Spatial Disaggregation – A Primer - PowerPoint Presentation

Slide1

Spatial Disaggregation – A Primer

Tom

D’Avello

–

NRCS-NSSC-GRU

c

ontact:

tom.davello@wv.usda.gov

Travis

Nauman

– NRCS-NSSC-GRU, WVU

c

ontact:

tnauman@mix.wvu.eduSlide2

Overview

Define ‘Disaggregation’

Approaches and Tools

West Virginia

Illinois

Arizona

Summary

Literature list for your referenceSlide3

What is spatial disaggregation?

The next opportunity for the NCSS

Add value to SSURGO

“

The process of separating an entity into component parts based on implicit spatial relationships or patterns” –

(Moore, 2008)

Getting more detail

Spatially refining maps to reflect the level of detail for current needs

Corresponding increased resolution of attributes

Trying to meet new types of demandsSlide4

What is spatial disaggregation?

Mapping of components within map units

Usually complexes or associations for Order 2 & 3 soil surveys (SSURGO)

STATSGO2 effort

Alaska (Moore, 2008)

New needs served

modeling community

maintenance and improvement of the product is a primary charge of NCSS

http://www.soilsurvey.org/tutorial/page1.aspSlide5

What is spatial disaggregation?

Ultimately, it is a refined segmentation of the landscape

Along with the spatial, the attributes are equally important

Map units have multiple parts with attributes

Example: Ponded parts of a larger map unit

Related to SDJR

Scope driven!

Area of InterestCan be relevant to one, some or all map units.Slide6

Purpose of the demonstration

Demonstrate case studies across varying physiographic regions

Get feedback from soil scientists on their assessment of current soil maps

Investigate different digital techniques

Evaluate results

Develop materials and guidelines for application by soil scientists Slide7

West Virginia early efforts

Gilpin

Pineville

Laidig

Guyandotte

Dekalb

Component Soils

Craigsville

Meckesville

Cateache

Shouns

Gilpin-Laidig

Pineville-Gilpin-Guyandotte

Other

SSURGO Map UnitsSlide8

General disaggregation workflow

Goals

Scope

What data is accessible to help

Choose method

Implement

Validate Quality

(evaluate

and iterate earlier

steps as needed)Slide9

Current workflow in West Virginia

Goals

S

oil series map on field scale grid

Scope

All

map units

in Pocahontas and Webster Counties, WV

What data is accessible to help

~30-meter DEM (NED), Landsat Geocover (Fed. MDA, 2004), lithology, SSURGOChoose methodSSURGO-derived expert rule training sets & classification tree ensemble (100 trees run on random subsets)

Implement

Run analysis with Access (SQL), GIS, and Python (or R)

Validate Quality

Independent

pedons

for ground truthSlide10

1. & 2. Goals and scope

Scope is key

define what needs to be disaggregated

Universal

vs

within map unit(s) (Local)

Local model

(confined to existing map unit) Keep original linesUniversal model

uses original survey to create but lines not used for final

Local

Universal

Figures courtesy of Dave Hoover, NSSCSlide11

3. What data: SSURGO

Component

Components (not explicitly mapped)

Inclusion

Legend

Map Unit

H

orizon

Geomorphology

Parent material

Landscape attributes

Horizon attributes

Soil physical properties

Soil chemical propertiesSlide12

3. What data: SSURGO

Most work done on SSURGO or equivalent scale maps

R

aster (grids) used for modeling

to match environmental data

West Virginia dataSlide13

3. What data: environmental

R

aster grids

Sometimes other polygon layers converted (e.g. geology)

C

haracterize variation within polygons using data that infer soil forming factors

SSURGO lines over DEM

SSURGO lines over Landsat

SSURGO lines over landforms

(Schmidt & Hewitt, 2004)

Examples from West VirginiaSlide14

4. Method: model techniques

Training Data

Match environmental data to components of interest

Use representative areas or

pedon

locations

Model Types

Expert landscape rulesHardened or fuzzyStatistical modelsArea to Point Interpolations (Goovaerts

, 2011)

Example Classification Tree Model

Dekalb

series training areas in WVSlide15

5. & 6. Implement & Validate

Create raster disaggregation map

Validate with ground truth data

Different methods available

WV example: universal model for Webster and Pocahontas Counties

validation

Spatial Support

match type

nearest

60-m radius

exact

26%

39%

like soil

45%

66%

any tree

57%

73%Slide16

Historical survey of Webster County, WV

These folks were pretty good

Milton Whitney

Curtis

Marbut

Hugh Bennett

Nice map, too!Slide17

Goals

Components or phases within Sable and Ipava units

Scope

All Sable and Ipava map units within Peoria County

What data is accessible to help

3-meter DEM (NED), SSURGO

Choose method

Expert rule training sets & classification

t

reesImplementRun analysis with R, ArcGIS and ArcSIEValidate Quality

Local soil

scientist review.

Peoria, Illinois investigationSlide18

Peoria,

Illinois investigation

1. Goals

Identification of Non-ponded and ponded phases in Sable units

Identification of

poorly drained components in

Ipava

unitsSlide19

Peoria,

Illinois investigation

2. Scope -

study site

~900,000 acres of Sable

~1,186,000 acres of Ipava

The project area is within MLRAs 95B, 108A,

108B, 108C and 115C

Why here?

Availability of high resolution DEMs

Representative setting for Sable and

Ipava

Good test for developing procedures to complete for entire extent of units when

LiDAR

coverage is completeSlide20

General setting

2. Scope

-

study site

soil

slope

profile

tangential

wetness

positionsinks

Ipava

Low

Plane

Plane

High

Broad summit/

Talf

Some

Sable

Lowest

Concave-plane

Concave-planeHighestDip on talf

Yes

Typical cross-section and qualitative description of

Sable and Ipava soils Slide21

Variables developed

3. Data

-

all derived from 3m DEM with ArcGIS/

ArcSIE

/SAGA GIS

Altitude above channel network

Curvature at numerous neighborhoodsHorizontal distance to flow channelMaximum curvature –numerous neighborhoodsMinimum curvature –numerous neighborhoods

Multi-resolution ridge top flatness indexMulti-resolution valley bottom flatness index

Profile curvature –numerous neighborhoodsRelative position-numerous neighborhoodsSink depth and Depression cost surfaceSlopeTangential curvature –numerous neighborhoodsTopographic position index

Vertical distance to flow channel

Wetness indexSlide22

Exploratory Data Analysis

4. Method

An extensive sample with soil series as a response was developed

Classification Tree

in R to determine explanatory variablesSlide23

Purpose of evaluation

4. Method

Spatial data needs to be the driver for modeling effort

Efficient determination of explanatory variables

Efficient determination

of thresholds for variables

Practical tools are needed to assist soil scientists in this effort Slide24

Results from classification tree

5. Implement

Altitude above channel network

Horizontal distance to channel

Minimum curvature 120m neighborhood

Multi-resolution ridge top flatness index

Profile curvature 150m neighborhood

Relative position 90m neighborhoodRelative position 60m neighborhoodRelative position 30m neighborhood

Sink DepthSlope 30m neighborhoodTopographic position index

Wetness index

Altitude above channel network

Relative Elevation (aka Relative position)

Sink Depth

Input variables

Important variables

Developed 20+ datasets – 12 showed promise from qualitative review – 3 were

i

dentified through classification tree as explanatory variables in this exampleSlide25

Results from classification tree

5. Implement

-

Ipava

and Sable independently Slide26

Altitude above channel network

>= 0.25

< 0.25

Results from classification tree

5. Implement

– walk through the splits Slide27

Relative position

>= 0.595

< 0.595

Results from classification tree

5. Implement

– walk through the splits Slide28

>= 1.472

< 1.472

Results from classification tree

5. Implement

– walk through the splits

Sink depthSlide29

Results from classification tree

5. Implement -

Results of rules applied for Sable and IpavaSlide30

Results from classification tree

5. Implement

Rule base compared with SSURGO for SableSlide31

Ponded

vs. non-ponded Sable

6. Validate

Local - using depression depth

Blue – likely depression/ponded

Red -Yellow – no depressionSlide32

Ponded vs. non-ponded Sable

6. Validate

Local -

using depression cost surface

Blue – likely depression/ponded

Red -Yellow – no depressionSlide33

Ponded

vs. non-ponded Sable

6. Validate

Local -

using 3m USGS NED

Zonal statistics indicate 41% of the area mapped as Sable is

ponded

Based on selected thresholdsVerification and tuning of threshold values is ongoingSlide34

Ponded

vs. non-

ponded

Sable

6. Validation/Data

Local -

using

10m

USGS NED

Bigger legend

Zonal statistics indicate 17% of the area is

ponded

Area “missed” with

c

oarser 10m DEMSlide35

Ponded

vs. non-ponded Ipava

6.

Validate

Local -

using 3m USGS NED

Zonal statistics indicate 9% of the area is

pondedSlide36

Future effort for Peoria County

Populate component table

-

based on verified and validated thresholds

Rename map unit phases if needed

What is reasonable to improve product

?

Accept line work and split components within existing map units? - A working copy in preparation for phase II of data

recorrelation

makes this feasibleSlide37

Arizona – arid example

Goal

match environmental classification of soil forming factor raster layers to soil types.

Scope

Entire soil

s

urvey: Organ Pipe Cactus National Monument (ORPI)

DataUsed DEM and ASTER imagery to represent topography, vegetation, and geologyMethod

Unsupervised classification (clustering)ImplementErdas

Imagine and ArcGISValidate (evaluation)Contingency tables (Chi2 Cramer’s V) to MUs; found separation of components in most complexes in field recon. (Nauman, 2009) Slide38

Arizona –

a

rid exampleSlide39

Arizona

More methods trials are planned for northeast AZ

Initial spatial data is being compiled

Model runs by late 2013Slide40

Summary

Webster and Pocahontas,

West Virginia

Peoria

, Illinois

ORPI,

Arizona

GoalSoil series map of entire area

Components/phases within Sable and

Ipava

units

Match environmental

raster patterns to MUs

Scope

Full

extent of both surveys (Webster and Pocahontas)

Sable and

Ipava

map units within Peoria Co.

Entire ORPI survey areaDataDEM (NED 30m), Landsat, geologyDEM (NED 3m)

DEM (NED 30m), ASTERMethodSSURGO component rules and classification treesExpert rules and classification

treesClustering (ISODATA)ImplementAccess (or SQL), ArcGIS, and Python (or R)

ArcGIS, ArcSIE, RArcGIS, Erdas ImagineValidateIndependent set of

pedonsExpert reviewCompare w/ SSURGO, expert review HighlightsSeries

map, harmonized surveys, maintained accuracyPicked out fine scale depressionsDetected components in complex MUsSlide41

Summary

Disaggregation is a process that is defined by a need for more detail

Needs a directed scope

T

remendous amount of new data and computing abilities to incorporate

Disaggregating classic soil surveys

improves the detail of final maps without loss of accuracy and with no new data

more realistic representation of soil distribution (continuous – background probabilities)Can use new field data in future to re-model for easy update (doing this in WV)Slide42

Match disaggregated data to ESDs

Further disaggregate to ESD state and transition models

Would better match imagery because management (e.g. pasture

vs

forest) is more easily detected with remote sensing.

Could map at state and/or community level for direct use in conservation planning

National Range and Pasture Handbook, 2003

Currently submitting article for peer review documenting WV case study Nauman, T., J.A. Thompson. (In prep). Semi-Automated Disaggregation of Conventional Soil Maps using Knowledge Driven Data Mining and Classification Trees

Next StepsSlide43

Spatial Analysis workshop

(distance learning)

Introduction to Digital Soil Mapping

(distance learning)

Digital Soil Mapping with ArcSIE

(conventional class)

Remote Sensing for Soil Survey Applications

(conventional class)Resources – Available TrainingNRCS offers the following courses which provide an introduction to some of these techniques –

check AgLearnSlide44

Literature

Bui, E., B. Henderson, and K.

Viergever

. 2009. Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbon mapping in Australia. Global Biogeochemical Cycles 23.

Bui

, E.N. and Moran, C.J., 2001. Disaggregation of polygons of surficial geology and soil maps using spatial

modelling

and legacy data. Geoderma, 103(1-2): 79-94.

Bui, E.N., A. Loughhead

, and R. Corner. 1999. Extracting soil-landscape rules from previous soil surveys. Australian Journal of Soil Research 37:495-508.de Bruin, S., Wielemaker, W.G. and Molenaar, M., 1999. Formalisation

of soil-landscape knowledge through interactive hierarchical disaggregation.

Geoderma

, 91(1–2): 151-172.

Goovaerts

, P., 2011. A coherent

geostatistical

approach for combining

choropleth

map and field data in the spatial interpolation of soil properties. European Journal of Soil Science, 62(3): 371-380.

Häring

, T., Dietz, E., Osenstetter, S., Koschitzki, T. and Schröder, B., 2012. Spatial disaggregation of complex soil map units: A decision-tree based approach in Bavarian forest soils. Geoderma

, 185–186(0): 37-47.Kerry, R., Goovaerts, P., Rawlins, B.G. and Marchant, B.P., 2012. Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale. Geoderma

, 170: 347-358.Li, S., MacMillan, R. A., Lobb, D. A., McConkey, B. G., Moulin, A., & Fraser, W. R. 2011. Lidar DEM error analyses and topographic depression identification in a hummocky landscape in the prairie region of Canada.

Geomorphology, 129(3), 263-275.McBratney, A.B., 1998. Some considerations on methods for spatially aggregating and disaggregating soil information. Nutrient Cycling in Agroecosystems, 50(1-3): 51-62

. MDA, Federal. 2004. Landsat Geocover TM 1990 & ETM+ 2000 Edition Mosaics Tile N-17-35 TM-EarthSat-MrSID

. USGS, Sioux Falls, South Dakota.Slide45

Literature

Moore, A. 2008. Spatial Disaggregation Techniques for Visualizing and Evaluating Map Unit Composition. NRCS 2008 National State Soil Scientist’s Workshop. Florence

, Kentucky.

ftp://

ftp-fc.sc.egov.usda.gov/NSSC/NCSS/Conferences/state/2008/moore.pdf

Nauman

, T.W., 2009. Digital Soil-Landscape Classification for Soil Survey using ASTER Satellite and Digital Elevation Data in Organ Pipe Cactus National Monument, Arizona.

MS Thesis. The

University of Arizona.

Nauman, T., J.A. Thompson, N. Odgers, and Z. Libohova. 2012. Fuzzy Disaggregation of Conventional Soil Maps using Database Knowledge Extraction to Produce Soil Property Maps, In B. Minasny, et al., (eds.) Digital Soil Assessments and Beyond: 5th Global Workshop on Digital Soil Mapping, Sydney, Australia

.

Schmidt, J. and Hewitt, A., 2004. Fuzzy land element classification from DTMs based on geometry and terrain position.

Geoderma

, 121(3-4): 243-256

.

Thompson

, J.A. et al., 2010. Regional Approach to Soil Property Mapping using Legacy Data and Spatial Disaggregation Techniques, 19th World Congress of Soil Science, Soil Solutions for a Changing World, Brisbane, Australia.

Wei, S. et al., 2010. Digital

Harmonisation

of Adjacent Soil Survey areas - 4 Iowa Counties, 19th World Congress of Soil Science, Soils Solutions for a Changing World, Brisbane, Australia.

Wielemaker, W.G., de Bruin, S., Epema

, G.F. and Veldkamp, A., 2001. Significance and application of the multi-hierarchical landsystem in soil mapping. Catena, 43(1): 15-34.Yang, L. et al., 2011. Updating Conventional Soil Maps through Digital Soil Mapping. Soil Science Society of America Journal, 75(3): 1044-1053.Zhu, A.X., 1997. A similarity model for representing soil spatial information.

Geoderma, 77(2-4): 217-242.Zhu, A.X., Band, L., Vertessy, R. and Dutton, B., 1997. Derivation of soil properties using a soil land inference model (SoLIM). Soil Science Society of America Journal, 61(2): 523-533.

Zhu, A.X., Band, L.E., Dutton, B. and Nimlos, T.J., 1996. Automated soil inference under fuzzy logic. Ecological Modelling, 90(2): 123-145.Slide46

Questions