pedometrics SI Semantic Import Model SoLIM Soil Land Inference Model SSURGO Soil Survey Geographic Database TM Thematic Mapper USDA-FSA United States Department of Agriculture,

Farm Service Agency USDA-NRCS

United States Department of Agriculture, Natural Resources Conservation Service

USDA-SCS United States Department of Agriculture, Soil Conservation Service

UTM Universal Transverse Mercator

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CHAPTER 1

INTRODUCTION

The traditional mapping approach of soil survey may soon be enhanced by Geographic

Information Science (GISc) (Zhu et al., 2001). Currently, soil surveys conducted by the

United States Department of Agriculture, Natural Resources Conservation Service

(USDA-NRCS, and formerly known as Soil Conservation Service, SCS) rely heavily on

field work, and professional soil scientists’ judgment of soil-landscape relationships. It

is a time consuming and somewhat subjective process that is prone to inconsistent

concepts and conflicting methodologies between surveys. The typical soil survey takes

years to complete. Each survey contains maps that are completed under varying

conditions while advances in soil science methodology change the focus and evaluation

of soils in subsequent surveys. Still, field verification and soil scientists’ experience

with the landscape are valuable and required methods of mapping soils. The human

experience with the landscape is the key to determining soil-landscape relationships. The

soil survey initiative may benefit from a method that captures the knowledge of the soil

scientist who has experienced and tested the defining characteristics of the landscape in a

logical and reliable manner.

Pedometrics (PM), the quantitative assessment of soils, is best defined as an

interdisciplinary science integrating Soil Science, Applied Statistics/Mathematics and

Geo-Information Science (Figure 1). McBratney (International Union of Soil Sciences,

1

2005), the Soil Scientist who founded the Commission on Pedometrics in the

International Union of Soil Sciences, has noted that PM:

“…can include numerical approaches to classification -- ways of dealing with a supposed deterministic variation. Whereas simulation models per se might not be considered pedometrics (though to dismiss models of pedogenesis would be inappropriate, even foolish) models that incorporate uncertainty by adopting chaos, statistical distributions or fuzziness should be embraced. The definition is certainly incomplete but as the subject grows its core will become well defined. Nevertheless, it will always intergrade to all areas of soil science and quantitative methods and no definition by circumscription or complete enumeration of methods can be unequivocal." (International Union of Soil Sciences, 2005)

Pedogenesis is the formation of soil profiles which are the “vertical arrangement of layers

of soil down to the bedrock” (United States Geological Survey, 2002), and are

symbolized as the individual soil mapping unit delineations in soil surveys. Pedogenesis

and soil profile modeling do not fall under the title of PM, but are recognized in PM as a

Figure 1. Pedometrics (PM) (Source: International union of soil sciences, 2005)

2

practical way of studying soils. Nevertheless, soil modeling provides a way to apply

quantitative GISc methods to help refine the subjective nature of some techniques in

traditional soil mapping.

This study intends to improve soil mapping efficiency and consistency with GISc.

USDA-NRCS in Missouri has completed all initial surveys, and has digital products for

every county. “Maintenance and update” is the next phase of soil survey in Missouri.

USDA-NRCS is interested in refining existing soil maps and defining methodology that

will accelerate the next soil survey phase (Radatz, 2005). There are many indicators of

type of soil; one of which is depth to bedrock. Depth of soil maps may facilitate field

mapping and verification of soil type. This study proposes to deviate from traditional soil

survey polygon-based maps and explore raster-based soil modeling. The approach

allows finer details of spatial gradation of soils to be captured. It also permits the use of

fuzzy logic and fuzzy membership of soil properties in individual pixels and to express

the transition of soil depth.

Under fuzzy logic, the soil at a given pixel can be assigned to more than one soil class

with varying degrees of class assignment (Burrough et al., 1992; Burrough et al., 1997;

McBratney and De Gruijter, 1992; McBratney and Odeh, 1997; Odeh et al., 1992). These

degrees of class assignment are referred to as fuzzy memberships. This fuzzy

representation allows the soil at each pixel to bear a partial membership in each of the

prescribed soil classes (Zhu et al., 2001).

3

A soil complex consists of areas with two or more component soils, or component soils

and a miscellaneous area, as well as acceptable inclusions in either case. The individual

component soils and miscellaneous area are intricately mixed or so small in size that they

cannot be delineated on the map at the scale used (U.S. Department of Agriculture,

Natural Resources Conservation Service, 2003). This study proposes to model the depth

of soil in the Goss-Gasconade-Rock outcrop complex, 5 to 35 percent slopes, through

fuzzy logic. The depth of soil data may serve as a tool for USDA-NRCS in Missouri to

separate the individual soil components of the Goss-Gasconade-Rock outcrop complex

into individual soil mapping units of the newly imposed statewide legend.

1.1 Research Background

Traditionally soil delineations are polygon-based. Each polygon represents an area that is

dominated by one taxon. In nature, soils may or may not change abruptly but are usually

transitional. Most polygons do not capture subtle changes or account for the numerous

less dominant soils called minor components.

Mapping soils is a very expensive and time consuming process. Field soil scientists

spend years judging the relationships of soils and the landscape. The knowledge soil

scientists gain as they traverse a survey area is indispensable. The challenge is to find a

method to capture the soil scientists’ understanding of the landscape in a logical and

consistent manner. It then may be possible to recreate or predict soils once certain

characteristics of the soil landscape relationship are translated into a model.

4

Zhu developed a Soil-Land Inference Model (SoLIM) (Zhu, 2005). The theory was

based on the Dokuchaeiv and Hilgard’s soil factor equation and Hudson’s soil-landscape

model but the model has been refined by using fuzzy logic, case-based reasoning (CBR),

and GISc techniques. Dokuchaeiv and Hilgard believed soils were formed as

independent natural bodies whose parent material was influenced over time by climate

and living organisms (Wilding et al. 1983). Hudson’s soil-landscape model contends that

soils may be predicted for a location if the environmental conditions are known for that

point (Zhu et al., 2001). These theories are the basis for SoLIM. Zhu promotes the use

of GIS/remote sensing, artificial intelligence techniques, and fuzzy logic concepts in

natural resource management and environmental modeling. Soils represented in raster

format using fuzzy logic allow each pixel to be “assigned to more than one soil class with

varying degrees of class assignment” (Zhu et al., 2001). Case-based reasoning is

“knowledge represented in specific cases to solve a new problem” (Shi et al., 2004).

This means that soil scientists may create fuzzy rules to describe the landscape so

predictions can be made about similar landscapes.

Soils have various relationships with the landscape and must be treated as unique

occurrences. However, the applications of predictive soil mapping are still practicable. A

study of the depth of soil in the Goss-Gasconade-Rock outcrop complex will lead to

similar studies with consistent results.

5

1.2 Research Objectives

USDA-NRCS is in need of methods for expediting intensive soil mapping. In some

cases the use of soils data for natural resource management requires a higher level of

detail than what is currently available. The Soil Survey of Callaway County, Missouri

states:

“The presence of inclusions in a map unit in no way diminishes the usefulness of accuracy of the soil data. The objective of soil mapping is not to delineate pure taxonomic classes of soils but rather to separate the landscape into segments that have similar use and management requirements. The delineation of such landscape segments on the map provides sufficient information for the development of resource plans, but onsite investigation is needed to plan for intensive uses in small areas.” (Horn, 1992).

USDA-NRCS in Missouri is willing to explore GISc methods that will facilitate soil

survey maintenance and update although the current soils data available for Missouri

already serves as an integral tool for natural resource management (Radatz, 2005).

Scale limits delineation of soil mapping units in highly variable landscapes although

more detailed text descriptions of soils may exist. In the vector data model, attributes are

uniformly assigned to polygons and, the corresponding encompassed ground areas

(DeMers, 2002). Internal variations within a polygon are typically ignored by most data

users. However, this is the current format of soils data available from USDA-NRCS.

An advantage of using raster-based modeling to quantify soils is that internal variations

may be represented and serves as a source for analysis with other raster datasets. Using a

combination of GISc techniques and professional soil scientist knowledge of the local

landscape, what are the depths of soil in the Goss-Gasconade-Rock outcrop complex in

Callaway County, Missouri when represented as continuous areas of fuzzy membership?

6

Overall, the nature of this study is to find an alternate way to represent soil as a

continuous landscape, unbroken by polygon boundaries. This raster-based approach to

soil modeling is somewhat unfamiliar to those who map soils, and to those who have

grown accustomed to viewing polygon-based soil maps. However, when data-users

begin to recognize the value of modeling transitional areas of soils, this method of

creating fuzzy membership maps may validate itself. The goal of this study is to model

the depth of soil within a specific soil mapunit originally mapped by a soil scientist as a

polygon. An aspiration of this study is to inspire those creating soil maps to consider

raster-based modeling of soil types or any other characteristic of soil such as depth class.

1.3 Study Area

Callaway County is located in central Missouri. Its southern portion is dominated by the

alluvial flood plains of the Missouri River. The Goss-Gasconade-Rock outcrop complex,

5 to 35 percent slopes soil mapping unit, is nestled among the forested hills adjacent to

the Missouri River flood plain (Figure 2). A component of this complex is made of Goss

soil on moderately steep to very steep upper backslopes and is well drained, meaning

water is removed from the soil readily, but not rapidly (Horn, 1992). The Gasconade

soils and rock outcrops are intermingled on short, steep upland slopes below the Goss soil

on the landscape (Horn, 1992). The Goss-Gasconade-Rock outcrop complex makes up

92,648 acres or 17.1 % of the Callaway County soil survey (U.S. Department of

Agriculture, Natural Resources Conservation Service, 2005). This study will focus on

7

the contiguous sections of the complex in southern and central Callaway County. This

portion of the complex is most accessible for field verification.

Figure 2. Extent of Goss-Gasconade-Rock outcrop complex in Callaway County, Missouri.

8

CHAPTER 2

LITERATURE REVIEW

The multiple methods this study is based on are summarized in this chapter. It is

important to understand how conventional soil classification is performed and how it

differs from fuzzy soil classification used by SoLIM. The following sections describe

traditional soil classification, fuzzy logic, and how they are used together in SoLIM.

2.1 Soil Classification

Soil surveys are traditionally produced through a series of field observations, detailed

analysis, and cartography (Horn, 1992). Figure 3 illustrates the traditional soil survey

process.

Soils are placed in taxonomic classes based on their physical and chemical properties. A

soil delineation, shown as a polygon on a map, shows the location of a dominant class or

type of soil or two or three dominant types of soil. The Goss-Gasconade-Rock outcrop

complex consists of two major kinds of soil and rock outcrops that occur in predictable

patterns but are so intermingled they could not be shown separately at the scale selected

for mapping (Horn, 1992). Management techniques, particularly for forestry and

wildlife, are different for the different soil types, especially in regards to depth of soil and

aspect. Predicting where the different soils occur within this complex could benefit land

managers.

9

Landscape Observation Document the slope characteristics, drainage patterns, vegetation, and type of bedrock.

Dig Soil Pits and Collect Samples Study the soil horizons (sequence of natural layers in the soil).

Develop Model of Soil Formation Combine landscape knowledge with soil knowledge.

Record Soil Characteristics Note color, texture, soil aggregates, rock fragments, plant roots, and reaction to chemicals.

Assign Soil to Taxonomic Classes Name soils in the survey area.

Define Significant Natural Bodies of Soil Delineate polygons representing a taxonomic class using an aerial photograph.

Figure 3. Traditional soil survey approach

10

2.2 Fuzzy Logic

"It is appropriate to use fuzzy sets whenever we have to deal with ambiguity, vagueness and ambivalence in mathematical or conceptual models of empirical phenomena" (BURROUGH 1989, Page. 479).

Many GIS applications, including land use categories, soil type, land cover classes, and

vegetation types, are impossible to establish membership cleanly between the mapping

boundaries. Two soil scientists mapping at the same location might disagree with each

other, not because of measurement error, but because the classes themselves are not

perfectly defined and because opinions vary. Also, objectives differ from one soil survey

to another resulting in diverse limits and ranges of attribute data (Soil Survey Division

Staff, 1993). In fuzzy logic, an object’s degree of belonging to a class can be partial.

One of the major attractions of fuzzy logic is that they appear to be able to deal with

features that are not precisely defined. The boundaries of classes are no longer clean and

crisp, and the sets of things assigned to a category can be fuzzy.

Spatial modeling may apply fuzzy logic to assign levels of yes or no associated to a class

(DeMers, 2002). Gradations of membership are allowed to define space instead of

limiting it to a quantitative number. In other words, an infinite number of intermediate

values may be assigned to a location in order to define degrees of truth (Worboys and

Duckham, 2004). It is common but not necessary to use a real number between 0 and 1

to measure the fuzzy membership to a class. The range between 0 and 1 represent the

strength of the relationship of a location to a fuzzy set (Worboys and Duckham, 2004).

This approach allows locational uncertainly to be visually modeled.

11

Fuzzy logic has the potential to improve soil classification because more intricate soil

patterns may be represented. Areas where small polygons were not kept due to scale

restraints may be more accurately modeled in a raster-based fuzzy logic environment.

2.3 Fuzzy Logic in Soil Classification

Fuzzy logic in soil science has become more prevalent in recent years. McBratney and

Odeh (1997) proposed soil depth as a possible soil fuzzy property. Their soil

classification methods are two different but complimentary approaches. Fuzzy C-Means,

which partitions space into natural occurring groups, or Semantic Import Model (SI),

classifies the class limits based on soil scientists’ experience. Composite maps of the

study sites produced enough information to ascertain site suitability using the fuzzy

approach. Examples of soil composite maps included continuous classes of textural

profiles and depth to bedrock. Their research demonstrates soil classification and

mapping, land evaluation, modeling and simulation of soil physical properties as

applications for fuzzy sets in soils.

Hengl and Rossiter (2003) utilize nine terrain parameters which were extracted from

DEMs (ground water depth, slope, plan curvature, profile curvature, viewshed,

accumulation flow, wetness index, sediment transport index, and the distance to nearest

watercourse) to classify landforms in eastern Croatia. They show that maps are much

easier to reproduce if more information about soil forming factors is considered. The

research results claim fuzzy classification algorithms offer better alternatives for

landscapes that may have uncertain classifications. It reveals the potential of

12

visualization of landscapes paired with fuzzy classification could improve methods to

edit soil boundaries of existing maps and improve their spatial accuracy within an

existing GIS. It also demonstrates that a more detailed survey could result in maps that

could be used for site-specific management.

2.4 Soil Land Inference Model (SoLIM)

SoLIM combines soil scientist’s knowledge with GIS techniques under fuzzy logic for

soil mapping (Zhu et al., 2001). There are three major components of SoLIM. The first

component is a similarity model for representing soils as a continuum in raster format.

This key concept identifies the benefit of each pixel representing transitional areas

instead of a single soils class. The second component is a set of automated inference

techniques. These techniques are the process that determines the similarity of each pixel

to the typical environment of each soil category. The inference engine evaluates the

environmental data for each pixel in the dataset and compares it to the knowledge base to

uncover its membership value. The third component is soil information products

generated by SoLIM. These products are soil maps derived from the combination of

individual fuzzy membership maps (Zhu et al., 2001).

Ultimately SoLIM is intended to help soil scientists produce soil survey and soil survey

products more efficiently and effectively (U.S. Department of Agriculture, Natural

Resources Conservation Service, 2004). USDA-NRCS recognizes the future of SoLIM

and its applicability within the agency. A regional newsletter states that SoLIM will be a

routine tool in every MLRA (Major Land Resource Area) Project Office (Carpenter,

13

2004). This establishes the confidence users have in SoLIM and their plan to utilize its

benefits for future soil survey products.

Hengl and Rossiter’s (2003) research emphasizes the application of fuzzy logic as a

better alternative for landscapes that may have uncertain classifications. McBratney and

Odeh (1997) actually site soil depth as a possible soil fuzzy property. This study will

focus on a soil complex, or soil mapunit whose properties are not easily separated at the

scale mapped.

Tomer and James (2004) conduct realistic terrain analysis and compare the results with

the model soil survey. Dissected terrains appeared appropriately placed, where near-level

terrains appeared more artificial. They conclude that targeting of conservation practices

based on 30 meter grid terrain analysis can be consistent with soil survey information, but

to a degree that varies by landscape and resource concerns. Their study succeeds in

producing 66% to 74% accuracy when modeling hydric, drainage, and topsoil-thickness

groups. This study will extend Tomer and James’ terrain analysis research to compare

the modeled results with its representative soil survey.

The separate elements: fuzzy logic, case-based reasoning, terrain analysis, and uncertain

terrain classifications are combined in this research to separate a soils complex by classes

of soil depth. The use of fuzzy logic and case-based reasoning are a move toward more

consistent soil modeling. Multiple variables, or data layers, may be used to solve for a

soil class or soil property. Modeling soils by levels of membership to a soil class or soil

14

property are an advantage because it recognizes areas of transition in soils rather than

limiting them to the edge of a polygon.

Shi et al. (2004) improve the SoLIM software to apply case-based reasoning and fuzzy

logic to create soil maps. One of their successes is the separation of a soils complex into

individual soil types using SoLIM. The improved SoLIM is also capable of separating a

soils complex into soil depths. Therefore, this study will use SoLIM software to utilize

fuzzy logic and case-based reasoning to separate the depth of soil within a soils complex.

15

CHAPTER 3

METHODOLOGY

3.1 Research Issues and Problems

The Goss-Gasconade-Rock outcrop complex, 5-35 percent slopes, in Callaway County, is

a beautiful yet rugged terrain. Unfortunately, this rough landscape poses problems for

the land user, the soil scientist who maps it, and researchers conducting field

investigations. It is possible that the landuse limitations within the soil complex

influenced the decision to map the soil as a complex, instead of separating it into

individual soils on the conventional polygon-based soil map.

This limitation leads to the question of how diverse the landscape is. Are there enough

environmental variables to distinguish one type of soil or soil property from another

within the complex? If so, what is the best way to model these? A model emphasizing

areas of transition should be one component. The model should also allow the use of

multiple variables to define the landscape by a person who has experience with it.

The solution is the use of SoLIM to model depth of soil within the Goss-Gasconade-Rock

outcrop complex. The process uses expert knowledge documented as tacit points to

create fuzzy membership maps. Areas of transition are modeled and may be tweaked by

changing the values associated with the environmental variables used in the inference.

16

This research has the benefit of applying methodology from the SoLIM process. The

challenge for this study is choosing the correct environmental variables to create the

model ultimately solving for the depth of soil on such a demanding landscape.

3.2 Description of Data

Most of the data used in this research are created by USDA-NRCS or USDA-FSA, and

are provided or projected into Universal Transverse Mercator (UTM) coordinate system,

zone 15, measured in meters in North American Datum 1983. All raster layers created

had a common grid cell size of 30 by 30 meters with 1704 cells on the x-axis and 1656

cells on the y-axis. The spatial extents in UTM coordinates are 567081.490882 meter on

the west side, 618201.490882 meter on the east side, 4319861.459406 meter on the north

side, and 4270181.45406 meter on the south side. The bounding coordinates in

latitude/longitude are West -92.229877 degrees, East -91.634603 degrees, North

39.025225 degrees, South 38.572234 degrees.

Table 1 lists the data applied in this study. The expert knowledge about the soil-

landscape relationships, such as environmental variables and geographic locations of

typical soil types, utilized in this study are provided by the professional USDA-NRCS

Missouri soil scientists. Environmental data such as Digital Elevation Models (DEMs),

vegetation, orthophotography, and geology are used to construct the GIS database. The

Soil Survey Geographic Database (SSURGO) for Callaway County, Missouri, is the

source of the soil data.

17

Table 1. Data sources

Data Type Data Source

10 meter DEMs

USDA-NRCS, made available on CD by Missouri Spatial Data Information Service (MSDIS), Columbia, Missouri, delivered 2005.

30 meter Landuse/Landcover

USDA-NRCS, released by Missouri Resource Assessment Partnership (MoRAP). Originally based on 1990 TM satellite data, new release based on 2003 county based DOQQ mosaics, delivered August 23, 2005.

1:24,000 SSURGO

USDA-NRCS, SSURGO dataset downloaded from: http://soildatamart.nrcs.usda.gov/

1 meter NAIP Orthophotography

USDA-FSA Aerial Photography Field Office, delivered 2004.

Key environmental variables that distinguish one soil from another

USDA-NRCS Missouri soil scientists

Geographic location where soil typically occurs (tacit points)

USDA-NRCS Missouri soil scientists

3.3 Research Methodology

SoLIM overcomes the limitations of conventional soil survey by using a collection of

common data layers such as elevation, slope, aspect, slope gradient, profile and planform

curvatures, drainage areas, wetness indices, distance to streams, and distances to ridges to

predict soil landscapes. The data layers’ attributes are provided by local soil scientists

who supply the list of environmental variables to be considered. The layers in raster

format are then analyzed under fuzzy logic using local soil scientists’ relational criteria.

The results are values which represent each pixel’s membership in a soil class. Each

pixel, commonly 30 by 30 meters, is associated with many soil classes, instead of being

limited to one discrete unit. Gradual and subtle changes in soils are detected and

therefore illustrate a more detailed model of the landscape.

18

Figure 4 outlines the steps suggested in the “SoLIM Operational Manual” (SoLIM LAB,

2004). There are five basic steps in order to properly prepare to use SoLIM. The

following is the detailed discussion of each fundamental step in the SoLIM setup for this

study.

Step 1: Spatial Data Collection Environmental layers are collected and organized so they may later be converted to a format used by SoLIM and 3dMapper software.

Step 2: SoLIM Software Setup The directory structure is setup to accommodate SoLIM and 3dMapper software.

Step 3: Knowledge Acquisition Environmental information specific to the study area is obtained through an interview with a soil scientist.

Step 4: Construct GIS Database Data layers are manipulated as needed then converted to ASCII format. 3dMapper converts files into .3dm and .3dr files.

Step 5: Perform Soil Inference Tacit points are chosen and environmental layer variables are given width values. Inferences may be run multiple times by changing width values to achieve desired results.

Figure 4. SoLIM process

19

3.3.1 Spatial Data Collection

Data collected in October, 2005 were: 10-meter DEMs (USDA-NRCS), 30-meter

Landuse/Landcover (through USDA-NRCS, released by Missouri Resource Assessment

Partnership), 1:24,000 SSURGO (USDA-NRCS), NAIP Orthophotography (USDA-

FSA), and key environmental variables (USDA-NRCS Missouri soil scientists), and tacit

points (USDA-NRCS Missouri soil scientists).

Data not collected were 1:500,000 geologic map of Callaway County, although it was

considered early in the research’s development stage to do so. Missouri soil scientists

who were interviewed agreed that the scale of the geology maps would not help model

soils for the study area. Another previous consideration was to use tacit points selected

from the Missouri Cooperative Soil Survey Website. Ultimately this method was not used

because further investigation of the SoLIM software demonstrated that tacit points should

be selected from evaluating each pixel for the appropriate environmental values.

3.3.2 SoLIM Software Setup

SoLIM software creates the maps and 3DMapper software is used to view the maps.

Both were acquired in October, 2005. Figure 5 illustrates the detail description of

directory structure needed for operating SoLIM.

3.3.3 Knowledge Acquisition

Specific environmental information was obtained through an interview with Caryl

Radatz, a Missouri soil scientist, in October, 2005 (Radatz, 2005). Based on the

20

discussion during the interview, the depth of soils for the study area was defined as listed

in table 2. However, for this study each depth class was not modeled. It was unlikely

that environmental layers could be used to distinguish five depth classes. Depth classes

were then consolidated to two depth classes (Table 3).

Gossgas2 (main directory)

data Stores base map (DEM and orthophotography), and other environmental layers used to perform inference.

header Stores a text file that defines the spatial extent, and a text file with a list of all environmental layers.

knowledge Stores tacit points.

relation Stores width files (information about the distribution curve).

result Stores inference results.

Figure 5: SoLIM data directory structure

21

Table 2: Soil depths in study area

Depth (inches) Depth Class 0-10 Very Shallow 10-20 Shallow 20-40 Moderately Deep 40-60 Deep >60 Very Deep

Table 3: Soil depths environmental variables modeled in study area

Depth (inches) Depth Class 0-20 Shallow >21 Deep

Caryl Radatz (2005) also established six environmental variables that would be useful to

determine soil depth in the Goss-Gasconade-Rock outcrop complex in Callaway County,

Missouri (Table 4). Figure 6 illustrates the environmental description of soils. Based on

her experience of the study area, the six environmental variables are ranked by the

potential importance for determining depth of soil (Table 5). The ranking serves as a

guide for selecting tacit points. Often difficulties arise when searching for appropriate

placement of a tacit point. If a compromise on the tacit point’s values must be made in

the case that no perfect location exists, the ranking guide helps set priorities of which

environmental variable is most important for that particular soil depth.

22

Table 4: Necessary environmental variables to determine soil depth

Environmental Variables aspect slope planform curvature profile curvature relative position vegetation

Table 5: Key to separate soil depths

Rank Environmental Variable Shallow Deep 1 Aspect South/Southwest North/Northeast 2 Vegetation Cedars/Sparse Deciduous 3 Curvature Convex Linear to Concave 4 Relative Position Below Above 5 Slope Shorter Longer

3.3.4 Construct GIS Database

Preparation of data before its conversion to ASCII format was done using ESRI

(Environmental Systems Research Institute) ArcGIS software and extensions version 9.0.

The first step was to create a 30 meter raster file of the soils in the Goss-Gasconade-Rock

outcrop mapunit and a 30 meter raster file of the soils with a 500 meter buffer. The first

file was later used as a mask in the inference process. The buffered soils were used as the

clip for all other environmental layers as they were prepared to be converted to ASCII.

Both were derived from a shape file. Thirty meter resolution was used due to

landuse/landcover being the lowest resolution of environmental variables.

23

(a) The environmental description of deep soil

(b) The environmental description of shallow soil.

Figure 6. The soil-environment description of the Goss-Gasconade-Rock outcrop complex in Callaway County, Missouri

24

Five of the environmental variables are extracted from elevation data using ArcGIS

software and extension version 9.0. The DEM derivatives were calculated using the

buffered soils as the analysis extent. Table 6 lists the DEM derivative and the tools for

calculating the environmental variables.

Relative position was found by using neighborhood statistics to determine the low and

high areas of the study area. The focal minimum and maximum, using a 50 meter radius

with the ArcGIS neighborhood statistics tool, created two DEM derivatives (focalmin and

focalmax). The raster calculator created a new raster file from the relative position

formula (equation [1]). High relative positions approach 1 and low relative positions

approach 0.

([dem] - [focalmin]) / ([focalmax] - [focalmin])..............(1)

Vegetation data (MoRAP landuse/landcover) and NAIP photography was clipped based

on the spatial extent of the buffered soils dataset. All environmental layers were

converted from 30 meter raster files to ASCII using ArcToolbox. ASCII files were

imported into 3dMapper Software and saved as 3dm files (base file containing dem and

orthophotography), and 3dr files (all other environmental layers). Figure 7 shows the

detailed list of environmental layers and the conversion process between software (from

ArcGIS to 3dMapper).

25

Table 6. DEM derivatives

DEM Derivative ArcGIS Tool or Extension Aspect Spatial Analyst Planform Curvature ArcToolbox Curvature Tool Profile Curvature ArcToolbox Curvature Tool Relative Position Spatial Analyst/Raster Calculator Slope Spatial Analyst

3.3.5 Perform Soil Inference

A tacit point is expected to be the ideal representative of a certain soil depth. The

environmental conditions at the tacit point are presumed to be “ideal” for the formation of

the certain soils depths based on the soil-landscape model. Two tacit points are placed on

the map, one for shallow soils and the other for deep soils. The best shallow soil tacit

point is preferably on a south/southwest aspect, has a convex planform and profile

curvature, a lower relative position, and is on short, steep slopes. The best deep soil tacit

point is preferably on a north/northeast aspect, has a linear to concave planform and

profile curvature, a high relative position, and is on longer slopes.

The membership values are set as 1.0 for the tacit points. The most undesirable condition

will be set as 0 in membership value. Each tacit point uses the environmental layer to

create the fuzzy membership maps. The membership value of the sample soil to the

certain soil depth increases when the value of environment variables is close to the

“ideal” condition. The membership function between an environmental variable and the

soil depth will be described with one of the three curve functions, bell-shaped, Z-shaped,

or S-shaped curve (Figure 8).

26

Convert Layers to ASCII using ArcToolbox

Aspect Planform Curvature Profile Curvature Relative Position Slope DEM NAIP Photography

Import ASCII files to 3dMapper

Save DEM and NAIP Photography Save Environmental Layers 3dm format (3dMapper base file) Aspect, Planform Curvature, Profile

Curvature, Relative Position, and Slope in 3dr format (3dMapper files that SoLIM uses for inference.)

Figure 7. Preparation of data layers for 3dMapper

Figure 8. The basic forms of membership function

27

The bell-shaped curve function assumes a normal distribution of the environmental

variable. It depicts the membership value decrease when the environmental condition

deviates from its optimal condition as in the tacit point. The bell-shaped function is

suitable for modeling soil-aspect relationship. For example, the shallow soil tends to

occur on south or southwest aspects. The degree of similarity to shallow soils decreases

when the aspect value moves to the west, east, or north.

The Z-shaped curve shows the membership value reaches unity when the value of the

environmental variable is below a threshold value set by the tacit point. Otherwise, the

degree of similarity decreases as environmental condition increases. The relationship

between deep soil and slope is best represented by the Z-shaped curve. For example, the

deep soils tend to occur on longer slopes. When the slope value moves beyond the

threshold value, the deep soils are less likely to occur.

The S-shaped function represents a reverse situation of the Z-shaped function. The

degree of similarity reaches unity when the value of the environmental variable is above a

threshold value set by the tacit point and it decreases as the environmental condition is

below a given value set by the tacit value. The relationship between shallow soil and

slope is best represented by the S-shaped curve. For example, the shallow soils tend to

occur on steep slopes. When the slope value shifts below the threshold value, the shallow

soils are less likely to occur.

28

Each environmental layer’s influence on the inference is determined by the width of

curve functions. The width file controls the spread of the curves, which depict the details

of the soils distribution relative to the environmental variable. It defines the difference

of the environmental condition between the value of the tacit point and the sample point.

The width is defined at a membership value of 0.5. Figure 9 illustrates the procedure for

setting up tacit points and their width files. Then the inference process may begin.

Select Tacit Points Two points were placed. One characterizes shallow soil of fuzzy membership value of 1.0. The other is for deep soil.

Set the Width Curve This is the difference of the environmental condition between that of the tacit point and the point which produces a 0.5 membership (SoLIM Lab, 2004).

Set Curve Type The options are bell-shaped, s-shaped, or z-shaped curves.

Figure 9. Setting up tacit points and their width files.

29

CHAPTER 4

ANALYSIS RESULTS AND DISCUSSION

Two series of fuzzy membership maps are created, one for shallow soils and one for deep

soils. After an inference is run, the final membership value of each pixel in the resulting

map is determined by the environmental variable with the least membership value. For

example, if the inference for a single pixel for shallow soil has a membership value of .5

for aspect, .35 for profile curvature, .35 for planform curvature, .8 for relative position

and .15 for slope, the pixel’s membership value to shallow soil will be .15 out of a

possible 1.0. In this example, slope is the determining environmental variable in the

inference mechanism. It is then necessary to run follow up inferences on the study area

to achieve a more desired result by finding the limiting environmental variables and

either increasing or decreasing the curve width value.

4.1 Parameters setting

The initial parameters are set up for operating the soil inference. One tacit point for each

shallow soil and deep soil are placed using 3dMapper software. The values of each

environmental layer located at the tacit points are extracted and used to determine

membership values. It is challenging to locate places on the landscape which meet all the

specifications considered to be ideal for shallow soils and for deep soils. The tacit point

for shallow soils meets all of the characteristics outlined in Chapter 3, shown in Table 7.

The shallow soil tacit point falls on a southern aspect, has convex planform and profile

curvatures, a low relative position, and a steep slope. The tacit point for deep soil meets

30

Table 7: Shallow soil and deep soil tacit point values

Environmental Layers Shallow Values Deep Values Aspect south, 236 degrees north, 13 degrees Planform Curvature -0.0067145 (convex) 0.13115 (concave) Profile Curvature -0.281 (convex) -0.31773 (convex) Relative Position 0.35151 0.78982 Landuse/Landcover (not used) (not used) Slope 16.603 percent 19.573 percent

most of the necessary environmental characteristics also. The deep soil tacit point falls

on a north aspect and has a high relative position. The planform curvature is concave,

and the profile curvature is somewhat convex. The convexity of the profile curvature is a

compromise in the tacit point’s values, although it would be more ideal to find either a

more linear or concave location. The deep soil’s tacit point also has a steeper slope than

ideal, which is another compromise. Landuse/landcover is not used as an environmental

layer because no single pixel could be located with the proper value in conjunction with

reasonable values for the five environmental variables.

Ultimately the landuse/landcover environmental layer was not used in the inference

process although it was loaded into 3dMapper software. Table 7 lists the environmental

condition of each tacit point.

The curve types for each environmental layer were set for each tacit point (Table 8) based

on the specification of soil scientists. Setting the width file defines where the 0.5

membership is for each curve. The default value is set as 1.33 * standard deviation of the

31

Table 8. Final shallow point and deep point curve types

Environmental Layer Shallow Point Curve Type

Deep Point Curve Type

Aspect Bell-shaped Bell-shaped Planform Curvature Z-shaped S-shaped Profile Curvature Z-shaped S-shaped Relative Position Z-shaped S-shaped Slope S-shaped Z-shaped

environmental variable. The initial inferences are operated based on the default width

value. The alterations of the width file are made based on the outcome of each inference

in the succeeding inference process. The inference process iterates until it reaches

desirable modeling results.

4.2 Shallow soils fuzzy membership inference series

The initial inference for shallow soils is run after software was set up with the necessary

data structure and variables. Table 9 lists the edition of the curve width after each

inference in order to alter the inference results.

Figure 10 shows the results from the initial inference on the shallow soil fuzzy

membership. The first inference produces a preliminary spatial extent of shallow soils

distribution and it also serves as a fundamental mechanism for the rest of the shallow soil

inferences. The results suggest the lack of south and southwest aspect dominance in the

environment variables. In figure 10, the yellow polygons outline areas in which higher

membership values for shallow soil are expected. Areas shown in gray have low

32

Table 9. The inference parameters for shallow soil fuzzy membership series

Initial Inference Second Inference Preliminary Results

Width Value

Curve Shape

Width Value

Curve Shape

Width Value

Curve Shape

Aspect 0.3644 bell 0.01 bell 0.01 bell Planform Curvature 0.3857 bell 0.3857 bell 0.3857 bell Profile Curvature 0.4655 bell 0.4655 bell 0.4655 bell Relative Position 0.3325 bell 0.055 bell 0.055 bell Slope 0.1154 bell 0.1154 bell 0.0005 s

Figure 10. Shallow soil fuzzy membership map from the initial inference (white = high membership, gray = low membership, green = elevation contours,

yellow polygons = areas shallow soil should dominate)

33

membership values for shallow soils. Stronger membership is designated in a solid white

color. Elevation contours are shown in green.

Edits are made to the curve width file in an attempt to place more emphasis on south and

southwestern aspects of the study area. The second inference is processed by tightening

the relative position and aspect curve widths values.

Figure 11 shows the results from the second inference on the shallow soils fuzzy

membership. There should not be shallow soil in the flood plain. More emphasis should

be placed on the steep slopes. Notice the area outlined in yellow on Figure 11 is the

flood plain. The position of the flood plain is low relative to positions around it. The

inference is successful in finding low relative position, however it was too low. The

relative position of the shallow soils should move up the slopes into steeper sections

where contour lines are closely spaced. The aspect modeled in this particular inference is

successful and should not change.

Edits are made by tightening the slope curve width value and changing the curve from

bell-shaped to s-shaped to proceed to the third inference of shallow soils fuzzy

membership. The curve shape was changed so each pixel would have a higher

membership value for shallow soil as the slope gets steeper. Figure 12 shows the result

from the third inference and is satisfactory after changes are made to the slope curve

width file. Shallow soils moved out of the flood plain. Southern aspects were properly

modeled. Notice areas where contour lines are closely spaced are where shallow soils are

34

modeled. The width files are tight enough to exclude all other areas that do not meet the

strict requirements of this inference.

Figure 11: Shallow soil fuzzy membership map from the second inference (white = high membership, gray = low membership, green = elevation contours,

yellow polygons = flood plain where shallow soils are not expected)

35

Figure 12: Shallow soil fuzzy membership map from the third inference (preliminary results)

(white = high membership, gray = low membership, green = elevation contours)

36

4.3 Deep soils fuzzy membership inference series

A series of fuzzy membership maps are created for deep soils. Each series of maps begin

with using the default values in the curve width files. Changes are made to the curve

width files after each inference in order to alter the inference results (Table 10).

Figure 13 represents the results from the initial inference on the deep soil fuzzy

membership. It indicates that the first inference places too much emphasis on soil in the

flood plain (outlined in yellow on the map) and on steep slopes with southern aspects.

The steep slopes with southern aspects should be dedicated to the shallow soils in the

previous set of inferences, not deep soils. It should be noted that the flood plain may

actually have deep soil in it, however it is not part of the Goss-Gasconade-Rock outcrop

complex.

Edits are made by altering the curve width file and changing the planform curvature and

profile curvature curves from bell-shaped to s-shaped curves. Also, slope is dropped as

an environmental layer for the second inference. Figure14 shows the results from the

second inference in the deep soil fuzzy membership. The second inference improves

from the initial inference because it models deep soils on longer slopes where contour

lines (shown in green) are not closely spaced. However, steep slopes that are not on

southern or southwestern aspects should be included in the deep soils. Figure 14

indicates areas outlined in yellow needs to be included with deep soils.

37

Table 10. The inference parameters for deep soil fuzzy membership series

Initial Inference

Second Inference

Third Inference

Preliminary Results

Width Value

Curve Shape

Width Value

Curve Shape

Width Value

Curve Shape

Width Value

Curve Shape

Aspect 0.3644 bell 0.0001 bell 0.0001 bell 0.0001 bell Planform Curvature 0.3857 bell 0.5 s 0.5 s 0.5 s Profile Curvature 0.4655 bell 0.5 s 0.5 s 0.5 s Relative Position 0.3325 bell 0.05 bell 0.05 bell 0.15 bell

Slope 0.1154 bell n.a. n.a. 0.5 z 0.5 z

Edits are made to the width file to include slope again using a z-shaped curve. Figure 15

shows the results from the third inference in the deep soil fuzzy membership. Overall,

more deep soil should be modeled. The majority of the Goss-Gasconade-Rock outcrop

soil complex is made of deep soil and the model should reflect that. Therefore, the next

inference needs more deep soil modeled with less emphasis on high relative position on

the landscape. Figure 15 highlights areas in yellow where deep soil should occur in

lower relative positions in addition to what is already modeled in the inference.

Edits are made to the width file to place less emphasis on relative position. Figure 16

shows the results from the fourth inference in the deep soil fuzzy membership. It is

satisfactory after making changes to the relative position curve width file. Deep soils are

modeled on high and low positions on slopes.

38

Figure 13: Deep soil fuzzy membership map from the initial inference (white = high membership, gray = low membership, green = elevation contours,

yellow polygons = flood plain where deep soils are not expected)

39

Figure 14: Deep soil fuzzy membership map from second inference (white = high membership, gray = low membership, green = elevation contours,

yellow polygons = areas deep soil should dominate)

40

Figure 15: Deep soil fuzzy membership map from the third inference (white = high membership, gray = low membership, green = elevation contours,

yellow polygons = areas deep soil should dominate)

41

Figure 16: Deep soil fuzzy membership map from the fourth inference (preliminary results)

(white = high membership, gray = low membership, green = elevation contours)

42

4.4 Preliminary Results

A hardened map (thematic map) from the third inference of shallow soil fuzzy

membership map and the fourth inference of deep soil fuzzy membership map are created

(Figure 17). Each pixel was placed in the category of the soil that had the highest

membership value.

Figure 17: Hardened map based on the third inference of shallow soil fuzzy membership and the fourth inference of the deep soil fuzzy membership

(red = shallow soil, blue = deep soil, black = no data)

43

44

Fuzzy membership maps and hardened maps are viewed and verified by soil scientists

Caryl Radatz and Ralph Tucker (2006), Missouri soil scientists using 3D Mapper. They

agree that short steep slopes (where contour lines are closely spaced) on northern and

eastern aspects should have less shallow soils modeled. They also find that shallow soils

should go farther up on the slope in southern and western aspects. The fuzzy

membership maps are refined and a new hardened map is created.

4.5 Refined Results

Figure 18 shows the results from the final inference of the shallow fuzzy membership.

Another environmental layer is added as a mask. All soils falling outside the Goss-

Gasconade-Rock outcrop mapunit are eliminated from the inference. The curve width

for aspect is tightened, and the curves of planform curvature and profile curvature are

changed to z-shaped as shown in Table 11.

Figure 19 represents the results from the final inference of the deep soil fuzzy

membership. The curve width value for relative position is increased and changed to an

s-shaped curve. Slope parameter is excluded (Table 11).

Figure 20 is the thematic hardened map of the combination between the final inference on

the shallow soil fuzzy membership and the deep soil fuzzy membership. The shallow

soils modeled, shown in red, dominate southern and southwestern aspects on steep

slopes. The relative position of shallow soils is not as emphasized as previous inferences

may have suggested. Deep soils, shown in blue, are more broadly distributed. The soils

45

Table 11.The final inference parameters for soil fuzzy membership series

Shallow Soil Final Inference

Deep Soil Final Inference

Width Value Curve Shape

Width Value

Curve Shape

Aspect 0.0001 bell 0.0001 bell Planform Curvature 0.8 z 0.5 s Profile Curvature 0.8 z 0.5 s Relative Position 2 z 0.3 s Slope 0.0005 s n.a. n.a.

Figure 18: Shallow soil fuzzy membership map from the final inference (white = high membership, gray = low membership, green = elevation contours)

46

are on all other aspects other than south and southwest. Steep slopes and long slopes are

included in the deep soils along with both high and low relative positions. Although

areas modeled as shallow soil in Figure 18 overlap with areas modeled as deep soil in

Figure 19 one soil must win to be placed in the hardened map.

Figure 19: Deep soil fuzzy membership map from the final inference (white = high membership, gray = low membership, green = elevation contours)

47

Figure 20: Final hardened map based on the final inference of shallow soil and depth soil fuzzy membership

(red = shallow soil, blue = deep soil, black = no data)

48

4.6 Model Verification

Field verification of the modeled soils was conducted in November, 2005. Permission is

granted by local landowners to use hand tools and GPS on their properties. A back saver,

mallet, and tile probe are used to penetrate soil to determine its depth. The tile probe’s

total length is 53 inches. Therefore if the tile probe is pounded into the ground to its

limit, “53+ inches” is recorded in the field notes. A USDA-NRCS GPS is used to capture

data points (Figure 21. The field crews (Figure 22) record field notes to document GPS

site number, depth of soil in inches, and slope (Table 12). A clinometer was used to

record slope.

Forty-two points are randomly created for field verification. Figure 23 shows field

sample locations. Among these 42 field sample points, 26 are color coded as pink for

those points that are field verified as shallow soil, and 16 are color coded as blue for

those points that are field verified as deep soil. These points are placed over the

hardened map where red is shallow soil and blue is deep soil. Labels attached to the

points show the actual depth of soil measured in inches. Pink samples should have been

in red polygons on hardened map, blue samples should have been in blue polygons on

hardened map. The white polygons falls outside of the Goss-Gasconade-Rock outcrop

complex soils.

Accuracy of field verified data points to the hardened map are calculated using SoLIM’s

errorMatrix. Although the labels “VeryShallow” and “VeryDeep” are used, they

49

represent shallow and deep soils as outlined in Table 3. The output of the error matrix is

shown in Figure 24.

50

Figure 21: Field Crew with tools. Left to right: Caryl Radatz with back saver, Mike VerBrugge with mallet and tile probe, Lydia VerBrugge with GPS. (Photographer: Bruce DeMurio, 2005)

Figure 22: Field crew traversing the landscape. (Photographer: Patrick Short, 2005)

51

Table 12: Field notes

Site Number

Depth (inches)

Slope (percent)

10 27.5" 18 11 5" 31 12 35" 24 13 22" 14 14 8" 19 15 6" 55 16 34" 31 18 23.5" 28 19 53" 36 20 24" 28 21 24.5" 35 22 53+" 25 23 53+" 22 24 33" 17 25 53+" 17 26 9" 19 27 30" 42 28 9" 32 29 8" 16 30 33" 22 31 8.5" 21 33 30" 19 34 23" 18 35 53+" 14 36 19" 21 37 31" 23 38 53+" 26 39 33.5" 42 40 53+" 26 41 8" 22 42 18.5" 52 43 11" 54 44 18" 46

45-46 11" 31 47 15.5" 25 48 6" 29 49 41 32 50 42 35 51 42 35 53 53 34 54 23 32 55 0 0 57 32 58

52

Figure 23: Example of field plots

The Error Matrix (C:\GISClasses\Thesis_Research\Depth30\Gossgas2\result\hardenMapUn.3dr vs C:\GISClasses\Thesis_Research\Fieldcheck_conversions\fldchkxl.tst) VeryDeep VeryShallow RowTotal User’s VeryDeep 17 11 28 0.6071429 VeryShallow 9 5 14 0.3571429 Col Total 26 16 22 Producers 0.6538461 0.3125000 1.0000000 22 cases correctly classified, out of 42 total cases Overall Accuracy: 0.5238096 KHAT: -0.0344828

Figure 24. Error matrix results

53

4.7 Discussion

The analysis results show through field verification that the depth of soil modeled in the

Goss-Gasconade-Rock outcrop complex was 52% accurate overall. The issues

encountered in the process, if resolved, may result in a higher accuracy model. One of

the major concerns of the analysis was the change of resolution from 10 meter to 30

meter resolution due to the landuse/landcover data. Perhaps a 10 meter resolution model

would have produced more accurate results. Furthermore, the placement of the tacit

points, or the movement of them, could have produced different results. The

specifications given by the soil scientists may have weighted too heavily on one

environmental variable, or simply omitted an environmental variable that could have

produced more accurate results.

54

CHAPTER 5

CONCLUSION

The purpose of this study is to model depth of soil in the Goss-Gasconade-Rock outcrop

complex, 5 to 35 percent slopes, in Callaway County, Missouri. This research uses fuzzy

logic and case-based reasoning to create fuzzy membership maps of both shallow and

deep soils. SoLIM software combines the fuzzy membership maps into thematic

“hardened” maps which can be viewed in 3dMapper software. Field verified points

compared to the hardened map show 52% accuracy.

A persistent problem in soil survey is characterizing soil spatial variability (Miller et al.,

1979; Nordt et al., 1991). Compositional purities for taxonomic map units are commonly

less than 50% (Powell and Springer, 1965; McCormack and Wilding, 1969; Crosson and

Protz, 1974; Amos and Whiteside, 1975; Bascomb and Jarvis, 1976; Ransom et al., 1981;

Edmonds and Lentner, 1986; Mokma, 1987; Nordt et al., 1991). The Soil Survey Manual

requires map unit purities of 85%, but is rarely attainable (Soil Survey Staff, 1951; Nordt

et al., 1991).

5.1 Limitations

The main research objective is to find depth of soil within a soils complex. One

expectation of the research was to use multiple data sources or environmental layers

without relying solely on DEM derivatives. Two specific data sources that were not

useful at the scale available were geologic data and vegetation data. It is noted at the

55

beginning of the interview with the soil scientists that geologic data at 1:500,000 scale

would be of little benefit to this study. Geologic data dropped from the list of possible

environmental layers to use. Landuse/landcover data is determined to be appropriate to

find vegetation classes to define shallow and deep soils. However, when seeking a tacit

point to represent shallow soil, it was evident that no pixel existed meeting the vegetation

requirement and all other DEM derivative requirements. Vegetation was therefore

eliminated as a data source.

The methodology requires an interview with a professional soil scientist to determine

environmental characteristics associated with the soil property modeled using SoLIM.

Presumably all environmental variables were discussed and noted in the interview.

Ideally the knowledge base of the soil scientist documented in the research was accurate.

It is possible that limitations of the soil scientist’s knowledge of the landscape caused less

accurate results. For example, several different soil scientists may observe the same

study area but have differing opinions about the characteristics of the soil and how it

should be mapped. One soil scientist may decide to take multiple soil samples and create

a map based on the soil observed at those points. Another soil scientist may have a more

holistic approach and map the soil based on their experience with the entire soil

landscape. A third soil scientist may center their opinion of the soil landscape on the

geology of the study area (Potter, 2006). These differing approaches, and how the soil

landscape is perceived, may elicit contrasting opinions about the environmental variables

that distinguish shallow soil from deep soil.

56

Soil depth is modeled by its level of fuzzy membership to a tacit point or location on the

landscape. A tacit point that represents each soil depth class has a fuzzy membership

value of 1.0. Each pixel included in the inference process is assigned a fuzzy

membership value of 0.0 to 1.0, depending on the environmental variables associated

with it, and how similar they are to the tacit point. It is important that the tacit points are

in optimal locations and are truly representative of the depth class that is modeled. It can

be questioned if the best points were chosen and are a limitation of the research. During

the process each tacit point was placed by specifications noted by the interviewed soil

scientist. The software used to find the points was 3dMapper. Areas where the tacit point

should be placed were done under the advisement of a soil scientist. The actual selection

of the tacit point was made by searching pixel by pixel for acceptable environmental

variable values.

Tomer and James (2004) discuss the controversies associated with modeling soils. They

agree with Zhu’s view that soil scientists’ have a unique and integrated knowledge of

topography, geomorphology, vegetation, and other factors that define soil relationships.

However, soil scientists’ ability to assess all soil collectively within a mapping area is

nearly impossible. Sometimes intricate soil patterns result in mapping soil complexes.

Tomer and James also illustrate the limitations of Digital Elevation Models (DEMs),

including resolution, and errors present in the data. It is important to keep in mind the

limitations of both the soil survey and DEMs so data will not be misused.

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Other limitations of the research are data quality and availability. Digital Elevation

Models (DEMs) and derivatives of the DEMs such as slope, aspect, and curvature models

are only as accurate as the DEM itself. DEMs in this study were reclassified from 10

meter resolution to 30 meter resolution to match the landuse/landcover environmental

layer. The process of lowering the resolution of the DEM and its derivatives may limit

the final model’s accuracy. The availability of a higher resolution landuse/landcover

layer may generate other model results.

5.2 Further improvements and future research

Ideally more data sources would have been available for the modeling process. If

geologic data and landuse/landcover data were available at an appropriate scale, better

inference results may have been achieved.

The proper placement of tacit points is critical to achieve appropriate inference results.

First, the criteria given by the soil scientist must be accurately captured by the person

creating the model. Secondly, the method to find the location noted as ideal by the soil

scientist has to be available within the environmental data layers. If no such scenario

exists, then the proper tacit point cannot be captured. If a less than quality tacit point is

chosen, it is still given a membership of 1.0. Resulting fuzzy membership classification

of individual pixels will be placed according to the tacit point.

Depth of soil in the Goss-Gasconade-Rock outcrop complex, 5-35 percent slopes, in

Callaway County, Missouri can be modeled using a combination of GISc techniques and

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professional soil scientist knowledge of the local landscape. Ideally accuracy results

should be higher. If data limitations were resolved perhaps there would be more accurate

results. It should be noted that the individual fuzzy membership maps for shallow and

deep soils may also be tools for soil scientists to refine or update conventional soil maps.

The hardened maps created by the combination of the fuzzy membership maps are only

one product of the SoLIM process. Multiple variations of fuzzy membership maps may

be created by changing widths values. If the depth classes for shallow and deep soils

were changed yet different inference results would occur and likely produce different

model accuracy.

This research contributes toward the process of updating and maintaining soils data more

systematically and efficiently. The benefit of SoLIM in Missouri Soil Survey is there is

already a SSURGO product. SoLIM, and the theory of fuzzy logic and case-based

reasoning for soils modeling, can be used to update soils data in Missouri. SoLIM may

also document the knowledge of professional soil scientists for use in the future.

The Goss-Gasconade-Rock outcrop complex may have other soil properties to explore.

Perhaps more research can be done on depth of soil using more soil depth classes.

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