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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
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field sample points, and alteration of the fuzzy membership map for shallow soils on
northern aspects may increase accuracy of depth of soil modeled.
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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
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CHAPTER 5: CONCLUSION …………………………………………………….…..54
5.1 Limitations ………………………………………………………………….54 5.2 Further improvement and future research …………………………………...57
REFERENCES...........................................................................................................…...59
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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
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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
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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
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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,
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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)
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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).
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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.
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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.
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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?
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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
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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.
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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.
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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
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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.
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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
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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,
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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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