Phillip R Owens Associate Professor Soil Geomorphology Pedology Digital Soil Mapping Also called predictive soil mapping Computer assisted production of soils and soil properties Digital Soil Mapping makes extensive use of 1 technological advances including GPS receivers field scan ID: 727635
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Slide1
Digital Soil Mapping: Past, Present and Future
Phillip R. Owens
Associate Professor, Soil Geomorphology/
PedologySlide2
Digital Soil Mapping
Also called predictive soil mapping.
Computer assisted production of soils and soil properties.
Digital Soil Mapping makes extensive use of: (1) technological advances, including GPS receivers, field scanners, and remote sensing, and (2) computational advances, including
geostatistical
interpolation and inference algorithms, GIS, digital elevation model, and data miningSlide3
Digital Soil Mapping
These techniques are simply tools to apply your knowledge of soil patterns and distributions. The maps can only be as good as your understanding of the soils and landscapes
DSM - Same type of advancement to the Soil Survey as aerial photographs and stereoscopes introduced by Tom Bushnell and others early in the Survey.Slide4
Key Point
It is
impossible
to use these products and create good maps if you do not know your soil-landscape relationship.Slide5
Opportunities
Available soil data are increasingly numerical
Tools (GIS, Scanners, GPS,…
Soil Data Models
Increasing soil data harmonization
The spatial infrastructures are growing
DEMs: Global coverage
Remote Sensing
Web servers
Quantitative mapping methods
Geostatistics
(
pedometrics
)
Data mining
Expert knowledge modelingSlide6
Models
Essential tools of science
Viewing and organizing thoughts
Conceptual Models – framework to ponder thoughts
Simplify reality
Must generate testable hypothesis to separate cause and effect
New models must be advanced before facts can be viewed differently – break ruling theoriesSlide7
Dynamic Nature of Soils
Society perceives soils as static
Pedologists deal with larger time scales – soils are dynamic
Many soil forming factors are active at a site – but only a few will be dominant
Importance of understanding soil dynamics- better predict results of management and evolution of soilsSlide8
Types of Models
Mental and Verbal – Most pedogenic models
Mathematical – Hope for the future
Simulation – Knowledge of rate transfersSlide9
Energy Model(Runge, 1973)
Similar to Jenny’s model, but emphasizes intensity factors of water (for leaching) and O.M. production
S = f(o, w, t) where:
W = water available for leaching (intensity factor)
O = organic matter production (renewal factor)
T = timeSlide10
Energy Model(Runge, 1973)
Many researchers continue to show that infiltrating water is a source of organizational pedogenic energy.
Many critics say designed for unconsolidated P.M. with prairie vegetation.Slide11
Factors of Soil Formation
S = (p, c, o, r, t, …) (Jenny, 1941)
Soils are determined by the influence of soil-forming factors on parent materials with time.
Parent
material
Climate
Organisms
Relief
Time
…Slide12
Functional Factorial Model(Jenny, 1941)
Good conceptual model, but not solvable
Factors are interdependent, not independent
Most often used in research by holding for factors constant – i.e.
topo
-,
clino
-, bio-, litho-,
chronosequences
Has had the most impact on pedologic research
Divide landscapes into segments along vectors of state factors for better understandingSlide13
Functional Factorial Model(Jenny, 1941)
Climate and organisms are active factors
Relief, parent material and time are passive factors, i.e. they are being acted on by active factors and
pedogenic
processes
Model has the most utility in field mapping – may be viewed as a field solution to the model
Very useful for DSM!Slide14
DEM Derived Terrain Attributes
These terrain attributes quantify the relief factor in Jenny’s Model
Some of the most commonly used are:
Slope;
Altitude Above Channel Network;
Valley Bottom Flatness;
Topographic Wetness Index (TWI). Slide15
Paradigm Shift in Pedology
S = (s, c, o, r, p, a, n, …) (McBratney, 2003)
Reformulation of Jenny 1941
Soil variability is understood as:
Soil attributes measured at a specific point
Climate
Organisms
Relief
Parent material
Age (time)
Space
…
Soils influence each other through spatial location!
GISSlide16
Paradigm Shift in Pedology
PCORT (Jenny, 1941)
Emphasizes soil column vertical relationships
Considers soils in relative isolation
Descriptive terms
used for landscapes
(e.g. “
noseslope
”)
SCORPAN (McBratney, 2003)
Accounts for lateral relationships and movements
Examines spatial relationships between adjacent soils
Terrain attributes
used to quantify landscapes
(“topographical wetness index”)
Catena – a “chain” of related soils (Milne, 1934)
Have properties that are spatially related by
hydropedologic
processes (
Runge’s
Model)Slide17Slide18Slide19
19
Digital Elevation Model
Dillon Creek, Dubois County, Indiana
Elevation
m
mSlide20
20
Aerial Photo draped over 3-d viewSlide21
21
AACH
Altitude Above Channel
Dillon Creek, Dubois County, IndianaSlide22
22
TWI
Topographic Wetness Index
Dillon Creek, Dubois County, IndianaSlide23
23
MRRTF
Multi Resolution Ridge Top Flatness
Dillon Creek, Dubois County, IndianaSlide24
24
MRVBF
Multi Resolution Valley Bottom Flatness
Dillon Creek, Dubois County, IndianaSlide25
No
Soil Series
MRRTF
MRVBF
Slope
AACH
TWI_29
1
Tilsit, Bedford, Apallona, Johnsburg (TBAJ)
> 2.4
< 2.9
< 2
2
Tilsit, Bedford, Apallona (TBA)
> 2.4
< 2.9
2-6
3
Zanesville, Apallona, Wellston (ZAW)
> 2.4
< 2.9
6-12
4
Gilpin, Wellston, Adyeville, Ebal (GWAE)
< 2.4
< 2.9
12-18
5
Gilpin, Ebal, Berks (GEB)
< 2.4
< 2.9
18-50
0.5-2.0
6
Pekin , Bartle (PB)
< 2.4
> 2.0
2-12
0.5-2.0
7
Cuba, (C)
< 2.4
> 2.9
0-2
> 0.09
< 12
8
Steff, Stendal, Burnside, Wakeland (SSBW)
< 2.4
0-1
0-2
<0.09
> 12
9
Rock Outcrops, Steep Slopes
< 2.4
> 50
Numerical Soil-Landscape Relationships, Indiana Site Slide26
SOLIM map
Hardened SoLIM MapSlide27
Dillion
Creek – Dubois County Indiana
Depth
to Limiting Layer
cm
cmSlide28
Low relief Landscape in the Glaciated Portion of IndianaSlide29
Slope
Slope in RadiansSlide30
Altitude above channel network (m)
Olaf Conrad 2005 methodology
Altitude above channel networkSlide31
Multi-resolution index of valley-bottom flatness
Gallant, J.C., Dowling, T.I. (2003): 'A
multiresolution
index of valley bottom flatness for mapping depositional areas', Water Resources Research, 39/12:1347-1359
Valley Bottom
FlattnessSlide32
TWI: 9
Topographic Wetness IndexSlide33
Soils in Howard County
5 soils cover 80% of the land on Howard County
Are there relationships between these 5 soils and terrain attributes?
Can we use those relationships to improve the survey in an update context
? Provide predicted properties?Slide34
SSURGO
Shaded Relief Elevation Model,
242 to 248 meters
Brookston
Fincastle
Wetness Index, 8 to 20
Slope, 0 to 4%Slide35
Frequency distributions
Fincastle
Terrain attribute:
Curvature
Brookston
Terrain attribute:
Altitude above channel network
Brookston
Fincastle
Frequency
Frequency
Frequency
ABCN
Curvature
*Data extracted with Knowledge Miner SoftwareSlide36
Frequency, Wetness Index
Frequency
Brookston
Fincastle
Terrain attribute:
Wetness Index
Wetness index
*Data extracted with Knowledge Miner SoftwareSlide37
Formalize the Relationship
Example:
If the TWI = 14 then assign Brookston
If TWI = 10 then assign Fincastle
Other related terrain attributes (or other spatial data with unique numbers) can be used.
That provides a membership probability to each pixel Slide38
Terrain-Soil Matching for Brookston
100%
2%
Fuzzy membership values (from 0 to 100%)
*Information derived from Soil landscape Interface Model (SoLIM)Slide39
Terrain-Soil Matching for Fincastle
5%
97%
Fuzzy membership values (from 0 to 100%)
*Information derived from Soil landscape Interface Model (SoLIM)Slide40
Create Property Map with SoLIM
D
ij
: the estimated soil property value at (i, j);
S
k
ij
: the fuzzy membership value for kth soil at (i, j);
D
k
: the representative property value for kth soil.
We already have S
k
ij
– the fuzzy membership value used to make the hardened soil map.
To estimate the soil property SoLIM uses:
So we only need to specify D
k
, the representative values of the property of interest for each soil
In this case, let’s assign values to carbonate depth for Fincastle and Brookston in the east section of the county.
Fincastle: 100 cm (low range of OSD)
Brookston: 170 cm (high range of OSD)Slide41
Predicted depth to carbonates
100 to 170 cm
100 to 170 cmSlide42
Fuzzy vs. Crisp Soil Maps
Imagine a heap of sand…
The Heap Paradox from 4
th
Century BCE, more than 2,000 years ago posed a problem that can be addressed by fuzzy logic
Take away 1 sand grain. Is it still a heap? Take away 1 more and keep doing it. When is it not a heap? And what is it? Is it a pile, a mound? How many grains of sand does a mound have, a pile, a heap?Slide43
Heap of Sand vs. Pile of Sand
How many grains of sand do you need to remove from a heap to get a pile? How many grains of sand do you need to add to make your pile of sand into a heap? Slide44
Fuzzy vs. Crisp Soil Maps
Fuzzy logic says that when you keep taking grains of sand away eventually you move from
definitely heap,
to
mostly heap, partly heap, slightly heap,
and
not heap.
You can express
heapness
with values from 0 to 1, with 1 being a perfect example of a heap and 0 being nothing at all like a heap.
How can we define a heap? It is a similar question to how can we define a mapping unit.
You can set rules like
a perfect heap is 2 tons or more of sand
and not heap is less than ½ a ton of sand. You might also want an upper limit to where you say that after a certain amount it becomes more of a dune or mountain than a heap. You can then set a mathematical curve for expressing the decline in
heapness
as a function of the removal of sand grains.Slide45
Black is Brookston in the map below
Orange is soil very different from Brookston.
Here we can express Brookston as values between 1 and 0
A given spot might have a 0.7 Brookston membership value
As we move up in elevation that membership value may decrease to 0.5, 0.3, 0.1, and 0 when we know we won’t find Brookston
Black is Brookston in the map below
Brown is a different soil, but similar to Brookston.
Orange is very different from Brookston and dark green is fairly different.
As we move away from Brookston in geographic space we cross a threshold and suddenly we are in a different soil. There is an abrupt conceptual change from one soil to another.
Crisp vs. Fuzzy Soil MapsSlide46
Brief History Of Digital Soil Mapping
1991-1993: publications of pioneer works
2003: Digital Soil Mapping as a body of soil science
2004: 1
st
International workshop on Digital Soil Mapping. Workshops: Rio (2006), Logan (2008), Rome (2010), Sydney (2012)
2009: GlobalSoilMap.netSlide47
SoLIM in the US
SoLIM
“soil landscape inference model” was developed at the University of Wisconsin by A-Xing Zhu and Jim Burt (late 90’s)
Knowledge based inference model, fuzzy logic, rule based reasoning. What does that mean?
There were Soil Survey pilot projects in Wisconsin and the Smoky MountainsSlide48
Challenges in Conducting Soil Survey
S <=
f
( E )
Soil-Landscape Model Building
Photo Interpretation
Manual Delineation
Polygon Maps
The Polygon-based Model
The Manual Mapping Process
Knowledge Documentation
(Slide from Zhu)Slide49
Spatial Distribution
Similarity Maps
Inference
(under fuzzy logic)
Perceived
as
S <=
f
( E )
Relationships between Soil and
Its Environment
Cl, Pm, Og, Tp
G.I.S.
Local Experts’ Expertise
Artificial Neural Network
Data Mining
Case-Based Reasoning
(
Zhu., 1997, Geoderma; Zhu, 2000, Water Resources Research
)
Overcoming the Manual Mapping ProcessSlide50
Valton
Lamoile
Elbaville
Dorerton
Churchtown
Greenridge
Urne
Norden
Gaphill
Rockbluff
Boone
Elevasil
Hixton
Council
Kickapoo
OrionSlide51
The Speed of Soil Survey Using SoLIM
The product is already in digital form, no need
to digitize it
A total of
500,499
acres since May 2001 over
526
person
days, about
950
acres per person per day
Overall
Currently the speed of manual mapping (including
Compilation and digitization) is about
80-100
acres
per person per day
(Slide from Zhu)Slide52
Quality of Results:
Inferred vs. Field Observed
Correct
Incorrect
Accuracy
Blue Mounds NE
Cross Plain SW
34
22
4
6
89%
78%
Watershed24
31
9
77%
(Slide from Zhu)Slide53
Cost Comparison
Cost about
$1.5 million
to complete field mapping of
the County using the manual approach
Cost about
$0.5 million
using the SoLIM approach
in digital form
(Slide from Zhu)Slide54
SoLIM
There were major advances in DSM using
SoLIM
.
Some minor setbacks – Smoky Mountain project
“If a guy who has mapped these mountains for 20 years can’t tell you what soil is on the other side of the hill, then you can’t use a computer to do it.” Bill Craddock, Former State Soil Scientist in KentuckySlide55
DSM – Current State
There are many options under the umbrella of DSM:
geostatistics
(
kriging
and co-
kriging
), clustering, decision trees, Bayesian models, and fuzzy logic with expert knowledge.
There are advantages and disadvantages to all methods. Slide56
DSM – Current State
Knowledge based inference model like
ArcSIE
and
SoLIM
allows soil scientists to utilize their understanding of soil landscape patterns
Requires less data but knowledgeable soil scientists
ArcSIE
is easier to use because it is within
ArcGIS
.
SoLIM
requires multiple file transfersSlide57
DSM Current State
ArcSIE
used successfully in initial soil surveys in Missouri, Vermont and Texas
Requires environmental covariates and depends heavily on the DEM, terrain attributes and remote sensing (in the dry climates)
Explicitly describes Jenny’s state factor model by the expansion through
McBratney’s
SCORPANSlide58
DSM - Future
DSM will be instrumental in soil survey updates. Research is currently underway to determine best methods
Digital delivery gives us the ability to illustrate and deliver soils in new formats (example
Isee
-
http://isee.purdue.edu/
)
Using the fundamentals of DSM, we can move towards predicting soil properties and incorporating other explanatory data (i.e. ecologic site descriptions, land use, etc.) Slide59
Dillion
Creek – Dubois County Indiana
Depth
to Limiting Layer
cm
cmSlide60
“Pros” to Digital Soil Mapping
Very consistent product due to the way it is created.
The soil landscape model is explicit. Updates can be completed more efficiently over large areas.
The variability or inclusions can be represented (in some cases)Slide61
“Pros” to Digital Soil Mapping
End users in the non traditional areas can more easily use some products.
We can use this information to make predictions of soil properties including dynamic soil properties.
All of these “pros” will increase the support and usefulness of the Soil Survey in the future.Slide62
“Cons” to Digital Soil Mapping
In some locations, the soil-landscape relationship is difficult to determine and represent. Examples are areas with heterogeneous parent materials.
Can be misused (It makes really pretty maps and a bad map is worse than no map at all)
Complications with data can stop a project.
Learning new
softwares
can be very frustratingSlide63
Saturated hydraulic conductivity (ksat , micrometers per second) from gridded SSURGO (Approximately 1:24,000 map. Gridded at 30 m resolution with STATSGO).
600
0Slide64
DSM Future
Harmonize the soil data
Disaggregate polygons
Create true DSM maps tied to landscapes
Provide alternate raster products at
multiple
resolutions
We
must embrace and use this technology and incorporate DSM into the long-term plan/vision.