Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman,...

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Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC State Soil Scientists’ Meeting Tues. 3/18 3:00 – 5:00pm Center Wed. 3/19 10:00am - Noon

Transcript of Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman,...

Page 1: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group IS. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC

State Soil Scientists’ MeetingTues. 3/18 3:00 – 5:00pm CenterWed. 3/19 10:00am - Noon

Page 2: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Introduction

• Laws of Krynine

– Let us see things in their proper places– Let us know what we are talking about– Let us think straight– Let us not fool ourselves

P.D. Krynine, known as the Father of Sedimentology, practiced Sedimentary Petrology in the 1930’s - 1950’s

Page 3: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Purpose• After this seminar you should be able

to recognize the four core geospatial data layers provided by NCGC in file geodatabase (FGDB – ArcGIS 9.2) format and to be aware of queries/analyses that your staff can perform to assess the published/historic soil survey database to support soil correlation decisions during MLRA Soil Survey Updates

Page 4: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Geospatial Data assessment is part of a sequential three step process:

1)Data assessment/GIS analyses2)Correlation Decision making3)Geometry editing (SSURGO polygons,

lines, points) – merge, split, re-label, or adjust

Page 5: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 6: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Introduction

• FGDB of core data layers– DEM (NEDS) – Hydrography (National Hydrology

Dataset-NHD)– Soils (SDM FY07 Q3 – next edition soon)– TeleAtlas reference layers (roads, zip

codes, counties, states, etc.)• Combine with Web Map Service

through NCGC (e.g DOQQ imagery)

Page 7: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Introduction

• Web Map Services (WMS) demos– Weather Station summaries (MAP,

MAAT)• WMS Server connection

– http://gis2/arcgis/services/WeatherStation/Mapserver/WMSserver

• ArcGIS Server connection– http://gis2.ftw.nrcs.usda.gov/arcgis/services

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Management of Soil Survey by MLRA Course• Marc Crouch is point of contact• Roles/Responsibility and Soil Correlation• Evaluation of historic soil survey• Prioritization and planning

– MLRA (long range) work plan (e.g. MLRA 105)

– Project (mid range) plan (MLRA SSA 10-10) – Annual work plan (plan of operations )

Page 9: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Management of Soil Survey by MLRA Course• Project Management • Role of Benchmark Soils • Assessment/Evaluation/Validation

– geospatial and attribute

• Correlation Decision Making• Certification and publication

Page 10: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Working with File Geodatabases (new in ArcGIS 9.2)• A series of instructions is under development

– Assistance is needed! If you have ideas or documents, let us know…

• The first set of instructions introduces relationship classes and provides examples of how to use them.

• Future instructions will (hopefully) include: – Querying and mapping data from component and horizon

tables;– Working with the MUAGGATT table; – Working with External Tables (e.g., SSURGO template); and – Visualizing Queries with Raster Data.

• http://www.ngdc.wvu.edu/software

Microsoft Word Document

Page 11: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Example I: Identifying soil survey areas in which a series is mapped

Page 12: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Example II: Viewing related mapunit, component, and horizon records.

Page 13: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Using the Mapunit Aggregate Table

• Part of the standard SSURGO download– Included in MO-wide file geodatabase

• Includes variety of soil attributes and interpretations that have been aggregated from the component level to a single value at the map unit level

• Can be joined to the MUPOLYGON feature class for easy mapping

Page 14: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Using the Mapunit Aggregate Table

• Fields include– Slope gradient, bedrock depth, water table depth,

flooding frequency, ponding frequency, available water storage, drainage class, hydrologic group, et.c…

• Refer to the SSURGO Metadata Table Columns Description report for a complete list of columns and their associated aggregation methods

• http://soildatamart.nrcs.usda.gov/documents/SSURGOMetadataTableColumnDescriptions.pdf

Slope Gradient, Drainage Class, and Hydrologic Group

Page 15: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Slope Gradient – Weighted Average

• MLRA 105

MUPOLYGON_albers_mlra105

muaggatt.slopegradwta

3.000001 - 5.000000

5.000001 - 8.000000

8.000001 - 12.000000

12.000001 - 20.000000

20.000001 - 35.000000

35.000001 - 55.000000

55.000001 - 75.000000

0.000000 - 3.000000

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Drainage Class – Dominant Component

• MLRA 105

Can also map Wettest component

MUPOLYGON_albers_mlra105

DRCLASSDCD

<Null>

Excessively drained

Somewhat excessively drained

Well drained

Moderately well drained

Somewhat poorly drained

Poorly drained

Very poorly drained

Page 17: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

• MLRA 105

Hydrologic Group – Dominant Condition

MUPOLYGON_albers_mlra105

HYDGRPDCD

<Null>

A

A/D

B

B/D

C

C/D

D

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Analyzing Data Outside of ArcGIS

• Soil attribute data can be analyzed and summarized in– NASIS– MS Access (SSURGO Template), 2 GB limit– SQL Server Enterprise, no size limit– SQL Server Express, 4 GB limit– Excel .xls and Dbase4 .dbf (~60,000 record

limit)– Other…

Page 19: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Analyzing Data Outside of ArcGIS

• Tables and queries developed in these systems can be mapped in ArcGIS, provided that MUKEY is included and the attribute summarized or aggregated to the map unit level

• Save or export queries and reports as *.dbf files, then join (or relate) using the MUKEY to the MUPOLYGON feature class for visualization– Can join all records or only matching records

Page 20: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Working in the SSURGO Template

• Data for all soil survey areas that overlap an MLRA can be exported easily from NASIS– Use NSSC Pangaea query

Area2/Mapunit/Datamapunit for mapunits by MLRA (uses MLRA overlap)

– If MLRA Overlap is not properly populated, additional survey areas may have to be added to the selected set

– Run SSURGO export, excluding interpretations and text notes

Page 21: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Working in the SSURGO Template• Import SSURGO export into the SSURGO

template– Final database must be less than 2 GB

• 162 SSAs in MO 13 resulted in a 500 MB database w/o interp and text tables

• Build queries to analyze data across the MLRA

– Existing reports and queries in the SSURGO template operate on a SSA basis

– Develop new queries using the Access query builder

– Export results as *.dbf or *.xls files

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Working in the SSURGO Template

• Map the results– Add *.dbf / *.xls files to ArcMap– Join to MUPOLYGON feature class (in the file

geodatabase) on MUKEY– Choose a field to map, and symbolize

appropriately

• Instructions on working with the SSURGO template can be found at http://soils.usda.gov/technical/access/

Page 23: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Correlation Date

Before 1970

1970 to 1994

1995 or later

Not in selection

Tables from NASIS export excludes SSAs that do not have Area Overlaps populated.

Tables in the file geodatabase include all data available from Soil Data Mart

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MUPOLYGON_albers_mlra105

FAYETTEG_2.GEOMDESC

valley floors

river valleys, terraces

stream terraces on river valley

river valleys, stream terraces

stream terraces, river valleys

stream terraces

valleys

hills, valley floors

ridges

ground moraines

valley sides

hills

hillslopes on uplands

IAIA

ILIL

WIWIMNMN

NM

NM

NM

NM

NM

NM

NMNM

NM

NM

NM

NINI

NI

NI

NI

NI

NM = Fayette series not mapped

NI = Survey area not included in export

Page 25: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

• MLRA 105• Dominant

component• First horizon,

mineral• Sandy textures

generalized to Sand, Loamy Sand, and Sandy Loam

Soil TextureSilt loam

Clay loam

Silty clay loam

Silty clay

Loam

Sandy loam

Loamy sand

Sand

Page 26: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Component Restrictions

• MLRA 105• Dominant

component• Lithic vs paralithic

bedrock• Cementation

classes

<Null>, <Null>

Lithic

Lithic, Indurated

Paralithic

Paralithic, Extremely weakly cemented

Paralithic, Weakly cemented

Paralithic, Moderately cementedParalithic, Indurated

Page 27: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Working with Soil Data Mart Data in Raster Format• At small scales (large extents), raster data may

display more quickly in ArcMap than vector data– Depends on pixel size

• In ArcGIS 9.2, rasters in various formats can be joined directly to other tables (*.dbf files, tables in file geodatabases, etc.)

• MUPOLYGON_AlbersEqualAreaUSGS feature class – Grid on MUKEY (this is possible in Arc 9.2)– 30 meter cell size is recommended– Join to *.dbf / *.xls files on MUKEY

Page 28: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Working with Soil Data Mart Data in Raster Format• Use raster representation to view data for

large areas– Switch to raster data at scales of ~ 1:50,000 –

1:100,000 or smaller– Some detail may be lost, but general shapes,

locations, and patterns will be apparent

• Use vector representation to view data for small areas– Switch to vector data when zoomed in to map scales

of ~ 1:50:000 – 1:100,000 or larger– See exact location and shape of polygons

Page 29: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Raster Data Images

• MLRA 105• Surface Texture,

Fayette Soils

Silt loam

Silty clay loam

Silt loam

Silty clay loam

Page 30: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

• MLRA 105• Geomorphic

Description, Dubuque Soils

Raster Data Imageshills

hillslopes

hillslopes on uplands

NULL

ridges

Page 31: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

hills

hillslopes

hillslopes on uplands

NULL

ridges

ground moraines

hills

hills, stream terraces

hillslopes on uplands

ridges

river valleys, terraces

stream terraces

stream terraces on river valleys

Geomorphic Description – Downs Soils

Page 32: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Analyzing Data Outside of ArcGIS

• Using MS SQL Server Enterprise or MS SQL Server Express to prepare MO-wide and Nation-wide queries – Major Component queries – Series– All component queries – Series

• Summed Component Percent for each map unit

– All component queries – Taxonomic • Summed component Percent for each map unit

Page 33: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Spatial selection of SSURGO or General Soil Map (GSM) polygons

– Those polygons that intersect or touch the MLRA SSA or MLRA line

– In the MLRA 105 example, the MLRA SSA coincides with the Ag Handbook 296 MLRA region called 105

– Note: this is not always the case across the nation

Page 34: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 35: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Spatial Selection of SSURGO Polygons-Those that intersect or touch the -MLRA 105 or MLRA SSA 10-10 -Boundary (1:250,000)

Page 36: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

1:250,000

1:250,000 Scale

Spatial Selection of SSURGO Polygons-Those that intersect or touch the -MLRA 105 or MLRA SSA 10-10 -Boundary (1:250,000)

Page 37: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 38: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Attribute selection of soil polygons joined to the MO-wide File GDB

– Major component queries can be performed and joined to MO-wide soils core layer using the MUKEY

– This will illustrate the MO-wide extent of the attribute in question

– These queries can be performed on the National Soil Data Mart attribute tables using MS SQL Server Enterprise

Page 39: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Major Component attribute query joined to MUPOLYGON• Query for component like ‘Fayette

%’ and major component flag = ‘Yes’

Page 40: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Selected table records are exported And later linked to MO10 MUPOLYGON

Page 41: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 42: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

General Soil Map of the US (GSM)Summed Component PercentFayette map unit components

Page 43: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Detailed Soil Survey (SSURGO)Within MLRA 105 (MLRA SSA 10-10)Major Component - Fayette

Page 44: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Detailed Soil Survey (SSURGO)Full Exent of Fayette SeriesMajor Component

Page 45: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Detailed Soil Survey (SSURGO)Fayette Series, Major Component 1:250,000 Scale

Page 46: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

All Components attribute query joined to MUPOLYGON• Query for component like ‘Fayette

%’ and comppct summed for those flagged components by map unit

Page 47: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 48: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 49: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 50: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.
Page 51: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Detailed Soil Survey (SSURGO)Full Extent of Fayette SeriesSummed Component Percent

Page 52: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Taxonomic Queries

– All component queries – Taxonomic • Summed component Percent for each map

unit

Page 53: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

– All component queries – Taxonomic • Summed component percent for each map unit• Taxclname like ‘%fragi%’ for GSM map units

Page 54: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

– All component queries – Taxonomic • Summed component percent for each map unit• Taxclname like ‘%fragi%’ for SSURGO map units

Page 55: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Review

• Recognize the four core geospatial data layers provided in file geodatabase (FGDB – ArcGIS 9.2) format

• Be aware of the resources available to assist with MLRA-wide analysis

• Be aware of the kinds of queries that can be used to identify problems, gaps, and inconsistencies the historic soil survey database

• Be aware of how the SSURGO template can be used in conjunction with soils data in FGDB and raster format

Page 56: Using Core Data layers (FGDB) to assist in MLRA Correlation Decision Making – Group I S. Waltman, A. Moore, S. Brown, P. Finnell – NGDC, NCGC and NSSC.

Questions?

• Demonstrations will be available during the evening session, please stop by

• Thank you