Digital Soil Mapping: Past, Present and Future - PowerPoint Presentation

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)Slide17
Slide18
Slide19

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.