CONTENTS	 

U.S. Department of the Interior

U.S. Geological Survey

Digital Mapping Techniques ‘06—

Workshop Proceedings

Edited by David R. Soller

June 11-14, 2006

Columbus, Ohio

Convened by the

Association of American State Geologists

and the

United States Geological Survey

Hosted by the

Ohio Geological Survey

U.S. GEOLOGICAL SURVEY OPEN-FILE REPORT 2007-1285

2007
II

This	report	is	preliminary	and	has	not	been	reviewed	for

conformity	with	the	U.S.	Geological	Survey	editorial	standards.

Any use of trade, product, or firm names in this publication is for

descriptive	purposes	only	and	does	not	imply	endorsement	by	the

U.S.	Government	or	State	governments
CONTENTS	 

III

CONTENTS

Introduction

David R. Soller (U.S. Geological Survey) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 1

Oral Presentations

The National Geologic Map Database Project: Overview and Progress

By David R. Soller (U.S. Geological Survey). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 7

The New Geology Ontario Web Portal - An “out-of-the-box”

Solution for Discovering and Delivering Ontario’s Geoscience Data

By Zoran Madon (Ontario Geological Survey) and Brian Berdusco

(Ministry of Northern Development and Mines). . . . . . . . . . . . . . . . . . . . . . . . . 	 15

Building a Water Well Database for GIS Analysis

By A. Wayne Jones and Kelly A. Barrett (Ohio Department of Natural

Resources, Division of Water). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 27

Sensitive Aquifers in Ohio – Relationship to Highly Susceptible Public Water

Systems

By Christopher Kenah, Michael W. Slattery, Linda D. Slattery, and Michael L.

Eggert (Ohio Environmental Protection Agency, Division of Drinking and

Ground Waters) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 35

GeoSciML – A GML Application for Geoscience Information Interchange

By Stephen M. Richard (U.S. Geological Survey and Arizona

Geological Survey) and CGI Interoperability Working Group. . . . . . . . . . . . . . 	 47

Open Source Web Mapping: The Oregon Experience

By David Percy (Portland State University). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 61

Lidar Basics for Mapping Applications

By James D. Giglierano (Iowa Geological Survey) . . . . . . . . . . . . . . . . . . . . . . . . 	 65

Geological Map Database – A Practitioner’s Guide to Delivering the

Information

By Jeremy R A Giles (British Geological Survey). . . . . . . . . . . . . . . . . . . . . . . . . 	 77

Preservation of North Carolina Legacy Geologic and Topographic Maps: A

Cooperative Effort with the North Carolina Geological Survey, North Carolina State University and the Library of Congress

By Jeffrey C. Reid (North Carolina Geological Survey), Jefferson F. Essic

(North Carolina State University), Steven P. Morris (North Carolina State

University), Smitha Ramakrishnan (North Carolina State University), and

Julia L. Harrell (North Carolina Department of Environment and Natural

Resources). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 85

Saturation and Value Modulation (SVM): A New Method for Integrating

Color and Grayscale Imagery

By David W. Viljoen and Jeff R. Harris (Geological Survey of Canada). . . . . . . . 	 87
Unpublished	Geologic	Evidence	and	Other	Databases	of	the	Kentucky	Digital	

Mapping	Program

By Gerald A. Weisenfluh and Douglas C. Curl (Kentucky Geological Survey)	. . 	101

The Challenges and Benefits of Distributing Digital Data: Lessons Learned

By	Kenneth	R.	Papp,	Susan	Seitz,	and	Larry	Freeman	(Alaska	Division	of	

Geological	&	Geophysical	Surveys),	and	Carrie	Browne	(formerly	with	

ADG&GS)	. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	107

The	Alabama	Metadata	Portal:	http://portal.gsa.state.al.us

By	Philip	T.	Patterson	(Geological	Survey	of	Alabama)	. . . . . . . . . . . . . . . . . . . . 	113

3D Geological Modeling: Solving as a Classification Problem with the

Support	Vector	Machine

By	Alex	Smirnoff,	Eric	Boisvert,	and	Serge	J.	Paradis	(Geological	Survey	

of	Canada) 	. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	119

Qualitative and Quantitative 3D Modeling of Surficial Materials at Multiple 

Scales

By	Erik	R.	Venteris	(Ohio	Geological	Survey)	. . . . . . . . . . . . . . . . . . . . . . . . . . . 	129

Poster Presentations

Arkansas	Geological	Commission	template	for	1:24,000	Scale	Geologic	Maps

By	Jerry	W.	Clark	and	William	D.	Hanson	(Arkansas	Geological	Commission)	. . 	151

Geologic	map	of	the	Ouachita	Mountain	region	and	a	portion	of	the	Arkansas	

Valley	region	in	Arkansas

By	William	D.	Hanson	and	Jerry	W.	Clark	(Arkansas	Geological	Commission)	. . 	153

ArcGS	Geodatabase	Schema	for	Geologic	Map	Production

By	Vic	Dohar	(Natural	Resources	Canada)	. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	155

Compression	of	Digital	Orthophotography	Collections:	Factors	to	Consider	in	

the	Compression	of	Large	Data	Sets	of	Geospatial	magery

By	Deette	M.	Lund	(llinois	State	Geological	Survey)	. . . . . . . . . . . . . . . . . . . . . 	159

Converting	Adobe	llustrator	Maps	to	ArcMap	Format

By	Jennifer	Mauldin	(Nevada	Bureau	of	Mines	and	Geology)	. . . . . . . . . . . . . . . 	165

Using	GS	to	Create	and	Analyze	Potentiometric-Surface	Maps

By	Paul	N.	Spahr,	A.	Wayne	Jones,	Kelly	A.	Barrett,	Michael	P.	Angle,	and	

James	M.	Raab	(Ohio	Department	of	Natural	Resources)	. . . . . . . . . . . . . . . . . 	169

Updates	to	the	Known	and	Probable	Karst	Map	of	Ohio

By	Donovan	M.	Powers	and	Dennis	Hull	(Ohio	Geological	Survey)	. . . . . . . . . . 	171

New Map of the Surficial Geology of the Lorain and Put-In-Bay 30 x 60

Minute	Quadrangles,	Ohio

By	Edward	M.	Swinford,	Richard	R.	Pavey,	Glenn	E.	Larsen,	and	K.E.	Vorbau		

(Ohio	Geological	Survey) 	. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	177

Rapid	Acquisition	of	Ground	Penetrating	Radar	Enabled	by	LDAR

By	David	Percy	and	Curt	Peterson	(Portland	State	University)	. . . . . . . . . . . . . . 	183
CONTENTS	 V

GS	and	GPS	Utility	in	the	Geologic	Mapping	of	Complex	Geologic	Terrane	

on	the	Mascot,	Tennessee	7.5’	Quadrangle

By	Barry	W.	Miller	and	Robert	C.	Price		(Tennessee	Division	of	Geology)	. . . 	187

Prototype	GS	Database	for	the	DNAG	Geologic	Map	of	North	America

By	Christopher	P.	Garrity	and	David	R.	Soller	(U.S.	Geological	Survey)	. . . . . . 	197

USGS	National	Surveys	and	Analysis	Projects:	Preliminary	Compilation	of	

ntegrated	Geological	Datasets	for	the	United	States

By	Suzanne	W.	Nicholson,	Douglas	B.	Stoeser,	Frederic	H.	Wilson,	Connie	L.	

Dicken,	and	Steve	D.	Ludington	(U.S.	Geological	Survey) 	. . . . . . . . . . . . . . . 	203

Banding	Birds	with	MapServer	CG

By	Robert	S.	Wardwell	and	Kevin	W.	Laurent	(U.S.	Geological	Survey) 	. . . . . . 	211

Appendix A.	List	of	Workshop	Attendees	. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 	 215
V	 CONTENTS
Introduction

By	David	R.	Soller

U.S.	Geological	Survey

926-A	National	Center

Reston,	VA	20192

Telephone:	(703)	648-6907

Fax:	(703)	648-6977

e-mail:	drsoller@usgs.gov

The Digital Mapping Techniques ‘06 (DMT‘06)

workshop was attended by more than 110 technical experts from 51 agencies, universities, and private companies, including representatives from 27 state geological

surveys (see Appendix A of these Proceedings). This

workshop was similar in nature to the previous nine meetings, which were held in Lawrence, Kansas (Soller, 1997),

Champaign, Illinois (Soller, 1998), Madison, Wisconsin (Soller, 1999), Lexington, Kentucky (Soller, 2000),

Tuscaloosa, Alabama (Soller, 2001), Salt Lake City,

Utah (Soller, 2002), Millersville, Pennsylvania (Soller,

2003), Portland, Oregon (Soller, 2004), and Baton Rouge,

Louisiana (Soller, 2005). This year’s meeting was hosted

by the Ohio Geological Survey, from June 11-14, 2006,

on the Ohio State University campus in Columbus, Ohio.

As in the previous meetings, the objective was to foster

informal discussion and exchange of technical information. It is with great pleasure that I note that the objective

was successfully met, as attendees continued to share and

exchange knowledge and information, and renew friendships and collegial work begun at past DMT workshops.

Each DMT workshop has been coordinated by the

Association of American State Geologists (AASG) and

U.S. Geological Survey (USGS) Data Capture Working

Group, the latter of which was formed in August 1996 to

support the AASG and the USGS in their effort to build a

National Geologic Map Database (see Soller, this volume,

and http://ngmdb.usgs.gov/info/standards/datacapt/). The

Working Group was formed because increased production

efficiencies, standardization, and quality of digital map 

products were needed for the database—and for the State

and Federal geological surveys—to provide more highquality digital maps to the public.

At the 2006 meeting, oral and poster presentations

and special discussion sessions emphasized: 1) methods

for creating and publishing map products (here, “publishing” includes Web-based release); 2) field data capture 

software and techniques, including the use of LIDAR; 3)

digital cartographic techniques; 4) migration of digital

maps into ArcGIS Geodatabase format; 5) analytical GIS

techniques; and 6) continued development of the National

Geologic Map Database.

ACKNOWLEDGEMENTS

I thank the Ohio Geological Survey (OGS) and their

Chief and State Geologist, Thomas M. Berg, for hosting

this meeting and for arranging for corporate sponsorship.

During the past 10 years, I have worked closely with the

Association of American State Geologists and, in particular, with Tom, who retired soon after the DMT’06

meeting. I wish to thank him profusely for his many years

of guidance, support, and friendship.

In the tradition of past DMT meetings, the attendees

were given a very informative, productive, and enjoyable

experience. I especially thank Jim McDonald (OGS), who

coordinated the events. Other OGS staff who deserve

thanks are those who provided essential support for this

meeting, including Lisa Van Doren (for preparing graphics, signs, and maps), Mac Swinford (poster boards),

Ed Kuehnle (poster boards), Madge Fitak (registration),

Sharon Stone (meeting logistics), Garry Yates, and Dennis

Hull (poster boards).

The meeting was co-hosted by The Ohio State University Department of Geological Sciences, and I thank them

for their hospitality and for their significant contributions 

to this meeting. Specifically, I thank Franklin Schwartz 

(Department Chair and meeting sponsor), Karen Tyler

(facilities setup), Garry McKenzie (general assistance), Ken

Shelberg (finances, registration), Sue Shipley (finances, 

registration), Mary Scott (Sunday reception host), Dale Gnidovec (Sunday reception host), Michael Seufer (web site),

Betty Heath (registration), Mary Hill (registration), and

Kelley Barrett, Kelley Carroll, Mike Fidler, Steve Goldsmith (setup, registration), and Brent Curtis (WiFi setup).

The meeting was greatly improved through the generous financial donations of the National Office and the 

Ohio Chapter of the American Institute of Professional

Geologists (AIPG), the Ohio Industrial Minerals and Aggregates Association, and the Ohio Oil and Gas Association. The Eastern Section of AAPG and the West Virginia

Geological Survey generously provided the poster boards,

and for this we owe them a warm thank you.

I also thank the members of the Data Capture Working

Group (Warren Anderson, Kentucky Geological Survey;
Elizabeth Campbell, Virginia Division of Mines and Geology; Rob Krumm and Barb Stiff, Illinois State Geological

Survey; Scott McColloch, West Virginia Geological and

Economic Survey; George Saucedo, California Geological Survey; and Tom Whitfield, Pennsylvania Geological 

Survey) for advice in planning the workshop’s content.

I warmly thank Lisa Van Doren (Ohio Geological

Survey) for typesetting the Proceedings. And, last but not

least, I thank all attendees for their participation; their

enthusiasm and expertise were the primary reasons for the

meeting’s success.

PRESENTATIONS AND POSTERS

The workshop included 32 oral presentations and 25

posters. Many are supported by a short paper contained in

these Proceedings. The papers describe technical and procedural approaches that currently meet some or all needs

for digital mapping at the respective agency. There is not,

of course, a single “solution” or approach to digital mapping that will work for each agency or for each program

or group within an agency; personnel and funding levels,

and the schedule, data format, and manner in which we

must deliver our information to the public require that each

agency design their own approach. However, the value

of this workshop and other forums like it is through their

roles in helping to design or refine these agency-specific 

approaches to digital mapping, and to find applicable approaches used by other agencies. In other words, communication helps us to avoid having to “reinvent the wheel.”

During the course of the 10 annual DMT meetings, it

has been my pleasure to meet, and work with, the many

talented people who have authored papers in these Proceedings. As the subjects addressed by the DMT meetings

have become even more essential to the Nation’s geological surveys, the demands placed on them have risen to the

point where many authors scarcely have time to address

their work fully. Predictably, less time is then available

to compose written summaries of their work; I’m sure

the readers (or at least other editors) can sympathize with

this predicament. Therefore, I include with this Introduction a list of all presentations and posters (Appendix A of

this paper). If the reader finds an interesting title that isn’t 

recorded in these Proceedings, I encourage them to contact the authors directly. Further, some presentations and

related information is available for download at http://ngmdb.usgs.gov/Info/dmt/DMT06presentations.html.

THE NEXT DMT WORKSHOP

The eleventh annual DMT meeting will be held in the

Spring of 2007, on the campus of South Carolina University, in Columbia, South Carolina. Please consult the

Web site (http://ngmdb.usgs.gov/Info/dmt/) for updated

information. While planning for that event, the Data Capture Working Group will carefully consider recommendations for meeting content and format offered by DMT’06

attendees.

REFERENCES

Soller, D.R., editor, 2005, Digital Mapping Techniques ‘05—

Workshop Proceedings: U.S. Geological Survey Open-File

Report 2005-1428, 268 p., accessed at http://pubs.usgs.

gov/of/2005/1428/.

Soller, D.R., editor, 2004, Digital Mapping Techniques ‘04—

Workshop Proceedings: U.S. Geological Survey Open-File

Report 2004-1451, 220 p., accessed at http://pubs.usgs.

gov/of/2004/1451/.

Soller, D.R., editor, 2003, Digital Mapping Techniques ‘03—

Workshop Proceedings: U.S. Geological Survey OpenFile Report 03-471, 262 p., accessed at http://pubs.usgs.

gov/of/2003/of03-471/.

Soller, D.R., editor, 2002, Digital Mapping Techniques ‘02—

Workshop Proceedings: U.S. Geological Survey OpenFile Report 02-370, 214 p., accessed at http://pubs.usgs.

gov/of/2002/of02-370/.

Soller, D.R., editor, 2001, Digital Mapping Techniques ‘01—

Workshop Proceedings: U.S. Geological Survey OpenFile Report 01-223, 248 p., accessed at http://pubs.usgs.

gov/of/2001/of01-223/.

Soller, D.R., editor, 2000, Digital Mapping Techniques ‘00—

Workshop Proceedings: U.S. Geological Survey OpenFile Report 00-325, 209 p., accessed at http://pubs.usgs.

gov/of/of00-325/.

Soller, D.R., editor, 1999, Digital Mapping Techniques ‘99—

Workshop Proceedings: U.S. Geological Survey Open-File

Report 99-386, 216 p., accessed at http://pubs.usgs.gov/of/

of99-386/front.html.

Soller, D.R., editor, 1998, Digital Mapping Techniques ‘98—

Workshop Proceedings: U.S. Geological Survey OpenFile Report 98-487, 134 p., accessed at http://pubs.usgs.

gov/of/of98-487/.

Soller, D.R., editor, 1997, Proceedings of a workshop on digital

mapping techniques: Methods for geologic map data capture, management, and publication: U.S. Geological Survey

Open-File Report 97-269, 120 p., accessed at http://pubs.

usgs.gov/of/of97-269/.
Appendix A

List of oral and poster presentations, and discussion sessions.

Oral Presentations

A 10-year retrospective on the Digital Mapping Techniques workshops

By David R. Soller (U.S. Geological Survey)

The new Geology Ontario web portal - an “out-of-thebox” solution for discovering and delivering Ontario’s

geoscience data

By Zoran Madon (Ontario Geological Survey)

Building a water well database for GIS analysis

By A. Wayne Jones and Kelly A. Barrett (Ohio Department of Natural Resources, Division of Water)

Identifying sensitive aquifers in Ohio

By Chris Kenah, Michael Slattery, Linda Slattery, and

Michael Eggert (Ohio EPA)

Discussion Session - “Topographic maps and framework

data in the future”

This session focused on plans for creating and updating

topographic and other framework map data, through local,

state, and national partnerships. The session began with

these presentations:

•	 Enhancing USGS topo quads, and GIS for the Gulf,

by Stafford Binder (U.S. Geological Survey)

•	 Building NSDI through local, state, and national

partnerships, by Stu Davis (National States Geographic Information Council (NSGIC)

•	 The National Map, by Charles Hickman (U.S.

Geological Survey)

GeoSciML, a GML application for geoscience information interchange

By the CGI Data Model and Testbed working group

Open source web-mapping, the Oregon experience

By David Percy (Portland State University)

High Resolution DEM's from digital photogrammetry,

stereo-autocorrelation, and morphological filtering as an 

alternative to LIDAR for mapping applications

By Peter G. Chirico (U.S. Geological Survey)

Integration of high-resolution satellite imagery for coastal

mapping and monitoring

By Ron Li, Xutong Niu, Sagar Deshpande, Feng

Zhou, and Kaichang Di (The Ohio State University)

GS	in	use	at	an	industrial	minerals	company

By	Steve	Murdoch	(Oglebay	Norton	Company	/	O-N	

Minerals)

Discussion	Session	on	LDAR

This	session	focused	on	LDAR	technology,	image	processing	techniques,	and	its	application	to	geologic	mapping.	The	session	began	with	these	presentations:

•	 LDAR	basics,by	Jim	Giglierano	(owa	Geological	

Survey)

•	 Airborne	Laser	Swath	Mapping	(LDAR)	and	geology:	The	B4	project,	by	Michael	Bevis,	David	Raleigh,	Shan	Shan,	Dana	Caccamise,	Eric	Kendrick,	

and	Wendy	Shindle	(The	Ohio	State	University),	

Ken	Hudnut	(U.S.	Geological	Survey),	and	Dorota	

Grejner-Brzezinska	and	Charles	Toth	(The	Ohio	

State	University)

•	 LDAR	and	various	levels	of	accuracy.	by	Mark	

Brooks	(Optimal	Geomatics)

The	National	Park	Service	Geologic	Resources	Evaluation;	Subtitle:	"Using	GS	to	get	GS"

By	Timothy	B.	Connors	(National	Park	Service)

The	concept	and	development	of	the	National	Geological	

Map	Database	in	the	Czech	Republic

By	Robert	Tomas	(Czech	Geological	Survey)

Geological	Map	Database	-	A	practitioner's	guide	to	delivering	the	information

By	Jeremy	Giles	(British	Geological	Survey)

The	Publishing	Process	ntegration	system	for	the	Geological	Survey	of	Canada	publication	products

By	Linda	Guay	(Geological	Survey	of	Canada)

Building	Geodatabase	coded-value	domains	from	National	Geologic	Map	Database	vocabularies

By	Steve	Richard	(Arizona	Geological	Survey),	and	

Jon	Craigue	and	Dave	Soller	(U.S.	Geological	

Survey)

Visualizing	earthquake	hazard	information	in	Google	

Earth

By	J.	Luke	Blair,	Marco	Ticci,	James	Lienkaemper,	

and	Heather	Lackey	(U.S.	Geological	Survey)
Preserving North Carolina legacy geologic and topographic maps

By Jeffrey C. Reid (North Carolina Geological

Survey), Jeff Essic (North Carolina State University Libraries), Steve Morris (North Carolina State

University Libraries), and Smitha Ramakrishnan

(University of North Carolina, Greensboro)

Saturation and value modulation: A new method for integrating colour and grey-scale imagery

By David Viljoen and Jeff Harris (Geological Survey

of Canada)

Geographic Imager software for Adobe Photoshop

By David Andrec and Doug Smith (Avenza Systems,

Inc.)

Acquiring unpublished geologic evidence to augment

Web dissemination of Kentucky's geologic maps

By Jerry Weisenfluh (Kentucky Geological Survey)

The challenges and benefits of distributing digital data: 

Lessons learned

By Kenneth Papp, Susan Seitz, and Larry Freeman

(Alaska Division of Geological & Geophysical Surveys), and Carrie Browne (formerly with

ADG&GS)

The Alabama Metadata Portal: A new solution for serving

large amounts of data

By Philip Patterson (Geological Survey of Alabama)

IT Security - How it affects digital mapping

By Harry McGregor (University of Arizona and the

U.S. Geological Survey)

From Geek to Illiterate Manager: following the road

wherever it leads, enjoying the scenery and ignoring the

address ranges

By Jay Parrish (Director, Pennsylvania Bureau of

Topographic and Geologic Survey)

3D geological modeling: Solving a classification problem 

with the Support Vector Machine

By Alex Smirnoff, Eric Boisvert, and Serge J. Paradis

(Geological Survey of Canada)

Qualitative and quantitative 3D modeling of surficial 

materials at multiple scales

By Erik Venteris (Ohio Geological Survey)

Discussion Session - "A vision for web-accessible 3D

geological mapping"

Led by Harvey Thorleifson (Director, Minnesota Geological Survey), this session offered for discussion this vision

and how it might be implemented:

“People require geological mapping to fulfill their 

objectives related to health, heritage, safety, and

economic development, and they expect public

information to be web-accessible and readily usable. Could the geoscience community cooperate in

order to make available an international database that

provides known and predicted subsurface conditions,

based on consistent global coverage, zoomable down

to the most detailed coverage available, with links

to the source map? To address societal issues, the

database should include bathymetry, soils, onshore

and offshore surficial and bedrock geology, and 3D 

geology depicting extent, thickness, and properties

of geologic units, so that web-accessible drillhole

forecasts can be issued for any point.”

Poster Presentations

Geologic map of the Ouachita Mountain region in Arkansas

By Boyd R. Haley and Charles G. Stone (Arkansas

Geological Commission)

Template for a geologic map at 1:24,000 scale

By William D. Hanson and Jerry W. Clark (Arkansas

Geological Commission)

A Geodatabase schema for geologic map production

By Vic Dohar (Geological Survey of Canada)

The Publishing Process Integration system for the Geological Survey of Canada publication products

By Linda Guay (Geological Survey of Canada)

The art of mapping with a catalogue of geo-knowledge:

Sable Island Bank and the Gully, Scotian Shelf, offshore

eastern Canada

By Edward L. King and Gary M. Grant (Geological

Survey of Canada)

Saturation and value modulation: A new method for integrating colour and grey-scale imagery

By David Viljoen and Jeff Harris (Geological Survey

of Canada)

Digital map production at the Czech Geological Survey,

Czech Republic

By Zuzana Krejci (Czech Geological Survey)
Compression of digital orthophotography collections

By Deette Lund (Illinois State Geological Survey)

Recent LGS StateMap geologic maps; Recent LGS geologic lithographs; The impact of Hurricane Katrina

By R. Hampton Peele, Richard P. McCulloh, Paul

Heinrich, John Snead, Lisa Pond, Robert Paulsell,

DeWitt Braud, Ahmet Binselam, Ivor van Heerden,

and Rob Cunningham (Louisiana Geological Survey and Louisiana State University)

Surficial and 3-D geological mapping in support of land 

and water management in Manitoba, Canada

By Greg Keller and Gaywood Matile (Manitoba

Geological Survey)

Converting Adobe Illustrator maps to ArcMap format

By Jennifer Mauldin (Nevada Bureau of Mines and

Geology)

Preserving North Carolina legacy geologic and topographic maps

By Jeffrey C. Reid (North Carolina Geological

Survey), Jeff Essic (North Carolina State University Libraries), Steve Morris (North Carolina State

University Libraries), and Smitha Ramakrishnan

(University of North Carolina, Greensboro)

Using GIS to create and analyze potentiometric-surface

maps

By Paul N. Spahr, A. Wayne Jones, Kelly A. Barrett,

Michael P. Angle, and James M. Raab (Ohio Department of Natural Resources, Division of Water)

Detailed, three-dimensional, surficial-geology mapping of 

the Milan, Ohio 1:24,000 Quadrangle

By Rick Pavey (Ohio Geological Survey)

Updates to the Known and Probable Karst Map of Ohio

By Donovan Powers (Ohio Geological Survey)

New map of the surficial geology of the Lorain and Put-

In-Bay 30 x 60 Minute Quadrangles, Ohio

By E.M. Swinford, R.R. Pavey, G.E. Larsen, and

K.E. Vorbau (Ohio Geological Survey)

Airborne Laser Swath Mapping (LIDAR) and Geology:

The B4 project

By Michael Bevis, David Raleigh, Shan Shan, Dana

Caccamise, Eric Kendrick, and Wendy Shindle

(The Ohio State University), Ken Hudnut (U.S.

Geological Survey), and Dorota Grejner-Brzezinska and Charles Toth (The Ohio State University)

The National Park Service Geologic Resources Evaluation; Subtitle: “Using GIS to get GIS”

By Timothy B. Connors (National Park Service)

GIS and GPS utility in the geologic mapping of complex

geologic terrane on the Mascot, Tennessee 7.5’ Quadrangle

By Barry W. Miller and Robert C. Price (Tennessee

Division of Geology)

Spatial adjustment and digital capture of unprojected

geologic data for the USGS 2004 oil and gas assessment

of the Michigan Basin

By Joseph A. East (U.S. Geological Survey)

Prototype GIS database for the DNAG Geologic Map of

North America

By Christopher Garrity and David Soller (U.S. Geological Survey)

Publications Warehouse: A database of verified, Web-enabled citations, USGS publications, and their metadata

(http://pubs.usgs.gov)

By Carolyn McCullough and Greg Allord (U.S. Geological Survey)

The National Geologic Map Database

By David R. Soller (U.S. Geological Survey), Thomas M. Berg (Ohio State Geologist), and Nancy R.

Stamm (U.S. Geological Survey)

USGS National Surveys and Analysis projects: Preliminary compilation of integrated geological datasets for the

United States

By Doug Stoeser, Ric Wilson, Steve Ludington, Connie Dicken, and Suzanne Nicholson (U.S. Geological Survey)

Banding birds with MapServer

By Rob Wardwell and Kevin Laurent (U.S. Geological Survey)

The National Geologic Map Database Project:

Overview and Progress

By David R. Soller

U.S. Geological Survey

926-A National Center

Reston, VA 20192

Telephone: (703) 648-6907

Fax: (703) 648-6977

e-mail: drsoller@usgs.gov

n	the	past	decade,	the	National	Geologic	Map	

Database	(NGMDB)	project	has	evolved	from	a	general	concept	to	a	set	of	resources	that	have	helped	the	

Nation’s	geological	surveys	provide	to	the	public,	in	a	

more efficient manner, standardized digital geologic map 

information.	Throughout	this	period,		have	had	the	honor	

of	serving	as	the	NGMDB	project	chief.	n	this	capacity,	

	worked	closely	with	the	Association	of	American	State	

Geologists	(AASG;	see	Appendix	A)	and,	in	particular,	

with	my	AASG	chief	liaison,	Thomas	M.	Berg	(State	Geologist	and	Chief,	Ohio	Geological	Survey).	Tom	retired	

soon	after	the	DMT’06	meeting,	and		wish	to	thank	him	

profusely	for	his	many	years	of	guidance,	support,	and	

friendship.

After	10	years	of	managing	the	NGMDB	project	and	

organizing	the	Digital	Mapping	Techniques	workshops,		

found	that	a	slight	reduction	in	certain	activities	has	been	

necessary	in	order	to	bring	new	priorities	and	direction	to	

the	project.	For	example,	by	comparison	to	the	progress	

reports	of	previous	years	(see	Appendix	B),	this	report	

is quite limited in scope. Below, I briefly document the 

project’s	progress	during	this	year;	for	detailed	descriptions	of	this	project’s	goals	and	accomplishments,	please	

refer	to	the	DMT’05	report	(Soller	and	others,	2005).

BACKGROUND

Development	and	management	of	science	databases	

for support of societal decisionmaking and scientific 

research	are	critical	and	widely	recognized	needs.	The	

National	Geologic	Mapping	Act	of	1992	and	its	subsequent	reauthorizations	stipulate	creation	and	maintenance	

of	a	National	Geologic	Map	Database	(NGMDB),	as	a	

national	archive	of	spatially	referenced	geoscience	data,	

including	geology,	paleontology,	and	geochronology.	The	

Act	further	stipulates	that	all	new	information	contributed	to	the	NGMDB	adhere	to	standards,	which	are	to	be	

developed	as	needed	under	the	guidance	of	the	NGMDB	

project.	Development	of	a	national	database	and	its	attendant	standards	is	a	daunting	task	that	requires	close	

collaboration	among	all	geoscience	agencies	in	the	U.S.,	

at	the	State	and	Federal	levels.	The	Act,	therefore,	creates	

the	environment	within	which	the	USGS	and	the	Association	of	American	State	Geologists	can	collaborate	to	build	

the NGMDB and serve as well the specific needs of their 

own	agencies.

From	the	guidelines	in	the	National	Geologic	Mapping	Act,	and	through	extensive	discussions	and	forums	

with	the	geoscience	community	and	with	the	public,	a	

general	strategy	was	designed	by	which	to	build	the	National	Geologic	Map	Database	(NGMDB).	This	strategy	

was	publicly	stated	in	1995	and,	based	on	public	input,	

has	gradually	evolved.	The	NGMDB	is	designed	to	be	

a	comprehensive	reference	tool	and	data	management	

system	for	spatial	geoscience	information	in	paper	and	

digital form. More specifically, the NGMDB will consist 

of	the	following:	1)	limited	metadata	in	its	Map	Catalog	

for	all	paper	geoscience	maps	and	book	publications	that	

contain	maps	(including	maps	of	any	part	of	the	Nation,	

published	by	any	agency),	online	viewable	images	of	

paper	and	digital	maps,	metadata	for	published	digital	

map	data,	and	links	to	online	data;	2)	ancillary	databases	

that	provide	further	information	about	geoscience	in	the	

USGS	and	the	state	geological	surveys	(e.g.,	the	Geologic	Names	Lexicon,	the	Mapping	in	Progress	Database,	

and	the	National	Paleontology	Database);	3)	nationwide	

geologic	map	coverage	at	intermediate	and	small	scales;	

4)	an	online	database	of	geologic	maps	(predominantly	in	

vector	format;	planned	as	a	distributed	system);	5)	a	set	of	

Web	interfaces	to	permit	access	to	these	products;	and	6)	

a set of standards and guidelines to promote more efficient 

use	and	management	of	spatial	geoscience	information.	

The	NGMDB	system	is	a	hybrid	–	some	aspects	are	centralized	and	some	are	distributed,	with	the	map	information	held	by	various	cooperators	(for	example,	the	State	

geological	surveys).	Through	a	primary	entry	point	on	

the	Web,	users	can	browse	and	query	the	NGMDB,	and	

obtain	access	to	the	information	wherever	it	resides.
Project Organization

The project has been designed as a set of related tasks

that will develop, over time, a NGMDB with increasing complexity and utility. This is being accomplished

through a network of geoscientists, computer scientists,

librarians, and others committed to supporting the objectives of the NGMDB. Phase One of this project principally involves the building of a comprehensive Map

Catalog of bibliographic records and online images of all

available paper and digital maps, and book publications

containing maps, that adhere to the earth-science themes

specified in the National Geologic Mapping Act of 1992. 

The first phase also includes the design and development 

of the Geologic Names Lexicon, the Mapping in Progress

Database, and the National Paleontology Database. Phase 

Two is the development of standards and guidelines for

geologic map and database content and format. Phase 

Three is a long-term effort to develop a database that

contains nationwide geologic map coverage at a variety of

map scales, according to a complex set of content and format specifications that are standardized through general 

agreement among all partners in the NGMDB (principally

the AASG); this database will be integrated with the databases developed in Phase One. The NGMDB project’s

technology and standards development efforts also are coordinated with various international bodies, including the

Federal Geographic Data Committee, the North American

Geologic Map Data Model Steering Committee, ESRI,

the U.S. National Science Foundation’s Geoinformatics

project “GEON,” the IUGS Commission on the Management and Application of Geoscience Information (“IUGS

CGI”), the IUGS Commission on Stratigraphy, the IUGSaffiliated Commission for the Geological Map of the 

World, and the International Association of Mathematical

Geology (IAMG).

A full realization of the project’s third phase is not

assured and will require a strong commitment among the

cooperators as well as adequate technology, map data, and

funding. The project will continue to assess various options for development of this database, based on realistic

funding projections and other factors. During the development of these phases of the NGMDB, extensive work

will be conducted to generate Web interfaces and search

engines, and continually improve them. Development of

the data management and administrative protocols will be

a priority as well, to ensure that the NGMDB will function efficiently in the future. The NGMDB’s databases 

and project information can be found at http://ngmdb.

usgs.gov.

PROGRESS

Phase One

As noted above, the objective of Phase One is to

provide quick access to existing geoscience resources. As

evidence of success in this activity, the NGMDB “Phase

One” databases (Map Catalog, Geolex, Mapping in Progress) receive about 135,000 visits a month from about

35,000 users (nearly all of whom are non-USGS). This

usage has increased dramatically since mid-2005 – most

likely this was achieved through: 1) increased content;

2) persistent (and more effective?) outreach to users at

scientific meetings and through email and publications; 

3) a successful appeal to USGS to identify NGMDB as

the link from “Geologic Information” on the USGS home

page; and 4) increased market prices for earth resource

commodities, which thereby increases the demand for

geoscience information. With this increased Web traffic 

has come an increase in user requests for information and

assistance – these users vary widely in interest and background, and include school children, homeowners, local

government planners, and professional geologists. With

many of these users we have personal contact by email to

ensure they find what they need.

Specific accomplishments this year include:

	1.	Expansion of the NGMDB Map Catalog by about

4000 records, to a total of about 75,000 records.

This includes 36,000 USGS maps in map, book,

and open-file series, essentially all relevant USGS 

publications. It also includes 26,000 State survey

publications and 13,000 products by other publishers.

	2.	Engagement of 45 states in the process of entering

Map Catalog records and processing of about 2000

new records for State geological survey publications.

	3.	Doubling of the number of links to online publications, including map images; the total is now

10,000.

	4.	Continued the expansion and revision of Geolex

(the geologic names lexicon), with a major update

completed in mid-year.

	5.	Completion of the incorporation of the prototype

Image Library into the Map Catalog. The Image

Library utilized a subset of Catalog records and

provided a Web interface that did not easily scale

upward to accommodate new images. This incorporation was a significant effort that will provide 
users	with	a	more	productive	search	process;	the	

project	is	now	focused	on	providing	a	geographicsearch	capability	within	the	Map	Catalog	to	give	

users	a	search	option	we	attempted	to	provide	via	

the	mage	Library.

	6.	Writing	of	a	prototype	application	that	generates	

a file to display Map Catalog search results in 

Google	Earth.	This	application	was	made	available	

for	public	comment.

	7.	Completion	of	several	hundred	productive	interchanges	with	Map	Catalog,	Geolex,	and	mage	

Library	users	via	the	NGMDB	feedback	form	and	

other	mechanisms.

	8.	Numerous	project	presentations	to	scientists	and	

managers at USGS, AASG, and other scientific 

meetings,	whereby	details	of	the	project	were	

explained	and	participation	of	professionals	in	

building	various	NGMDB	standards	and	databases	

was	increased	(e.g.,	Map	Catalog,	Geolex,	online	

map	database).

	9.	Participation	with	USGS	National	Cooperative	

Geologic	Mapping	Program	(NCGMP)	in	an	effort	

to begin to revise significantly the Mapping in 

Progress	database,	focusing	on	database	redesign	

and	adding	information	that	will	be	useful	to	NCGMP	management.

	10.	 The	providing	of	index	maps,	in	response	to	

requests	by	USGS	and	AASG	management,	that	

show	areas	in	the	U.S.	that	have	been	geologically	mapped	at	various	scales	and	time	periods	

(see	Soller,	2005).	These	maps	and	statistics	(e.g.,	

square	miles	mapped	at	1:24,000-scale	from	year	

2000	to	2005)	were	presented	at	various	venues	

and	were	used	by	NCGMP	to	prepare	responses	to	

the Office of Management and Budget during their 

annual	performance	appraisal.

Phase Two

Phase	Two	addresses	a	Congressional	mandate	to	develop	standards	and	guidelines	for	geologic	map	and	database content and format. Specific accomplishments this 

year	by	members	of	the	NGMDB	project	staff	include:

	1.	Coordination	of	work	on	the	Federal	Geographic	

Data	Committee’s	draft	standard	for	geologic	map	

symbolization;	revisions	to	the	standard,	based	on	

FGDC	Standards	Working	Group	review;	gaining final approval from the FGDC Coordination 

Committee	for	release	as	the	Federal	standard.	This	

standard	includes:	a	new	terminology	for	representing the scientific and locational confidence associated	with	geologic	map	features	(e.g.,	contacts,	

faults,	sample	locations),	a	Postscript	implementation	of	the	standard	(the	ArcGS	implementation	is	

under	development),	and	a	comprehensive	response	

to	all	comments	received	in	the	FGDC-sanctioned	

Public	Review.

	2.	Serving	as	Chair	of	the	FGDC	Geologic	Data	Subcommittee.

	3.	Organization	and	leadership	of	the	tenth	annual	

“Digital	Mapping	Techniques”	workshop,	and	

publication	of	the	workshop	Proceedings	from	the	

previous	year’s	meeting	(DMT	‘05,	Baton	Rouge,	

LA).	These	meetings	have	proven	to	be	a	principal	

means	by	which	to	help	the	geoscience	community	

converge	on	more	standardized	approaches	for	

digital	mapping	and	GS	analysis.

	4.	Serving	as	committee	Secretary	and	as	member	of	

the	newly-formed	U.S.	Geologic	Names	Committee,	and	assistance	in	proposal	of	geologic	time	

scale	and	color	scheme	for	adoption	by	USGS.

	5.	Serving	as	Coordinator	of	the	North	American	

Geologic	Map	Data	Model	Steering	Committee	

(NADMSC)	and	managing	the	NADM	website.

	6.	Continuing	to	provide	strong	intellectual	input	

on	design	and	implementation	of	the	NADMSC	

conceptual	data	model	(“NADM	C1.0”).	This	data	

model	was	published	in	late	2004	and	is	based	in	

part	on	results	of	the	NGMDB-Kentucky	database	

prototype	that	was	developed	in	2002-03.

	7.	Serving	as	U.S.	representative	to	DMAS,	the	

global	standards	body	that	serves	the	Commission	

for	the	Geological	Map	of	the	World.	Provision	of	

technical	information	and	guidance	on	data	model	

and	science	language	standards	under	development	

in	North	America,	and	participation	in	DMAS	

initiatives	to	develop	global	standards.

	8.	Serving	as	the	U.S.	Council	Member	to	the	UGS	

Commission	for	the	Management	and	Application	

of	Geoscience	nformation	(“CG”).

	9.	Participation	in	the	UGS	CG-sponsored	“nternational	Data	Model	Collaboration	Working	

Group.”	Assistance	in	developing	consensus	for	

international	standards	for	a	geologic	data	model.	

Contributing	to	development	of	the	XML-format	

“GeoSciML”	schema,	which	will	be	proposed	as	

an	international	data-exchange	standard.

	10.	 Serving	as	UGS	CG	liaison	to	the	“Multi-Lingual	Thesaurus	Working	Group.”	This	group	is	
10	 DGTAL	MAPPNG	TECHNQUES	‘06

enabling	global	exchange	of	geoscience	information	by	developing	a	common	science	vocabulary	

that	is	translated	into	many	languages.

	11.	 Continued	interaction	with	ESR,	regarding	collaboration	on	an	ArcGS	Geology	Data	Model.

	12.	 Serving	as	member	of	AMG	Strategic	Planning	

Committee	and	providing	guidance	regarding	GS	

and	T	issues.

Phase Three

As	noted	above,	Phase	Three	is	a	long-term	effort	to	

develop	a	database	that	contains	nationwide	geologic	map	

coverage	at	a	variety	of	map	scales,	according	to	a	complex set of content and format specifications that are standardized	through	general	agreement	among	all	partners	in	

the	NGMDB.	Project	activities	this	year	included:

	1.	Continued	development	of	the	prototype	database,	

focusing	on	compilation	of	a	standard	science	

terminology,	implementation	of	the	NADM	conceptual	data	model	in	ESR’s	ArcGS,	and	creation	

of	a	data-entry	tool	to	assist	geologists	and	GS	

specialists	in	creating	standardized	map	databases.	

The	prototype	data	model	was	posted	to	the	ESR	

Geology	Data	Model	website	for	evaluation	by	the	

international	community.

	2.	Participation	in	the	Database	nteroperability	

Testbed	#2,	sponsored	by	the	UGS	CG’s	Working	

Group	on	Data	Model	Collaboration.	This	testbed	

required	disparate	map	data	to	be	managed	in	a	

prototype	online	map	database	system	that	could	

demonstrate	various	query	and	symbolization	

functionality	as	well	as	the	ability	to	output	selected	map	data	to	the	GeoSciML	data	interchange	

format.	A	critical	part	of	this	task	was	identifying	

and	contracting	for	highly	skilled	geologists	with	

strong	backgrounds	in	programming,	GS,	spatial	

database	design,	and	Web	delivery	of	information.	

This	is	a	vitally	important	testbed	involving	at	

least	8	agencies	worldwide.	NGMDB	participation	

involved	the	Arizona	Geological	Survey,	Portland	

State	University,	DOGAM	(Oregon	GS),	and	the	

University	of	Arizona.

	3.	n	order	to	have	modern,	small-scale,	consistent	

geologic	map	coverage	for	the	U.S.,	the	NGDMB	

project	is	converting	the	recently	published	Geologic	Map	of	North	America	(GMNA)	to	digital	

format.	This	is	a	daunting	task,	and	so	an	area	was	

selected	in	which	a	prototype	map	database	would	

be developed (it included part of the U.S., Canada,

and the Pacific Ocean). The prototype map was created and subjected to peer review at the DMT’06

meeting (see Garrity and Soller, this volume). This

prototype demonstrated the feasibility of converting the enormously complex map files from Adobe 

Illustrator to ArcGIS. Participating agencies (Geological Society of America, Geological Survey of

Canada, Woods Hole Oceanographic Institute, and

USGS) have since been contacted with regard to

finalizing the NGMDB proposal so as to create and 

manage the GMNA map database.

ACKNOWLEDGEMENTS

I principally thank my long-time colleague on this

project, Thomas M. Berg (State Geologist and Chief,

Ohio Geological Survey), for his friendship and innumerable contributions to the success of this project. I also

thank the NGMDB project staff and collaborators for

their enthusiastic and expert support, without whom the

project would not be possible. In particular, I thank: Nancy Stamm (USGS, Reston; Geolex database manager and

associate project chief); Ed Pfeifer, Alex Acosta, Dennis

McMacken, Jana Ruhlman, and Michael Gishey (USGS,

Flagstaff and Tucson, AZ; Website and database management), Chuck Mayfield (USGS; Map Catalog content), 

Robert Wardwell and Ben Carter (USGS, Vancouver,

WA, and Reston, VA; Image Library), Steve Richard

(Arizona Geological Survey, Tucson, AZ; data model and

science terminology), Jon Craigue (University of Arizona

/USGS, Tucson, AZ; data-entry tool), and David Percy

(Portland State University; Google Earth application

for the Map Catalog). I also thank the many committee

members who provided technical guidance and standards

(Appendix A).

REFERENCES

Soller, D.R., 2005, Assessing the Status of Geologic Map

Coverage of the United States—A New Application of the

National Geologic Map Database, in D.R. Soller, ed., Digital Mapping Techniques ’05—Workshop Proceedings: U.S.

Geological Survey Open-file Report 2005-1428, p.41-47, 

available at http://pubs.usgs.gov/of/2005/1428/soller2/.

Soller, D.R., Berg, T.M., and Stamm, N.R., 2005, The National

Geologic Map Database Project: Overview and Progress, in

D.R. Soller, ed., Digital Mapping Techniques ’05—Workshop Proceedings: U.S. Geological Survey Open-file 

Report 2005-1428, p. 23-40, available at http://pubs.usgs.

gov/of/2005/1428/soller1/.
THE	NATONAL	GEOLOGC	MAP	DATABASE	PROJECT:	OVERVEW	AND	PROGRESS 11

Appendix A

Principal committees and people collaborating with the National Geologic Map Database project.

Digital Geologic Mapping Committee of the Association of American State Geologists:

Tom Berg (Ohio Geological Survey and Committee

Chair)

Rick Allis (Utah Geological Survey)

Larry Becker (Vermont Geological Survey)

Rick Berquist (Virginia Division of Mineral Resources)

Jim Cobb (Kentucky Geological Survey)

Ian Duncan (Texas Bureau of Economic Geology)

Rich Lively (Minnesota Geological Survey)

Jay Parrish (Pennsylvania Geological Survey)

Bill Shilts (Illinois State Geological Survey)

Nick Tew (Alabama Geological Survey)

Harvey Thorleifson (Minnesota Geological Survey)

Geologic Data Subcommittee of the Federal Geographic Data Committee:

Dave Soller (U.S. Geological Survey and Subcommittee Chair)

Jerry Bernard (USDA-Natural Resources Conservation

Service)

Mark Crowell (Dept. of Homeland Security, Federal

Emergency Mgmt. Agency)

Jim Gauthier-Warinner (U.S. Forest Service, Minerals

and Geology Management)

Laurel T. Gorman (U.S. Army Engineer Research and

Development Center)

John L. LaBrecque (National Aeronautics and Space

Administration)

Lindsay McClelland (National Park Service)

Jay Parrish (State Geologist, Pennsylvania Geological

Survey)

George F. Sharman (NOAA National Geophysical Data

Center)

Dave Zinzer (Minerals Management Service)

Map Symbol Standards Committee:

Dave Soller (U.S. Geological Survey and Committee

Coordinator)

Tom Berg (State Geologist, Ohio Geological Survey)

Bob Hatcher (University of Tennessee, Knoxville)

Mark Jirsa (Minnesota Geological Survey)

Taryn Lindquist (U.S. Geological Survey)

Jon Matti (U.S. Geological Survey)

Jay Parrish (State Geologist, Pennsylvania Geological

Survey)

Jack Reed (U.S. Geological Survey)

Steve Reynolds (Arizona State University)

Byron Stone (U.S. Geological Survey)

AASG/USGS Data Capture Working Group:

Dave Soller (U.S. Geological Survey and Working

Group Chair)

Warren Anderson (Kentucky Geological Survey)

Rick Berquist (Virginia Geological Survey)

Elizabeth Campbell (Virginia Division of Mineral

Resources)

Rob Krumm (Illinois State Geological Survey)

Scott McCulloch (West Virginia Geological and Economic Survey)

Gina Ross (Kansas Geological Survey)

George Saucedo (California Geological Survey)

Barb Stiff (Illinois State Geological Survey)

Tom Whitfield (Pennsylvania Geological Survey)

DMT Listserve:

Maintained by Doug Behm, University of Alabama

North American Data Model Steering Committee:

Dave Soller (U.S. Geological Survey and Committee

Coordinator)

Tom Berg (Ohio Geological Survey)

Boyan Brodaric (Geological Survey of Canada and

Chair of the Data Model Design Technical Team)

Peter Davenport (Geological Survey of Canada)

Bruce Johnson (U.S. Geological Survey and Chair of

the Data Interchange Technical Team)

Rob Krumm (Illinois State Geological Survey)

Scott McColloch (West Virginia Geological and Economic Survey)

Steve Richard (Arizona Geological Survey)

Loudon Stanford (Idaho Geological Survey)

Jerry Weisenfluh (Kentucky Geological Survey)

IUGS Commission for the Management and Application of Geoscience Information:

Dave Soller (U.S. Geological Survey, Council Member)

Conceptual Model/Interchange Task Group (of the 

Data Model Collaboration Working Group of the 

IUGS Commission for the Management and Application of Geoscience Information):

Steve Richard (Arizona Geological Survey, Task Group

Member)

DIMAS (Digital Map Standards Working Group of 

the Commission for the Geological Map of the World):

Dave Soller (U.S. Geological Survey, Working Group

Member)
12	 DGTAL	MAPPNG	TECHNQUES	‘06

NGDMB contact-persons in each State geological 

survey:

These people help the NGMDB with the Geoscience

Map Catalog, GEOLEX, the Geologic Map Image

Library, and the Mapping in Progress Database. 

Please see http://ngmdb.usgs.gov/info/statecontacts.

html for this list.

These groups have fulfilled their mission and are no 

longer active:

NGMDB Technical Advisory Committee:

Boyan Brodaric (Geological Survey of Canada)

David Collins (Kansas Geological Survey)

Larry Freeman (Alaska Division of Geological & Geophysical Surveys)

Jordan Hastings (University of California, Santa Barbara)

Dan Nelson (Illinois State Geological Survey)

Stephen Richard (Arizona Geological Survey)

Jerry Weisenfluh (Kentucky Geological Survey)

AASG/USGS Metadata Working Group:

Peter Schweitzer (U.S. Geological Survey and Working

Group Chair)

Dan Nelson (Illinois State Geological Survey)

Greg Hermann (New Jersey Geological Survey)

Kate Barrett (Wisconsin Geological and Natural History

Survey)

Ron Wahl (U.S. Geological Survey)

AASG/USGS Data Information Exchange Working 

Group:

Dave Soller (U.S. Geological Survey and Working

Group Chair)

Ron Hess (Nevada Bureau of Mines and Geology)

Ian Duncan (Virginia Division of Mineral Resources)

Gene Ellis (U.S. Geological Survey)

Jim Giglierano (Iowa Geological Survey)

AASG/USGS Data Model Working Group:

Gary Raines (U.S. Geological Survey and Working

Group Chair)

Boyan Brodaric (Geological Survey of Canada)

Jim Cobb (Kentucky Geological Survey)

Ralph Haugerud (U.S. Geological Survey)

Greg Hermann (New Jersey Geological Survey)

Bruce Johnson (U.S. Geological Survey)

Jon Matti (U.S. Geological Survey)

Jim McDonald (Ohio Geological Survey)

Don McKay (Illinois State Geological Survey)

Steve Schilling (U.S. Geological Survey)

Randy Schumann (U.S. Geological Survey)

Bill Shilts (Illinois State Geological Survey)

Ron Wahl (U.S. Geological Survey)
THE	NATONAL	GEOLOGC	MAP	DATABASE	PROJECT:	OVERVEW	AND	PROGRESS 13

Appendix B

List of progress reports on the National Geologic Map Database,

and Proceedings of the Digital Mapping Techniques workshops.

Soller, D.R., editor, 2005, Digital Mapping Techniques

‘05—Workshop Proceedings: U.S. Geological Survey

Open-File Report 2005-1428, 268 p., accessed at

http://pubs.usgs.gov/of/2005/1428/.

Soller, D.R., editor, 2004, Digital Mapping Techniques

‘04—Workshop Proceedings: U.S. Geological Survey

Open-File Report 2004-1451, 220 p., accessed at

http://pubs.usgs.gov/of/2004/1451/.

Soller, D.R., editor, 2003, Digital Mapping Techniques

‘03—Workshop Proceedings: U.S. Geological Survey

Open-File Report 03-471, 262 p., accessed at http://

pubs.usgs.gov/of/2003/of03-471/.

Soller, D.R., editor, 2002, Digital Mapping Techniques

‘02—Workshop Proceedings: U.S. Geological Survey

Open-File Report 02-370, 214 p., accessed at http://

pubs.usgs.gov/of/2002/of02-370/.

Soller, D.R., editor, 2001, Digital Mapping Techniques

‘01—Workshop Proceedings: U.S. Geological Survey

Open-File Report 01-223, 248 p., accessed at http://

pubs.usgs.gov/of/2001/of01-223/.

Soller, D.R., editor, 2000, Digital Mapping Techniques

‘00—Workshop proceedings: U.S. Geological Survey

Open-File Report 00-325, 209 p., accessed at http://

pubs.usgs.gov/of/of00-325/.

Soller, D.R., editor, 1999, Digital Mapping Techniques

‘99—Workshop proceedings: U.S. Geological Survey

Open-File Report 99-386, 216 p., accessed at http://

pubs.usgs.gov/of/of99-386/front.html.

Soller, D.R., editor, 1998, Digital Mapping Techniques

‘98—Workshop Proceedings: U.S. Geological Survey

Open-File Report 98-487, 134 p., accessed at http://

pubs.usgs.gov/of/of98-487/.

Soller, D.R., editor, 1997, Proceedings of a workshop on

digital mapping techniques: Methods for geologic

map data capture, management, and publication: U.S.

Geological Survey Open-File Report 97-269, 120 p.,

accessed at http://pubs.usgs.gov/of/of97-269/.

Soller, D.R., Berg, T.M., and Stamm, N.R., 2005, The

National Geologic Map Database Project: Overview

and Progress, in D.R. Soller, ed., Digital Mapping

Techniques ’05 – Workshop Proceedings: U.S.

Geological Survey Open-File Report 2005-1428, p.

23-40, accessed at http://pubs.usgs.gov/of/2005/1428/

soller1/.

Soller, D.R., Berg, T.M., and Stamm, N.R., 2004, The National Geologic Map Database project: Overview and

progress, in Soller, D.R., ed., Digital Mapping Techniques ‘04—Workshop Proceedings: U.S. Geological Survey Open-File Report 2005-1451, p. 15-31,

accessed at http://pubs.usgs.gov/of/2004/1451/soller/.

Soller, D.R., and Berg, T.M., 2003, The National Geologic Map Database project: Overview and progress,

in Soller, D.R., ed., Digital Mapping Techniques

‘03—Workshop Proceedings: U.S. Geological Survey

Open-File Report 03-471, p. 57-77, accessed at http://

pubs.usgs.gov/of/2003/of03-471/soller1/.

Soller, D.R., and Berg, T.M., 2002, The National Geologic

Map Database: A progress report, in Soller, D.R.,

editor, Digital Mapping Techniques ‘02—Workshop

proceedings: U.S. Geological Survey Open-File

Report 02-370, p. 75-83, accessed at http://pubs.usgs.

gov/of/2002/of02-370/soller2.html.

Soller, D.R., and Berg, T.M., 2001, The National Geologic

Map Database--A progress report, in Soller, D.R.,

editor, Digital Mapping Techniques ‘01—Workshop

proceedings: U.S. Geological Survey Open-File

Report 01-223, p. 51-57, accessed at http://pubs.usgs.

gov/of/2001/of01-223/soller1.html.

Soller, D.R., and Berg, T.M., 2000, The National Geologic

Map Database--A progress report, in Soller, D.R.,

editor, Digital Mapping Techniques ‘00—Workshop

proceedings: U.S. Geological Survey Open-File

Report 00-325, p. 27-30, accessed at http://pubs.usgs.

gov/of/of00-325/soller2.html.

Soller, D.R., and Berg, T.M., 1999a, Building the National

Geologic Map Database: Progress and challenges, in

Derksen, C.R.M, and Manson, C.J., editors, Accreting the continent’s collections: Geoscience Information Society Proceedings, v. 29, p. 47-55, accessed at

http://ngmdb.usgs.gov/info/reports/gisproc98.html.

Soller, D.R., and Berg, T.M., 1999b, The National

Geologic Map Database—A progress report, in

Soller, D.R., editor, Digital Mapping Techniques

‘99—Workshop proceedings: U.S. Geological Survey

Open-File Report 99-386, p. 31-34, accessed at http://

pubs.usgs.gov/of/of99-386/soller1.html.

Soller, D.R., and Berg, T.M., 1998, Progress Toward Development of the National Geologic Map Database,

in Soller, D.R., editor, Digital Mapping Techniques

‘98—Workshop proceedings: U.S. Geological Survey

Open-File Report 98-487, p. 37-39, accessed at http://

pubs.usgs.gov/of/of98-487/soller2.html.

Soller, D.R., and Berg. T.M., 1997, The National Geologic Map Database—A progress report: Geotimes,

v. 42, no. 12, p. 29-31, accessed at http://ngmdb.usgs.

gov/info/reports/geotimes97.html.

Soller, D.R., and Berg, T.M., 1995, Developing the National Geologic Map Database: Geotimes, v. 40, no.

6, p. 16-18, accessed at http://ngmdb.usgs.gov/info/

reports/geotimes95.html.
14	 DGTAL	MAPPNG	TECHNQUES	‘06
15

The New Geology Ontario Web Portal—

An “out-of-the-box” Solution for Discovering

and Delivering Ontario’s Geoscience Data

By	Zoran	Madon1,	P.Geo.	and	Brian	Berdusco2,	M.Sc.

1Ontario	Geological	Survey	(OGS)/Ministry	of	Northern	Development	and	Mines

Willet	Green	Miller	Centre,	B7

933	Ramsey	Lake	Road

Sudbury,	ON	P3E	6B5

Telephone:	(705)	670-5991

Fax:	(705)	670-5905

e-mail:	zoran.madon@ontario.ca

2Business	Solutions/Land	and	Resources	Cluster

Ministry	of	Northern	Development	and	Mines	(MNDM)

159	Cedar	Street,	Suite	605

Sudbury,	ON	P3E	6A5

Telephone:	(705)	564-7093

Fax:	(705)	564-7919

e-mail:	Brian.Berdusco@ontario.ca

INTRODUCTION

The new Geology Ontario web portal (www.ontario.

ca/geology) was developed during the last two years as a

direct result of the Minister’s Office directive to improve 

the discovery and delivery of Ministry of Northern Development and Mines (MNDM) geoscience data (Figure 1).

This directive came about in response to clients’ complaints about MNDM’s existing delivery mechanisms.

Many clients work in mining jurisdictions throughout

North America and around the world, and are accessing

geoscience data in a variety of ways. Not surprisingly,

they quickly adopt the best methods for accessing data

and, subsequently, demand this high level of service delivery from other jurisdictions.

BACKGROUND

The GeoPortal Project was initiated in the spring of

2004. The project team, made up of a project manager

and a small group of individuals seconded from various

sections within MNDM, held several meetings initially to

scope out the requirements and to assign responsibilities.

Figure 1. Geology Ontario home page allowing access

to both the map search window as well as the text search

windows for the MNDM’s geoscience data archives

(AFRI, PUBS, MDI, LGC, ODHDB and AMIS).

15
16	 DGTAL	MAPPNG	TECHNQUES	‘06

The project specifications were constrained by the following	core	requirements:

•	 create	a	delivery	website	using	an	“out-of-the-box”	

solution	that	meets	current	needs

•	 concentrate	on	delivering	all	OGS	publications	

(PUBS), mineral exploration assessment files 

(AFR),	the	Mineral	Deposit	nventory	(MD),	

the	Ontario	Drill	Hole	Database	(ODHDB),	the	

Lithogeochemical	Database	(LGC),	land	tenure	information	(CLAIMaps III),	and	Abandoned	Mines	

nformation	(AMS)	–	totaling	in	excess	of	300	

gigabytes	of	data	including	2	million	text	pages	

and	168,000	maps.

Work	on	the	Geology Ontario	web	portal	began	

on	several	fronts,	including	the	selection	of	a	software	

vendor	to	provide	the	web	service,	a	benchmarking	study,	

a	client	survey,	development	of	appropriate	metadata	

records	for	all	data	holdings,	and	conversion	of	various	

existing digital image files to a more useful, popular and 

web-friendly	format.	This	initial	work	was	followed	up	

with	business-impact	and	threat-risk	studies,	focus	group	

sessions,	and	a	Quality	Assurance/Quality	Control	process	

to	test	the	web	portal	functionality.

Software Vendor Selection Process

n	1997,	MNDM	consolidated	nine	regional	Provincial Mining Recorder Offices into a single, centralized 

office in Sudbury, Ontario. This necessitated a new way to 

distribute	40,000	mining	land	tenure	(claim)	maps	annually,	as	well	as	a	new	process	for	maintaining	information	

on	these	maps.	The	Ministry	decided	to	use	the	internet	as	

a	service	delivery	mechanism,	and	as	a	result,	the	Crown	

Land	Automated	nternet	Mapping	System	(CLAIMaps)	

was	developed.	CLAIMaps,	originally	a	simple	ArcView	

MS	application,	delivered	scanned	images	of	claim	maps	

over	the	internet.	However,	the	process	of	updating	these	

maps	was	limited	to	editing	hard	copies,	rescanning	them,	

and	then	reposting	them	on	the	Ministry’s	website.

n	2000,	the	decision	was	made	to	update	the	

CLAMaps	application	based	on	Environmental	Systems	

Research	nstitute	(ESR)	Map	Objects	nternet	Map	Service	(MOMS)	software.	The	new	application,	CLAMaps	

,	quickly	grew	in	use,	and	the	heightened	demand	placed	

on	the	system	resulted	in	critical,	technical	challenges	on	

server	infrastructure.	n	addition,	the	solution	was	based	

on complex custom code that required significant human 

and financial resources to maintain. This system ran from 

March	2001	to	December	2002.

The	next	phase	of	the	application	was	built	on	an	

out-of-the-box	philosophy,	and	a	new	CLAIMaps	website	

based	on	ESR	ArcSDE,	ArcMS,	and	Oracle	RDBMS	

was	launched.	The	new	system,	CLAIMaps III,	turned	

out	to	be	a	stable	application	that	required	few	resources	

to	maintain	and	resulted	in	a	greatly	reduced	total	cost	

of	ownership.	t	was	also	recognized	internationally	as	a	

world	class	system	capable	of	generating	and	delivering	

daily-updated	map	images	and	polygon	data	(Figure	2).

The	success	of	CLAMaps		made	ArcSDE,	ArcMS,	and	Oracle	the	obvious	choices	as	the	base	for	

building	Geology Ontario.	The	Ministry	approached	companies	on	the	provincial	Vendor	of	Record	and	requested	

a	time	and	materials	quote	for	developing	the	new	portal.	

Companies had to show a proficiency in developing webbased	applications	based	on	ESR	ArcSDE,	ArcMS,	Oracle	RDBMS,	and	the	ArcMS	Portal	Toolkit.	Like	CLAIMaps,	the	development	environment	for	Geology Ontario

was	the	Java	2	Plaform,	Enterprise	Edition	(J2EE).

Benchmarking Study

Benchmarking is a crucial step in helping define 

customer	requirements,	establishing	objectives,	developing	productivity	measures,	and	remaining	competitive.	

n	this	benchmark	study,	over	a	hundred	geoscience	

web	sites	were	examined	in	order	to	document	industry	

best	practices	and	ensure	that	the	Geology Ontario	web	

portal	would	provide	the	necessary	services	to	meet	client	

requirements.	Data	discovery	methods	(i.e.,	map	search	

tools,	text	search	tools),	data	content,	and	data	format	

were	tabulated	for	each	site	visited.

Over	50%	of	the	sites	offer	text-based	search	tools,	

while	more	than	30%	offer	map-based	search	tools.	Approximately	25%	of	these	use	ArcMS	technology.	The	

rest	maintain	either	simple	listings	in	some	order	(i.e.,	

chronological,	alphabetical,	by	subject	matter)	because	

of	the	low	volume	of	downloadable	data	or	do	not	offer	

any	data	for	download,	but	simply	provide	a	link	to	their	

publications office. Most of the sites with downloadable 

data	have	some	form	of	metadata,	and	many	of	these	

are	FGDC/Z39.50	compliant.	Some	sites	even	produce	

thumbnail	images	of	the	document	or	map.

Almost	all	of	the	downloadable	sites	supply	geoscience	publications,	both	maps	and	reports,	as	well	

as	regional	thematic	data	that	encompasses	their	entire	

jurisdiction.	These	would	typically	include	regional	

planimetric,	geological	and	geophysical	maps,	mineral	

deposit	and	regional	geochemical	data,	and	in	some	cases	

regional	terrain	models	(DEMs)	and	satellite	imagery.	

Some	sites	offer	mineral	land	tenure	maps	and	assessment	

file reports, as well.

By	far,	the	most	popular	format	for	disseminating	

both	reports	and	maps	is	the	Adobe	Systems	PDF	(Portable	Document	Format).	GS	and	vector	data	downloads	

are	available	in	various	formats,	but	the	ESR	Arcview/Arcnfo	formats	predominate.	Tabular	data	stored	in	spreadsheets	is	generally	provided	in	Microsoft	Excel	and	ASC	

CSV	formats.	Very	few	sites	supply	image	and	grid	data	
THE	NEW	GeOLOGy OntArIO	WEB	PORTAL 17

Figure 2. Sample of a typical map product generated from the CLAIMaps III web site.

(satellite	images,	geophysical	data),	so	no	dominant	format	emerged	here.	Rather,	they	appear	to	be	spread	evenly	

across	the	following	types	–	Lizardtech	MrSD,	Geosoft	

GRD/GXF,	ERMapper	ERS,	TFF,	JPG,	and	BMP.

All	sites,	with	the	exception	of	one,	provide	free	

downloads.	Some	sites	require	individuals	to	register	online	before	being	allowed	to	download,	particularly	sites	

that provide access to very large files such as geophysical 

data	and	satellite	imagery.	Disclaimers	and	liability	waivers	vary	from	the	non-existent	to	those	that	appear	before	

each	download.	Some	sites	offer	the	possibility	of	downloading	data	in	more	than	one	format	and,	for	maps,	in	

more	than	one	projection.	Most	sites	provide	only	current	

data	for	download	–	generally	less	than	5	to	10	years	old.

Client Survey

A	client	survey	was	posted	on	MNDM’s	e-consultation	web	site	for	approximately	6	weeks	in	the	summer	

of	2004.	The	survey	consisted	of	26	questions,	primarily	

in	multiple	choice	format,	and	generally	took	less	than	

10	minutes	to	complete.	Throughout	the	questionnaire,	

respondents	were	given	the	opportunity	to	comment,	

critique,	and	provide	suggestions.	The	survey	was	subdivided	into	the	following	sections:

•	 background information (business activity, geographic area of interest, internet connection used,

etc.),

•	 data discovery/access preferences (map-based vs.

text-based searches, text-based search criteria),

•	 data content/format preferences (which government

geoscience data sets were most important, what

formats do you use for report data, map data, GIS

data, tabular data, etc.)

•	 data usage (plotting on maps, incorporating into

reports, spatial modeling, reference material, etc.)

and

•	 data quality issues (method for reporting data errors, etc.).

Approximately 200 respondents completed the survey, which provided the project team with a better profile 

of clients’ needs and preferences as well as a wealth of

constructive suggestions to incorporate into the web

design. Suggestions included the following requests: list

examples or provide pick lists for all text-search criteria, develop an on-line help utility, produce hyperlinks

to documents directly from the search results page, and

provide an easy link to communicate problems or issues

pertaining to the web portal.
18	 DGTAL	MAPPNG	TECHNQUES	‘06

Metadata Development and Data Conversion 

Process

Metadata	pertaining	to	MNDM	publications	is	currently	housed	in	two	different	databases.	n	1994,	the	

Ministry	developed	a	database	that	contains	attributes	on	

all	publications	produced	since	1891.	n	2002,	the	Ministry	began	entering	metadata	into	a	centralized	metadata	

server	based	on	the	Government	of	Ontario	nformation	

Technology	Standard	(GOTS)	72.0.	GOTS	72.0	is	a	

metadata	standard	that	was	derived	from	the	U.S.	Federal	

Geographic	Data	Committee	(FGDC)	standards.	Though	

new	efforts	have	concentrated	on	the	centralized	metadata	

server,	MNDM	continues	to	populate	the	older	publications	database	simply	because	this	database	contains	

records	for	all	publications.

The	Ministry	has	created	a	process	of	extracting	

metadata	from	the	publications	database	and	populating	

the	metadata	catalog	available	in	ArcMS.	n	this	implementation,	MNDM	has	utilized	the	FGDC	compliant	catalog	but	will	review	moving	to	the	new	SO	standard	in	

the	near	future.	The	metadata	server	is	a	Z39.50	protocol	

compliant	server	that	enables	direct	access	from	library	

search	engines.

Metadata values were also used to populate fields 

within	the	PDF	documents	themselves.	The	purpose	of	

populating these fields or meta-tags is to facilitate the 

rapid	discovery	of	pertinent	information	either	through	the	

website	search	engine	or	through	other	common	search	

engines	such	as	Google	and	Yahoo.	These	search	engines	

not	only	index	content,	but	also	index	information	contained	within	PDF	meta-tags.	The	Ministry	also	embedded meta-tag data within the mineral assessment files to 

facilitate	discovery.

The majority of publications and assessment files 

were	scanned	in	the	early	1990s	and	stored	as	raster	images.	MNDM	utilized	raster	to	PDF	conversion	software	

available	from	JRAPublish	and	converted	approximately	

2,000,000	pages	and	168,000	maps	to	85,000	PDF	documents.	These	documents,	in	turn,	were	subjected	to	optical	character	recognition	software	and	converted	to	“PDF	

searchable”	format.	The	Ministry	built	a	40,000	entry	

geoscientific and geographic names dictionary to enhance 

word	pattern	recognition	by	the	conversion	software.	The	

net	result	is	that	the	majority	of	these	millions	of	raster	

images	can	now	be	searched	for	content	using	the	dtSearch	engine	that	has	been	implemented	on	the	Geology 

Ontario	website.

Simple	and	complex	Boolean	functions	coupled	with	

meta-tag search fields ensure that clients are provided 

with	simple	tools	that	return	powerful	results	(Figure	3).	

For	example,	using	the	following	Boolean	syntax:	visible 

w/3 gold	returns	those	occurrences	where	the	word	visible

is	found	within	three	words	of	the	word	gold.	Though	this	

may	seem	like	an	obvious	query	to	perform,	what	must	be	

realized is that performing this type of query will also return occurrences of no visible gold. A simple modification 

of the above query to: visible w/3 gold not w/2 no will remove the occurrences of no visible gold. Combining these

Boolean queries with meta-tag data can assist the client in

narrowing down results and pinpointing searches.

In the future, metadata will probably be entered, stored,

and managed through ESRI ArcCatalog. Not only will

searches align with the Z39.50 protocol, but the data distributed through the website will also include necessary attributes for proper spatial queries. ArcCatalog also conforms

to the out-of-the-box philosophy that has contributed to a

reduction in the total cost of the development and maintenance of internet-facing, GIS-related web applications.

Business-Impact Analysis and Threat-Risk 

Analysis

As work on Geology Ontario progressed, the project

team undertook a business-impact analysis, a threat-risk

analysis, and a business continuity plan, using guidelines developed by the Ontario government’s business audit group.

The business impact study attempts to evaluate how

an interruption in the Geology Ontario web service would

impact clients over time. Although several client impact

variables are available in the guidelines (including public

and employee health and safety, business service delivery,

public confidence, internal perception, regulatory and 

legal, environmental impact, financial reporting, revenue 

loss), it was concluded that disruption of the web service

would have minimal impact for only business service

delivery in the short term (< 1 day) and moderate to high

impact for public confidence and internal perception in 

the longer term (2 weeks to 1 month).

The threat-risk analysis evaluated all the Geology 

Ontario assets, including data, software/hardware architecture, network, and staff. For each asset, a sensitivity assessment with respect to confidentiality, integrity, 

availability, and accountability was estimated to determine

the potential severity of harm if the asset was compromised in any way. Threat agents, both internal (i.e., users,

system administrators) and external (i.e., clients, hackers,

contractors, natural and man-made disasters, etc.), as well

as threat events, were identified. Once determined, an estimate for the likelihood (i.e., low, moderate or high) of each

threat event as well as its exposure (i.e., the impact to the

government if the threat is realized) was established.

Finally, a business continuity plan was developed to

recommend alternate strategies for delivering Geology 

Ontario services during an unexpected interruption.

Focus Group

When a beta version of the website was available

for review and testing, three individuals who represented
THE	NEW	GeOLOGy OntArIO	WEB	PORTAL 19

Figure 3. Geology Ontario “PDF searchable” files allow for advanced search capability; A) in this example, a Boolean search for the word “felsic” within 5 words of “pyrite”

returned almost 2000 hits from the Geology Ontario AFRI archives; B) in this example, a

simple search using an OGS geologist’s last name returned over 300 documents, including maps from the Geology Ontario PUBS archive.

A

B
20	 DGTAL	MAPPNG	TECHNQUES	‘06

MNDM’s	main	client	groups	were	invited	to	Sudbury	for	

a	“test	drive.”	After	a	few	sessions,	the	individuals	completed	a	Ministry	evaluation	form	to	provide	feedback	on	

all	aspects	of	the	Geology Ontario	web	portal,	including:

•	 the	portal	home	page	functionality,	layout,	and	

content

•	 on-line	help

•	 layout	and	functionality	of	the	search	pages	for	the	

various	themes

•	 layout	and	functionality	of	the	map	viewer	window

•	 overall	search	functionality

•	 layout	and	functionality	of	the	search	results	window

•	 functionality	of	the	download	process

•	 general	comments	when	navigating	between	different	windows.

Their input helped to further refine and improve the 

Geology Ontario	web	portal.

Quality Assurance and Quality Control

The	Geology Ontario	website	replaces	the	Earth	

Resources	Mineral	Exploration	webSite	(ERMES).	However,	since	both	Geology Ontario	and	ERMES	operate	on	

copies	of	the	same	Oracle	databases,	quality	assurance	

processes	were	developed	whereby	query	results	generated	in	ERMES	were	compared	with	the	same	results	

generated	in	Geology Ontario.	Any	discrepancies	between	

the	two	pointed	to	possible	inherent	problems	within	

query	structures.	n	some	cases,	it	was	discovered	that	the	

original	queries	in	ERMES	were	incorrect.

Data	integrity	issues	were	a	persistent	problem	in	

both	the	OGS	Publications	and	AFR	databases.	Historically, files were loaded using a complex data loading 

process	developed	in	the	early	1990s,	which	involved	

populating	Oracle	databases	and	storing	raster	images	

of	individual	pages	and	maps.	t	was	not	uncommon	for	

data	loading	problems	to	cause	orphaned	attribute	or	image	data.	Orphaned	documents	could	not	be	queried	and	

would	remain,	essentially,	undiscoverable.	However,	once	

these	scanned	images	were	converted	to	searchable	PDF	

format	and	indexed,	they	became	discoverable	through	

the text search engine. Orphaned files have now been 

identified and targeted for correction so that both attribute 

and	text-based	queries	return	similar	results.

The	original	scanned	documents	from	the	early	1990s	

were	stored	in	either	JPG	or	TFF	format,	and	as	thumbnail	GF	images.	On	occasion,	the	original	TFF	images	

were	scanned	and	stored	incorrectly,	which	rendered	them	

unconvertible	to	PDF.	MNDM	worked	with	the	PDF	

software	vendor	(James	Rile	Associates)	and	developed	a	

process	whereby	a	specially	designed	page	was	inserted	

in	place	of	any	unusable	TFF	image.	Embedded	within	

the “bad page” notice was a special code that was, in turn,

indexed by the text search engine. By searching for this

code, a record was generated that indicated which file 

pages were troublesome. The “bad page” notice, therefore, serves the dual purpose of informing clients that a

page was scanned incorrectly and failed to convert, and

assisting the development and maintenance team in identifying which pages required re-scanning.

JPG images that were created at that time failed to

populate the data pertaining to resolution (dots per inch or

dpi) in the JPG header. The PDF conversion application

defaulted to an incorrect dpi which, when the JPGs were

converted, caused them to display at the wrong scale.

Since the original scanning was done at 200 dpi for JPG

images, an image conversion utility was run to update the

headers in every JPG file with the 200 dpi setting. As a 

result, these images converted to the correct scale in the

final PDF document.

The Ministry will develop a problem report page

where clients can submit information on any problems

they encounter with products downloaded from the Geology Ontario website. In addition, clients can recommend

changes that would assist them in using the web-based

products.

GEOLOGY ONTARIO WEB PORTAL

Benefits of Out-of-the-Box Software

The entire application development environment

adhered to the same “out-of-the-box” philosophy that has

made the CLAIMaps website so successful. Minimal custom code generation will facilitate the Ministry’s ability

to manage and modify the Geology Ontario website on an

ongoing basis with the limited resources that the Ministry

has at its disposal. Other benefits to an “out-of-the-box” 

solution include:

•	 large pool of skills available to select from (i.e.

software vendor)

•	 limited programming expertise required

•	 with version upgrades, solution systems can easily

implement new functionality

•	 puts pressure on software vendors to enhance “outof-the-box” functionality (as opposed to custom

coding)

•	 develops common skill sets and solution methodologies across an organization.

In January 2003, the CLAIMaps III website was

launched by the Mineral Development and Lands Branch.

This application utilizes ESRI ArcSDE and ArcIMS

technologies and ORACLE 8i RDBMS together with

Macromedia JRun 3.1 to create maps that are served

through Microsoft Internet Information Server on a Mi-
THE	NEW	GeOLOGy OntArIO	WEB	PORTAL 21

crosoft Windows 2000 Server. The CLAIMaps application

website, which provides 3 million map images and 70,000

final maps per year, has garnered international recognition 

and awards by delivering daily updated land tenure mapsto-scale for Ontario.

The graphical user interface of the CLAIMaps

website is intuitive and easy to use, which are key selling

points with MNDM’s client base. In addition, clients can

link into the ArcIMS map services and incorporate land

tenure data into their private, proprietary database systems

to generate mining land tenure maps and other complex

analytical products in the privacy of their work or home

environments.

The CLAIMaps website is updated every night

through the Automated CLAIMap Management System

(ACMS) Replication manager software synchronizes land

tenure databases between the internal production server

(ACMS) and the public facing internet server (CLAIMaps) thus fulfilling the additional function of providing 

offsite storage of the Ministry’s data. If required, these

data can readily be used for business resumption in

the event of catastrophic failure of either the ACMS or

CLAIMaps servers.

The Ministry’s key clients have clearly indicated

that they would prefer the delivery of the other Ministry

products, specifically geoscience data and reports, through 

an application that operates in a fashion comparable to the

CLAIMaps website.

Out-of-the-Box Software

The new Geology Ontario portal website was built

using the latest versions of the same software utilized by

the CLAIMaps website, namely, Oracle 9i, ArcSDE 9,

ArcIMS 9, XML, java, JavaScript, and Apache Tomcat

(J2EE). Data is served through the Internet Information

Server component available in Microsoft Server 2003.

The Oracle and ArcSDE software will reside on the database server, while ArcIMS and Windows Internet Information Server will reside on a separate application server.

The CLAIMaps website, which currently resides on

a Windows 2000 server, will be moved to the Geology 

Ontario servers. All environment variables will be modified so that the CLAIMaps application points to the new

Oracle/ArcSDE server. The ArcSDE component of the

Oracle database will be tuned to optimize performance.

The data that reside within the current Oracle 8i databases

will be migrated to Oracle 9i. The replication process that

currently updates the CLAIMaps server will be revised to

point to the new Oracle/ArcSDE server so that data can be

updated in a similar fashion. The critical components of

the new Geology Ontario website include:

•	 Land Information Ontario

•	 Ontario Land Information Directory

•	 Internet Geospatial Data Delivery

• 	 XML, ArcXML, Java, Javascript

• 	 ESRI ArcIMS 9, AcSDE 9, ArcGIS 9

•	 ESRI ArcMap Server, ArcGIS Server, Portal Toolkit

• 	 ESRI Metadata Server, Java ADF

•	 Apache Tomcat

•	 Oracle 8i migration to 9i

•	 Ontario Mining Land Tenure Data

•	 Geoscience Digital Data

• CLAIMaps III Functionality

•	 ArcIMS

•	 Windows 2003 Server

•	 iSERV Production Facilities and the processes.

Highlights

The guiding principle driving this project was to enhance discovery and download of all MNDM geoscience

data, maps, reports, and publications. In excess of 300

gigabytes of data have been made available on the Geology Ontario web portal. All existing assessment files and 

publications (over 2 million pages and 168,000 maps),

stored as image files, were converted to “PDF searchable” format for more rapid and convenient downloading.

As mentioned, the “PDF searchable” format allows for

powerful data mining capabilities, since all PDF documents are indexed and can be queried for virtually any

text string or combination of text strings. Also, the “PDF

searchable” format makes it possible to incorporate

government data into a client’s work environment using

copy/paste functionality (Figure 4). Once downloaded

onto a client’s personal computer, these documents can

be indexed and their contents discoverable using desktop

search tools like Google Desktop and Copernic.

Data discovery is based on the ESRI Portal Toolkit

and metadata tools (Figure 5). Clients enter search criteria,

generate a search, and receive the results in a tabulated

format with a description of the documents and their file 

sizes as well as a hotlink for direct download (Figure 6). In

addition, Geology Ontario provides links to other Ministry

web sites (Figure 7) and map services, which allows clients simultaneous access to services of other government

agencies, including those in other Provinces (Figure 8).

For digital data products, such as CAD drawings,

ArcView shapefiles, and database files, clients will be 

required to accept a disclaimer prior to download. The disclaimer is in the form of an html/asp document, controlled

by the Internet Information Server component of Windows

2003 server. The Government of Ontario is limited with

respect to placing cookies on client computers and, as a

result, the disclaimer acceptance avoids leaving cookies.

The Ministry has also developed a map browsing site,

much like the CLAIMaps website, where clients can simultaneously view numerous thematic datasets (Figure 9).

Clients have clearly indicated their preference for viewing
22	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 4. Geology Ontario “PDF searchable” files allow for copy and paste functionality with all 

its downloadable documents.

Figure 5. Geology Ontario text search window for querying the mineral exploration assessment files database 

(AFRI).

Figure 6. Search results from a typical query of the AFRI 

database with linkages to detailed metadata and to the

folder containing the downloadable files.
THE	NEW	GeOLOGy OntArIO	WEB	PORTAL 23

Figure 7. The Geology Ontario web site provides links to other Ministry web sites – either directly using hypertext (solid arrows) or indirectly using reference numbers in the metadata (dashed

arrow). (abbreviations: DDH – diamond drill hole database; RGP – Resident Geologist Program;

AFRI – Assessment File Research Imaging database; MCI – Mining Claims Information database;

PDF – Portable Document Format).

Figure 8. Because Geology Ontario uses ArcIMS technology, clients can simultaneously link to

ArcIMS-based web sites from other jurisdictions – in this illustration land tenure and geology are

being accessed concurrently from both the Geology Ontario web portal and the Manitoba portal.
24	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 9.	Samples	of	the	Geology Ontario	map	browser	window	illustrating	clockwise	from	top	

left, a DEM, total field magnetics draped over a DEM, geology, magnetics, and geology draped 

over	a	DEM	(center	image).	Over	2	dozen	pan-Ontario	themes	are	available	for	viewing	in	the	

map	browser	window.

mineral	land	tenure	together	with	geology,	geophysics,	

geochemistry,	and	other	thematic	data	in	order	to	relate	

land tenure to known geoscientific characteristics. The 

GeoPortal	Project	Team	has	created	geological,	geophysical,	geochemical,	and	geomorphologic	thematic	datasets	

for	multi-theme	viewing.	Clients	can	also	print	maps	to	

scale	in	a	fashion	comparable	to	the	CLAIMaps	website	

and	include	various	geoscience	themes	in	the	map	output.

FUTURE WEB DELIVERY

n	addition	to	delivering	geoscience	data	to	MNDM	

clients	worldwide,	the	Ministry	is	also	looking	at	enhancing	the	visual	web	delivery	of	its	data.	Some	avenues	

being	explored	include	using	3-dimensional	perspective	

tools	with	multi-thematic	overlay	capabilities	through	

a	standard	web	browser	(Figure	10)	and	reviewing	best	

practices	in	use	by	other	organizations,	as	far	as	delivering	geoscience	data	through	interfaces	such	as	Google	

Earth	(Figure	11),	Microsoft	Virtual	Earth,	NASA	World	

Wind,	or	ESR	ArcReader and	ArcGS	Explorer.	Utilizing	

these	additional	applications	requires	careful	planning	and	

a	balanced	client-server	approach.	Large,	static	datasets	

such	as	geophysical	grids	digital	elevation	and	remotely	

sensed	imagery	can	be	downloaded	and	reside	on	the	client	side	while	dynamic	datasets	such	as	drilling	data	and	

rock	sample	analyses	can	be	accessed	directly	from	the	

Geology Ontario	server	through	keyhole	markup	language	(kml)	or	Open	Geospatial	Consortium	(OGC)	web	

mapping	services.	

ACKNOWLEDGEMENTS

The	authors	would	like	to	thank	Terry	Johnston,	a	

member	of	the	Geology Ontario	project	team,	for	her	

critical	review	of	this	paper.
THE	NEW	GeOLOGy OntArIO	WEB	PORTAL 25

Figure 10. Possible future web viewing enhancements for

Geology Ontario – providing 3-dimensional perspective

tools with multi-thematic overlay capabilities through a

simple web browser; in this illustration, the mineral land

tenure fabric is draped over a DEM.

Figure 11. MNDM is reviewing best practices as far as

incorporating Geology Ontario geoscience data into sites

like Google Earth.
26	 DGTAL	MAPPNG	TECHNQUES	‘06
27

27

Building a Water Well Database for GIS Analysis

By A. Wayne Jones and Kelly A. Barrett

Ohio Department of Natural Resources

Division of Water

2045 Morse Rd. B-2

Columbus, Ohio 43229-6693

Telephone: (614) 265-1075

Fax: (614) 265-6767

e-mail: wayne.jones@dnr.state.oh.us

OBJECTIVES

The Ohio Department of Natural Resources (ODNR),

Division of Water (DOW), is the official repository for all 

water well records in Ohio. Currently, over 775,000 paper

well records are on file at the DOW (Figure 1). The water 

well records dataset has multiple uses, including requests

for homeowner private well information, a usable resource for the water well drilling community, an information source for environmental consultants, and a base for

mapping and ground water related research. The DOW is

in the process of converting paper well logs to digital data

in a database and scanning images of each well record.

These data are accessed through a public database. To use

these data for more advanced projects, like DOW modeling and potentiometric mapping, it is essential for the data

type to be compatible with use of Geographic Information

Systems (GIS). Ultimately, the database needs to support

the public access activities, while also handling the more

advanced GIS functions.

VENDOR

The DOW had three vendors for the data conversion

and database construction project. The DOW staff and inmates at an Ohio state penal institution converted the paper well log data into a database. As these labor-intensive

steps are costly, the DOW chose to work through a vendor

who trained and supervised the inmates on data entry. A

separate vendor scanned the well logs as images. Finally,

a third vendor redesigned the “front-end” for the database

that is used for public data requests. Staff members were

responsible for overseeing these operations. A major challenge was quality assurance and quality control.

SOFTWARE USED

The well log database is an Oracle 10G Release

1-enterprise database. Oracle, which uses a Unix interface, is administered by the ODNR, Office of Information Technology. As an enterprise system, the DOW has

limited write-authority and relies on the administrator to

perform updates and maintenance. The DOW purchased

from a vendor a custom-built Oracle front-end application called Flotiva from Workiviti version (8-1-6), which

is used for data entry, simple string queries, and public

service requests (Figures 2 and 3). The Oracle database is

accessible to some internal users by using a link through

Microsoft Access (2002). No write privileges are associated with this application. Queries and data dumps in Access are saved as tables that are usable in Microsoft Excel

(2002). GIS applications are based on the Environmental

Systems Research Institute (ESRI) products of Arc Map,

Arc Catalog, Arc Toolbox (9.1) (Spatial Analyst, 3-D

Analyst), and Arc Workstation (9.1).

WELL LOGS

The DOW began collecting water well records in

1947. Of the 775,000 paper well logs on file, less than 

200,000 are located by spatial coordinates. Most of the

well log locations are from DOW staff traveling into the

field with a topographic map and the well log to plot the 

location. The field location process was abandoned in the 

early 1990s due to personnel reductions. Recently, geocoding has been used, with excellent results, for unlocated

well log data for three counties.

Water well location maps (Figure 4) were digitized by

the vendor and written to CD. The information was organized as an ESRI shapefile for each county in Ohio. To add 

the spatial coordinates of the wells to the Oracle well log

database, a common field between the CD records and the 

database had to be identified. Each well log has a unique 

well log number, and therefore, this would have been the

logical link between the digitized file and the Oracle database. Unfortunately, the well log locations were digitized

using four fields (none of which are the well log number) 

to tag the locations. These four fields include county code, 

township code, location map year (the year in which the

well logs were located in the field, since many counties 
28	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 1. A typical water well log and drilling report.

Figure 2. Search well log

area screen, from Flotiva

Oracle front-end custom

application.
BULDNG	A	WATER	WELL	DATABASE	FOR	GS	ANALYSS 29

Figure 3. Location details entry screen, from Flotiva Oracle front-end custom application.

Figure 4. Map showing field-located sites of water well 

records.

have been field located multiple times), and location area. 

If these four fields match, then the link is made between 

the field location map points with spatial coordinates and 

the data fields in the Oracle database. In addition, paper 

copies of the well logs were scanned as TIFF type 4 images so they could be linked to the Oracle database.

Errors in linking CD data to the appropriate records

in the database can be categorized as either duplicate errors or no-match (Figures 5 and 6). The shapefile of the 

digitized well locations is sent to the Oracle administrator

to be run in a simulation to compare the digitized locations with the records in the Oracle database. The duplicate list shows all of the points with identical coordinates.

Often, the errors come from inadvertently clicking the

digitizing puck twice on a location. The no-match list

contains those points where the four fields of data from 

the digitized file do not match the records in the well log 

database. No-match errors are either data entry or missing

record errors in the Oracle database.

After error correction is completed, the Oracle administrator performs a finalized update to the Oracle database. Now, the well locations are linked to the database

and to the scanned image of the well log.
30	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 5. List of duplicates, showing matching records

with different spatial coordinates.

Figure 6. The no-match list (well log database records

where the actual record is lost).

POTENTIOMETRIC SURFACE MAPPING

Mapping ground water flow directions (potentiometric	surface	mapping)	is	extremely	important	to	most	

hydrogeological	studies.	The	DOW	is	currently	producing	

potentiometric	surface	maps	for	Ohio.	Producing	electronic	datasets	to	support	potentiometric	surface	mapping	

from	our	database	is	a	high	priority.

The	process	of	generating	a	potentiometric	surface	

map begins by downloading the necessary fields from the 

database using Access (2002). Important fields are well 

log	number,	county,	township,	location	map	year,	location	

number,	static	water	level,	and	geologic	formation.	The	

data	are	complied	in	Excel	(2002)	and	converted	into	a	

DBF4	table.	The	table	is	imported	into	ArcView	9.1	as	an	

event theme, then converted into a shapefile. This shapefile is edited to confirm that the well’s coordinates lie in 

the	correct	county	and	township	(Figure	7).

The	Digital	Line	Graphs	(DLG)	hypsography	layer	

for	each	quadrangle	is	downloaded	from	the	Center	for	

Mapping	at	the	Ohio	State	University	(Figure	8).	The	

DLGs	are	projected	to	State	Plane	South	NAD27	coordinate	system.	The	hypsography	(elevation	contours)	

layer is recoded to display the “elevation” item using

code written in arc macro language (AML). The resulting

surficial elevation from the hypsography layer is used to 

construct a Triangular Integrated Network (TIN) model

(Figure 9). A TIN model is a 3-dimensional surface of

the X, Y, and Z values interpolated at locations between

data points. The ArcInfo Workstation command Tinspot

selects the elevation at the spatial location of each point in

the coverage and picks the elevation from the TIN model.

This elevation item will be significantly more accurate 

than selecting the elevation from a DEM surface, where

the cell size can skew the value returned. The static water

level is subtracted from the surface elevation at each point

to get the elevation of the water table. USGS 1:24,000
BULDNG	A	WATER	WELL	DATABASE	FOR	GS	ANALYSS 31

Figure 7. Water well location map. Franklin County wells

highlighted in light gray (light blue in online version). Figure 8.	A	typical	Digital	Line	Graph	(digitized	topographic	elevation	contours).

Figure 9. The 3-D TIN (Triangular Irregular Network)

model constructed from Digital Line Graph file, then 

shaded by elevation. Well log points also are shown.
32	 DGTAL	MAPPNG	TECHNQUES	‘06

quadrangle	plots	of	the	water	elevation	point	data	are	

printed	on	Mylar	for	mapping	hydrogeologists	to	interpret	

and map ground water flow direction. After mapping is 

completed,	the	contours	drawn	on	the	Mylar	sheets	are	

digitized to create a final layout (Figure 10).

USING GIS TO CREATE AND ANALYZE 

POTENTIOMETRIC SURFACE

n	our	latest	effort	with	the	digital	database,	the	

data	points	from	the	well	log	database	were	plotted	to	

create	sand	and	gravel	and	a	bedrock	aquifer	potentiometric	surface	map	near	Darby	Creek	in	Western	

Franklin	County,	Ohio.	These	potentiometric	surface	

maps	were	combined	to	show	where	groundwater	is	

flowing. The shapefiles were turned into TIN models, 

which	were	turned	into	grids.	Using	grid	subtraction	in	

ArcMap,	Spatial	Analyst,	a	raster	difference	map	was	

generated	from	the	potentiometric	surface	map	data.	On	

the Difference Map, negative values (blues) show areas

where water can move downward from the sand and

gravel to the bedrock aquifers. Positive values (reds)

show areas where the bedrock aquifer has a net upward

effect, which means that water moves from the bedrock

into the sand and gravel. For more information on this

technique, please view the PDF of this work as a poster

session from the DMT 06 conference, http://ngmdb.usgs.

gov/Info/dmt/docs/angle06.pdf.

APPLICATIONS FOR DIGITAL DATA

The DOW receives many requests for our digital

water well log database. The requests may be for an area,

county, watershed, or localized site. Using the capacities

of GIS to clip the data to the desired configuration has 

made completing those requests easier. Some requests

(often from environmental consulting companies) are for

an area within a given radius from a natural feature or

Figure 10. Potentiometric surface map (contour map of water table elevation) of shallow consolidated aquifers in Geauga County, Ohio.
BULDNG	A	WATER	WELL	DATABASE	FOR	GS	ANALYSS 33

intersection.	The	buffer	function	in	ArcMap	makes	these	

requests	straightforward	(Figure	11).	For	example,	if	you	

were	looking	for	ground	water/surface	water	interaction,	

you	can	easily	create	a	query	that	returns	all	of	the	water	

wells	within	a	1-mile	radius	of	a	major	stream.	A	further	

refinement of the query might be to return only the sand 

and	gravel	wells	with	a	total	depth	of	less	than	50	feet	and	

a static water level of less that 20 feet. Consulting firms 

often	request	a	1-mile	radius	around	a	site	(Figure	12).	GS	

capacity allows for quick fulfillment of these requests.

FINAL PRODUCTS FOR DIGITAL DATA

Templates	are	developed	in	ArcMap	for	all	of	our	

mapping	projects.	This	allows	for	a	standard	design	to	all	

maps	of	the	same	series.	The	templates	require	the	input	

of new data and some minor modifications and the map is 

published	on-line	as	a	PDF.	Examples	of	these	maps	can	

be	viewed	on-line	at	http://www.dnr.state.oh.us/water/	

gwpsurface/County_List/tabid/3621/Default.aspx	and	

http://www.dnr.state.oh.us/water/gwppmaps/default/tabid/	

3541/Default.aspx.

SOFTWARE CITED

ESR,	ARCGS	–	Environmental	Systems	Research	nstitute,	

nc.,	380	New	York	St.,	Redlands,	CA	92373-8100	USA,	

(909)	793-2853,	http://www.esri.com

Flotiva-Workiviti,	Document	maging	Solutions,	8529	N.	Dixie	

Drive,	Dayton,	Ohio	45414	USA,	1-937-890-5135,	

http://www.disolutions.com

Microsoft, Microsoft Office 2002 – Microsoft Corporation, One 

Microsoft	Way,	Redmond,	WA	98052-6399	USA,	1-800-

642-7676,	http://www.microsoft.com

Oracle,	Oracle	10G	Release	1	–	Oracle	Corporate	Headquarters	

500	Oracle	Parkway,	Redwood	Shores,	CA		94065	USA,	

1-650-506-0024,	http://www.oracle.com

Figure 11. Water wells within one-mile buffer around Big

Darby Creek in Franklin County, Ohio. Wells within buffer are highlighted in light gray (green in online version).

Figure 12. One-mile search radius centered on a road

intersection, indicating water wells within the search area.
34	 DGTAL	MAPPNG	TECHNQUES	‘06
35

35

Sensitive Aquifers in Ohio –

Relationship to Highly Susceptible Public Water Systems

By Christopher Kenah, Michael W. Slattery, Linda D. Slattery, and Michael L. Eggert

Ohio Environmental Protection Agency

Division of Drinking and Ground Waters

50 W. Town Street

Columbus, OH 43215

Telephone: (614) 644-2752

Fax: (614) 644-2909

e-mail: christopher.kenah@epa.state.oh.us

INTRODUCTION

One of Ohio Environmental Protection Agency’s

goals is to identify aquifers in Ohio that are sensitive to

ground water contamination as a result of land use activities. The primary benefit of identifying sensitive aquifers 

within the state is to help prioritize limited resources in

order to maximize ground water protection efforts. This

article outlines the approach Ohio has used to identify

sensitive aquifers (Ohio EPA, 2006) by integrating geologic and chemical water quality data with digital mapping to develop a derivative map product. Data utilized

in this effort includes geologic information presented in

Ohio Department of Natural Resources Aquifer Maps

(ODNR, 2000), water quality information from Ohio’s

Ambient Ground Water Monitoring Program, and Ohio’s

public water system (PWS) water quality compliance

monitoring data. PWSs with chemical water quality impacts (criteria outlined in Ohio’s Source Water Protection

and Assessment Program) are also used to evaluate Ohio’s

sensitive aquifers. The complex nature of ground water

contamination will always require site-specific investigations to identify sources and pathways for impacts to

ground water. These site-specific studies help refine our 

hydrogeologic knowledge and may result in refinement of 

sensitive aquifers. The goal here, however, is to use statewide data to identify aquifer settings that are most likely

to be impacted by land use activities.

OHIO SENSITIVE AQUIFERS

The major aquifers in Ohio include widespread,

unconsolidated sand and gravel units, sandstone bedrock

in the eastern half of Ohio, and carbonate bedrock in

the western half, as illustrated in Figure 1. The sand and

gravel aquifers fill pre-glacial and glacial valleys cut into 

bedrock and are referred to as buried valley aquifers.

The sandstone and carbonate bedrock aquifers generally

provide sufficient production for water wells except where 

dominated by shale, as in southwest and southeast Ohio.

Glacial drift overlies most of Ohio except for the unglaciated southeastern quarter of the state.

An understanding of ground water recharge pathways

and water quality data were used to identify those aquifers

most likely to be impacted by land use activities; that is,

sensitive aquifers. The concept that short or rapid recharge

pathways increase aquifer sensitivity is widely accepted

and used to identify and evaluate sensitive aquifers. Applying this concept within the state of Ohio suggests that

sand and gravel aquifers are the most sensitive. Shallow

bedrock aquifers, particularly fractured or karst bedrock

aquifers that underlie thin glacial drift (tills or lacustrine

deposits), comprise a second group of sensitive aquifers.

Elevated nitrate concentrations from PWS compliance

monitoring data confirmed that these aquifers were sensitive based on ground water quality impacts (Ohio EPA,

2003a). An underlying assumption is that the distribution

of potential contaminant sources is widespread and evenly

distributed, and the results of this analysis reflect the 

influence of recharge pathways, not potential source distribution. The widespread distribution of nitrate sources

(agricultural, residential) in Ohio makes this a reasonable

assumption; however, high concentrations of potential

sources close to a PWS well increases the likelihood that

the well will exhibit water quality impacts.

The geologic information used in this analysis was

derived from the Glacial Aquifer Map (ODNR, 2000),

and consequently, the analysis does not provide information about the sensitivity of aquifers not included within,

or occurring below, glacial deposits. Geologic settings

with rapid recharge are identified as sensitive aquifers and 

include the following as described in the ODNR Glacial

Aquifer Map:
36	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 1. Distribution of major aquifers in Ohio, modified from ODNR Aquifer Maps (Ohio 

EPA, 2000).

Sand	and	Gravel	Aquifers:

•	 Buried	Valley

•	 Alluvial

•	 Valley	Fill

•	 Outwash/Kame

•	 Beach	Ridge

Bedrock	Aquifers	Below	Thin	Uplands	and	Lacustrine	Deposits	(<25	feet):

•	 Thin	Uplands	(thin	till)

•	 Lacustrine

n	contrast,	areas	of	thick	till	(e.g.,	glacial	moraines)	

generally	retard	recharge,	thereby	reducing	the	sensitivity	

of	aquifers	within	or	below	the	tills.	The	following	geologic	settings	in	the	ODNR	Glacial	Aquifer	Map	are	not	

sensitive	where	the	till	is	relatively	thick	(>25	feet):

Moraine	Deposits:

•	 Ground	Moraine

•	 End	Moraine

•	 Complex

These	geologic	settings	are	associated	with	different	

glacial	material	thicknesses	across	the	state.	t	is	generally	

assumed	that	the	greater	the	glacial	thickness,	the	greater	

the	protection	provided	to	the	aquifer.	The	longer	recharge	

pathways and increased recharge travel time to the aquifer

reduces the overall sensitivity of the aquifer. The ODNR

Aquifer Maps groups glacial drift into three thickness categories: thin (<25 feet), moderate (25-100 feet), and thick

(>100 feet). This thickness describes either the thickness

of the glacial drift that includes the aquifer or the thickness of glacial material that overlies a bedrock aquifer.

Each of the hydrogeologic settings was separated into

these thickness groups to evaluate whether water quality

impacts were influenced by the glacial overburden thickness. Even though these groupings are coarse, differences

between geologic setting and thickness groups are clear,

as discussed in later sections.

This analysis was also performed using lithologic

attributes included in the Glacial Aquifer Maps. The

lithologic attributes describe the primary materials

within mapped polygons in the ODNR Aquifer Maps and

provide further division of some geologic settings. The

lithologic parameters were divided into three groups for

this analysis:

•	 Sand and Gravel Lithologies: Includes coarse to

fine sand and gravel units with minor fine-grained 

material, including thin lenses of alluvium, lacustrine deposits, or till.

•	 Fine Grained Lithologies: Predominantly fine 

grained geologic materials with minor sand and
SENSTVE	AQUFERS	N	OHO	–	RELATONSHP	TO	HGHLY	SUSCEPTBLE	PUBLC	WATER	SYSTEMS 37

gravel	lenses.	Alluvium,	slack	water,	till,	and	colluvium	deposits	are	included	in	this	group.

•	 Till	Lithologies:	Predominantly	tills	with	little	evidence	of	sand	and	gravel	lenses.	n	thin	tills,	wells	

generally	penetrate	through	the	till	into	bedrock	

aquifers.	n	thicker	tills,	limited	production	may	be	

associated	with	sand	and	gravel	lenses,	but	larger	

production	wells	will	generally	be	drilled	through	

the	till	into	bedrock	aquifers.

Overall,	the	coarser	lithologies	would	be	expected	to	

allow	more	rapid	recharge	and,	consequently,	be	associated	with	more	sensitive	aquifers.	Figure	2	illustrates	the	

distribution	of	the	sand	and	gravel	aquifers	that	are	sensitive,	and	the	areas	of	thin	glacial	drift	that	overlie	sensitive	bedrock	aquifers.	

PWSs WITH GROUND WATER QUALITY 

IMPACTS

The	1996	Amendments	to	the	Safe	Drinking	Water	

Act	established	a	program	for	states	to	assess	drinking	

water	sources	for	all	public	water	systems	(PWSs).	The	

purpose	was	to	provide	PWSs	information	for	developing	

drinking water protection plans. These assessments included a susceptibility analysis. As outlined in the Source

Water Assessment and Protection (SWAP) Program, Susceptibility Analysis Process Manual (Ohio EPA, 2003b),

a PWS in Ohio is considered to have “high susceptibility”

if it has been impacted by anthropogenic contaminants,

regardless of its geologic setting or lithology. Water quality impacts are defined as two or more nitrate concentrations greater than 2.0 mg/L, or two or more confirmed 

detections of organic constituents (VOC or SVOC) using

PWS compliance monitoring data since 1991. The SWAP

staff reviewed more than 60,000 samples, including over

1,100,000 results, to identify the subset of high susceptibility PWSs based on water quality impacts. This effort

identified a subset of 561 PWSs out of a total of 5,151 

ground water sourced PWSs. This subset of PWSs can

be used as an independent data set to evaluate the identification of sensitive aquifers in Ohio. Figure 3 shows the 

locations of the 561 PWSs with documented water quality

impacts (highly susceptible in SWAP terminology) in

relationship to sensitive aquifers. The visual association

of the PWSs that exhibit water quality impacts to sensitive

aquifers is most obvious along buried valleys, but is not

particularly obvious on a state scale map.

Figure 2. Distribution of sensitive sand and gravel aquifers and thin glacial drift over sensitive bedrock aquifers.
38	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 3. PWSs with documented water quality impacts in relation to sensitive aquifers.

ASSOCIATION OF HIGH SUSCEPTIBILITY 

PWSs WITH SENSITIVE AQUIFERS

The	following	analysis	utilized	the	ODNR	Glacial	

Aquifer	maps	to	determine	whether	the	highly	susceptible	

PWSs	are	located	where	sensitive	aquifers	occur,	with	

the	implication	that	the	well	is	probably	completed	in	that	

aquifer.	To	accomplish	this,	the	561	highly	susceptible	

PWSs	were	associated	by	location	to	the	attributes	of	the	

ODNR	Glacial	Aquifer	Maps.	These	attributes,	including	geologic	setting,	thickness,	and	lithology,	were	used	

to	count	the	number	of	impacted	PWSs	that	occur	in	

different	glacial	settings	or	the	number	associated	with	

different	glacial	lithologies.	These	counts	were	compared	

to	the	number	of	all	ground	water	based	PWSs	associated	

with	the	same	groupings	of	glacial	settings	or	lithologies	

to	normalize	the	results	for	accurate	comparisons.	The	

following	section	presents	the	results	as	percentages	of	

ground	water-based	PWSs.

Extracting	the	Glacial	Aquifer	Map’s	attribute	data	

based	on	PWS	locations	has	several	limitations	that	need	to	

be	considered	with	regard	to	the	results	presented	in	Tables	

1	and	2.	The	thickness	of	the	glacial	drift	at	a	given	PWS	

location	is	not	the	well	depth	or	average	well	depth	of	the	

PWS	wells,	but	rather	an	estimate	of	the	glacial	drift	thickness	that	controls	the	recharge	pathways.	The	PWS	well	

may	be	producing	water	from	the	glacial	drift,	or	the	well	

may be cased through the glacial drift and producing water

from a bedrock aquifer. If the glacial thickness is thin

(< 25 feet thick), it is reasonable to deduce that the well is

a bedrock well. In thicker glacial drift, it is not possible to

know whether the well is producing water from the glacial

aquifers or bedrock aquifers without additional information

such as well depth or casing length. If the PWS wells are

located in thick glacial drift in geologic settings that are

considered sensitive (buried valley, beach ridge, outwash/

kame), it is likely that the well is producing from sand

and gravel aquifers that lie within the drift. For example,

a well may be 45 feet deep in an area of drift greater than

100 feet thick. These data limitations need to be taken into

account when reviewing the data presented in Tables 1 and

2. Overall, there appears to be a strong association between

impacted PWSs and glacial attributes associated with sensitive aquifers, in spite of these data limitations.

GEOLOGIC SETTING ASSOCIATIONS

The percentages of impacted PWSs associated with

categories of geologic settings by increasing thickness

of glacial drift are listed in Table 1. Overall the highest

percentages of PWSs with impacted source water are

associated with the sand and gravel aquifers. The major aquifer groups associated with geologic settings are

discussed below.
SENSTVE	AQUFERS	N	OHO	–	RELATONSHP	TO	HGHLY	SUSCEPTBLE	PUBLC	WATER	SYSTEMS 39

Table 1. Documented	water	quality	impacts	at	PWSs,	and	associations	between	impacts	and	hydrogeologic	setting*	of	

glacial	units.

Thickness of Glacial

Unit (Feet)

Number of ground 

water-based PWSs 

with Impacts 

Total Number of 

ground waterbased PWSs

Percentage of

Impacted PWSs

Sand and Gravel Aquifer Settings

(Aquifer	Map	settings	-	Buried	Valley,	Alluvial,	Valley	Fill,	Outwash/Kame,	Beach	Ridge)

0-25	 10	 39	

	 25-100	 92	 487	

>100	 153	 1066	

Bedrock Aquifers Below Thin Uplands and Lacustrine Deposits

(Aquifer	Map	settings	-	Thin	Uplands	and	Lacustrine)

0-25	 133	 904	

	 25-100	 103	 1032	

>100	 8	 28	

Moraine Deposits

(Aquifer	Map	Settings	-	Ground	Moraine,	End	Moraine,	Complex)

0-25	 0	 0	

	 25-100	 27	 704	

>100	 13	 559	

Unglaciated Areas

(No	glacial	units	on	ODNR	Aquifer	Map)

0	 22	 332	

26%

19%

14%

15%

10%

29%

0%

4%

2%

7%

*Hydrogeologic	settings	are	from	ODNR	Glacial	Aquifer	Map	(ODNR,	2000).

Table 2. Documented	water	quality	impacts	at	PWSs,	and	associations	between	impacts	and	lithology*	of	glacial	units.

Thickness of Glacial

Unit (Feet)

Number of ground 

water-based PWSs 

with Impacts 

Total Number of 

ground waterbased PWSs

Percentage of

Impacted PWSs

Sand and Gravel Lithologies

(including: sand and gravel, minor fines, confined, thin till included within or over unit) 

0-25	 6	 9	

25-100	 50	 133	

>100	 113	 495	

Fine Grained Lithologies

(including: fine grained sediments undifferentiated, fines with minor sand and gravel lenses)

0-25	 4	 36	

	 25-100	 44	 405	

>100	 37	 464	

Till Lithologies

(including:	till,	till	with	sand	and	gravel	lenses)

0-25	 133	 898	

	 25-100	 128	 1682	

>100	 24	 697	

Unglaciated Areas

(no	glacial	units	on	ODNR	Aquifer	Map)

0	 22	 332	

67%

38%

23%

11%

11%

8%

15%

8%

3%

7%

*Lithology	divisions	are	from	ODNR	Glacial	Aquifer	Map	(ODNR,	2000).
40	 DGTAL	MAPPNG	TECHNQUES	‘06

Sand and Gravel Settings

The percentage of PWSs with documented water quality impacts is higher for the geologic settings dominated

by sand and gravel deposits (buried valley, alluvial, valley

fill, outwash/kame, and beach ridge) as illustrated in Table 

1, column 4. The percentage of impacted systems decreases with increasing glacial drift thickness: 26 percent

for thin sand and gravel deposits (statistic based on small

number of PWSs), 19 percent for intermediate sand and

gravel deposits, and 14 percent for sand and gravel deposits of more than 100 feet thick. This trend supports the

concept of increased protection as the recharge pathways

lengthen. Based on the statutory requirement of 25 feet of

casing for PWS wells, wells associated with the 0-25 foot

group of sand and gravels probably represent wells drilled

through the glacial sand and gravel into bedrock.

Bedrock Aquifers Below Thin Uplands and 

Lacustrine Deposits

PWS wells producing from bedrock aquifers below

thin and intermediate-thickness upland till or lacustrine

deposits (< 25 and 25-100 feet thick) exhibit ground

water quality impacts in 15 and 10 percent of the PWSs,

respectively. These percentages suggest that thin till and

lacustrine material provides a bit more protection from

land use activity than sand and gravel. The low yield from

till and lacustrine deposits requires most of these wells to

be bedrock production wells. The 29 percent of PWSs that

showed ground water impacts with glacial drift greater

than 100 feet thick is based on relatively few wells and

appears anomalous. Two of the eight impacted wells include sensitive buried valley or beach ridge geologic settings within their Drinking Water Source Protection Area

inner management zone (one year time-of-travel). These

settings provide rapid recharge pathways. The other six

wells are deep wells (>120 feet) and, based on available

well logs, are presumed to produce water from confined 

aquifers. This is an unlikely set of wells to exhibit elevated nitrate, since most nitrate detections > 2.0 mg/L occur

in Ohio wells with depths less than 75 feet. Three of these

four wells were drilled in the 1950’s. One explanation

is that well construction deficiencies or corrosion of the 

casing may allow leakage of shallow, nitrogen rich ground

water into these wells. Several of these wells are within a

few miles of each other and exhibit similar nitrogen time

series patterns, which suggests a regional rather than a local control. This example illustrates some of the problems

with applying broad interpretations, as shown in Table 1,

to site-specific cases.

Moraine Deposits

Protection of ground water resources by thick till

cover is demonstrated in the moraine deposits category

of Table 1, where the percentage of impacted PWSs does

not exceed 4 percent. In Table 1, the thin tills are grouped

with the thin upland and lacustrine deposits; 15 percent

of PWSs located in thin till settings exhibit water quality

impacts. Thicker tills, however, are associated with relatively few impacted PWSs; 4 percent for the intermediate

(25-100 feet) group, and 2 percent for the thick (>100

feet) group of the moraine deposits category. These low

percentages suggest that thick till provides significantly 

more protection than sand and gravel or thin tills. The role

that fractures and macropores play in controlling recharge

to aquifers through tills is a current topic of discussion in

Ohio (Weatherington-Rice and Christy, 2000; Weatherington-Rice and others, 2006). Fractures and macropores

certainly affect the movement of recharge through thin

tills; this is supported by the high percentage (15 percent) of impacted PWSs associated with thin upland and

lacustrine settings. Fractures and macropores appear to be

significantly less important in recharge and contaminant 

transport in thick tills as documented by the much lower

percentages (4 percent and 2 percent) of impacted PWSs

associated with thicker moraine deposits. This is believed

to be caused by the limited vertical extent of fractures in

till, typically 20-25 feet (Scott Brockman, personal communication, 2006).

Unglaciated Areas

The last category in Table 1 includes PWSs located

in the unglaciated areas of Ohio, primarily the southeastern uplands. These areas include weathered bedrock

(colluvium) that overlies late Paleozoic sandstones and

shales, and in places is overlain by loess deposits. In

most cases, wells produce water from bedrock aquifers;

however, yields are generally low and relatively few

PWSs use these aquifers. Consequently, we have limited

data for the unglaciated areas. The available data, however, indicates that the colluvium is better than sand and

gravel and thin till in glaciated areas, but not as good as

thicker tills at protecting wells from land use impacts.

From Table 1, PWSs in unglaciated areas are impacted

in 7 percent of the sites, a value that is intermediate

between sand and gravel and thicker tills. The lower

population density and reduced number of potential pollution sources in unglaciated, southeastern Ohio uplands

also tends to depress the percentage of impacted PWSs

in this region.
SENSTVE	AQUFERS	N	OHO	–	RELATONSHP	TO	HGHLY	SUSCEPTBLE	PUBLC	WATER	SYSTEMS 41

LITHOLOGIC ASSOCIATIONS

The lithologic attributes can be divided for further

analysis into sand and gravel, fine grained, and till lithologies. The percentages of impacted PWSs associated with

these lithologic groups, ordered by increasing thickness of

glacial drift, are presented in Table 2. The percentage of

PWSs with documented water quality impacts is highest for the sand and gravel lithologies and lowest for the

till materials. This is similar to the results presented in

Table 1 for geologic settings, but the percentages associated with the lithologic divisions indicate that recharge

processes are more dependent on the lithologic material

than on the geologic setting. The major lithologic groups

are discussed individually below.

Sand and Gravel Lithologies

For sand and gravel lithologies, the low number of

wells in the < 25 feet thickness category results from the

statutory requirement that PWS wells have a minimum

of 25 feet of casing installed. Thus, the few wells in this

thickness grouping are either old wells with short casing

lengths or bedrock wells cased through thin glacial sand

and gravel. With 67 percent of these wells having water

quality impacts, it appears that they are highly susceptible, but because the percentage is based on a very low

number of wells, it cannot be given much significance. 

The percentage of impacted PWSs decreases to 38 percent

and 23 percent for the 25-100 feet and >100 feet groupings, respectively. These wells probably do not penetrate

the entire glacial thickness, but it appears that the thicker

section of sand and gravel provides more filtration and 

increased travel time, as one would expect.

Almost all of the areas of sand and gravel lithologies

in Table 2 are included within the geologic settings that

are considered most sensitive in Table 1. The sensitive

geologic settings that include sand and gravel aquifers also

include fine grained lithologies. The higher correlation between coarser sand and gravel lithologies and PWSs with

water quality impacts (Table 2) emphasizes the importance

of identifying coarser sand and gravel lithologies as more

sensitive than finer grained sand and gravel lithologies. 

This is consistent with the understanding that aquifer

sensitivity is controlled by recharge and transport rate of

surface or near surface contaminants to aquifers.

Fine Grained Lithologies

The fine grained portions of the glacial lithologies 

in the ODNR Aquifer Maps are predominantly undifferentiated fine grained sediments with minor sand and 

gravel lenses. Approximately 8 percent to 11 percent of

the PWSs associated with these fine grained lithologies 

exhibit water quality impacts, which is significantly less 

than coarse sand and gravel deposits (23 percent to 38

percent for the sand and gravel lithologies). The percentage of impacted PWSs decreases with increasing glacial

drift thickness, but not appreciably, which may result

from the variability of well depths independent of the glacial deposit thickness. The low number of samples in the

0-25 feet thickness group (36) suggests that the result (11

percent) should not be given much significance, except 

that the 25-100 feet group exhibits the same percentage of

impacted PWSs.

Till Lithologies

Table 2 documents a sizable difference between

impacted PWSs in thin tills and the thicker tills. Only

limited protection is provided by thin glacial drift overlying bedrock aquifers, as evidenced by the 15 percent of

impacted PWSs associated with thin tills (< 25 feet).

Wells producing from fractured bedrock just below thin

glacial cover will be the most vulnerable and probably account for the bulk of the PWSs with water quality impacts

in this category. The percentage of PWSs with water quality impacts drops dramatically for thicker tills: 8 percent

for tills in the 25-100 feet group, and 3 percent for tills

greater than 100 feet thick.

Unglaciated Areas

The unglaciated areas include the same subset of

wells in Table 2 as presented in Table 1. As stated earlier,

the limited data restrict broad conclusions, but the low

percentage of impacted PWSs suggests that colluvium

provides some protection for these wells from land use activities. The lower population density and reduced number

of potential pollution sources in southeast Ohio may also

help to keep this percentage low.

DISCUSSION

This analysis documents the importance of distinguishing the coarser grained lithologies in buried valleys, alluvial, valley fill, outwash/kame, and beach ridge 

deposits as being more sensitive to contaminant impact

than the finer grained deposits in these same settings. 

Figure 4 illustrates the association of the PWSs with water

quality impacts along a section of the Great Miami buried

valley aquifer in southwest Ohio. This figure illustrates 
42	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 4. Impacted PWSs and sensitive aquifers and in southwest Ohio.

the relationship between the high concentrations of water

quality impacts and sections of the buried valley identified 

as sand and gravel. Water quality impacts are also associated with finer grained buried valley deposits and thin tills 

in the uplands (where wells penetrate the till to produce

from bedrock aquifers), but with lower frequency as documented in Table 2. The locations of PWS treatment plants

are provided to illustrate the wide distribution of PWSs

used in the analysis.

Highlighting Sand and Gravel Aquifers

The divisions on the glacial aquifer map are general groupings that were selected to represent glacial

material on a state scale based on geologic and well log

data. Analyses using PWS empirical water quality data

(elevated nitrate, VOC detections) exhibit significant 

correlations with the identified sensitive geologic settings 

and lithologies. The stronger association of impacted

PWSs with coarser and more permeable sand and gravel

lithologies underscores the role recharge plays in making aquifers sensitive, and suggests that coarse sand and

gravel units should be emphasized in the identification of 

Ohio’s sensitive aquifers.

Figure 5 illustrates the distribution of sensitive

glacial geologic settings across Ohio, utilizing attributes

identified in ODNR’s Glacial Aquifer Maps (ODNR, 

2000). This figure represents the combined analysis of 

sensitive aquifers and water quality impacts based on

nitrate and VOC concentrations. In the glacial settings where sand and gravel deposits are common, the

lithology is divided into fine and coarse grained units. 

The analysis presented in this paper documents that the

coarser grained units are more sensitive. The classification processes used in developing the glacial aquifer

maps required a great deal of simplification, and consequently, these generalizations need to be considered

in any application of Figure 5. Nevertheless, the correlation of the empirical PWS water quality data to the

lithologic and geologic setting descriptors supports the

simplifications made in developing the ODNR Glacial 

Aquifer map and the validity of using recharge controls

in determining sensitive aquifers.

The goal of identifying sensitive aquifers is to help

set priorities for protecting the state’s ground water

resources. Statewide, aquifers that have more than 25 feet

of till or fine grained glacial deposits overlying the well 

production zone are less likely to exhibit anthropogenic

water quality impacts than unconfined sand and gravel 

aquifers. If till that overlies a bedrock production aquifer

is less than 25 feet thick, the bedrock aquifer’s sensitivity is elevated, but the sand and gravel aquifers are even

more sensitive. The data summaries in Tables 1 and 2, and

Figure 5, illustrate the high geologic sensitivity (based on
SENSTVE	AQUFERS	N	OHO	–	RELATONSHP	TO	HGHLY	SUSCEPTBLE	PUBLC	WATER	SYSTEMS 43

Figure 5. Distribution of sensitive aquifers in Ohio.

chemical	water	quality	data)	of	Ohio’s	productive	sand	

and	gravel	aquifers,	in	particular	the	areas	of	coarser	sand	

and	gravels.	These	conclusions	are	not	unexpected,	and	

they	support	the	generally	accepted	views	of	hydrogeologists	familiar	with	the	fate	and	transport	of	contaminants	

within	sensitive	aquifers	in	Ohio.	These	empirical	data	

provide significant support for judgments based on professional experience and, consequently, increase our confidence in applying the identified sensitive aquifer settings 

to	protecting	Ohio’s	ground	water	resources.

Thin Till Over Bedrock Aquifers

Several	situations	in	areas	of	thin	glacial	till	(<	25	

feet) over bedrock have been identified and emphasize the 

sensitivity	of	this	geological	setting,	especially	where	the	

till	that	overlies	the	bedrock	is	less	than	10	feet.	Wastewater	management	(septic,	manure)	and	agricultural	or	

residential	facilities	placed	in	close	proximity	to	private	

or	public	wells	can	result	in	health	issues	if	contaminants	

move	rapidly	through	the	thin	till	along	macropores,	fractures,	or	other	pathways	to	fractured	bedrock	aquifers.	t	

appears	that	microbiological	contamination	is	more	likely	

in	these	thin	till	settings	than	in	sensitive,	unconsolidated	

sands due to the limited filtration capacity of macropores 

and	fractures	in	till	and	bedrock.	nvariably,	ground	water	

microbiological contamination increases proportionally

to increasing population concentration in areas of closely

spaced septic systems and wells. Figure 6 is a schematic

geologic cross section that illustrates the sensitive geologic setting of fractured bedrock below thin glacial drift.

Water quality impacts associated with similar geologic

setting are recognized across the state. The 2004 infectious disease outbreak at South Bass Island in Lake Erie,

which was determined to be associated with ground water

contamination (Ohio Department of Health, 2005), is an

example of the potential water quality impact to sensitive

bedrock aquifers in areas of thin to no glacial overburden.

Horizontal Flow Pathways

The previous analysis has assumed that recharge

pathways are dominantly vertical. Although this is generally true, there are geologic settings where recharge contributions can have significant horizontal components that 

increase the sensitivity of aquifers. The schematic cross

section in Figure 7 illustrates this situation. Wells located

in flood plains close to rivers are examples of locations 

where horizontal flow paths can become significant 

contributors to recharge. Many of these wells are located

close to the river to increase production by inducing

recharge from the river. At flood stage, the elevated river 
44	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 6. Schematic cross section illustrating rapid pathways in areas of thin glacial drift. The

ground water table is indicated by the thick line with triangles above it.

Figure 7. Schematic cross section illustrating shortened flow paths for wells in flood 

plains.

level can shorten the horizontal flow path and increase 

the pressure gradient to a pumping well, with the result

of shortening recharge pathways and reducing transport time. There are also areas where fractured bedrock

exposed in streams and stream banks can provide rapid

recharge to bedrock aquifers, especially during flooding 

events. The areas of the state where horizontal components of recharge are potentially significant also need to 

be identified as areas of sensitive aquifers.

CONCLUSIONS AND FUTURE

ACTIVITIES

This approach to identifying sensitive aquifers demonstrates a practical integration of digital geologic mapping and water quality data analysis to generate a derivative map of sensitive aquifers. The intention of this map is

to help prioritize ground water protection activities. The

map is certainly not a final product but rather a work in 
SENSTVE	AQUFERS	N	OHO	–	RELATONSHP	TO	HGHLY	SUSCEPTBLE	PUBLC	WATER	SYSTEMS 45

progress. The current draft illustrates the core logic of using recharge pathways to determine sensitivity and documents the validity of the approach based on water quality

impacts. Refinements of this map will include:

•	 Assignment of glacial drift thickness to PWS wells

for more precise analysis of water quality impact

and drift thickness;

•	 Incorporation of well depth and casing length into

the analysis;

•	 Evaluation of differences in aquifer sensitivity

between dissolved components and particulate

components such as pathogens;

•	 Completion of analysis of variability of aquifer

sensitivity as related to increased concentration

or more persistent occurrences of anthropogenic

contaminants; and

• Identification of areas where horizontal flow paths 

are significant.

REFERENCES

Ohio Department of Health, 2005, Director Investigation - Gastrointestinal Illness, South Bass Island, Lake Erie, August

2004: Multiple Agency Investigation and Actions, February

22, 2005; amended February 16, 2006, accessed at http://

www.epa.state.oh.us/ddagw/SBweb/Reports/SB_ODH.

pdf.

Ohio	Department	of	Natural	Resources,	2000,	Glacial	Aquifer	

Map	and	Bedrock	Aquifer	Map	(digital	format),	accessed	at	

http://www.dnr.state.oh.us/water/samp/.

Ohio	EPA,	2000,	Ohio’s	Ground	Water	Quality,	2000	305(b)	

Report:	Division	of	Drinking	and	Ground	Waters,	38	p.,	

accessed	at	http://www.epa.state.oh.us/ddagw/pdu/gw_

305b.html.

Ohio	EPA,	2003a,	Ohio’s	Ground	Water	Quality,	2002	305(b)	

Report:	Division	of	Drinking	and	Ground	Waters,	78	p.,	

accessed	at	http://www.epa.state.oh.us/ddagw/pdu/gw_

305b.html.

Ohio	EPA,	2003b,	Susceptibility	Analysis	Process	Manual:	

Division	of	Drinking	and	Ground	Waters,	64	p.,	accessed	at	

http://www.epa.state.oh.us/ddagw/Documents/swap_

saprocman.pdf.

Ohio	EPA,	2006,	Ohio’s	Ground	Water	Quality,	2006	305(b)	

Report:	Division	of	Drinking	and	Ground	Waters,	93	p.,	

accessed	at	http://www.epa.state.oh.us/ddagw/pdu/gw_

305b.html.

Weatherington-Rice,	J.,	and	Christy,	A.D.,	Eds.	2000,	Fractures	

in	Ohio’s	Glacial	Tills:	The	Ohio	Journal	of	Science,	Special	ssue.	Vol.	100,	no.3/4,	accessed	at	http://www.oardc.

ohio-state.edu/fractures.

Weatherington-Rice,	J.,	Christy,	A.	D.,	and	Angle,	M.P.,	Eds.,	

2006,	Fractures	in	Ohio’s	Glacial	Tills	-	Further	Explorations:	The	Ohio	Journal	of	Science,	Special	ssue,	Vol.	106,	

no.2,	assessed	at	http://www.oardc.ohio-state.edu/fractures/.
46	 DGTAL	MAPPNG	TECHNQUES	‘06
47

47

GeoSciML – A GML Application for

Geoscience Information Interchange

By Stephen M. Richard and CGI Interoperability Working Group

U.S. Geological Survey and Arizona Geological Survey

416 W. Congress #100

Tucson, AZ 85701

Telephone: (520) 770-3500

Fax: (520) 770-3505

e-mail: steve.richard@azgs.az.gov

CGI Interoperability Working Group is (in alphabetic order): Eric Boisvert,

Boyan Brodaric, Simon Cox, Tim Duffy, Jonas Holmberg, Bruce Johnson, John

Laxton, Tomas Lindberg, Stephen Richard, Alistair Ritchie, Francois Robida,

Marcus Sen, Jean Jacques Serrano, Bruce Simons, and Lesley Wyborn.

INTRODUCTION

The	GeoSciML	application	is	a	standards-based	data	

format	that	provides	a	framework	for	application-neutral	

encoding	of	geoscience	thematic	data	and	related	spatial	

data.	GeoSciML	is	based	on	Geography	Markup	Language	(GML,	Cox	et	al.,	2004)	for	representation	of	features	and	geometry,	and	the	Open	Geospatial	Consortium	

(OGC)	Observations	and	Measurements	Best	Practices	

(Cox, 2006) for observational data. Geoscience-specific 

aspects	of	the	schema	are	based	on	a	conceptual	model	for	

geoscience	concepts	and	include	geologic	unit,	geologic	

structure,	and	Earth	material	from	the	North	America	Data	

Model	(NADMC1,	North	American	Geologic-Map	Data	

Model	Steering	Committee,	2004),	and	borehole	information	from	the	eXploration	and	Mining	Markup	Language	

(XMML,	https://www.seegrid.csiro.au/twiki/bin/view/

Xmml/WebHome).	Development	of	controlled	vocabulary	

resources	for	specifying	content	to	realize	semantic	data	

interoperability	is	underway.

The	intended	scope	for	initial	versions	of	GeoSciML	

includes	information	typically	found	on	geologic	maps	

as	well	as	information	typically	recorded	with	boreholes.	

The	possible	uses	for	GeoSciML	include	transporting,	

storing,	and	archiving	information.	Amongst	these,	the	

most significant is transport—or information exchange—

which	enables	information	to	be	visualized,	queried,	and	

downloaded	in	spatial	data	infrastructures.	This	role	for	

GeoSciML	is	particularly	important,	as	geoscience	information	consumers	are	becoming	more	digitally	sophisticated and are no longer satisfied with images and portrayals	of	data,	but	want	digital	data	in	standardized	formats	

that	can	be	used	immediately	in	applications.	Hours,	days,	

or	weeks	spent	merging	data	sets	obtained	separately	from	

multiple	agencies	is	time	wasted.	Use	of	a	standardized	

markup for serializing geoscience information supports a

commitment by data providers to publish data to users in

a standardized format. Thus, GeoSciML allows applications to utilize globally distributed geoscience data and

information.

The GeoSciML (https://www.seegrid.csiro.au/twiki/

bin/view/CGIModel/GeoSciML) project was initiated

in 2003 under the auspices of the Commission for the

Management and Application of Geoscience Information (CGI) working group on Data Model Collaboration

(https://www.seegrid.csiro.au/twiki/bin/view/CGIModel/

WebHome). The CGI is a commission of the International

Union of Geological Sciences and has the objective to

enable the global exchange of geoscience information for

legal, social, environmental, and geoscientific reasons. 

The project is part of what is now known as the CGI Interoperability Working Group (https://www.seegrid.csiro.

au/twiki/bin/view/CGIModel/InteroperabilityWG), which

has the specific objectives to:

• develop a conceptual model of geoscientific information that draws on existing data models,

•	 implement an agreed subset of this model in an

agreed schema language,

•	 implement an XML/GML encoding of the model

subset,

•	 develop a test bed to illustrate the potential of the

data model for interchange, and

• identify areas that require standardized classifications to enable interchange.

GeoSciML draws from many geoscience data model

efforts and from them establishes a common suite of feature types based on geological criteria (units, structures,

fossils) or artifacts of geological investigations (speci-
48	 DGTAL	MAPPNG	TECHNQUES	‘06

mens,	sections,	measurements).	Supporting	objects	are	

also	considered	(timescale,	lexicons,	etc),	so	that	they	can	

be used as classifiers for the primary objects. Predecessor 

projects that have had a strong influence on the development	of	GeoSciML	include	activities	undertaken	within	

national	statutory	bodies	(e.g.,	the	USGS/AASG	National	

Geologic	Map	Database,	British	Geological	Survey,	

and	Japanese	Geological	Survey)	in	multi-jurisdictional	

contexts	(the	North	American	Data	Model,	http://nadmgeo.org/,	for	geological	maps),	and	activities	oriented	

to	an	industry	sector	(eXploration	and	Mining	Markup	

Language	–	XMML,	https://www.seegrid.csiro.au/twiki/

bin/view/Xmml/WebHome).	Currently,	several	external projects are leveraging GeoSciML for more specific 

applications,	including	Water	Resources	monitoring	and	

management,	Soils,	Geotechnical	and	Engineering,	Assay	

Data,	and	Geochemistry.

This	report	summarizes	the	schema	and	instance	

documents	as	implemented	in	a	test	bed	demonstrated	

at	the	AMG	meeting	in	Liege,	Belgium	in	September,	

2006.	The	working	group	met	subsequent	to	the	test	bed	

demonstration and has identified a number of aspects of 

the	model	and	schema	in	need	of	update,	as	well	as	model	

elements	that	need	to	be	added.	Anticipated	changes	are	

discussed	here	as	well.	Version	1.1	is	the	current	version	

of	the	markup	language,	with	schema	available	at	https://

www.seegrid.csiro.au/subversion/xmml/GeoSciML/

tags/1.1.0/schema/.	Planning	is	underway	for	evolution	of	

the	schema	to	version	2	to	expand	the	scope	and	clarify	

some	of	the	top	level	model	issues.	Working	group	activity	is	currently	focused	in	several	task-groups	(pending	

formalization):

•	 Use-cases	and	requirements	task	group,	responsible	

for	setting	technical	goals.

•	 Design	task	group,	responsible	for	the	structural	

and	syntactic	aspects	of	the	“nformation	Model”	

of	a	GeoSciML-based	service	architecture.

•	 Service	architecture	task	group,	responsible	for	

the	“Computational	Model”	of	GeoSciML-based	

service	architecture.

• Concepts definition task group, responsible for 

the	“Semantic	Model,”	which	will	be	a	standard	

set	of	concepts	(ontology)	for	the	content	used	to	

populate	GeoSciML,	and	will	facilitate	semantic	

interoperability	with	GeoSciML.

•	 mplementation	test	bed	task	group,	responsible	

for	liaison	with	GeoSciML	Design	and	Service	Architecture	task	groups	to	ensure	that	requirements	

are satisfied and coordinate and deliver TestBed3 

demonstrating	the	GeoSciML	v.2	use-cases.

•	 Outreach	and	technical	assistance	task	group,	

responsible	for	providing	advice	and	assistance	

to	direct	collaborators,	assisting	them	to	deploy	

conformant	GeoSciML	services.

GEOLOGIC MAP DATA SCHEMATIC 

INTEROPERABILITY

The	development	of	standardized	markup	languages	

is	a	critical	step	necessary	to	achieve	interoperability,	

which is defined by ISO/IEC 2382-01 (SC36 Secretariat, 

2003)	to	mean:	“The	capability	to	communicate,	execute	

programs,	or	transfer	data	among	various	functional	units	

in	a	manner	that	requires	the	user	to	have	little	or	no	

knowledge	of	the	unique	characteristics	of	those	units.”	

Technical	requirements	to	meet	this	goal	include	system-level	shared	protocols	for	network	communication,	

resource	discovery,	and	service	invocation	(Figure	1).	

Applications	that	use	these	protocols	must	communicate	

by way of a shared data language that defines how information	will	be	encoded.	Geography	Markup	Language	

Figure 1. Multiple levels of interoperability (Brodaric and Gahegan, 2006).
GEOSCML	–	A	GML	APPLCATON	FOR	GEOSCENCE	NFORMATON	NTERCHANGE 49

(http://www.opengeospatial.org/standards/gml) is the

data language adopted for GeoSciML development. GML

provides a framework for encoding geometry, defining 

features and associating them with properties (including

geometry), and constructing dictionaries in which controlled vocabularies can be defined.

GeoSciML is a GML application scheme, which is

defined by a collection of XML schema that utilize and 

extend elements from GML to represent standard geologic

observations and descriptions in a geospatial context.

GeoSciML is not a database structure. GeoSciML defines 

a format for data interchange (Figure 2). Agencies can

provide a GeoSciML interface onto their existing data

base systems, with no restructuring of internal databases

required.

The semantic level of interoperability (Figure 1)

requires agreement on the meaning of words used to

express property values contained in GeoSciML elements.

Developing common meanings for GeoSciML contents

that can be applied to various multi-lingual vocabularies is a planned future activity. At present, we anticipate

that implementation of schematic interoperability will

demonstrate the need for data content standards to enable

semantic interoperability.

IMPLEMENTATION

GeoSciML was developed by representatives from

an international group of geologic map data providers in a

series of face-to-face meetings and online discussion (see

Twiki at https://www.seegrid.csiro.au/twiki/bin/view/CGIModel/GeoSciML). One design objective was to re-use,

revise, and extend existing standards wherever possible.

The design philosophy of this interchange format has

focused on an accurate representation of geoscience information in a general way. This results in great representational flexibility at the price of complexity and verbose 

encoding. Fortunately, text-based XML compresses very

efficiently, and the markup is designed for machine input 

and output, not human readability.

Model development has utilized UML notation with a

UML profile to enable systematic mapping from UML to 

XML schema. The mapping from UML models to GML is

described in https://www.seegrid.csiro.au/twiki/bin/view/

AppSchemas/UmlGml and https://www.seegrid.csiro.au/

twiki/bin/view/AppSchemas/UmL2GMLAS. A detailed

procedure for generating a GML-compliant XML schema

is summarized in https://www.seegrid.csiro.au/twiki/

bin/view/AppSchemas/HollowWorld and

Figure 2. Communication between data providers and consumers utilizes standard GeoSciML

schema. Clients that can interpret GeoSciML can operate with any GeoSciML-enabled data

source.
50	 DGTAL	MAPPNG	TECHNQUES	‘06

http://www.seegrid.csiro.au/twiki/bin/view/AppSchemas/

OandMCookbook. See also Boisvert et al. (2004) from

the USGS DMT 2004 workshop. Use of a standard graphical notation for model representation during development

makes group analysis and review of the evolving model

much easier.

Major Entities

Only a small part of the GeoSciML model is discussed here. See the GeoSciML Twiki (https://www.

seegrid.csiro.au/twiki/bin/view/CGIModel/GeoSciML)

for more information about the full model. Figure 3

presents the logical framework that underlies the draft

GeoSciML version 2 GeologicFeature implementation, a

core aspect of GeoSciML. Starting from the center left,

a MappedFeature associates a GeologicFeature with a

GML_geometry that specifies a location on or within 

the Earth. The mapped feature may be the result of an

Observation if observation-related metadata concerning

identification of the mapped feature are recorded. Each 

GeologicFeature is associated with a ControlledConcept

classifier that specifies the intention of what the GeologicFeature represents. A GeologicFeature may have one or

more associated GeologicFeatureDescriptions that specify

properties assigned to the feature. Each description may

also be represented as the result of an Observation. Table

1 summarizes the packages included in the GeoSciML

UML model. Each package is implemented as a separate

XML schema.

Geologic Feature

In the draft GeoSciML version 2 model, GeologicFeature is an association class that binds mapped

feature(s) and description(s) with one or more classification concepts. Geologic feature is an entity that represents some particular phenomenon that may be observed

in the Earth. It has a primary classification in terms of 

a controlled concept, and this association establishes a

content model or concept space within which the feature

is located/given identity by specification of a collection 

of properties in a description. A MappedFeature instance

specifies a particular located occurrence of a geologic 

feature by associating it with a location (GML_geometry).

GeologicFeatures may be classified by geologic unit or 

geologic structure ControlledConcepts terms. In addition

to its primary classification (e.g. a lithostratigraphic designation), a feature may carry alternative classifications (e.g. 

geotechnical classification). GeologicFeature corresponds 

with a “legend item” from a traditional geologic map and

with “occurrence” in conceptual models presented by

Brodaric and Gahegan (2006) or Richard (2006). GeologicFeatures may have one or more associated GeologicFeatureDescriptions. Multiple descriptions associated

with a feature may be the result of different observations

(different observer, different time, different observation

procedure...), or may specify different properties

Mapped Feature

A MappedFeature is a specific bounded occurrence, 

such as an outcrop or map polygon that carries a geometry

or shape (through its samplingFrame association). It has

an associated GeologicFeature instance that specifies what 

kind of thing is represented by the mapped geometry, both

by classification with a vocabulary term (ControlledConcept) and through association with one or more description objects (GeologicUnitDescription) that specify

property values.

Figure 3. Core GeoSciML 2.0 logical model.
GEOSCML	–	A	GML	APPLCATON	FOR	GEOSCENCE	NFORMATON	NTERCHANGE 51

Table 1.	Packages	in	GeoSciML	model.

 Package Name Contents

TopLevel The	core	model	for	mapped	entities	distinguishes	between	geologic	features,	mapped	features,	

and	controlled	concepts

BasicTypes Simple package, extends gml:MeasureType to represent quantification of measurements using 

relative	comparisons,	e.g.	greater	than,	less	than.

LiteralValue	 The	GeoSciML	“value”	model	provides	a	generic	way	of	encoding	“literal”	values,	both	textual	

and	numeric,	which	have	uncertainty	and	may	be	a	range.	These	values	are	usually	obtained	as	

the	result	of	an	observation.	The	description	of	the	associated	observation	event	will	provide	

more	detail	about	the	observation	method,	result	quality,	etc.

RootDoc Generic	collection	element	for	packaging	objects	from	the	GeoSciML	schema.

GeologicMetadata	 nterim	model	for	representation	of	dataset,	feature,	and	attribute-level	metadata.	SO	19115	

metadata	would	be	preferred,	but	the	XML	implementation	(SO	19139)	is	not	yet	supported	by	

common	software	The	interim	model	is	intended	to	have	some	forward	compatibility	with	SO.	

For	example,	the	scope-codes	are	a	subset	of	MD_ScopeCode	from	SO	19115.

GeologicVocabulary	 Model	for	controlled	vocabularies	of	terms	linked	to	normative	descriptions,	link	to	ontology.	A	

GeologicVocabulary	is	a	collection	of	terms	(ControlledConcept)	and	relationships	(VocabRelation).

BoreHole Support	for	borehole	data	in	GeoSciML	is	provided	by	XMML	components.	Borehole	is	modeled as a kind of sampling profile that may have various sorts of associated ‘logs’, modeled as 

kinds	of	coverages.

EarthMaterial	 Earth	Material	is	a	class	that	holds	a	material	description.	A	naturally	occurring	substance	in	

the	Earth.	Earth	Material	represents	substance,	and	is	thus	independent	of	quantity	or	location.	

Ideally, Earth Materials are defined strictly based on physical properties, but because of standard 

geological	usage,	genetic	interpretations	enter	into	the	description	as	well.

GeologicAge	 The	age	of	a	particular	geological	event	or	feature	expressed	in	terms	of	years	before	present	

(absolute	age),	referred	to	the	geological	time	scale,	or	by	comparison	with	other	geological	

events	or	features	(relative	age).	A	GeologicAge	can	represent	an	instant	in	time,	an	interval	of	

time, or any combination of multiple instants or intervals. Specifications of age in years before 

present	are	based	on	determination	of	time	durations	based	on	interpretation	of	isotopic	analyses	

of	EarthMaterial	(some	other	methods	are	used	for	geologically	young	materials).	Ages	referred	

to	geological	time	scales	are	essentially	based	on	correlation	of	a	geological	unit	with	a	standard	

chronostratigraphic	unit	that	serves	as	a	reference.	Relative	ages	are	based	on	relationships	between	geological	units	such	as	superposition,	intruded	by,	cross-cuts,	or	‘contains	inclusions	of’.

GeologicRelation	 Geologic	Relations	are	typed,	directed	associations	between	geologic	objects.	Represents	any	

of	a	wide	variety	of	relationships	that	can	exist	between	two	or	more	GeologicFeatures.	For	

example,	the	GeologicRelation	‘intrudes’	is	a	relationship	between	an	intrusive	igneous	rock	and	

some	host	rock.	ncludes	spatial,	temporal,	sequence,	correlation,	and	parent/child	relations.

Two	or	more	GeologicFeatures	are	associated	in	a	GeologicRelation;	each	has	a	role	in	the	

relationship.	Examples	of	geological	roles	include	“overlies”,	“is	overlain	by”,	“is	younger”,	

“is	older”,	“intrudes”,	“is	intruded	by”,	and	so	forth.	n	a	relationship	where	an	igneous	unit	

intrudes	a	sedimentary	unit,	the	geological	relationship	is	‘intrudes’,	the	intruded	sedimentary	

unit	has	the	role	‘host’,	and	the	igneous	unit	has	the	role	‘intrusion’.	Many	other	types	of	relationships	can	also	be	accommodated	via	GeologicRelation,	for	example,	topological	relations	

between spatial objects could be described where they are scientifically significant.

GeologicTime	 The	GeoSciML	Geologic	Timescale	model	and	encoding	is	described	in	detail	in	the	paper	‘A

formal	model	for	the	geologic	time	scale	and	global	stratotype	section	and	point,	compatible	

with	geospatial	information	transfer	standards’	(Cox	and	Richard,	2005).

The	classic	“geological	time	scale”	is	a	hierarchical	ordinal	system,	in	which	the	eras	are	

ranked:	“stages”	nest	within	“series”	within	“systems”	within	“eras”	within	“eons”	(in	the	most	

common	version	of	the	ranking	system).

GeologicUnit	 Package	containing	content	model	for	geologic	unit.	Geologic	unit	is	a	notional	unit,	whose	complete	and	precise	extent	is	inferred	to	exist.	Practically,	spatial	properties	are	only	available	through	

association	with	a	MappedFeature.	ncludes	both	formal	units	(i.e.	formally	adopted	and	named	in	

the official lexicon) and informal units (i.e. named but not promoted to the lexicon) and unnamed 

units (i.e. recognizable and described and delineable in the field but not otherwise formalized).

StructureObject Package	containing	content	model	for	geologic	structure.	Version	1	includes		fault	system,		fault,	

contact,	and	fault	displacement.
52	 DGTAL	MAPPNG	TECHNQUES	‘06

Controlled Concept

ControlledConcepts represent human concepts in

computer form, typically as words (lexical objects) with

an associated definition. Because GeoSciML extends 

GML, each controlled concept instance may have one or

more gml:names, but the GeoSciML model adds a preferred name element that specifies one term that is used to 

identify the concept. Practically speaking, each preferredName should be associated with a unique concept, but in

a distributed system, this cardinality cannot be enforced.

ControlledConcepts are aggregated into GeologicVocabulary collections, which are derived from a GML dictionary. Data producers should ensure that preferredNames

are unique within a particular vocabulary. A Controlled

Concept may have an associated prototype entity (not

shown in Figure 3) that can be a GeologicFeature, EarthMaterial, or Specimen. The prototype entity provides a

mechanism to associate machine-analyzable properties

with ControlledConcept terms. Similar functionality

might be provided by links from the ControlledConcept to

some other formal ontology representation.

Geologic Feature Description

Descriptions are collections of properties with assigned values (e.g. attributes) that characterize some

feature. Different kinds of descriptions specify different

properties. Descriptions may be associated with Observation elements that supply information on the origin of the

property value assignments.

Observation

Observation describes the “metadata” associated with

an information capture event, together with a value for the

result of the observation. Observations are the basis for

classified features, interpretations, and models. GeoSciML 

uses the Observation and Measurement model from the

Open Geospatial Consortium (Cox, 2006), which models

observation as a kind of event, in which a result value is

assigned to some property of a feature of interest, using

some procedure.

SOME SCHEMA DESIGN AND USAGE 

PATTERNS ISSUES

Names and Identifiers

Any GML Object or Feature may have an unlimited

number of gml:name properties, which reflects the fact 

that the same object often has different identifiers assigned 

by different authorities. To assert “this is the name or

identifier assigned by authority XYZ corporation,” use the 

codeSpace attribute on gml:name (i.e. the scope identifier). 

If the codespace for the gml:name is not specified, then the 

value is implicitly under the authority of the organization

or service that supplies the document, which should be

indicated by associated document-level metadata.

Note that GML document elements also include a

gml:id attribute, which plays a different role from the gml:

name element. The value of the gml:id has type=”xsd:

ID”, so it must be unique within the (XML) document. It

is a document fragment identifier that acts as a handle for 

an XML element in the scope of its appearance within a

particular document, and is usually assigned by the information management system since it is primarily significant in that context. The gml:id supports cross-references

within a document and references that involve individual

nodes (elements) within a system of documents. The

value of a gml:name has type=”gml:CodeType”, which is

a string with a “codeSpace” attribute. In the context of a

GML object, the value of a gml:name is a label or identifier for the object described by the containing element, 

and is typically assigned by the data provider agency. The

gml:name should be used for identifiers that are required 

to be persistent and are subject to constraints (e.g. uniqueness) applicable to a context wider than just the document

scope. Different authorities may have different authoritative identifiers for the same item.

Namespace and Packaging

The namespace for GeoSciML version 2.0 schema

is http://www.cgi-iugs.org­/xml/­GeoSciML/2. Versioning

strategy for namespace evolution will follow practice described in OGC 05-062r3. For future upgrades, each minor version of any such schema that retains the namespace

of the predecessor shall not introduce any new XML

types or elements that could not be safely ignored by

existing application based on the previous minor version,

which ensures a strong form of backward compatibility.

Components from other namespaces (e.g., http://www.

opengis.net/om) may also constitute a “canonical” part

of GeoSciML but will be incorporated using the WXS

import mechanism and, thus, retain their own namespace

names.

The physical document location (path) for GeoSciML

schema will include the complete version number—initially 1.0.0, moving to 1.0.x for bug-fix releases, and 1.1.x 

(etc.) for extensions that do not change the scope of the

schema. Schema documents are hosted in the GeoSciML

publish/build repository, which is at https://www.seegrid.

csiro.au/subversion/xmml/GeoSciML/tags/.

Use of Scoped Names

Use of scoped names, i.e., a term or word with an

identifier for the source of the term, provides a method for 

linkage to formal controlled vocabularies (e.g. an ontology) that may then be used for semantic mediation. For
GEOSCML	–	A	GML	APPLCATON	FOR	GEOSCENCE	NFORMATON	NTERCHANGE 53

example, a GeoSciML file might have a property value 

specified by the following element:

<CGI_TermValue>

	 <qualifier>common</qualifier>

	 <value	codeSpace=”http://www.iugscgi.org/outcropCharacterVocabulary”>	

ledge	forming</value>

</CGI_TermValue>

The	<value>	element	contains	a	scoped	name	“ledge	

forming” from the vocabulary specified by the codeSpace 

attribute.	f	the	data	interpreter	is	familiar	with	the	

“http://www.iugs-cgi.org/	outcropCharacterVocabulary”	

codeSpace	(vocabulary),	then	they	may	use	the	scoped	

name	directly	or	by	correlation	with	a	preferred	term	in	a	

different vocabulary. On the other hand, if the identified 

codeSpace (vocabulary) is not familiar and its identifier is 

a	resolvable	URL	that	points	to	service	that	can	provide	

a definition of the term in a known format (e.g. free text, 

OWL,	KF...),	it	is	possible	to	interpret	the	term.	This	may	

be as simple as someone studying a free text definition 

and	determining	the	closest	corresponding	term	in	their	

vocabulary.	An	automated	semantic	mediator	might	be	

able to use a formal definition (e.g., OWL) to match with 

the	closest	subsuming	term	in	a	different	formal	vocabulary	that	is	preferred	by	the	data	interpreter.

Value specification

The GeoSciML data model includes a flexible value 

specification scheme that is designed to capture value 

descriptions	conventionally	recorded	by	geologists.	All	

values may carry a qualifier. Numeric values include units 

of measure. Values may be specified in several manners:

•	 by a single numeric value with optional uncertainty, e.g., 5.24 +/- 0.12

•	 by a numeric range, e.g., 5.7-13.6

• by a term with an identifier for the source vocabulary, e.g., “thick-bedded (NADM SLTTs)”

•	 as a range with bounds assigned by terms or by

a term and a numeric value, e.g. “fine- to medium-grained (Folk 1968)” or “Miocene (IUGS

2004)” – 1.7 Ma.

Instance Document Example

Example instance documents associated with

each version of the schema in the subversion repository (https://www.seegrid.csiro.au/subversion/xmml/

GeoSciML/tags/) are stored in an “Instances” subdirectory in the directory for that version. The following listing

provides an example usage of many of the elements for

geologic unit description. The base element in the document is a GeoSciML collection (gsml:); each member

of the collection starts with a <member> element.

GeoSciML collection members may be:

	1.	Geologic features (a kind of GML feature)

	2.	GML geometry elements

	3.	Mapped Features (outcrops, sample locations,

traverses/sections)

4.Controlled concepts (vocabulary definitions)

	5.	Geologic relationships

	6.	Dictionaries (collections of controlled concepts)

Comments in the following listing are delimited by ‘<!--’ and ‘-->’.

<?xml version=”1.0” encoding=”UTF-8”?>

<Gsml xmlns=”http://www.cgi-iugs.org/xml/GeoSciML/1” ... other namespace declarations>

	 <!-- The lexicon would probably be in a separate file. The StratigraphicLexicon element extends GML dictionary (through GeologicVocabulary GeoSciML element)

-->

<member>

	 <StratigraphicLexicon gml:id=”AZGSGeologicUnits”>

	 <!-- This is a lexicon element that includes three units -->

	 	 <gml:description>Collection of geologic units defined by State of Arizona</

gml:description>

	 	 <gml:name>Arizona stratigraphic unit lexicon</gml:name>

	 	 <member>

	 	 <ControlledConcept gml:id=”MartinFormationConcept”>

	 	 <gml:description>lithostratigraphic formation defined by ... </gml:

description>

	 	 <gml:name>urn:x-cgi:def:lithostratigraphy:USGS:2006:Geolex:MartinFormation </gml:name>

	 	 <preferredName>Martin Formation</preferredName>

	 	 <prototype xlink:href=”#Feature2524”/>
54	 DGTAL	MAPPNG	TECHNQUES	‘06

	 	 <vocabulary xlink:href=”#AZGSGeologicUnits”/>

	 	 <metadata/>

	 	 </ControlledConcept>

	 	 </member>

	 	 <member>

	 	 	 <ControlledConcept gml:id=”LS2”> ... </ControlledConcept>

	 	 </member>

	 	 <member>

	 	 	 <ControlledConcept gml:id=”LS3”> ...</ControlledConcept>

	 	 </member>

	 </StratigraphicLexicon>

</member>

<member>

	 <GeologicFeatureRelation gml:id=”rel-100”>	

	 <!-- This is a geologic relationship element-->

<gml:name>urn:x-cgi:def:lithostratigraphy:USGS:2006:featureRelation:

Stratigraphic position</gml:name>

<role codeSpace=”http://www.iugs-cgi.org/featureRelationVocabulary”>overli

es</role>

<source xlink:href=”#BeckersButteMemberPrototype”/>

<target xlink:href=”#JeromeMemberPrototype”/>

	 </GeologicFeatureRelation>

</member>

<member>

<!-- GeologicFeature is derived from GML AbstractFeature, it associates a description, a classifier (what is described) and an extent (where it was described, if defined).The Classifier element defines the type of a feature. Multiple descriptions may be associated with a GeologicFeature -->

<GeologicFeature gml:id=”Feature2524”> <!-- This is a geologic unit GML feature,

which is the basic container for geologic unit descriptions in GeoSciML v.

1 -->

	 <gml:description>The type section of the Martin Formation at Mt. Martin near

Bisbee consists almost entirely of medium-gray to medium dark-gray

aphantiic to fine-graine limestone. dolostone is entirely subordinate, ...

	 </gml:description>

	 <gml:name>urn:x-cgi:def:lithostratigraphy:USGS:2006:Geolex:TypeMartinFormation</gml:name>

	 <gml:boundedBy>

	 	 <gml:Envelope>

	 	 <gml:lowerCorner/> <!-- corners of a bounding box for type area of the

Martin Formation; geometry specification elements not included

here-->

	 	 <gml:upperCorner/>

	 	 </gml:Envelope>

	 </gml:boundedBy>

	 <purpose>typicalNorm</purpose>	

	 <age>

<!-- Geologic age element includes a date value specification (see below), and an

event specification that explicitly identifies the event to which the age is assigned (e.g. deposition, cooling through biotite closure temperature...) -->

	 	 <GeologicAge>

	 	 <value>

	 	 <CGI_TermValue>

	 	 <value codeSpace=”http://www.iugs-cgi.org/geologicAgeVocabulary”>Middle
GEOSCML	–	A	GML	APPLCATON	FOR	GEOSCENCE	NFORMATON	NTERCHANGE 55

Devonian</value>

	 	 			</CGI_TermValue>

	 	 		</value>

	 	 		<event>

	 	 				<CGI_TermValue>

	 	 	 <value	codeSpace=”http://www.iugs-cgi.org/EventVocabulary”>deposition</

value>

	 	 				</CGI_TermValue>

	 	 		</event>

	 	 </GeologicAge>

	 </age>

	 <classifier	xlink:href=”#MartinFormationConcept”/>	<!--	here’s	the	link	to	the	

controlled	concept	that	defines	the	intention	of	the	Martin	Formation.	

Link	is	reference	to	controlled	concept	instance	in	this	document-->

	 <description>

	 <LithostratigraphicUnitDescription>

	 <metadata/>	<!--	xlink	to	metadata	for	this	description;	this	provides	tie	to	

Observation	model-->

	 <partOf>

	 		<GeologicUnitDescriptionPart>	<!--	310-	340	thin	bedded,	non	fossiliferous	

dolostone	-->

	 	 <unit>

	 	 		<LithostratigraphicUnitDescription	gml:id=”GeoUnitPart0235”>	

<!--	part	is	also	a	lithostratigraphic	unit,	uses	same	description	schema	as	containing	unit;	it	could	have	parts	itself;	partonomy	is	recursive.	-->

	 	 	 <descriptionSource	xlink:href=”reference	to	description	source	observation”	/>	<!—Source	observation	element	not	included	here	-->

	 	 	 <bodyMorphology	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 	 	 <outcropCharacter	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 	 	 <grossGenesisTerm	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 	 	 <exposureColor	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 	 	 <grossChemistry>

	 	 	 	 <CGI_TermValue>

	 	 	 	 			<qualifier>always</qualifier>

				 	 	 			<value	codeSpace=”http://www.iugs-cgi.org/grossChemistryList”>carb

onate</value>

	 	 	 	 </CGI_TermValue>

	 	 	 </grossChemistry>	

	 	 	 <rank	codeSpace=”http://www.iugs-cgi.org/Vocabulary”>DescriptionPart</

rank>

	 	 	 <weatheringCharacter	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 	 	 <metamorphicGrade/>	 <!--	not	specified	so	implies	same	as	containing	

unit	-->

	 	 	 <unitThickness>

	 	 	 	 <CGI_NumericValue>

	 	 	 	 	 <principalValue	uom=”meter”>30</principalValue>

	 	 	 	 	 <plusDelta	uom=”meter”>20</plusDelta>

	 	 	 	 	 <minusDelta	uom=”meter”>10</minusDelta>

	 	 	 	 </CGI_NumericValue>

	 	 	 </unitThickness>

	 	 	 <beddingStyle	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 	 	 <beddingPattern	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 	 	 <beddingThickness>

	 	 	 		<CGI_TermValue>

	 	 	 				<value	codeSpace=”http://www.iugs-cgi.org/ThicknessVocabulary”>Thin-
56	 DGTAL	MAPPNG	TECHNQUES	‘06

bedded</value>

	 	 	 		</CGI_TermValue>

	 	 	 </beddingThickness>

	 	 		</LithostratigraphicUnitDescription>

	 	 </unit>

	 	 <role	codeSpace=”http://www.iugs-cgi.org/unitPartRoleVocabulary”>Stratigrap

hic	part</role>

	 	 <type>codeSpace=”http://www.iugs-cgi.org/unitPartTypeVocabulary”>Descriptiv

ePart</role>

	 	 <proportion>

	 	 			<CGI_NumericValue>

	 	 	 <qualifier>approximate</qualifier>

	 	 	 <principalValue	uom=”percent”>12</principalValue>

	 	 	 <plusDelta	uom=”percent”>0</plusDelta>

	 	 	 <minusDelta	uom=”percent”>0</minusDelta>

	 	 			</CGI_NumericValue>

	 	 </proportion>

					</GeologicUnitDescriptionPart>

	 </partOf>

<!--	end	of	part	descriptions.	Following	properties	apply	to	entire	described	

unit	-->

	 <descriptionSource	xlink:href=”reference	to	description	source	observation”	/>

	 <bodyMorphology	xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 <outcropCharacter>

	 			<CGI_TermValue>

	 						<qualifier>common</qualifier>

	 						<value	codeSpace=”http://www.iugs-cgi.org/outcropCharacterVocabulary”>le

dge	forming</value>

	 			</CGI_TermValue>

	 </outcropCharacter>

	 <grossGenesisTerm

	 			<CGI_TermValue>

	 						<qualifier>always</qualifier>

	 						<value	codeSpace=”http://www.iugs-cgi.org/GenesisVocabulary”>Sedimentary

,	marine</value>

	 			</CGI_TermValue>

	 </grossGenesisTerm>

	 <exposureColor>

	 			<CGI_TermValue>

	 						<qualifier>common</qualifier>

	 						<value	codeSpace=”http://www.color.org/ColorVocabulary”>Light	gray</value>

	 			</CGI_TermValue>

	 			<CGI_TermValue>

	 						<qualifier>common</qualifier>

	 						<value	codeSpace=”http://www.color.org/ColorVocabulary”>Medium	gray</

value>

	 			</CGI_TermValue>

	 			<CGI_TermValue>

	 						<qualifier>rare</qualifier>

	 						<value	codeSpace=”http://www.color.org/ColorVocabulary”>Pink</value>

	 			</CGI_TermValue>

	 <exposureColor/>

	 <grossChemistry>

	 	 <CGI_TermValue>
GEOSCML	–	A	GML	APPLCATON	FOR	GEOSCENCE	NFORMATON	NTERCHANGE 57

	 	 <qualifier>common</qualifier>

	 	 <value codeSpace=”http://www.iugs-cgi.org/grossChemistryList”>carbonate</

value>

	 	 </CGI_TermValue>

	 	 <CGI_TermValue>

	 	 <qualifier>occasional</qualifier>

	 	 <value codeSpace=”http://www.iugs-cgi.org/grossChemistryList”>siliceous</

value>

	 	 </CGI_TermValue>

	 </grossChemistry>

	 <rank codeSpace=”http://www.iugs-cgi.org/Vocabulary”>Formation</rank>

	 <weatheringCharacter xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 <metamorphicGrade>

<CGI_TermValue>

<qualifier>always</qualifier>

<value codeSpace=”http://www.iugs-cgi.org/metamorphicGradeVocabulary”>not

metamorphosed</value>

</CGI_TermValue>

	 </metamorphicGrade>

	 <unitThickness>

	 	 <CGI_NumericValue>

	 	 	 <principalValue uom=”meter”>340</principalValue>

	 	 	 <plusDelta uom=”meter”>10</plusDelta>

	 	 	 <minusDelta uom=”meter”>10</minusDelta>

	 	 </CGI_NumericValue>

	 </unitThickness>

	 <beddingStyle>

	 	 <CGI_TermValue>

	 	 	 <qualifier>common</qualifier>

	 	 	 <value codeSpace=”http://www.iugs-cgi.org/Vocabulary”>Planar bedding</

value>

	 	 </CGI_TermValue>

	 </beddingStyle>

	 <beddingPattern xlink:href=”urn:x-ogc:def:nil:OGC:unknown”/>

	 <beddingThickness>

	 	 <CGI_TermRange>

	 	 <lower>

	 	 	 <CGI_TermValue>

	 	 	 	 <qualifier>common</qualifier>

	 	 	 	 <value codeSpace=”http://www.iugs-cgi.org/Vocabulary”>thin bedded</

value>

	 	 	 </CGI_TermValue>

	 	 </lower>

	 	 <upper>

	 	 	 <CGI_TermValue>

	 	 	 	 <qualifier>rare</qualifier>

	 	 	 	 <value codeSpace=”http://www.iugs-cgi.org/Vocabulary”>thick bedded</

value>

	 	 	 </CGI_TermValue>

	 	 </upper>

	 	 </CGI_TermRange>

	 </beddingThickness>

	 </LithostratigraphicUnitDescription>

	 </description>

</GeologicFeature>

</member>

</Gsml>
58	 DGTAL	MAPPNG	TECHNQUES	‘06

TEST BED DEMONSTRATION

Six	national	and	two	state	geological	survey	agencies,	

in	Australia,	Europe,	and	North	America,	participated	in	a	

proof-of-concept	demonstration	of	GeoSciML	at	the	nternational	Association	of	Mathematical	Geologists	(AMG)	

meeting	in	Liege,	Belgium,	in	September	2006.	The	demonstration	showed	that	it	is	possible	to	access	information	

in	real	time	from	globally	distributed	data	sources.	Geological	map	polygons	and	attribute	information,	and	borehole	data,	were	displayed,	queried,	and	re-portrayed	using	

web	applications	hosted	by	the	Geological	Survey	of	

Canada	and	the	French	Bureau	de	Recherche	Géologiques	

et	Minières	(BRGM).	Functions	demonstrated	included	

continuous map portrayal with attribute query, reclassification	according	to	attributes,	and	download	of	complex	

data	structures	encoded	in	GeoSciML.

nformation	delivery	from	different	complex	data	

stores	using	a	community	standard	schema	demonstrated	

that	GeoSciML	provides	a	data	model	and	format	capable	

of	supporting	transfer	of	geology	data	from	multiple	

jurisdictions.	This	also	demonstrated	that	a	distributed	

data	delivery	system	can	be	constructed	by	specifying	

standard	interfaces,	not	limited	to	single	vendor	software.	

New	services	can	be	added	easily,	providing	they	conform	to	the	interface.	All	of	the	services	in	the	test	bed	

used	different	data	stores,	wrapped	by	a	variety	of	server	

software applications. Deployment requires configuration 

of	server-	and	client-side	software	to	conform	to	the	data	

model,	but	does	not	require	development	of	new	software	

“from	scratch.”

Three	use	cases	were	demonstrated	at	the	AMG	

2006	meeting	in	Belgium.	Use	Case	1	demonstrated	display	of	map	data	and	query	for	the	description	of	a	single	

map object. When the client asks for the map, the server

returns a map with default symbolization. A user can

then click on any graphic feature from a layer to retrieve

information for the feature, which can be presented to

the user as raw GeoSciML or as a more clearly-rendered

HTML version. Presentation formats other than HTML

can be requested by the client if the server supports them.

The types of features used must include at least one of the

following: geologic units, faults, contacts or boreholes.

Use case 2 demonstrated selection and download

of features; a geographic bounding box is specified and 

the contents downloaded as a GeoSciML document. The

GeoSciML document can be reformatted (e.g. by XSLT

for display in a browser) or serve as input for another

process in a workflow. The GeoSciML document contains 

a collection of GeologicFeatures or Boreholes.

Use case 3 demonstrated dynamic query and re-symbolization of mapped features on the basis of age, using the

IUGS standard geologic age color scheme, or on the basis

of lithology, using a CGI defined lithology color scheme. 

The results of symbolization by lithology for data from

Canada, the U.S., and Scandinavian countries is shown in

Figure 4. A very simple lithologic classification and symbolization was used, with four classes and related colors:

igneous (pink), sedimentary (green), metamorphic (purple),

and unconsolidated (yellow). Each participant had to implement a mechanism to map from properties associated with

the mapped features to the standardized lithology classes. It

is the service provider’s prerogative to determine the mapping from the data source to the classification.

SUMMARY

A standardized schema and syntax for information encoding is a fundamental requirement for interoperable inforFigure 4. Use Case 3 from Testbed 2, re-symbolization of geologic units by lithology for Canada, U.S. and Scandinavian countries: igneous (medium gray), sedimentary (light grey), metamorphic (dark gray), and unconsolidated (nearly

white).
GEOSCML	–	A	GML	APPLCATON	FOR	GEOSCENCE	NFORMATON	NTERCHANGE 59

mation	systems.	The	UGS	CG	Data	Model	collaboration	

working	group	has	developed	GeoSciML,	an	XML-based	

GML	(geography	markup	language)	application,	to	meet	

this	requirement	for	the	interchange	of	geoscience	information.	The	schema	for	this	application	reuses	existing	markup	

languages where possible. Newly developed markup specifications	are	based	on	existing	conceptual	models	in	most	cases.	This	standards-based	data	format	provides	a	framework	

for	application-neutral	encoding	of	geoscience	thematic	data	

and	related	spatial	data.	t	is	intended	for	use	in	publishing	or	interchanging	data	between	organizations	that	use	

different	database	implementations	and	software/systems	

environments.	Full	realization	of	data	interoperability	at	the	

semantic	level	will	require	development	of	controlled	vocabulary	resources	for	specifying	actual	content.	A	Testbed	

demonstrated	simple	interoperability	using	web	map	and	

feature	services	(WMS,	WFS)	between	geological	surveys	

in	several	different	countries.	GeoSciML	is	being	considered	as	a	national	standard	for	geoscience	data	exchange	

by	federal	and	state	geological	surveys	in	Australia	and	the	

European	Union	Spatial	Data	nfrastructure	(NSPRE),	and	

will be submitted in 2007 as an IUGS-CGI specification.

Development	of	GeoSciML	is	an	open	process	with	

the	intent	to	involve	as	many	participants	as	possible.	

This	will	ensure	development	of	a	schema	and	services	

that	will	meet	the	needs	of	a	wide	variety	of	geoscience	

data	producers	and	users.	Three	types	of	participation	are	

available:	1)	direct	participation	in	GeoSciML	development,	2)	monitoring	GeoSciML	development	via	the	webcollaboration	tools	and	3)	deploying	an	internet	server	to	

provide	data	in	GeoSciML	format.

REFERENCES

Boisvert,	E.,	Johnson,	B.R.,	Cox,	S.J.,	and	Brodaric,	B.M.,	2004,	

GML	Encoding	of	NADM	C1,	in	Soller,	D.R.,	ed.,	Digital	

Mapping	Techniques	‘04—Workshop	Proceedings:	U.S.	

Geological	Survey	Open-File	Report	2004–1451,	p.	95-

103.	Available	at	http://pubs.usgs.gov/of/2004/1451/boisvert/index.html	(accessed	12/12/2006).

Brodaric,	B.M.,	and	Gahegan,	Mark,	2006,	Representing	geoscientific knowledge in cyberinfrastructure: some challenges, 

approaches	and	implementations,	in	Sinha,	Krishna,	ed.,	

Geoinformatics-Data	to	knowledge:	Geological	Society	of	

America	Special	Paper	397,	p.	1-20.

Cox,	S.J.D.,	ed.,	2006,	Observations	and	measurements:	Open	

Geospatial	Consortium,	nc.,	document	OGC	05-087r4,	

version	0.14.7,	168	pages.	Available	at	http://portal.

opengeospatial.org/files/?artifact_id=17038	(accessed	

12/13/2006).

Cox,	S.J.D.,	Daisey,	P.,	Lake,	R.,	Portele,	Clemens,	and	Whiteside,	Arliss,	eds.,	2004,	OpenGS	Geography	Markup	

Language (GML) Implementation specification: Open GIS 

Consortium,	nc,	document	OGC	03-105r1,	version	3.1.0,	

580	p.

Cox,	S.J.D.,	and	Richard,	S.M.,	2005,	A	formal	model	for	the	

geologic	time	scale	and	global	stratotype	section	and	point,	

compatible	with	geospatial	information	transfer	standards:	

Geosphere,	v.	1,	p.	119-137,	DO:	10.1130/GES00022.1.	

Available	at	http://geosphere.geoscienceworld.org/cgi/content/abstract/1/3/119	(accessed	12/12/2006).

North	American	Geologic-Map	Data	Model	Steering	Committee,	2004,	NADM	Conceptual	Model	1.0,	A	Conceptual	

Model	For	Geologic	Map	nformation:	U.S.	Geological	

Survey	Open-File	Report	2004-1334,	61p.,	available	at	

http://pubs.usgs.gov/of/2004/1334/.

Richard,	S.M.,	2006,	Geoscience	concept	models,	in	Sinha,	

Krishna,	ed.,	Geoinformatics-Data	to	knowledge:	Geological	Society	of	America	Special	Paper	397,	p.	81-108.

SC36	Secretariat,	2003,	Proposed	Draft	Technical	Report	for:	

SO/EC	2382,	nformation	technology—Learning,	education, and training—Management and delivery—Specification and use of extensions and profiles: ISO/IEC 2382-01, 

SO/EC	JTC1	SC36	N0646,	available	at	http://jtc1sc36.

org/doc/36N0646.pdf	(accessed	12/12/2206).
60	 DGTAL	MAPPNG	TECHNQUES	‘06
61

61

Open Source Web Mapping: The Oregon Experience

By David Percy

Portland State University

Department of Geology

1721 SW Broadway

Portland, OR 97201

Telephone: (503) 725-3373

Fax: (503) 725-3325

e-mail: percyd@pdx.edu

SUMMARY

Open source tools have enabled a superior level of

productivity for our online interactive map data delivery

efforts. At Portland State University (PSU), we have delivered a web-accessible interactive map of the geology of

Oregon since 1999. We also began delivering glacier data

and coastal data in the ensuing years as it became obvious

that all scientific data needed a web presence. Initial versions of our interactive web maps were based on MapObjects, followed by ArcIMS, both of which are products of

ESRI, the dominant provider of proprietary GIS software.

As more scientific data needed to be delivered via the Internet, the limitations of ESRI’s software became evident,

and the search began for better solutions.

Eventually, a new direction was established using

Open Source software, which allows for a greater degree

of customization and the transfer of existing skills in web

design. Since PSU has an expert group of developers that

use open source tools, such as PHP and MySQL, the training was minimized. Many of the skills that are already

used for other web applications are directly transferable to

web mapping when we use open source tools. Additionally, we have a culture of Open Source software use and

development at PSU and, in general, within the state of

Oregon, so this direction of development makes sense on

many levels.

Overall, this effort has resulted in a web-mapping

framework that provides considerably faster web page

updates than its proprietary counterparts. Additionally, we

have the ability to re-use components from applications,

developed initially for certain organizations, to solve new

problems for other organizations. Each time we initiate

a project, we consider how it can benefit the larger goals 

of the web-mapping framework, which is essentially to

provide feature for feature replacement of our proprietary

competition, and thus everyone benefits.

BACKGROUND

Since Linux and Apache are supported on our campus

as the defacto web platform, it is obvious that, to minimize the support required, we should use this platform.

Mapserver was developed by researchers at the University

of Minnesota under an NASA grant and is a mature server

side application for delivering map data. PostGreSQL is

an open source project that traces its roots to UC Berkeley, but is currently maintained in Germany. It is a hybrid

relational-object oriented database, similar in functionality to Oracle. PostGIS, which was developed by Refractions Research (an open source consulting company in

Victoria, BC), is a set of extensions that enhance PostGreSQL to give it a full set of GIS capabilities. PostGIS

implements the full set of “OpenGIS Simple Features for

SQL” capabilities as specified by the Open Geospatial 

Consortium. We refer to this mapping platform as LAMP

for Linux/Apache/Mapserver/PostGIS.

METHODS

Once an organization has decided to use this set of

open source mapping tools, they must decide how to deliver the data in an interactive web application. The previously enumerated tools provide the back end for web data

delivery, but a front-end is needed to allow the end-user to

interact with the data in a web browser.

Several mature web-mapping frameworks exist, but

on close examination, it was clear that some had the patina

of an older web application. That is, applications on the internet mature and age quickly, and new developments also

happen quickly—the term “internet time” has currency

because it is true that things happen rapidly on the internet.

Thus, even though we could use one of the existing mapframeworks, that would not mean that it would be as functional as something developed with an eye to the future.
62	 DGTAL	MAPPNG	TECHNQUES	‘06

Initially, we considered using one of the existing

web-mapping frameworks. There are several robust applications in existence such as Chameleon, Mapbuilder,

and Ka-Map. After examining these mapping frameworks,

which were already deployed, and in light of the previous notes and the ascendency of Web 2.0 and AJAX, we

decided to develop our own. The functional requirements

were to zoom/pan, query, turn off and on thematic layers,

and dynamically resize the window to maximize map

area. In a single weekend of development, several PSU

graduate students wrote a new framework. One year later,

the resulting product was named Map-Fu and became its

own open-source project on Sourceforge in December,

2006 (http://sourceforge.net/projects/map-fu/).

In the meantime, several other mapping front-end

products have become available that provide the same

types of features we developed in Map-Fu. Thus, there

are many options for the open source enthusiast to pursue.

The main cautionary note is for the potential user to follow the listserve of any particular project for a few weeks

to determine how active the community is. A healthy open

source project will have several posts to the listserve every day, usually even 10 to 20. A project that has not had

any posts to the listserve for more than a month is probably dead or perhaps mature, yet used only by one group.

Regardless of what front-end an organization

chooses, the first step is to develop a mapfile Mapserver 

can read and generate images from. The mapfile will consist of names of data sources such as shapefiles for vector 

data and geotiffs for basemaps. It will also specify how to

symbolize individual classes of data, for example a “Qal”

unit would likely be displayed with an RGB value of 255

255 0.

In terms of optimizing data for web delivery, a few

tasks are required. Large raster data sets need to be tiled

and have internal overviews built. This is done via a series

of command line operations that utilize an open source

library known as GDAL. To build internal tiles we issue a

command like this:

gdal_translate -of GTiff -co “TILED=YES” shaded_

relief.tif shaded_relief_tiled.tif

After this, it is useful to build internal overviews

(similar to “pyramids” in ArcMap) using a command like:

gdaladdo shaded_relief_tiled.tif 2 8 32 128

Note that the first command, gdal_translate, creates 

a new file, while the second command, gdaladdo, works 

“in situ” (without creating a new file). Also, the execution 

order of commands matters. Overviews are not copied

during a gdal_translate operation, so the user should build

tiles first, followed by overviews, as illustrated above.

To optimize the vector data, the shapefiles are imported to the open source database PostGIS, which is an

extension of PostGreSQL. From here a command line

function is executed that produces a lower resolution data

set for initial delivery at low resolution (“zoomed-out”)

levels. It uses the conversion from postgresql to shapefile 

with the addition of an SQL operation. In this example,

we have a table named lithology in the database named

geology. We request that the output be a shapefile name 

simplelith and the sql command simplify the vertices

down to one every 1000 feet:

pgsql2shp -f simplelith -h localhost -u mapserve geology -s “select simplify(the_geom, 1000) as the_geom,

gnlith_u from lithology”

Techniques like this can considerably speed up the

delivery of web-accessible data. While it may seem

strange to do such things, it is simply the reality of providing data on the internet, where delivery times mean

the difference between users accessing your site or simply

abandoning it for lack of responsiveness.

With regard to returning query results, we have implemented an approach that uses the geospatial database

PostGIS. When the user clicks on the map with the “infoquery” tool, the coordinate pair they clicked on is sent to

the database, which then returns all objects from all tables

that intersect the point that was clicked. We then have

a query handler that outputs data related to the objects,

depending on which layers in the view are on or off.

It is relatively trivial at this point to set up an Open

Standards based output system. Note that Open Standards

are different from Open Source; in the first case we are 

talking about a committee of vendors and organizations

that decide upon a protocol for data interoperability, while

in the latter we refer to a formal system by which users

are allowed to view and legally modify and redistribute

the source code of programs.

The Open Standards protocols that are of interest are

Web Map Services (WMS) and Web Feature Services

(WFS), though the suite of open standards-based web services are collectively called Open Web Services (OWS).

The entire set of OWS is still under development, though

it is maturing rapidly and there are some viable uses now.

By inserting certain metadata statements into the same

mapfiles we use for our interactive maps, we can simultaneously serve as OWS providers. This allows our data to

be aggregated by others into other useful web-interfaces.
OPEN	SOURCE	WEB	MAPPNG:	THE	OREGON	EXPERENCE 63

CONCLUSION

We have had great success and satisfaction using

open source tools for our web delivery of scientific 

data. Using open source tools has given us the ability

to leverage existing strengths, as opposed to having

to learn techniques that only apply to one monolithic

proprietary software program. We also have the ability

to use data from other OWS providers, such as NASA or

the USGS, as base layers on which to overlay our data.

In the end, we assume that these “stove pipe” solutions we are building will be converted into pure open

standards based formats like WMS and WFS, such that

any standards-based interface can integrate our data with

whatever other data they deem useful to addressing the

situation at hand.
64	 DGTAL	MAPPNG	TECHNQUES	‘06
65

65

Lidar Basics for Mapping Applications

By James D. Giglierano

Iowa Geological Survey

Iowa Department of Natural Resources

109 Trowbridge Hall

Iowa City, IA 52242-1319

Telephone: (319) 335-1594

Fax: (319) 335-2754

e-mail: jgiglierano@igsb.uiowa.edu

INTRODUCTION

The purpose of this article is to help newcomers understand the basics of lidar data collection and processing,

especially non-engineering, mapping specialists such as

geologists, soils scientists, and those interested in land

cover characterization. Many states in the U.S. are embarking on large-scale lidar acquisitions. This will make

lidar elevation and other derived products widely available to many different audiences. To make full use of this

new source of information, mappers must have or acquire

some knowledge of the lidar data collection and handling

procedures, and have the capability to convert the vendor

supplied files into useful products. In some cases, mappers will do the processing themselves; in others, they

will opt to have the processing performed by a vendor or

third party. Another case may be that lidar derived topographic data supplied by a local government entity will

have no metadata. In this case, the user will have to make

some educated guesses as to the type of processing that

may have been performed on the data.

HOW LIDAR DATA ARE COLLECTED 

AND REPRESENTED

The term LiDAR is an acronym for Light Detection and Ranging. Light Detection and Ranging basically

consists of a laser rangefinder that operates in some form 

of airborne platform (helicopter, plane, or satellite). The

rangefinder takes repeated measurements of the distance from the platform to the ground. The position and

elevation of the platform is precisely known by way of

airborne GPS along with ground control, so the elevation of the ground surface can be calculated by subtracting the laser rangefinder distance from the height of the 

platform. Compensation must be made for the tilt and

pitch of the airborne platform by way of gyroscopes and

accelerometers in the aircraft’s inertial measurement unit.

A good technical overview of lidar scanning technology is

provided by Wehr and Lohr (1999).

Lidar systems record thousands of highly accurate

distance measurements every second (newer systems

operate at frequencies up to 150 kHz; older systems 30-80

kHz) and create a very dense coverage of elevations over

a wide area in a short amount of time. Because lidar is

an active sensor that supplies its own light source, it can

be used at night and, thus, avoid routine air traffic, or it 

can be flown under some types of high cloud conditions. 

Most lidar systems record multiple surface reflections, or 

“returns,” from a single laser pulse. When a laser pulse

encounters vegetation, power lines, or buildings, multiple

returns can be recorded. The first return will represent 

the elevation near the top of the object. The second and

third returns may represent trunks and branches within a

tree, or understory vegetation. Hopefully, the last return

recorded by the sensor will be the remaining laser energy

reflected off the ground surface, though at times, the tree 

will block all the energy from reaching the ground. These

multiple returns can be used to determine the height of

trees or power lines, or give indications of forest structure

(crown height, understory density, etc.). Figure 1 shows a

single 2 x 2 km tile that consists of 3.3 million first return 

lidar points.

Another feature of an airborne lidar system is the use

of mirrors or other technology to point the laser beam

to either side of the aircraft as it moves along its path.

Depending on the scanning mechanism, the lidar scans

can have a side-to-side, zigzag, sinusoidal, or wavy pattern. While the laser itself pulses many thousands of times

per second, the scanning mechanism usually moves from

side-to-side at around 20-40 cycles per second. This scanning, combined with the forward motion of the aircraft,

produces millions of elevations in a short distance and

time. The field of view or angle the scan makes from 

side-to-side can be adjusted by the operator, but is usually

set at 30 to 40 degrees. This creates a swath of around 1

kilometer wide or less. Adjacent swaths overlap from 15

to 30% so that no data gaps are left between flight lines.

The spacing of lidar points on the ground, called

“postings,” is a function of the laser pulse frequency, scan
66	 DGTAL	MAPPNG	TECHNQUES	‘06

frequency, and flight height (Baltsavias, 1999). While 

there is usually a nominal posting spacing specified in a 

lidar project, actual data points have variable spacing that

are smaller and larger than the specified spacing. Mappers need to be aware of these effects when viewing final 

products that were derived from the raw data. The second

aspect is that, because the laser scans from side to side,

it interacts with the ground in different ways, depending

on the angle of incidence. Lidar pulses at the edge of a

scan will strike the sides of buildings, whereas pulses at

the center of a scan will only strike the roof tops. Likewise, pulses at the edges of scans will pass through trees

at an angle. Sometimes this will create “shadows” on the

other side where no lidar passes through. In addition, less

energy will return to the lidar receiver as it reflects away 

from the aircraft. This is evident in the intensity images

created from the intensity values for each return: one

can see overall darkening of the intensity at the edges of

swaths. Edges of swaths appear darker than the returns at

centers of swaths.

HOW LIDAR POINTS ARE PROCESSED 

INTO TINS AND DEMS

In the Spring of 2005, the Iowa Department of Natural Resources (DNR) and others, had lidar with a nominal

resolution of 1 meter collected by a commercial vendor

over the Lake Darling watershed located in Washington

County, Iowa. The vendor’s lidar system collected a first 

and last return from each lidar pulse. From the first and 

last returns, a so-called “bare earth” return was created

using a proprietary classification algorithm developed by 

the vendor. These classification systems try to sort out 

non-bare earth returns (tree tops, buildings, power lines,

automobiles) from bare earth returns. To distinguish bare

earth in forested areas, differences in elevation between

the first and last returns, relative changes in elevation, 

and slope were used. Intensity data were used to identify

vegetation and man-made materials. The lidar data for

the Lake Darling watershed were collected in April with

mostly leaf-off conditions. There are some data voids in

forested areas due to non-penetration of the laser through

tree canopy, but these areas are generally less than 10

meters across and are easily filled in by interpolation. 

Leaf-on conditions and tall crops, such as corn, do not

allow easy penetration of the laser beam to the ground and

should be avoided.

Lidar data for the Lake Darling area were supplied

by the vendor in ASCII text format, consisting of 2 x 2

kilometer tiles with x and y coordinates, z elevations, and

intensity values. With a nominal 1 meter posting spacing, some tiles had up to 3.3 million points. Postings near

the center of the flight lines were close to the nominal 1 

meter spacing (Figure 2), while toward the ends of scans,

the points converge with the start of the next scan (Figure

3). While some scans converge, others diverge. Where

the scans converge, the points can be less than half of

the nominal spacing, and likewise, where they diverge,

they can be twice the nominal spacing. Because some

points can be as close as 0.5 meters, the tiles were initially

interpolated to create grids with 0.5 meter resolution, with

the idea that no data points should be merged or averaged

with any other points. There is a tendency among some

users to create grids with resolutions of 3, 5, and even

10 meters to save storage space or reduce the volume of

data to process. We desired to create the grids as close as

possible to the native resolution of the lidar data to evaluate their full potential to represent the smallest surface

features.

To make digital elevation models (DEM) from the

tiles, the Surfer 8 software (http://www.goldensoftware.

com/products/surfer/surfer.shtml) was used. This software

first creates a triangulated irregular network (TIN) before 

it interpolates the points into a raster DEM; however, once

the DEM tiles were initially put together into mosaics, it

became obvious that there were noticeable gaps between

each tile. To remedy this, a C program was created to

sort through the ASCII text files of the adjacent tiles and 

find points within a 3 meter buffer of the edge of the tile 

to be processed. Then the tiles were reprocessed adding

the 3 meter buffers. When these raster tiles were merged

together into a mosaic, the gaps were almost completely

eliminated. Leica Imagine (http://gi.leica-geosystems.

Figure 1. Gray scale image consisting of 3.3 million lidar

first return points or “postings.” First returns indicate the 

tops of trees and buildings as well as bare ground in open

areas. White areas are data voids where no returns were

recorded, usually due to non-reflecting water surfaces.
LDAR	BASCS	FOR	MAPPNG	APPLCATONS 67

Figure 2. Data near the middle of a lidar flight line. Posting	spacing	is	around	one	meter	at	the	center	of	back	and	

forth	scans.

Figure 3. Data along overlap of two adjacent lidar flight 

lines (top and bottom of figure). The posting spacing is 

highly variable at edges of flight lines. Some postings are 

less	than	one	meter	apart	at	the	end	of	one	and	beginning	

of	the	next	scan,	while	the	distances	between	points	in	different	sets	of	scans	are	as	much	as	1.5	meters	apart.

com/)	was	used	to	mosaic	all	the	tiles	into	one	large	raster	

DEM file. From the DEM, shaded relief images were 

created	and	compressed.	DEMs	and	shaded	relief	images	

were	then	easily	imported	into	ArcGS	software	(http://

www.esri.com/)	for	display	and	further	analysis.

Field	examination	of	the	lidar	bare	earth	shaded	relief	

images	was	conducted	in	January,	2006.	t	was	surprising	how	well	lidar	shaded	the	relief	images	represented	

the	smallest	topographic	features,	including	small	slope	

changes	of	less	than	half-a-meter,	even	in	forested	areas.	

There	were	some	data	voids	due	to	lack	of	penetration	

through	the	dense	tree	canopy,	but	there	were	enough	data	

points to show good definition of incised stream channels, 

meander	scars,	and	gullies	(Figure	4).	Man-made	features	

such	as	road	ditches	and	embankments,	terraces,	and	

dams were also well defined. Tillage patterns are evident 

as regular lineated textures on crop fields parallel to the 

road	grid.	These	are	not	scanning	artifacts	as	the	individual scans are at a slight angle to the east-west flight lines.

Because	the	bare	earth	processing	does	not	remove	

100%	of	the	forest	artifacts,	a	distinctive	bumpy	pattern	

is	left	in	the	bare	model,	which	indicates	the	presence	of	

forest cover (Figure 5). During field examination, it was 

noticed	that	different	canopy	structures	were	represented	

by	different	patterns	in	the	artifacts.	n	the	tall	canopy	

floodplain forest, most of the bumps were removed, which 

left	a	mostly	smooth	surface,	while	on	side	slopes	with	

thick	understory	or	brush	cover,	the	texture	on	the	shaded	

relief	image	is	rougher	in	appearance.	nterestingly,	the	

bare	earth	processing	removed	nearly	all	of	the	numerous	

tree	falls	in	the	stream	channels,	which	allows	drainage tracing programs to work well when following flow 

paths	downstream.	Also,	areas	with	pine	trees	were	very	

smooth,	which	indicated	nearly	complete	penetration	by	

the	laser	beam.

HOW TO USE LIDAR PRODUCTS FOR 

MAPPING APPLICATIONS

Once	the	raw	lidar	point	tiles	are	processed	into	

high-resolution	DEMs,	other	useful	mapping	products	can	

be	derived.	The	derived	shaded	relief	image	previously	

mentioned	(Figure	4)	is	very	useful	for	visual	display	and	

interpretation,	and	can	be	combined	with	colorized	elevation	images	for	extra	information	content.	Another	useful	

display	product	is	the	slope	map,	which	can	be	derived	

from	the	DEM	using	the	grid	processing	tools	found	in	

almost	every	GS	package.	Usually,	a	choice	can	be	made	

whether	to	calculate	the	slope	rate	in	degrees	or	as	percent	

(45	degree	slope	=	100%).	A	slope	map	based	on	percent	

can	be	grouped	into	slope	classes	typically	used	by	soil	

survey	mappers	(slope	class	A	=	0-2%,	B	=	2-4%,	etc.)	

and	readily	compared	to	soil	polygons	displayed	by	slope	

class	(Figure	6).	Figure	7	shows	the	new	level	of	detail	

available	in	slope	classes	derived	from	lidar	data.	

n	addition	to	the	elevation	component	of	the	lidar	

return,	many	systems	produce	an	intensity	component	that	

indicates	the	strength	of	the	lidar	return.	This	intensity	

value is mostly influenced by the reflectance of the material struck by the laser pulse, but is also influenced by the 

scan	angle.	(Laser	pulses	directed	away	from	the	airplane	

at significant angles do not reflect back as much light 

energy	as	a	pulse	directed	straight	down	from	the	plane).	

Because	most	lidar	systems	use	a	laser	that	emits	light	in	
68	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 4. Portion of the bare earth shaded relief image of the Lake Darling watershed, showing

natural and man-made features readily apparent in the lidar data.

Figure 5. Portion of a bare

earth shaded relief image

showing artifacts (bumpy

texture) in deciduous forest areas. These artifacts

are lidar elevations classified as bare earth, but 

probably are from tree

trunks, branches, or understory close to the ground

and classified as bare earth 

by the vendor’s algorithm.
LDAR	BASCS	FOR	MAPPNG	APPLCATONS 69

Figure 6. Soil survey soil polygons shaded by slope class range: light shades are lower slopes and

darker shades indicate steeper slopes.

Figure 7. Slope class ranges derived from Lake Darling lidar data. While low slope areas on

lidar look similar to the soil polygons, lidar shows more detail on steep slopes such as gullies and

stream channels.
70	 DGTAL	MAPPNG	TECHNQUES	‘06

the	near-infrared	portion	of	the	spectrum	(lidar	used	for	

Lake	Darling	had	a	wavelength	of	1064	nm),	the	intensity	of	lidar	return	is	directly	related	to	the	near-infrared	

reflectance of the target material. An image constructed 

from	the	intensity	component	of	the	returns	(Figure	8)	

looks	very	much	like	a	black	and	white	near-infrared	

aerial	photograph.	An	intensity	image	has	one	interesting	

peculiarity: tree shadows point away from the flight lines, 

so	one	can	see	shadows	pointing	in	opposite	directions	

close together at the edge of two flight lines. Because 

intensity	is	recorded	from	each	lidar	return,	it	is	possible	

to construct first return intensity images as well as last 

return	intensity	images,	and	have	them	look	quite	different.	This	may	especially	occur	in	forested	areas	where	the	

first return might mainly represent the treetops, while the 

last	return	intensity	represents	many	features,	including	

the forest floor.

VERTICAL ACCURACY TEST AND 

INFLUENCE OF LAND COVER

Usually, one of the first questions new users of lidar 

have	is	about	the	vertical	accuracy	of	the	elevation	data.	

n	the	Lake	Darling	project,	the	stated	accuracy	was	15	

cm	(.5’)	RMSE	(root	mean	square	error)	in	bare	earth	

areas	and	37	cm	(1’)	in	vegetated	areas.	Because	there	are	

no	high	accuracy	geodetic	monuments	in	the	watershed	

and access to survey grade GPS equipment was unavailable, another way to test the vertical accuracy needed

to be found. Fortunately, a digital terrain model and

associated 2’ contours produced by aerial photography

and photogrammetric techniques for a road project were

available from the Washington County engineer’s office. 

This digital terrain model and contours were created by a

local aerial photography firm and had a stated vertical accuracy of 6.1 cm (.2’). The area covered by the model was

over 2 miles long and a quarter of a mile wide. The digital

terrain model consisted of elevation points and break lines

(Figure 9) in CAD format. Using the 3D_ANALYST

extension in ArcGIS, the photogrammetrically derived

terrain model was converted into a triangulated irregular

network or TIN, and interpolated into a 1 meter elevation

grid. The lidar elevation grid was subtracted from the grid

made by photogrammetry to produce a simple difference

grid. The overall average difference between the two grids

was only 3.3 cm (.11’). To compare the two grids to their

stated accuracies, the RMSE had to be calculated. First,

the simple difference grid was multiplied by itself to create the squared difference grid. Using a polygon coverage

of land cover from 2005, the mean squared difference

was calculated for each land cover class using the zonal

statistics command in ArcToolBox. By using the spatial

calculator function in the SPATIAL_ANALYST extension, the square root of the values in the “mean” field 

Figure 8. Portion of a lidar intensity image of the Lake Darling watershed, constructed from bare

earth return intensity values.
LDAR	BASCS	FOR	MAPPNG	APPLCATONS 71

of the table was calculated, and the RMSE was found

for each land cover class. The zonal statistic tool also

computes a “count” of cells for each class and a “sum” of

the elevations within that class. By calculating the sum of

all the “count” field values and “sum” field values for all 

the classes, and dividing the total sum by the total count,

the average squared difference for the entire dataset was

found. By taking the square root of this value, the RMSE

was found for the whole area. Initially, RMSE between

the lidar DEM and the photogrammetry DEM was found

to be .79 feet or 24.1 cm.

Upon examination of the squared difference image, it was apparent that the terrain in several areas had

changed significantly between the time of the airphoto 

flight in 2000 and the lidar flight in 2005. These mainly 

included areas where the installation of sediment retention structures and dams, and road grading had occurred.

When these areas were digitized and excluded from the

squared difference calculation, the overall RMSE was

found to be .57 feet or 17.4 cm (Figure 10). The RMSE

of the row crop area was .46’ (14.3 cm), grass areas .62’

(18.9 cm), and forested areas .85’ (25.8 cm). If the DEM

derived by photogrammetric means is accepted as the

higher accuracy source, then the lidar meets its stated

accuracy of 15 cm in the bare ground areas, and under 37

cm in the vegetated areas. This appears to be a good test

of lidar accuracy because it includes many types of land

cover conditions, not just a few high accuracy locations at

benchmarks on roads or nearby ditches.

COMPARING OLD AND NEW DATA

One of the first tests of any new lidar data set is to 

compare it with the existing DEM derived from the 10’

contours from the USGS topographic quadrangle mapping

projects of the latter half of the last century. Displayed at

smaller scales, it is difficult to see much difference between the shaded relief images derived from the 30 meter

resolution National Elevation Dataset or NED (http://ned.

usgs.gov/) and lidar shaded relief. Only when the display is zoomed into larger scales is it possible to see the

marked differences between the 30 meter NED (Figure

11) and lidar DEM (Figure 12). Visible on the lidar image

(but not on the 30 meter NED shaded relief image), are

man-made features such as roadways, ditches, fence lines,

terraces and dams. Natural features such as stream channels, gullies, and floodplains are also visible.

Lidar excels at mapping topographically challenged

areas: areas with little relief. Figure 13 is a shaded relief

image, derived from the 30 meter NED, that shows typical glaciated terrain in north-central Iowa, east of Spirit

Lake in Dickinson County. Figure 14 shows the same area

using 1 meter resolution lidar, which focuses the indistinct mounds seen on the NED shaded relief into sharply

defined, circular, and elongated features. These are interpreted to be remnants of ice walled lakes, which formed

on the surface of the glacier. These lakes had varying

amounts of sediment deposited in them, and after the ice

melted, these sediments formed indistinct, low mounds

(Quade et al., 2004).

Figure 15 shows the Missouri River floodplain north 

of Council Bluffs, Iowa, in a view, which again, uses the

30 meter resolution NED to create a shaded relief image.

It reveals numerous defects in the original conversion of

widely spaced contours on a very flat surface. With a 10’ 

contour interval, there is not enough information to interpolate features on the floodplain adequately. The shaded 

relief image reveals cross-shaped artifacts within the

DEM, which were created by the interpolation software’s

attempt to connect widely spaced data. Figure 16 shows

the great improvement afforded by interpolating a surface

from closely spaced lidar points (about 2 meter lidar postings). Missouri River meander scars, levees along drainage ditches, fence lines, interstate lanes, railroad right-ofways, borrow pits, and sewage lagoons are all visible on

the lidar shaded relief image.

When using shaded relief images for on-screen

digitizing, geological mappers will need to become accustomed to recognizing and separating man-made as well

Figure 9. Portion of shaded relief digital terrain model

derived from low altitude aerial photos. The black dots are

elevation mass points and the black lines are break lines.
72	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 10. Root mean square error (RMSE) calculation of photogrammetrically derived DEM and

lidar DEM, after 2000/2005 landscape-change areas removed from calculation.

Figure 11. Portion of a shaded relief image made from

a National Elevation Dataset (NED) 30 meter resolution

DEM. Area is from Lake Darling watershed in Washington County, Iowa.

Figure 12. Portion of shaded relief image made from a 1

meter resolution lidar DEM for the same area in Washington County, Iowa.
LDAR	BASCS	FOR	MAPPNG	APPLCATONS 73

Figure 13. Portion of

a shaded relief image

showing recently glaciated

terrain near Spirit Lake in

Dickinson County, Iowa.

The shaded relief was

created from a 30 meter

resolution DEM from the

National Elevation Dataset

(NED).

Figure 14. Portion of a

lidar-derived shaded relief

image of the same area of

glacial terrain near Spirit

Lake in Dickinson County,

Iowa. Notice how the

shapes of subtle, low relief

glacial features are now

readily apparent.
74	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 15. Portion of

a shaded relief image

showing the Missouri

River floodplain north of 

Council Bluffs, Iowa. The

shaded relief image was

created from a 30 meter

resolution DEM from the

National Elevation Dataset

(NED). Notice the crossshaped features that are

artifacts of the interpolation of the original 10’

contours from USGS topographic maps.

Figure 16. Portion of a

lidar-derived shaded relief

image of the same area

on the Missouri River

floodplain (see Figure 15). 

Notice the much finer detail showing the interstate

cloverleaf, river meander

scars, borrow pits, and a

ditch and levee system.

Lidar DEM was obtained

from the Pottawattamie

County GIS Department.
LDAR	BASCS	FOR	MAPPNG	APPLCATONS 75

as	geomorphic	features.	Because	shaded	relief	images	

can	represent	the	encoding	of	relatively	small	changes	in	

slopes,	mappers	will	need	to	build	up	criteria	for	recognition	of	everyday	features	using	the	clues	in	contrast,	

shading,	shape,	texture,	pattern,	and	context	contained	in	

these	images.	n	the	past,	geological	mappers	learned	how	

to	interpret	aerial	photos	by	poring	over	example	after	

example	of	natural	and	man-made	features.	They	also	

learned	how	to	interpret	geological	features	by	looking	at	

geomorphic	signatures	on	topographic	maps.	Lidar	will	

require	relearning	and	reinventing	both	techniques	by	

moving	the	geomorphic	scale	down	to	the	realm	of	the	

airphoto,	roughly	at	resolutions	from	1	to	5	meters.	While	

qualitative	information	on	slopes	was	available	by	way	of	

stereo	viewers	and	aerial	photos,	there	has	never	been	as	

much	quantitative	slope	information	available	until	now	

with	the	advent	of	lidar	data.	With	digital	elevation	data	

derived from lidar, new computer assisted classification 

strategies	for	geomorphic	feature	interpretation	can	be	developed,	as	can	new	types	of	imagery	to	support	manual	

interpretations.

SUMMARY

Large-scale	lidar	acquisitions	will	provide	mapping	professionals	with	an	increase	of	new,	high	quality	

elevation	data	to	use	as	base	maps	for	their	projects.	To	

take	full	advantage	of	this	new	data	source,	those	who	are	

mapping	need	to	be	aware	of	how	lidar	data	are	collected	

and	what	data	reduction	processes	commercial	vendors	

use	to	make	deliverable	products	for	their	clients.	n	many	

cases,	mappers	will	want	to	manipulate	the	raw	lidar	

returns	into	their	own	TNs,	DEMs,	and	derived	products,	

but	sometimes	they	will	only	have	access	to	vendor-supplied, finished products that have undergone unknown 

procedures	to	make	the	visual	appearance	more	appealing.	Mappers	can	use	shaded	relief	images	derived	from	

lidar	DEMs	or	TNs	for	on-screen	digitizing,	as	well	as	

new	derivative	products	such	as	terrain	slope	and	lidar	

intensity	to	identify	geologic	features	and	other	features.	

Anyone	using	lidar	data	will	be	interested	in	the	absolute	

vertical	accuracy	of	elevations	and	will	need	to	know	how	

land	cover	type	affects	that	accuracy.

REFERENCES

Baltsavias,	E.P.,	1999,	Airborne	laser	scanning:	basic	relations	

and	formulas:	SPRS	Journal	of	Photogrammetry	and	Remote	Sensing,	no.	54,	p.	199-214.

Quade, D.J., Giglierano, J.D. and Bettis, III, E.A., 2004, Surficial	geologic	materials	of	the	Des	Moines	Lobe	of	owa,	

Phase	6:	Dickinson	and	Emmett	Counties:	DNR/GS,	

OFM-0402,	1:100,000	scale,	accessed	at	http://www.igsb.

uiowa.edu/gsbpubs/pdf/ofm-2004-2.pdf.

Wehr,	A.	and	Lohr,	U.,	1999,	Airborne	laser	scanning	–	an	introduction	and	overview:	SPRS	Journal	of	Photogrammetry	

and	Remote	Sensing,	no.	54,	p.	68-82.
76	 DGTAL	MAPPNG	TECHNQUES	‘06
77

77

Geological Map Database – A Practitioner’s Guide

to Delivering the Information

By Jeremy R A Giles

British Geological Survey

Kingsley Dunham Centre

Keyworth

Nottingham, United Kingdom

NG12 5GG

Telephone: +44(0)115 936 3100

Fax: +44(0)115 936 3200

e-mail: jrag@bgs.ac.uk

INTRODUCTION

The British Geological Survey (BGS) has been

practicing geological mapping since 1835. One would

think that we should be getting quite good at it by now.

The simplistic view is that the geology doesn’t change,

or at least not very quickly, so why is the job not done?

There are two related answers to that question. The first 

is that our understanding is continually improving, so that

we can know more about any given area. The second is

that the demands upon the outputs of geological surveying are ever increasing. In his Presidential Address to the

Geological Society of London in 1836, Sir Charles Lyell

explained the process of setting up the world’s first national geological survey “to cover the cost of geologically

coloring the topographical maps of the trigonometrical

Survey.” He said: “...we drew up a joint report in which

we endeavoured to state fully our opinion as to the great

advantages which must accrue from such an undertaking not only as calculated to promote geological science,

which would alone be a sufficient objective, but also as a 

work of great practical utility bearing on agriculture, mining, road-making, the formation of canals and railroads

and other branches of national industry”.

Those demands have now grown considerably as the

number and variety of the branches of national industry

has grown and developed. Roger Tym & Partners estimated in November 2003 that: “the total value added of total value added of

national output to which BGS contributes for 2001 lies

in the range of $64 billion – $116 billion, representing

around 5%–8% of total UK output (GVA). This is of

course orders of magnitude greater than BGS’s annual

turnover of approximately $75 million.”

The aims of BGS geological mapping are stated in

Walton and Lee (2001). They said: “the key objectives

of the programme are to (i) deliver high quality detailed

information on bedrock and superficial geology of the UK 

landmass as digital, map-based and text data, (ii) provide

increased information on Quaternary and other superficial deposits, (iii) provide increased understanding of 

three-dimensional structure and process, (iv) deliver the

near-surface component of the Digital Geoscience Spatial

Model (DGSM) and (v) deliver the remaining ‘sheets’ in

the current programme for incorporation into the Digital

Geological Map of Great Britain (DiGMapGB).”

To achieve these stated aims, BGS has been progressively developing the key components of a geological

mapping system (Figure 1). This multi-component system

has been developed by a large team of scientists and developers (see Acknowledgements). The key components

of the system, which are described below, are as follows:

•	 BGS-geoIDS and the associated databases and data

stores in which BGS information is managed

•	 NGDC

•	 SIGMA

•	 LithoFrame

•	 Internal Discovery

•	 Internet

•	 DiGMapGB

•	 GeoSure

BGS GEOSCIENCE INTEGRATED

DATABASE SYSTEM (BGS-geoIDS)

BGS is the custodian of a wealth of geoscience information that has been collected by its own scientists or

deposited by industry under various government statutes

and voluntary agreement. Acquisition of this information

has been continuous since the Survey’s formation in 1835,

and material created prior to this date is also stored in its

archives. The range of information types includes materials (such as rocks, fossils, minerals, and borehole core),

paper records, microfiche, reports, digital databases, digi-
78	 DGTAL	MAPPNG	TECHNQUES	‘06

tal files, digital models, etc. A range of systems and tools 

has been developed to manage these information assets in

a holistic manner.

Peebler (1996) made the following observation:

“Lack of basic data integration costs the average E&P

professional a considerable amount of time. According to

various estimates geoscientists and engineers spend from

20% to 30% of their total project time searching for, loading and formatting data.”

Similarly, Adam Dobson (Pers Comm: 2002), representing Shell, a major international oil company, said that

an internal audit undertaken in 2002 showed that, for new

frontiers areas, staff spent their time as follows:

• 	 Finding data – 53%

• 	 Archiving data – 9%

• 	 Documenting the data – 15%

• Interpreting (adding value) – 23%

On the basis of this audit Shell set a target of reducing the time spent finding data to 30% and increasing the 

adding-value time to 46%.

Several years earlier the BGS-geoIDS Project had

been established to resolve a number of similar problems. BGS recognized that it held a wealth of valuable

and important data, but that this resource was largely

inaccessible to staff. Some of the data might be held

and managed in well designed databases, but these were

isolated “islands of excellence.” There was little interoperability between these islands, so that routine integration

and onward use or enhancement of the data were rarely

straightforward. There were few corporate standards, and

the local standards that did exist were not shared between

databanks. BGS had no maintained metadata, so most

BGS staff had no idea what data were held corporately,

how these data might be used, or what their quality was.

Finally, there were no corporate application standards, so

data were accessed through the use of a multitude of different tools that had been built with no consistent design

standards and no thought for future interoperability.

Key drivers recognized in planning and undertaking

the BGS-geoIDS work were the opportunities to

• reduce staff effort in finding data,

Figure 1. Geological Mapping Implementation (BGS-geoIDS – BGS Geoscience Integrated

Database System; GDI – Geoscience Data Index; GLOS – Geoscience Large Object Store;

GSD – Geoscience Spatial Database; GSF – Geoscience Spatial Framework; GSI3D – Geological

Surveying and Investigation in 3D; IDA – Intranet Data Access; MIDAS – Mobile Integrated Data

Acquisition System; NGDC – National Geoscience Data Centre; SIGMA – System for Integrated

Geospatial Mapping).
GEOLOGCAL	MAP	DATABASE	–	A	PRACTTONER’S	GUDE	TO	DELVERNG	THE	NFORMATON 79

•	 make	quality-assured quality-assured	data	availableavailable	to	staff	and	

customers,

•	 encourageencourage	and	facilitatefacilitate	collaboration collaboration	across	BGS,

•	 improve	access	to	the	unique	BGS	informationinformation	

base,

•	 keep	BGS	at	the	forefrontforefront	of	the	development development	of	

digital	geoscience	systems,

•	 inform	and	support	management management	decisions,decisions,

•	 create	and	implementimplement	corporatecorporate	standardsstandards	and	

establish	best	practice

The	BGS-geoDS	Project	produced	a	range	of	deliverables,	including	a	CorporateCorporate	Data	Policy,	a	system	for	

data	management	planning,	metadata	at	various	levels,	a	

documented	corporate	data	model,	an	application	standard	

implemented	through	an	ntranet	data	access	tool,	and	

the	adoption	of	BGS-wide	best	practice.	Above	all,	the	

Project imposed a significant culture change within BGS, 

transforming	data	from	being	personal	property	to	being	

corporate	property.

Subsequently,	the	DGSM	(Digital	Geoscience	

Spatial	Model)	Project	extended	the	data	management	

system	to	deal	with	digital	3D	models	and	introduced	the	

Geoscience	Large	Object	Store	(GLOS)	to	hold	the	full	

models	in	their	proprietary	software	format	with	associated	metadata.	However,	it	was	recognized	that	the	various	proprietary	software	formats	were	unlikely	to	remain	

unchanged	and	could	not	safely	be	used	for	archiving	the	

models.	Such	models	would	probably	have	a	life	expectancy	of	less	than	10	years.	Thus,	a	second	component	was	

introduced,	which	“sampled”	the	model	and	produced	a	

series	of	X,	Y,	Z	coordinates	for	each	stratigraphical	horizon	represented	in	the	model.	As	this	information	is	stored	

as a simple digital file, it is more suitable for long-term 

preservation.

•

All	BGS	corporate	data	are	managed	through	the	

National	Geoscience	Data	Centre,	which	is	the	Natural	

Environment	Research	Council	(NERC)	designated	center	

for	geoscience	data	and	information	management,	and	

which has five main elements:

•	 NationalNational	GeoscienceGeoscience	Records	Centre

•	 NationalNational	GeoscienceGeoscience	MaterialsMaterials	CollectionCollection

•	 NationalNational	Hydrocarbons Hydrocarbons	Data	Archive

•	 NGDC	Earth	Science	AcademicAcademic	Archive

•	 NGDC	Digital	Data	Management Management

The	top	level	aim	of	the	NGDC	is	to	manage	all	

BGS	data	and	information	in	accordance	with	the	NERC	

and	BGS	Data	Policies.	NGDC	staff	members	manage	a	

wide	range	of	information	types,	aiming	to	preserve	them	

for	use	by	future	generations.	Thus,	the	environments	in	

which	the	collections	are	held	are	monitored	carefully,	

and	action	is	taken	to	manage	environments	where	conditions	fall	outside	accepted	norms.	For	example,	localised	

high	humidity	in	a	room	holding	a	palaeontological	

collection	triggered	an	investigation	that	discovered	a	

fractured	rain	waste	pipe	on	the	exterior	of	the	building.	

A	robust	metadata	system	is	recognized	as	being	indispensable,	and	an	active	program	of	metadata	management	

is	operated	within	the	NGDC.	Where	appropriate,	digital	

indexes are created and maintained as aids for finding 

individual	records	or	specimens.

Overall,	the	NGDC	activities	attempt	to	strengthen	

users’ confidence by creating and maintaining validated 

and verified datasets to agreed standards, and providing	tools	that	enable	geoscientists	and	others	who	need	

geoscientific insight, both inside and outside BGS, to use 

BGS information with confidence.

SYSTEM FOR INTEGRATED 

GEOSPATIAL MAPPING (SIGMA)

http://www.bgs.ac.uk/scripts/downloads/start.cfm?id=381

Once	the	key	elements	of	information	that	underpin	

geological	mapping	are	in	place	and	tools	have	been	

provided	to	facilitate	access,	it	becomes	possible	to	build	

a digital geological workflow that starts with digital field 

capture,	progresses	through	digital	map	compilation,	and	

passes	into	a	digital	map	production	and	management	system. The project that specified and developed this process 

is	called	SGMA.	The	project	has	two	key	elements,	the	

first being the MIDAS system (Mobile Integrated Data 

Acquisition	System),	the	second	the	Geoscience	Spatial	

Database	(GSD).

Mobile Integrated Data Acquisition System 

(MIDAS)

Designed to allow use in the field, each MIDAS setup	is	mounted	on	a	weather	proof,	robust,	impact-resistant	

computer.	t	is	based	on	standard	ESR	software	that	has	

been customized to meet the project’s specific requirements. The field geologist’s base map and an analysis of 

existing	information	are	loaded	onto	the	computer,	and	

the GIS provides digital field slip functionality. A global 

positioning	system	is	used	to	locate	the	sites	of	observations,	and	forms	are	called	up	to	support	the	population	of	

a	Microsoft	Access	Database	with	a	range	of	information.

Geoscience Spatial Database (GSD)

Once the geologist returns from the field, the GSD 

is	used	to	compile	the	geological	map	from	existing	

information	and	from	the	data	captured	by	the	MDAS	

system.	The	product	is	a	traditional	geological	standard	
80	 DGTAL	MAPPNG	TECHNQUES	‘06

map that is created and managed in the digital environment along with its accompanying digital databases. This

suite of information is then passed into the cartographic

map production system for final delivery in the form of 

the DiGMapGB product or as printed maps.

LITHOFRAME

http://www.bgs.ac.uk/scripts/downloads/start.cfm?id=535

Merger of aspects of the data captured by the digital

geological mapping workflow with appropriate digital 

information managed by the NGDC permits the creation

of three-dimensional models of the geology of the whole

or part of Great Britain. For example, the BGS receives

approximately 50,000 borehole logs a year from industry.

These range from shallow construction industry boreholes

to deep energy exploration boreholes. The borehole logs

are scanned and the metadata entered into the appropriate

database by the NGDC registration team. The borehole

logs are then available for use, on every desktop in the

BGS, and can be accessed through a range of application

and GIS tools. Two principal tools are used to undertake the modeling. The first is GSI3D, which is used for 

modeling superficial deposits, and the second is GoCAD, 

which is used for bedrock modeling. LithoFrame models

are prepared at various resolutions:

• LithoFrame – shows the most significant stratigraphical divisions and major faults

• 	 LithoFrame250 – prepared for stratigraphical

groups

• 	 LithoFrame50 – modelled at the formation level

• 	 LithoFrame10 – focuses on well-characterized and

relatively shallow superficial deposits

Geological Surveying and Investigation in 3D 

(GSI-3D)

http://www.bgs.ac.uk/science/3Dmodelling/gsi3d.html

The GSI-3D software tool and methodology has been

developed over the last decade by Dr Hans-Georg Sobisch

of INSIGHT Geological Software Systems GmbH, based

in Cologne. During the past 3 years, BGS has acted as a

test bed for the accelerated development of the tool and

methodology. GSI-3D utilizes a Digital Terrain Model,

surface geological linework, and downhole borehole

data to enable the geologist to construct cross sections

by correlating boreholes and the outcrops to produce a

geological fence diagram. Mathematical interpolation

between the nodes along the drawn sections and the limits

of the units produces a solid model comprising a stack

of triangulated objects, each of which correspond to one

of the geological units present. Geologists draw their

sections based on facts such as borehole logs correlated

by intuition	– the shape “looks right” to a geologist. This – the shape “looks right” to a geologist. This

“looks right” element draws on each geologist’s wealth of

understanding of earth processes, examination of exposures, and theoretical knowledge gathered during a career

in geology.

GoCAD

http://www.gocad.org/www/

GoCAD is the tool used in BGS for modeling bedrock geology, as it has additional features, such as fault

and fold handling capabilities, which GSI-3D lacks. The

GoCAD Research Program is run by the Computer Science Group of the National School of Geology in Nancy,

France. This project is currently undertaken in collaboration with the Institut National Polytechnique de Lorraine

and the Centre de Recherches Petrographiques et Geochimiques, France. The aim of the research program is to develop a new computer-aided approach for the modeling of

geological objects. This approach is specifically adapted 

to geophysical, geological, and reservoir engineering applications.

KNOWLEDGE DELIVERY

The knowledge created during the geological mapping process is delivered through a range of products and

services that are available from the BGS (see “Internal

Discovery” and “Internet”, in Figure 1). These include

not only the geological map itself but also the elements of

data that were used to develop the map. These products

include:

•	 The BGS Website

•	 The BGS Intranet

•	 Discovery Metadata

•	 GeoIndex

•	 GeoReport

•	 GeoRecord

•	 DiGMapGB

•	 GeoSure

Website

http://www.bgs.ac.uk/

The award winning BGS website is accessed extensively by a wide range of users. It caters specifically 

to the needs of various groups, ranging from children to

professional geoscientists. Its aim is to inform users about

BGS activities and provide them with access to information and data. The site offers well over 900 downloads, a

number set to rise to well over 10,000 in the near future.

There is access to a range of definitive data sources. For 

example the four BGS Rock Classification Scheme (RCS) 
GEOLOGCAL	MAP	DATABASE	–	A	PRACTTONER’S	GUDE	TO	DELVERNG	THE	NFORMATON 81

reports are available from http://www.bgs.ac.uk/bgsrcs/

home.html. The BGS Rock Classification Database can 

be accessed and searched using a web-based form at

http://www.bgs.ac.uk/bgsrcs/searchrcs.html, and the data

that it holds can be downloaded in spreadsheet format at

http://www.bgs.ac.uk/data/dictionaries.html.

Intranet

In parallel to the BGS Website, there is a comprehensive Intranet that provides information and data to BGS

staff. Intranet information and data suites are normally

more comprehensive than their Internet equivalents.

Discovery Metadata

www.bgs.ac.uk/metadata

Published BGS discovery metadata can be accessed

as part of the main BGS Website. A profile of ISO19115 

is used to describe each dataset at a level that is appropriate for a user to assess whether the data contained in the

dataset are appropriate for their needs and to allow recognition of data limitations. The full record is posted on the

Internet, and the tools to manage the underpinning Oracle

database are on the Intranet.

Technical Metadata

The BGS Technical Metadata, which describe the

numerous components to the BGS Oracle database system, are only available on the Intranet. The information The information

provided includes details of databanks, tables, views, indexes, synonyms, etc., and the system as a whole contains

some of BGS’s most critical digital data.

As the Technical Metadata system is complex, it is

actively maintained to help users. The system extends

Oracle’s own data dictionary and is designed to help those

with a basic understanding of Oracle to navigate the objects that make up the BGS Data Architecture. Its application front-end also provides “Best Practice Guidelines

for Oracle Development,” procedures for changing the

structure of database objects and some documentation on

data models.

GeoIndex

http://www.bgs.ac.uk/geoindex/home.html

The GeoIndex provides detailed metadata about

selected datasets. Using a web-based GIS, it shows the

locations of data points within each featured dataset and

provides basic information about each data point. For example, the sites of geochemical stream sediment samples

are shown in the GIS along with a list of elements analyzed in each sample.

Geoscience Data Index (GDI)

The intranet version of the GeoIndex is the Geoscience Data Index (GDI). It is built in ESRI’s ArcGIS and

allows BGS staff to discover the availability of spatiallyreferenced information, drill down to it, and gain access to

it. It allows a rapid assessment to be made of information

and data that are available for any given location within

Great Britain.

Data Portal

The BGS Data Portal is an Intranet tool that allows

access to the datasets and information to support 3D modeling. Its functionality overlaps partially with that of the

GDI and the two applications will eventually be merged.

Intranet Data Access

Intranet Data Access (IDA) is an Intranet tool available to BGS staff to facilitate access to BGS Oracle

databases through a user-friendly forms interface. The

interface has been developed using Adobe’s ColdFusion,

and its components have been developed to a standard

design template so that users are presented with a consistent look and feel across the application. Examples of the

databases that can be accessed include:

•	 Borehole locations

•	 Borehole lithology

•	 Borehole materials (samples collected from boreholes)

•	 Geophysical log index

• Geological maps and field slips

•	 Palaeosaurus (paleontology specimen data)

•	 Britrocks (rock specimens)

GeoReport

http://shop.bgs.ac.uk/georeports/

If you are investigating land or property, GeoReports

could save you time and money. GeoReports will:

• 	 tell you about the condition of the ground—its geology, hydrogeology and any related hazards (such

as subsidence or radon potential),

• 	 let you know what information about your site

might already be held in the national geological

archive,

• 	 provide cost-effective access to expert advice from

BGS scientists who know about your local area.

There are a wide range of GeoReports that are available from the BGS Internet site, including:
82	 DGTAL	MAPPNG	TECHNQUES	‘06

•	 Building	stone	assessments

•	 Data	listings

•	 Geological	assessments

•	 Geological	map	extracts

•	 Ground	source	heat	pump

•	 Ground	stability

•	 Radon	protection

•	 Water	borehole	prognosis

GeoRecord

http://www.bgs.ac.uk/boreholes/home.html

The	BGS	provides	a	comprehensive	range	of	scanned	

documents,	including:

•	 Borehole	records

•	 Site	investigation	reports

•	 Technical	reports

•	 Mine	records

DIGMAPGB

http://www.bgs.ac.uk/products/digitalmaps/home.html

BGS	maps	are	increasingly	being	offered	digitally,	

either	as	raster	images	or	as	vector	data,	in	a	variety	of	

formats	and	structured	into	themes.	This	allows	them	

to	be	used	in	Geographical	nformation	Systems	(GS)	

where	they	can	be	integrated	with	other	types	of	spatial	

data	to	provide	powerful	aids	to	problem	solving	in	many	

earth	science	situations.	Map	data	are	availableavailable	at	a	range	

of	resolutions,	from	small	to	large	scale,	and	they	cover	

many	aspects	of	the	geological	and	related	sciences.

Layers	include:

• Superficial deposits

• Artificial ground

•	 Mass	movement

• Thickness of superficial deposits

•	 Bedrock	geology

•	 Elevation	of	bedrock

GEOSURE

http://www.bgs.ac.uk/products/geosure/home.html

Hazards	that	go	unrecognized	by	developers,	householders, or local government may lead to financial loss, 

which	can	be	avoided.	The	cost	of	arresting	or	repairing	a	

ground	failure	is	far	greater	than	the	cost	of	prevention.

Understanding geology is vital when determining the

stability (and thus the value) of land and property, and

ensuring the safety of its occupiers. The GeoSure datasets

from the British Geological Survey provide information about potential ground movement or subsidence in

a helpful and user-friendly format. The datasets can help

inform planning decisions and indicate potential causes of

subsidence:

• 	 Soluble rocks (dissolution)

• 	 Shrink-swell clays

• 	 Landslides (slope instability)

• 	 Compressible ground

• 	 Running sands

• 	 Collapsible deposits

As well as being able to license any of these datasets

in digital form, the BGS provides a report generation

service GeoReport, whereby reports can be produced giving details of six ground stability issues for specified areas 

or properties.

CONCLUSION

The BGS has aspired to develop a digital workflow 

for its data and information acquisition, management,

manipulation, and delivery. This aspiration is now approaching fulfillment. Tools that have been developed will 

allow digital capture of field mapping data and support its 

enhancement right through to its digital delivery.

Digital information created by the process is being

managed systematically across the entire organization in

a manner that allows its rapid discovery, retrieval, and

exploitation.

ACKNOWLEDGEMENTS

The BGS multi-component geological mapping system has been built by the efforts of a large team of people

including:

•	 Keith Adlam, GIS Expert

•	 Garry Baker, Deputy Programme Manager for

Information Management

•	 Brian Cannel, Webmaster

•	 Bill Hatton, Programme Manager for Information

Systems Development

•	 Andrew Howard, Programme Manager for National Geoscience Framework
GEOLOGCAL	MAP	DATABASE	–	A	PRACTTONER’S	GUDE	TO	DELVERNG	THE	NFORMATON 83

•	 Richard Hughes, Programme Manager for Information Delivery

•	 Ian Jackson, Programme Director for Information

•	 David Lowe, Geoscience Data Manager

•	 Hans-Georg Sobisch of INSIGHT Geological Software Systems

•	 Martin Nayembil, Data Architect

•	 Rob Pedley, Application Architect

•	 Jenny Walsby, Programme Manager for Information Products

REFERENCES

Peebler, R., 1996, Extended Integration: The Key To Future

Productivity Leap, Oil and Gas Journal May 20, 1996,

Vol.94. No.21.

Tym, R., 2003, The Economic Benefit of the BGS: British 

Geological Survey, 8pp, accessed at http://www.bgs.ac.uk/

about/economicbenefits.html.

Walton, G and Lee, M. K., 2001, Geology for our diverse

economy: report of the Programme Development Group

for Onshore Geological Surveys: Keyworth, Nottingham,

British Geological Survey, 99p.
84	 DGTAL	MAPPNG	TECHNQUES	‘06
85

85

Preservation of North Carolina Legacy Geologic and

Topographic Maps: A Cooperative Effort with the

North Carolina Geological Survey, North Carolina

State University and the Library of Congress

By Jeffrey C. Reid1, Jefferson F. Essic2, Steven P. Morris2,

Smitha Ramakrishnan2, and Julia L. Harrell3

1North Carolina Geological Survey

1612 Mail Service Center

Raleigh, NC 27699-1612

Telephone: (919) 733-2423

Fax: (919) 733-0900

e-mail: jeff.reid@ncmail.net

2North Carolina State University, Raleigh, D.H. Hill Library

e-mail: {jeff_essic, steven_morris, snramakr}@ncsu.edu

3North Carolina Department of Environment and Natural Resources

e-mail: Julia.Harrell@ncmail.net

Geologic	and	historic	maps	are	in	high	demand,	and	

are	critical	for	earth	science	instruction	and	research.	

Digital	georeferencing	of	scanned	maps	provides	new	

capabilities	not	possible	with	paper	maps	by	allowing	

other	data	to	be	overlain	by,	and	analyzed	with,	these	

map	images	using	GS	software.	Therefore,	geologic	and	

topographic	maps	for	North	Carolina	are	being	collected,	

scanned,	georeferenced,	and	preserved	in	a	collaboration	

between	the	North	Carolina	Geological	Survey	(NCGS)	

and	North	Carolina	State	University	Libraries	(NCSU	

Libraries).	The	legacy	geologic	and	topographic	maps	

have	no	digital	counterparts,	and	paper	copies	are	scarcely	

accessible.

Geologic	maps,	including	collars,	are	scanned	with	

a	large	format	(42-inch)	HP	Designjet	815	mfp	scannerplotter device to create a 300 dpi TIF file. Using ArcMap 

9.1’s	georeferencing	extension,	at	least	four	geographic	

locations	were	interactively	selected	from	the	TF,	based	

on	coordinates	and	grids	printed	on	the	maps.	ArcMap	

creates	a	table	of	these	selected	coordinate	values,	and	

with	U.S.	Army	Corps	of	Engineers	Corpscon	software,	

each	coordinate	pair	is	converted	to	NC	Stateplane	

NAD83	meters	and	then	appended	to	the	table.	ArcMap	

then creates a TFW world file and transforms the TIF image	so	that	it	is	represented	in	the	data	view	in	Stateplane	

Coordinates (NAD83). Each image file is rectified with 

Arcnfo	workstation	and	compressed	with	MrSD.	The	

workflow is shown in Reid et al. (2006a), Reid et al. 

(2006c), and Essic et al. (2006); Ramakrishnan (2006)

also provided details. Previously, Cahill et al. (2002) reported on the scanning and delivery of historic maps over

the Internet as done by the Library of Congress.

The TIF images and world files will become part of 

the North Carolina Geospatial Data Archiving Project,

a partnership between NCSU Libraries and the Library

of Congress (North Carolina Geospatial Data Archiving

Project, http://www.lib.ncsu.edu/ncgdap). As of June,

2006, the inventory consists of 101 U.S. Geological

Survey geologic maps, 130 North Carolina Geological

Survey geologic maps, 47 maps from theses and dissertations, 8 N.C. Department of Transportation maps, and 165

legacy 15-minute topographic maps, all 451 of which are

backed up on multiple secure servers (http://wfs.enr.state.

nc.us/NCGeologicMaps/; Reid et al. (2006d)).

Data are planned for dissemination through the North

Carolina Geological Survey’s Geologic Map Catalog,

(http://wfs.enr.state.nc.us/NCGeologicMaps/), the NCSU

Library’s campus-wide server (http://www.lib.ncsu.edu/

gis/geolmaps.html) pointing at the NCGS’ Geologic map

catalog URL, and by contribution to NCOneMap (http://

www.nconemap.com) and the National Geologic Map

Database’s Map Catalog (http://ngmdb.usgs.gov/).

The compressed MrSID geologic and topographic

maps accompanied by their world files and supplemental 
86	 DGTAL	MAPPNG	TECHNQUES	‘06

data (MS Excel and ESRI shape files) are in Reid et al. 

(2006d). The legacy topographic maps are now online at:

http://www.lib.ncsu.edu/gis/historictopos.html in JPEG

2000 format. Other geologic maps to fill in geographic 

gaps are actively sought. An annual map service and data

listing update is planned.

REFERENCES

Cahill, C. R., Roberts, G. E., and Shug-O’Neill, D., 2002, Scanning and Delivery of Historic Maps Over the Web: The

Library of Congress Experience, in Soller, David R., ed.,

Digital Mapping Techniques ’02—Workshop Proceedings:

U.S. Geological Survey Open-File Report 02-370, pp.

99-104, available at http://pubs.usgs.gov/of/2002/of02-370/

cahill.html.

Essic, J., Reid, J. C., Morris, S., and Ramakrishnan, S., 2006,

Preserving North Carolina Legacy Geologic and Topographic Maps, abstract, Geological Society of America,

Abstracts with Programs, Vol. 38, p. 30.

NCOneMap, accessed 16 June 2006 at http://www.nconemap.

com.

North Carolina Geological Survey’s Geologic Map Catalog, accessed 16 June 2006 at http://149.168.30.180/NCGeologic

Maps.

North Carolina Geospatial Data Archiving Project, accessed 16

June 2006 at http://www.lib.ncsu.edu/ncgdap.

Ramakrishnan, S, 2006, Georegistration for digital archiving and

distribution of scanned geologic maps of North Carolina: A

cooperative effort with the Library of Congress and NCSU

Libraries, unpublished master’s degree thesis, The University of North Carolina, Greensboro, North Carolina, 35p.

Reid, J. C., Essic, J. F., Morris, S. P., Ramakrishnan, S., and

Harrell, J. L., 2006a, Preserving North Carolina Legacy Geologic and Topographic Maps, MS PowerPoint, accessed at

http://ngmdb.usgs.gov/Info/dmt/DMT06presentations.html.

Reid, J. C., Essic, J. F., Morris, S. P., Ramakrishnan, S., and

Harrell, J. L., 2006b, Preserving North Carolina Legacy

Geologic and Topographic Maps, poster part A, accessed 29

June 2006 at http://ngmdb.usgs.gov/Info/dmt/DMT06

presentations.html.

Reid, J. C., Essic, J. F., Morris, S. P., Ramakrishnan, S., and

Harrell, J. L., 2006c, Preserving North Carolina Legacy

Geologic and Topographic Maps, poster part B, accessed 29

June 2006 at http://ngmdb.usgs.gov/Info/dmt/DMT06

presentations.html.

Reid, J. C., Essic, J. F., Morris, S. P., Ramakrishnan, S., and

Harrell, J. L., 2006d, Preserving North Carolina Legacy

Geologic and Topographic Maps: North Carolina Geological Survey, Open-file report 2006-01, DVD (also contains 

Reid et al 2006a—c).
87

87

Saturation And Value Modulation (SVM): A New Method

For Integrating Color And Grayscale Imagery

By David W. Viljoen1 and Jeff R. Harris2

Geological Survey of Canada

615 Booth St.

Ottawa, ON, K1A 0E9

Telephone: (613) 995-1207

Fax: (613) 995-9273

e-mail: viljoen@nrcan.gc.ca; harris@nrcan.gc.ca

ABSTRACT

Algorithms for integrating color imagery with

grayscale imagery have long been an important feature of

many remote sensing (RS) image analysis and geographic

information systems (GIS). Traditional methods for data

integration include Red-Green-Blue (RGB) /Hue-Saturation-Value (HSV) transformation and RGB modulation.

However, these techniques are either inflexible or present 

a compromise between the quality of the color and the

contribution of the shading. Furthermore, these techniques

can also result in serious color distortions. Layer transparency is another popular technique for integrating data that

is available in most RS and GIS software packages. However, optimal integration of color and grayscale imagery is

difficult to achieve using this method.

We briefly review the shortcomings of these traditional image integration methods and introduce a new method

(Saturation-Value-Modulation [SVM]) for raster image

integration developed by David Viljoen at the Geological

Survey of Canada. SVM is flexible and does not compromise the color or grayscale components of the resulting

integrated image. The general concepts behind this algorithm as well as the five parameters used to control the integration process are discussed. Various examples of how

SVM can be used to integrate various geoscience data are

also presented. Finally, we provide a brief overview of the

ESRI ArcGIS implementation of SVM, though we do not

include a detailed presentation of the actual Visual Basic

code or the algorithm.

The ArcGIS map document (MXD) that contains the

VBA (Visual Basic for Applications) code is available for

download for those who wish to use SVM.

INTRODUCTION

There are two primary reasons for integrating a color

image with a grayscale image. The first is to provide visual enhancement of a single dataset by combining different characteristics. For example, a color image of a digital

elevation model (DEM) can be integrated with a grayscale

image of the shaded relief DEM (Figure 1). The second

is to visualize the relationship between two very different types of data. For example, gamma ray spectrometer

data can be combined with Landsat Thematic Mapper

band 7 (Figure 2). Many methods have been developed to

integrate imagery in remote sensing image analysis and

geographic information systems. It is instructive to review

a few of these methods to appreciate some of the advantages of the SVM method.

Modern remote sensing software and GIS often have

a layer transparency feature that facilitates the integration

of data and allows the user to increase or decrease the

transparency of one layer to reveal the layer that would

otherwise be hidden. The advantage of this method is that

it is instantaneous, as it does not involve pixel-by-pixel

computations and color transformations. This method can

be used with two or more color images or a color image

and a grayscale image. The resulting integrated image is

a weighted interpolation of the colors of the contributing

images. The disadvantage of this method in integrating

color and grayscale imagery is that the resulting integrated image compromises either the color or the shading

(Figure 3).

Remote sensing and GIS software often have tools

for performing transformations between Red-Green-Blue

(RGB) and Hue-Saturation-Value (HSV) color models.

Figure 4 graphically shows the components of the HSV

model where hue is the dominant wavelength of the color,

saturation is the presence or absence of color, and value

is the brightness and darkness. Color transformations

involve pixel-by-pixel conversion of RGB color components into equivalent HSV components. Integration of

color and grayscale imagery is achieved by replacing the

value component (V) with the values from the grayscale

image (Figure 5). One of the problems with this technique

is that the value component is often important in defining 

colors in the color image (Harris et al. 1990, 1994). That
88	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 1. a. Color image of a digital elevation model (DEM) of Mt. Logan. b. Shaded relief of Mt.

Logan DEM. c. Integrated image using Saturation-Value-Modulation (SVM) method.

Figure 2. a. Ternary gamma ray spectrometer color composite image K-Th-U (RGB) – imagery

supplied by E. Schetselaar – ITC) b. Landsat TM 7 c. Integrated image using Saturation-ValueModulation (SVM) method.
SATURATON	AND	VALUE	MODULATON	(SVM) 89

Figure 3. Layer transparency feature of ArcGIS.

Figure 4. Hue-Saturation-Value color model.
90	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 5. Traditional Value Replacement Method of image integration.

is, often the difference between lighter and darker colors

in an image is higher and lower numbers representing the

value component of the colors. If the value components

are modulated or replaced, then the difference between

the colors will be changed or eliminated. Figure 6 shows

how replacing the value component of a dark and lighter

green results in an image where the dark and lighter

greens cannot be differentiated. In this example, the only

difference between the two greens in the original color

image is in the value component. A second problem with

this technique is that the original colors can be corrupted

when the value component is replaced (Harris et al., 1990,

1994). For example, yellow can appear as dirty green in

the integrated image, and red can appear brown. Another

problem with this method is that, if the saturation of the

colors is low, then replacing the value component results

in an image where the saturation of the color is further

reduced (Figure 7).

Another traditional method of image integration

involves pixel-by-pixel multiplication of the RGB components by the grayscale values scaled between 0 and 1

(Figure 8). The main problem with this method is that the

scaled values of nearly all pixels in a grayscale image are

less than one, so the colors in the resulting integrated image are darker than the original image. This scaled values

problem can also corrupt the apparent hue of the color.

For example, a yellow might appear to be some kind of

green (Figure 9).

Both value replacement and RGB modulation methods offer very little flexibility on how the integration is 

performed, and there are few or no parameters that can be

used to control the result of the calculations.

Unlike layer transparency, the SVM method is not interactive and involves pixel-by-pixel computations similar

to those associated with the value replacement and RGB

modulation methods. However, the SVM method provides

more flexibility on the integration process which results in 

integrated images that are superior to those produced by

traditional methods.

OVERVIEW OF THE SATURATIONVALUE-MODULATION (SVM) METHOD

The saturation and value color components of an illuminated object change with the angle of incidence. For

example, Figure 10 shows a cylinder illuminated from

the right side. The colors of the parts facing the source of

illumination have a lower saturation and a higher value

component, whereas the parts facing away from the

source of illumination have a higher saturation and lower

value. In the area around the “cutoff” line (Figure 10),

the saturation and value components will be that of the

natural color of the object.

In Figure 11, the “shade value” (x-axis) is zero on

those surfaces that face away from the source of illumination; the highest values (e.g., 255 or 100) will be assigned

to surfaces that face toward the source of illumination.

Multiplier curves that range between 0 and 1 can be used

to modulate the saturation and value color components

depending on the shade value. The value of a pixel in the

grey-scale image (Shade value) defines a vertical line that 

intersects the saturation and value multiplier curves. The

points of intersection of this vertical line and multiplier

curves are the saturation and value multipliers ranging

from 0 to 1 (Figure 11). The saturation and value components of the color image at the same pixel location are

multiplied by their respective multipliers. The resulting

modulated saturation and value components are integrated

with the original hue component to create the SVM image

in HSV coordinate space. The final step is a transformation of the hue and modulated saturation and value components to RGB coordinates for display purposes.

Figure 12 presents a schematic of the SVM method.

Not shown is the transformation of the color image from
SATURATON	AND	VALUE	MODULATON	(SVM) 91

Figure 6. Loss and corruption of color with value replacement method. The three circled areas

have three shades of green differentiated only by the value component as shown in the table.

Replacing the value component of these green areas with values in the shaded relief image results

in a loss of the shades of green. Replacing the value component in yellow and red areas results in

colors that appear “dirty”.

Figure 7. Low saturation colors become lower with value replacement method.

Figure 8. RGB modulation method of image integration.
92	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 9. a. Color DEM image of northern Manitoba. b. Shaded relief of northern Manitoba DEM.

c. Integrated image using RGB modulation method. The box highlights an area where light green,

yellow, and light red pixels have been transformed to darker colors since all pixels have been

multiplied by a value from the shaded relief image of less than 1. In fact, virtually all pixels will

be multiplied by multipliers less than 1 which creates a darker overall integrated image product.

Figure 10. A rainbow colored cylinder illustrates the

Saturation-Value-Modulation (SVM) Concept. For surfaces facing the illumination source, the colors have full

value but lower saturation. For surfaces facing away, the

colors have full saturation but lower value. For surfaces at

the “cutoff”, the color has full saturation and value.

RGB to HSV components. The schematic shows how the

saturation (S) and value (V) components are modulated

by multipliers that are determined by the pixel value in

the grayscale image. The modulated saturation (Sm) and

value (Vm) are used with the original hue component and

transformed to an RGB composite image file that can be 

displayed in remote sensing software or a GIS. The multiplier curves and the parameters that define their shape are 

key elements of the SVM method.

SVM Parameters

There are five SVM parameters necessary to define 

the shape of the saturation and value multiplier curves.

Together, they provide the ability to control various characteristics of the resulting integrated image.

Grayscale Value Cutoff (CutOff)

In cases where the pixel values in a grayscale image

are lower than the “grayscale value cutoff” (see Figure

11), the value component (V) of the color image will be

modulated, and the saturation component will be equal to

the saturation in the original color image (i.e. saturation

multiplier equals one). For grayscale pixel values greater
SATURATON	AND	VALUE	MODULATON	(SVM) 93

Figure 11. SVM saturation and value multiplier curves are used to model the lower saturations

for surfaces that face towards an illumination source and lower color values for surfaces that face

away from an illumination source. Surfaces that neither face towards nor away from the illumination source (e.g. horizontal surfaces in a digital elevation model) will have minimal or no change

to their original color values.

Figure 12. Schematic of the SVM method.
94	 DGTAL	MAPPNG	TECHNQUES	‘06

than the cutoff, the saturation component (S) will be

modulated, and the value component will equal the value

in the original color image (i.e., multiplier equals one). At

the cutoff value, the value and saturation multipliers are

equal to one, so the color in the integrated image will be

the same as the original color image at the cutoff value.

A general SVM rule of thumb is to maximize the

number of pixels in the integrated image that have the

same color as the original color image. This means that

a cutoff value that maximizes the number of pixels with

saturation and value multipliers of 1 should be selected.

In most cases, this cutoff is represented by the peak in the

grayscale image histogram.

For a typical shaded relief digital elevation model, the

peak in the histogram coincides with pixels that represent

horizontal surfaces. For shaded relief images, the cutoff

can therefore be computed from the following equation:

Cutoff = 255 * sin(A)

This assumes a range of values in the grayscale image

is 255 and A is the illumination source altitude in degrees

(0-90). For example, the peak of histogram of a shaded

relief DEM with an illumination angle of 45 degrees will

be approximately 180 (Figure 13).

Figure 14 shows the impact of changing the cutoff

on the integrated image. Lowering the cutoff below the

optimal value of 180––the peak in the grayscale histogram––results in an image with lower overall saturation

(i.e., washed out colors) than the original color image.

Increasing the cutoff value above the cutoff results in an

image with lower overall value (i.e., darker).

Minimum Value Multiplier (Vmin)

The minimum value multiplier, which can vary

between 0 and 1, determines how dark the pixels will be

in areas where the grayscale pixel values are low. For

example, if Vmin is 0, then for grayscale pixel values of 0

the value component of the color image will be multiplied

by 0. Any color with a value component of 0 is black.

This means that color pixels that have the same location

as grayscale pixel values of 0 will be black in the integrated image. As Vmin increases, these same pixels will

become brighter. A Vmin value of 1 will result in no value

modulation. In this case, the pixels in the integrated image

that have the same location as grayscale pixels with values lower than the cutoff will be the same as the original

color image.

Figure 15 shows the impact of changing the value of

Vmin. Note how the shadows become brighter as Vmin

increases. Values greater than 0 and less than 0.4 are generally recommended.

Value Multiplier Exponent (Vexp)

The value multiplier exponent will increase or

decrease the number of pixels that will have their value

component multiplied by a value close to Vmin. Higher

Vexp values mean that the multiplier will rise slowly from

Vmin. Figure 16 shows how increases in Vexp increase

the proportion of “dark pixels” in the integrated image.

Values of 1 or less generally provide good results.

Minimum Saturation Multiplier (Smin)

The minimum saturation multiplier, which varies

between 0 and 1, determines how much color there will

be for pixels where the grayscale values are high. For

example, if Smin is 0, then for grayscale pixel values of

255 (the maximum in the image), the saturation multiplier

will be 0 and the saturation of the color in the integrated

image will be 0. In this case the color in the integrated

image will have “no” color and will typically be white or

light gray. As Smin increases, these same pixels will have

higher saturation and more color. If Smin is 1, then there

will be no saturation modulation and the pixels above the

cutoff will appear to be “flat”. Figure 17 shows the impact 

of increasing Smin from 0 to 0.6. Smin values between 0

and .4 are recommended.

Saturation Multiplier Exponent (Sexp)

The saturation multiplier exponent will increase or

decrease the number of pixels that will have their saturation component multiplied by a value close to Smin.

Higher Sexp values mean that more pixels will be multiplied by a multiplier close to Smin. Figure 18 shows how

higher values of Sexp decrease the proportion of “washed

out pixels” in the integrated image. Generally values

between 1 and 3 provide good results.

ARCGIS IMPLEMENTATION OF SVM

The SVM method was implemented as a Visual Basic

for Applications (VBA) application in ESRI’s ArcMap

application. It works with ArcGIS (ArcView, ArcEditor,

or ArcInfo) and does not require any special ESRI extensions (e.g., Spatial Analyst).

The VBA implementation allows the user to set each

of the five SVM parameters described above. The input 

images can be one of the following:

• 8- or 16-bit image with an associated CLR file

• 8- bit TIF image (colors embedded in file)

• 32-bit TIF image (color defined by RGB channels)

•	 3 separate 8-bit RGB images
SATURATON	AND	VALUE	MODULATON	(SVM) 95

Figure 13. Histogram of pixels values for shaded relief of Mt. Logan (Figure 1). Peak is approximately 255 * sin(45) = 180 = recommended SVM cutoff value.

Figure 14. SVM Parameters – Grayscale value cutoff (CutOff).
96	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 15. SVM Parameters – Minimum value multiplier (Vmin).

Figure 16. SVM Parameters – Value multiplier exponent (Vexp).
SATURATON	AND	VALUE	MODULATON	(SVM) 97

Figure 17. SVM Parameters – Minimum saturation multiplier (Smin).

Figure 18. SVM Parameters – Saturation multiplier exponent (Sexp).
98	 DGTAL	MAPPNG	TECHNQUES	‘06

The CLR file contains space delimited values for 

pixel	value	and	RGB	components.	An	example	record	

from a CLR file might be:

18	244	64	120

This	means	that	pixel	values	of	18	in	the	color	image	

have	RGB	coordinates	of	244,	64,	and	120	respectively.

The	output	from	SVM	is	a	3-band	RGB	Band	nterleaved	by	Line	(BL)	image	which	is	easily	imported	or	

used	directly	by	remote	sensing	software.	t	can	also	be	

easily	exported	to	TF	or	ESR	Grid	format	in	ArcGS.	

The	ArcMap	document	(MXD)	that	contains	the	VBA	

code	can	be	downloaded	from	the	SVM	FTP	site	along	

with	sample	data	(Viljoen,	2006).

APPLICATION OF SVM IN 

GEOSCIENCE

The	SVM	method	has	broad	application	to	geoscience	studies	that	require	integration	of	a	color	and	

grayscale	image.	Many	geological	mapping	applications,	

for	example,	require	interpretation	of	various	types	of	

remotely	sensed	and	geophysical	data.	The	integration	

of	these	data	types	often	provides	images	that	offer	a	

unique	perspective	of	the	Earth’s	surface,	which	enables	the	interpretation	of	many	geological	features	

that,	without	integration,	would	have	been	impossible	to	

make.	Furthermore,	the	relationships	often	evident	in	the	

resulting	integrated	imagery	offer	a	unique	interpretation	

tool. The following three examples highlight the value of

integrating different geoscience data and demonstrate the

advantages of the SVM method over traditional integration methods.

Integrating Geological Map Units with 

Shaded Relief Aeromagnetics

The magnetic characteristics of rocks at, and below,

the Earth’s surface often reflect mappable variations in 

lithologies. The magnetic characteristics of rocks are measured with aeromagnetic sensors, and these measurements

are often processed into colorful images that represent

total field, vertical gradient, and other derivative products. 

Integration of colored geological units with shaded relief

versions of total field aeromagnetics can provide an image 

that is extremely useful for geological mapping.

Geological units are usually represented by vector

polygons in a GIS and, given that SVM is entirely a raster-based method, these vector polygons must be rasterized and have the same projection, pixel resolution, and

map extent as the total field shaded relief image. Detailed 

step-by-step instructions on how to use the ArcGIS version of SVM for this kind of integration are available for

download (Grant and others, 2006).

Figure 19 shows the result of using SVM to integrate rasterized geological units with shaded relief total

field aeromagnetics. As can be seen, rock units, after 

SVM integration, are characterized by different magnetic signatures on the integrated image. This image

Figure 19. Comparing image integration methods – a. Rasterized geological map units. b. Shaded

relief aeromagnetic image for the same area as the geology map. c. Integrated image using the

value replacement image integration method. d. Integrated image using the SVM method. e. Integrated image using RGB modulation method.
SATURATON	AND	VALUE	MODULATON	(SVM) 99

can	greatly	assist	mapping	endeavours,	as	the	rock	units	

can be modified based on variations in their magnetic 

signature.	With	respect	to	the	integration	methods,	note	

how	the	integrated	image	produced	by	the	SVM	method	

retains	the	original	colors	of	the	geology	map	compared	

to	the	other	two	traditional	methods.	The	small	squares	

highlight	areas	where	color	corruption	and	loss	are	

evident	in	the	integrated	image	produced	by	the	other	

methods.

Integrating a Ternary U-Th-K RGB Gamma 

Ray Spectrometer Image with a Shaded 

Relief Digital Elevation Model

ntegration	of	gamma	ray	spectrometer	data,	which	

measures	the	emission	of	the	elements	U,	Th	and	K	from	

the	Earth’s	surface	with	a	shaded	DEM,	can	also	provide	

a	very	useful	image	for	geological	mapping.	Variations	

in the above radioelements often reflect different rock 

units	as	well	as	areas	of	potential	mineralization.	However,	since	the	gamma	ray	data	comprise	three	channels	

(U,	K,	Th)	that	are	often	correlated,	the	color	variations	in	ternary	images	are	often	low.	The	integration	of	

ternary	imagery	presents	a	challenge	to	all	methods	of	

image	integration.	The	Value	replacement	method,	for	

example,	often	results	in	almost	complete	color	loss	as	

shown	in	Figure	20.	As	with	the	previous	example,	the	

SVM method results in an integrated image that retains

the original color to a much greater degree than the other

methods.

Pan Sharpening Landsat Multi-spectral With 

A Panchromatic Image

Pan sharpening is the term given to an image processing technique that uses higher resolution (smaller

pixels) grayscale imagery to improve the visualization

of lower resolution (large pixels) color or color composite images. Many satellite and airborne sensor systems

have multi-spectral channels and panchromatic channels.

Landsat 7, for example, provides 6 channels of 30 meter

pixel resolution for portions of the visible, near infrared

and short-wave infrared of the electromagnetic spectrum.

It also contains a panchromatic channel that covers the

entire visible part of the electromagnetic spectrum with 15

meter pixel resolution. Integrating the higher resolution

panchromatic channel with a RGB color composite of the

lower resolution visible channels results in a sharper color

composite image as shown in Figure 21. The boxes in the

images are areas that highlight the differences between

these image integration methods. If the accuracy of the

original colors is important, than SVM is the preferred

method. If the original colors are not important, then

value replacement is another pan-sharpening option.

Figure 20. Comparing image integration methods – a. Ternary gamma ray spectrometer color

composite image K-Th-U (RGB) composite image. b. A shaded relief digital elevation model

of the same area as the K-Th-U image. c. Integrated image using the value replacement image

integration method. d. Integrated image using the SVM method. e. Integrated image using RGB

modulation method.
100	 DGTAL	MAPPNG	TECHNQUES	‘06

CONCLUSIONS

The	Saturation-Value-Modulation	(SatValMod	or	

SVM)	method	is	based	on	the	real-world	concept	of	darker	colors	for	surfaces	in	shadow	(lower	value	component)	

and	color	loss	(lower	saturation	component)	on	illuminated	surfaces.	This	real-world	concept	is	implemented	in	

SVM	as	a	pair	of	multiplier	curves	that	modulate	the	saturation	and	value	components	of	colors	in	the	color	image.	

These	modulated	saturation	and	value	components	are	

combined	with	the	original	hues	in	the	color	image	and	

transformed	to	red-green-blue	components	for	display.

The	SVM	method	of	integrating	color	and	grayscale	

imagery	provides	superior	results	over	many	other	integration	methods	because	there	is	no	compromise	between	

color	and	shading,	as	is	the	case	for	layer	transparency.	

n	addition,	there	is	no	distortion	of	colors,	as	can	result	

from	value	replacement,	RGB	modulation,	and	other	

methods.	Unlike	the	other	integration	methods,	which	

provide	little	or	no	control	over	the	integration	process,	

SVM uses five different parameters that provide a great 

deal	of	control	over	the	characteristics	of	the	resulting	

integrated	image.

The	use	of	color	is	important	in	conveying	geoscience	information	such	as	geological	units,	geophysical	

properties, radiometric characteristics, and many others.

Visualizing the relationships between these data is made

possible through image integration techniques. The SVM

method is a superior algorithm for integrating color and

grayscale imagery, which results in integrated images that

preserve the original color and grayscale characteristics of

the input imagery. SVM is freely available from the SVM

FTP site listed in Viljoen (2006).

REFERENCES

Harris, J.R., Murray, R., and Hirose, T., 1990, IHS Transform

for the Integration of Radar Imagery with Other Remotely

Sensed Data: Journal of Photogrammetric Engineering and

Remote Sensing, Vol. 56, No. 12, p. 1631-1641.

Grant, G., Fraser P., and Viljoen, D., 2006, How to SVM:

accessed at ftp://nrd:imanrd2@ftp.gis.nrcan.gc.ca/viljoen/

downloads/satvalmod/HowToSVM.pdf.

Harris, J.R., Bowie, C., Rencz, A.N., and Graham, D., 1994,

Computer Enhancement Techniques for the Integration of

Remotely Sensed, Geophysical and Thematic Data for the

Geosciences: Canadian Journal of Remote Sensing, Vol. 20,

No. 3, p. 210-221.

Viljoen, D., 2006, Saturation-Value-Modulation FTP site:

accessed at ftp://nrd:imanrd2@ftp.gis.nrcan.gc.ca/viljoen/

downloads/satvalmod/.

Figure 21. Comparing image integration methods. a. Landsat 7 RGB color composite image of

Thematic Mapper (TM) bands 3, 2, and 1 respectively (30 meter pixels). b. Landsat TM Band 8

(15 meter pixels) c. Pan sharpened image using the value replacement image integration method.

d. Pan sharpened image using the SVM method. e. Pan sharpened image using RGB modulation

method.
101

101

Unpublished Geologic Evidence and Other Databases

of the Kentucky Digital Mapping Program

By Gerald A. Weisenfluh and Douglas C. Curl

Kentucky Geological Survey

228 Mining and Mineral Resources Bldg.

University of Kentucky

Lexington, KY 40506-0107

Telephone: (859) 257-5500

Fax: (859) 257-1147

e-mail: {jerryw, doug}@uky.edu

INTRODUCTION

Between 1996 and 2005 the Kentucky Digital Mapping Program digitized 707 USGS Geologic Quadrangle

maps produced by the joint USGS-KGS geologic mapping

program that took place from 1960 to 1978. One of the

products of this effort is a seamless 1:24,000-scale spatial

database of geologic information. The database includes

map features and all the explanatory information found

on the collar of the printed USGS maps. Since 2001, the

Kentucky Geological Survey has been developing Web

services to integrate the digital geologic maps with other

geoscience databases (Weisenfluh, and others, 2005).

The seamless, 1:24,000-scale database is essentially

complete and available as an ESRI ArcIMS map service

at the Kentucky Geologic Map Information site (http://

kgsmap.uky.edu/website/KGSGeology/viewer.asp). Some

supporting data are still being added to the site. Here,

we discuss database design elements that were found to

be particularly useful for implementing the Web project

and current KGS database activities that will extend the

usefulness of the Internet mapping site.

MAP EXTENT DATABASE

The first database that was built to support the 

geologic map system was a catalog of KGS and USGS

reports and maps. Map coordinate extents were assigned

to publications to facilitate geographic searches for information of this kind, where a single coordinate value does

not adequately represent the area to which the information

applies. At the onset, most publications were associated

with quadrangles or counties, but subsequently, additional

area types such as state parks and drainage basins were

added. A database of map extents was constructed that

contains a geographic area type and name along with the

minimum and maximum coordinates for the enclosing

rectangular area.

At about the same time, Internet map developers in

other Kentucky state government agencies were creating

new Internet map services for a variety of information.

Among the early drawbacks to these maps were the slow

redraw speeds and the difficulty of finding a specific area 

on a statewide map. Most users utilized repeated zoom

and pan methods to find an area, and so finding an area 

of interest was taxing for both the user and the computer

servers. KGS used the map extents database to develop

a search and zoom function that could be used by any

Kentucky Internet map developer to simplify this process.

This service, called the KGSGeoPortal, functions much

like the Geographic Names Information System with the

added ability to link to most Kentucky-based Internet

maps from a single base map. Moreover, the initial view

provided by the GeoPortal is typically closer to the user’s

area of interest because the database stores the full extent

of the feature, not just its central value. The GeoPortal is

found at <http://kgsmap.uky.edu/website/KGSGeoPortal/

KGSGeoPortal.asp>.

The database was enhanced to help resolve similar

geographic names. For example, many stream names are

duplicated in different parts of the state. Additional attributes were assigned to identify the beginning and ending

county name, the drainage basin name, and the name of

the stream of the next higher order so that users could

identify the correct stream.

All of the initial map extents for this database were

for standard geographic features that were readily available in GIS format. The extents were calculated using

a simple Avenue function in ArcView or with AML

programs in Arc for more complex requirements. More

recently, the need to store informally-defined areas has 

increased, but it was more difficult to calculate these 

areas. For example, many reports are published for study

areas that do not conform to a standard quadrangle or

other named geographic features. KGS undertook a project to help the Kentucky Transportation Cabinet catalog
102	 DGTAL	MAPPNG	TECHNQUES	‘06

over 5,000 of its geotechnical reports for roadways and

structures. Because each of these projects pertains to a

small and unique area, an Internet base map service was

developed with a function to create a custom map extent

by dragging a rectangle across the area as viewed on a

topographic map or aerial photograph (Figure 1). Most

recent Transportation Cabinet project areas can be defined 

by the drillholes taken at the site, and therefore, another

function was provided to upload the drillhole coordinates

and plot them on the map. Users can then draw the project

extent on the map or use the minimum and maximum hole

coordinates to define the rectangle programmatically.

An unanticipated benefit of this work was discovered 

late in the project. The Transportation Cabinet has tens

of thousands of historical drillholes, but only a few are

referenced in a geographic coordinate system. Transportation engineers typically use a local survey system of

route alignment station footages and offsets. Locating

these holes on a map normally would be time prohibitive.

Because the holes are associated with a project report and

the reports have been assigned a map extent that, in most

cases, is only hundreds of feet square, most of these data

can now be placed in a geographic context that is suitable

for comparison to geologic maps.

MAP DESIGN DATABASE

The KGS Geologic Map Information site was

designed so that users could create a highly customized

geologic map of a project area overlain with other related

site information, such as oil and gas wells or sinkholes.

Over 40 themes are available for geology, derivative classifications of map units, well and sample sites, economic 

features, hazards, and various base maps. Geologic maps

can be customized by selecting individual layers or by

choosing one of five predesigned layouts (Figure 2). The 

service also has a bookmark function that allows users

to save a browser “favorite” for the current view that

includes the map extent and the map layout. With a bookmark, users can return to the map exactly as designed or

send it to a colleague. Bookmarks are stored as URL text

strings, and saving in that string the necessary information about the visibility of 40 layers is not practical due

to length considerations. Therefore, a database of map

layouts (the “IMSLayers” database) was created so that

a custom configuration could be stored in the URL as a 

simple variable number.

Columns in the IMSLayers database (Figure 3)

designate the visibility of individual layers, while each

Figure 1. Creating non-standard map extents by interactively drawing a rectangle around plotted

drill hole locations on an ArcIMS map service. The holes are plotted by means of a coordinate

upload function.
UNPUBLSHED	GEOLOGC	EVDENCE	OF	THE	KENTUCKY	DGTAL	MAPPNG	PROGRAM 103

Figure 2. Pre-designed and customized map layouts on

the KGS geologic map service.

row	represents	a	unique	combination	of	visible	layers.	To	

simplify	map	development,	database	column	names	for	

layers	are	equivalent	to	layer	names	used	in	the	ArcMS	

AXL file used for map rendering. Additional columns in 

the	database	record	the	layout	D	number	(an	internal	D),	

a layout name for standard maps (this field is blank for 

map layouts defined by users), a counter for the number 

of	users	who	have	used	that	layout,	and	the	name	of	the	

help file that is available for the layout on the map. For 

simplicity,	the	geologic	map	service	has	only	one	link	

to a help file for explanatory information. Because the 

content of the map can change significantly, the database 

can specify a different help file dynamically according to 

which layers are visible. For example, a karst help file is 

activated	when	the	karst	potential	theme	is	turned	on.

When	a	user	requests	a	bookmark,	the	system	compares	the	current	state	of	visible	layers	to	the	database.	f	

the	same	layout	exists,	its	D	number	is	appended	to	the	

URL	along	with	the	coordinate	bounds	of	the	view,	and	

its	count	is	incremented.	f	the	layout	does	not	exist,	a	

record	is	appended	to	the	table,	and	the	new	D	number	is	

returned.	When	an	nternet	browser	receives	a	URL	with	

an	embedded	layout	D	for	this	map	site,	the	process	is	

reversed.	The	D	number	is	used	to	obtain	visible	layers	

from	the	database,	and	the	application	draws	the	map	in	

this	initial	state.

The	MSLayers	database	provides	an	easy	method	

of	storing	customized	map	layouts	that	are	useful	to	a	

diverse	user	base.	Tabulation	of	layout	counts	provides	

information	about	frequency	of	use	that	can	be	used	to	

identify	“favorite”	designs	so	that	these	can	be	provided	

as	quick	links.

DERIVATIVE GEOLOGIC MAPS

One	of	the	objectives	of	the	KGS	Digital	Mapping	

Project	was	to	make	geologic	maps	accessible	to	a	wider	

user	audience.	n	Kentucky,	this	includes	many	people	

without	formal	geologic	training.	One	way	to	do	this	

is to make derivative classifications of the geology that 

are	conveyed	in	simpler	terms	appropriate	to	the	target	

audience.	Derivative	maps	were	made	using	a	variety	of	

methods	and	are	available	on	the	map	service	as	predefined layouts.

An	oil	and	gas	map	was	created	by	simple	layer	

manipulation.	This	layout	excludes	most	of	the	geologic	

themes,	except	faults	and	near-surface	structure	contours,	

then overlays oil and gas wells and fields. A dominant 

lithology	map	was	created	by	reviewing	lithology	descriptions	for	each	map	unit,	then	assigning	an	appropriate	

Figure 3. Design of the IMSLayers database table. Layers assigned the value “-1” will be made

visible.
104	 DGTAL	MAPPNG	TECHNQUES	‘06

term in a stratigraphic database. Combined units were

treated similarly to heterogeneous stratigraphic units.

Lithology terms were reduced to seven unconsolidated

types and 23 rock terms, all of which were derived from

the original geologic map legends. A karst potential map

was developed using a multivariate analysis of factors that

affect the dissolution of limestone units. This classification, developed by KGS geologists, is intended to show

the likelihood that a unit may develop karst features. The

four factors considered were percentage CaCO3 in the

carbonate portion of the unit, carbonate grain size, bedding thickness, and percent insoluble material. Each factor

was ranked for all geologic units, and a combined score

was tabulated. From this ranking, a five-level classification was devised, and adjustments were made based on

the experience of KGS geologists. Examples of these map

types are shown in Figure 4.

Two additional map classifications are in progress. 

The first is a map that depicts shale behavior by grouping 

units according to the amount of shale present and properties such as slake durability, expandable clays, and sulfide 

content. The second is an endangered species potential

map that will highlight units that are associated with occurrences of endangered plant and animal species.

IMAGE LIBRARY

One of the most useful ways of communicating the

character of geologic units is with photographic evidence.

In fact, most geologists’ offices are filled with hundreds, 

if not thousands, of photographs of outcrops, scenes,

and specimens that have been accumulated during their

careers. The challenge of making these images available

to a general audience always relates to the time needed

to catalog them—few working geologists have sufficient 

time. Recognizing this, KGS developed a digital image

application for its staff that simplifies some of the tasks 

related to the processing and cataloging of photos. The

application has Web-based data entry and search functions

to provide ease of user access and efficient programming 

maintenance. The search function < http://kgsweb.uky.

edu/geology/image_search.asp > is currently available for

public access.

The initial step in cataloging a photograph is to upload the image to the Web server and, optionally, overprint credit text on the picture. No limits to resolution are

imposed. Images are stored in their original resolution,

and a thumbnail version of 100-pixel width is created.

Those who wish to include a credit on the image can type

Figure 4. Examples of derivative classifications provided for Kentucky geologic maps.
UNPUBLSHED	GEOLOGC	EVDENCE	OF	THE	KENTUCKY	DGTAL	MAPPNG	PROGRAM 105

the	text,	then	specify	the	font	size,	color,	and	position	(upper	or	lower	left	corner).	A	preview	function	is	provided	

to	verify	that	the	selected	options	work	well	for	the	given	

image. Once the user is satisfied, the image is uploaded 

using	the	program	AspUpload	(Persits	Software	nc.,	

http://www.aspupload.com/index.html),	and	the	credit	text	

is	appended	during	the	operation.	Some	properties,	such	

as pixel resolution and file size, are harvested from the 

image file, and a unique file name is assigned at the time 

of	upload.

After	the	upload	is	complete,	a	form	is	presented	

for	the	geologist	to	describe	the	image.	One	of	the	most	

important	characteristics	to	record	is	the	location	of	the	

photograph. Locations can be specified with a variety 

of	methods	and	levels	of	precision,	ranging	from	“in	a	

quadrangle”	to	a	GPS	coordinate	value.	Only	images	with	

a	coordinate	location	can	be	posted	on	an	nternet	map;	

however,	those	with	general	locations	can	be	searched	

from	a	map	using	the	same	methodology	that	is	applied	

to	publications	(i.e.,	by	assigning	a	mapextent	value	as	

described	above).

The	remainder	of	the	cataloging	process	allows	users	

to	supply	information	about	stratigraphic	context,	authorship,	and	image	properties.	t	also	allows	users	to	provide	brief	and	long	descriptions	of	the	photo,	and	assign	

keywords.	Standard	keywords	are	provided	as	checkboxes	

to simplify data entry and maximize the efficiency of the 

search	function.	Another	keyword	function	is	supplied	to	

assign	fossil	names	from	a	hierarchical	taxonomic	list.

The	image	search	function	(Figure	5)	permits	users	

to	search	by	combinations	of	all	the	criteria	discussed	

above.	The	initial	results	screen	(Figure	6)	contains	a	list	

of	matching	photos	with	a	brief	description,	a	thumbnail	

image,	and	links	to	a	full	description	and	a	geologic	map	

view	of	the	location.	mages	can	be	downloaded	in	their	

original	resolution.	Users	of	the	geologic	map	service	can	

view	photo	locations	on	the	map,	and	these	symbols	are	

linked	to	the	detail	description	(download)	page.

Figure 5. Part of the KGS photo search Web site.
106	 DGTAL	MAPPNG	TECHNQUES	‘06

GEOLOGIC ANECDOTES

The geologic map service provides information about

the geology of Kentucky from published geologic maps,

reports, and databases. However, some sources of information are unpublished, yet provide important context or

explanation about the geology of an area. Unpublished

evidence may include field notes from a mapping project, 

but often is represented as personal knowledge in anecdotic form. The Kentucky Survey sought a way of preserving

this “institutional knowledge” and making it accessible to

others. A simple form was developed to allow a geologist

to submit such information to the existing geologic description database. The user must indicate his or her name

and the geographic area to which the information applies

(by assigning a value from the mapextents database). A

description category is specified (e.g., geotechnical or 

hydrologic), and one or more geologic units can also be

designated. The anecdote is entered as free text.

Using mapextent methods, anecdotal descriptions can

be searched from the geologic map interface along with

published descriptions. If the user’s map view overlaps

the coordinate extent assigned to a particular description, it will be returned in a query. There may be concerns about making information of this kind available to

the public, since it has not been formally reviewed. It is

important, therefore, to advise users about the nature of

the data.

REFERENCE

Weisenfluh, G.A., Curl, D.C., and Crawford, M.M., 2005,The

Kentucky Geological Survey’s Online Geologic Map and

Information System, in D.R. Soller, ed., Digital Mapping

Techniques ‘05—Workshop Proceedings. U.S. Geological

Survey Open-File Report 2005-1428, p. 5-10, available at

http://pubs.usgs.gov/of/2005/1428/weisenfluh/index.html.

Figure 6. Photo search results page with links for detailed image information and a geologic map

location.
107

107

The Challenges and Benefits of Distributing

Digital Data: Lessons Learned

By Kenneth R. Papp1, Susan Seitz1, Larry Freeman1, and Carrie Browne2

1Alaska Division of Geological & Geophysical Surveys

3354 College Rd.

Fairbanks, AK 99709-3707

Telephone: (907) 451-5039

Fax: (907) 451-5050

e-mail: {ken.papp, susan.seitz, larry.freeman}@alaska.gov

2Formerly with the Division of Geological & Geophysical Surveys

INTRODUCTION

The	Alaska	Division	of	Geological	&	Geophysical	

Surveys	(DGGS)	has	been	producing	geologic	maps	using	a	Geographic	nformation	System	(GS)	since	1983	

(Davidson,	1998).	The	maps,	reports,	and	informational	

publications	produced	by	the	DGGS	are	widely	utilized	

by	oil,	mining,	and	resource-based	companies,	as	well	

as	consultants,	universities,	schools,	government	agencies,	scientists,	and	private	individuals.	These	users	have	

become	more	technologically	savvy	over	time,	and	as	a	

result,	user	requests	for	digital	data	in	addition	to	or	in	lieu	

of	paper	reports	have	grown	exponentially.	Since	1983,	the	

DGGS	has	provided	several	web-based	digital	geologic	

data-distribution	tools	to	accommodate	the	needs	of	its	users	(DGGS	Staff,	2005),	including	a	database-driven	publications	query	page	(http://www.dggs.dnr.state.ak.us/pubs/

pubs.jsp),	a	geochemistry	search	engine	(http://www.dggs.

dnr.state.ak.us/webgeochem/index.jsp),	and	a	geologic	

map	indexer	(http://maps.akgeology.info/).	Currently,	the	

DGGS	provides	users	with	digital	versions	of	its	maps	and	

reports as Portable Document Format (PDF) files. The raw 

digital	data	that	generate	each	map	can	be	burned	to	a	CD	

or	DVD	and	purchased	for	a	small	fee	through	a	general	

order	process.	At	the	time	of	this	writing,	a	focused	effort	

is	underway	to	upgrade	the	DGGS	web	site	to	provide	

users	with	the	digital	data1	used	to	create	the	state	survey’s	

geological	and	geophysical	maps.	This	paper	discusses	the	

challenges and benefits of distributing digital data on-line.

PROJECT BACKGROUND

The	primary	goal	of	digital	data	distribution	is	to	

make	the	data	available	to	the	widest	possible	user	audi1Note: Underlined words are defined in Appendix A.

ence in formats that are easily adaptable to typical enduser systems. The DGGS Digital Data Distribution (D3)

Project is the result and distillation of a series of discussions convened in 2005 in response to numerous public

requests for digital geologic map data and the need to

fulfill digital data delivery requirements of some projects. 

The project provides end-users with a means by which to

acquire all of the digital datasets used to create, “on the

fly,” DGGS geological and geophysical maps in the form 

of ESRI shape files, raster images (i.e. GeoTIFF), various 

other data, and metadata as compressed files for download 

via the World Wide Web. The scope of the D3 project

includes the following: (1) Enhance the current publications pages to distribute compressed digital data packages,

(2) develop a secure, internal application that will allow

DGGS staff to create, on-line and off-line, packages for

distribution to the public, (3) develop a means by which to

insert metadata file elements into the database, (4) modify 

database structures as needed for application design, and

(5) write accurate documentation for project process steps

and changes made to the database.

The process work-flow for the distribution process 

is shown in Figure 1. The first step will mainly involve 

“cleaning up” the GIS data, ensuring that each dataset has

valid metadata, and loading the metadata into the DGGS

database (Freeman, 2001a, 2001b). Once this step is

completed, all of the digital data files that comprise each 

publication (project file) will be archived and indexed

into the DGGS database system, creating a record of the

subsequent distributable dataset. Steps 2 – 7 will be accomplished by providing the authors with a secure, internal, web-based application that will allow users to index

their digital geospatial data files and organize them into 

“packages” according to publication number, dataset, and

then data format type. The GIS Manager will then review

the dataset packages for data quality purposes (Step 7).

Finally, the distribution package will be published to the
108	 DGTAL	MAPPNG	TECHNQUES	‘06

Metadata, cleaning up project files, archiving

data on network

Entry point to internal application.

Log in, find dataset via publication information

Enter and describe layer names of the dataset

Browse to archive location via application,

locate files for distribution

Associate files to be distributed with

dataset layers

Identify files to be distributed together

GIS Manager reviews distribution packages

for data quality

Final approval and release to the public

Note: Author tasks shown in gray, bold boxes.

STEP 1: DISTRIBUTION PREPARATION

STEP 2: FIND DATASET

STEP 4: IDENTIFY LAYERS

STEP 3: FIND PROJECT FILES

STEP 5: INDEX FILES BY LAYER

STEP 6: CREATE DISTRIBUTION PACKAGE

STEP 7: REVIEW DISTRIBUTION PACKAGE

STEP 8: PUBLISH DISTRIBUTION PACKAGE

Figure 1. General workflow diagram for the DGGS data distribution process.

DGGS	Web	site	and	available	to	a	wide	array	of	end	users	

upon final approval by the DGGS Director (Step 8).

PROBLEMS THAT NEED SOLVING

The	old	saying	“Garbage	n,	Garbage	Out”	is	certainly relevant in this case, and the process of finding and 

cleaning	up	the	raw	digital	data	can	be	daunting	and	consume	a	large	portion	of	the	D3	project’s	resources.	Once	

the data files for publications have been located, recovered,	updated,	and	stored	in	a	central	location,	a	decision	

needs	to	be	made	regarding	the	data	format	that	will	be	

distributed	to	the	end	user.	A	project	of	this	type	and	magnitude also requires a well thought-out process work-flow 

and flexible database structure to store and manipulate the 

data.	Moreover,	internal	policies	and	procedures	must	be	

created	so	that	all	those	involved	understand	the	project’s	

purpose	and	goals,	as	well	as	the	assigned	roles	of	staff	

members	and	what	is	expected	of	them	throughout	the	

entire	process.	Once	the	data	are	published	on-line	and	

available	to	the	end	user,	several	steps	must	be	taken	to	

ensure that the customer will be satisfied not only with the 

data,	but	also	the	experience	he	or	she	will	have	in	obtaining	the	data.

Archiving the Data Pile

The	initial	step	of	preparing	and	archiving	the	data	is	

similar	to	taking	an	inventory	of	all	items	in	a	warehouse.	

Many	organizations	have	dealt	with	this	issue	and	have	

had	to	make	“executive	decisions”	regarding	unknown	

aspects	of	legacy	data	after	project	managers	or	veteran	

geologists	retire	or	leave	(Steinmetz	et.	al,	2002).	Moreover,	Steinmetz	et	al.,	(2002)	pointed	out	that,	within	

the petroleum industry, approximately 60 – 80% of a

geoscientist’s time is spent searching for data, while the

remainder is spent organizing and analyzing the data. Ensuring that data files are properly cataloged and archived 

should therefore be a critical priority for any organization that provides data to the public. Documentation and

ensuring data quality for legacy datasets are crucial in

order to make the datasets meaningful and usable. Over

the past several years, the DGGS has participated in the

interagency Minerals Data and Information Rescue in

Alaska (MDIRA) project. The MDIRA project has allowed the DGGS to overcome many challenges regarding

its data archive, specifically database design (Freeman, 

2001a, 2001b), restructuring and archiving legacy data

by developing an appropriate geologic map model (Freeman and Sturmann, 2004), and writing and organizing

metadata (Browne et al., 2003).

At the time of this writing, the total size of geologic

geospatial data in the DGGS central server archive is

approximately eight times greater than what it was in

2001, which is substantial, given that the average size of a

single GIS layer file is on the order of tens to hundreds of 

kilobytes. There are also many large airborne geophysical

datasets, personal databases, and datasets lingering on PCs

that have yet to be archived. It is, therefore, the responsibility of project managers and authors to ensure that data

files are not lost as tenured staff members leave the organization. In this day and age, the DGGS geologist is expected to solve complex geological problems in the field 

and in the office, gather information, process data, create a 

geologic map, and, additionally, archive and document all

of the data associated with the project according to current

division and FGDC standards. The time required to manage the magnitude of data associated with a given publication often dwarfs the time needed to analyze, understand,
THE	CHALLENGES	AND	BENEFTS	OF	DSTRBUTNG	DGTAL	DATA:	LESSONS	LEARNED 109

and	publish	the	data.	Documentation	of	many	geospatial	

datasets	has	been	neglected	because	of	geologists’	need	

to	initiate	new	mapping	projects.	Our	hope	at	the	DGGS	

is	that	the	D3	Project	will	provide	authors	and	geologists	

with	an	effective	user	interface	that	would	allow	them	to	

manage and publish their data more efficiently, granting 

the	geologist	more	time	to	“do	geology.”

What Should We Distribute?

The	goal	of	the	D3	Project	is	to	provide	datasets	

in	formats	that	are	easily	adaptable	to	typical	end-user	

systems.	On	the	basis	of	the	DGGS	staff	comments	gathered	during	the	project-planning	phase,	there	will	be	two	

different file groups for data distribution (Table 1): digital	

data files	and	digital	data	product.	After	considering	the	

two options, the DGGS decided to use “digital data files” 

in	widely	accepted	data	formats	as	the	minimum	standard	

for	all	forms	of	digital	data	distribution.	Providing	data	

to	the	DGGS’s	customers	in	the	native	data	environment	

format	is	not	the	primary	goal;	however,	it	is	an	author-selected	option	for	over-the-counter	distribution	(see	below).	

Although	providing	all	supporting	native	dataset	environment files with the digital dataset may provide more information	with	end-user	appeal,	it	is	not	the	standard	delivery	

we	recommend.	Key	reasons	for	not	choosing	“digital	data	

products”	include:	(1)	the	end-user	will	require	the	native	

software	to	use	the	data,	(2)	greater	data	liability	(e.g.	

the	misuse	or	misinterpretation	of	conclusions	made	by	

the	DGGS),	(3)	high	obsolescence	risk,	and	(4)	the	need	

for	special	knowledge	to	distribute	and	use	the	data	(e.g.	

data/software	compatibility	and	end-user	familiarity	with	

the	software).	Examples	of	the	digital	data	types	distributed by the DGGS are shown in Table 2. These file formats 

include	the	most	basic	data	format	types	that	are	capable	

of	being	adapted	and	used	by	a	larger	end-user	audience.	

Because some data format types are technically proprietary

formats (i.e. ESRI shape files, Microsoft Access databases, 

GeoSoft grid files), providing them in “more generic” 

formats would be unreasonable due to prohibitive file sizes 

or the complexity of the common format (i.e. distributing

relational databases as ASCII, comma-delimited text files). 

The proposed methods of data distribution include

provisions for both over-the-counter and on-line distribution. The goal is to make each method distribute the same

digital data, but in a different package. Custom distribution orders are always available.

Over-The-Counter Distribution

Digital data will be distributed “on-demand” on

transportable media such as a CD-ROM for the over-thecounter (off-line) method. In this case, the publication

number is the basis for distribution. Each CD-ROM will

sell for $10 to cover the cost of the media, plus applicable

postage/shipping costs. Over-the-counter contents could

include:

• “ReadMe” file comprised of the table of contents, 

general information, and instructions in the use of

the data or data product (standard)

•	 Metadata (HTML, ASCII text, and XML formats)

(standard)

•	 PDFs of maps and reports for the publication (standard)

• Digital data files; format depends on data type as 

per Table 2 (DGGS Standard)

• Native dataset files, where different than digital 

data and if centrally stored and cleaned up (at

author’s option)

• Native dataset environment files, if centrally stored 

and cleaned up (at author’s option).

Table 1.	Comparison	of	two	categories	of	data	distribution.

Digital Data Files Digital Data Product

Pros: Cons: Pros: Cons:

1. Simple to distribute

2. Wide audience

3. Easy to index

4. Consistent between

 projects and publications

5. Minimizes obsolescence

6. Smaller number of files

7. Not dependent on

 directory structure

1. User processing

 required before use

2. Annotation may be

 missing or in metadata

3. Requires export from

 native dataset

4. Larger file size

1. Data immediately

 usable/viewable

2. No file conversion (in

 native environment)

3. Full annotation

4. Contains all built-in

 logical relations

5. Often used by authors -

 formats already exist

1. Dependent on directory

 structure

2. Requires native data

 environment

3. Larger chance of data

 liability

4. High obsolescence risk

5. Requires more indexing

6. Difficult to manage

7. May require special

 knowledge to distribute

 and use

ASCII (comma or tab delimited), ESRI Shape, Geo-referenced 

TIFF, MSExcel, ArcExport

Digital data in native data environment (e.g., Geodatabase)

AND supporting information like symbols, fonts, workspace

files, base maps, etc.
110	 DGTAL	MAPPNG	TECHNQUES	‘06

Furthermore, the data storage for the distribution files 

will	use	the	existing	directory	structure	(Freeman	and	

Sturmann, 2004). All files distributed will be indexed in 

the	database	such	that	they	can	be	located	and	copied	onto	

distribution	media	on	an	“as	needed”	basis.

On-line Distribution

Digital data will be distributed on-line, free of

charge, in compressed files to reduce volume and increase 

download speed. Compressed files will allow the DGGS 

to package metadata and other necessary documentation

with the selected data as well as preserve any required

internal file structure. Each compressed file will contain 

an individual digital dataset and metadata file, and will be 

listed with documentary information as an extension to

the existing DGGS Publication Web Page for any given

publication.

1.	Each digital dataset distributed on the Web will

display an abstract and have links to:

• Compressed file containing a digital dataset as 

digital data files and metadata

• Metadata file (including code set documentation) for the digital dataset

•	 A link to the “ReadMe” File

•	 Decompression instructions

2.	Information about availability of over-the-counter

“data on disk” will be included on the publication

page with the following information:

•	 Ordering instructions

• A copy of the “ReadMe” file which includes 

the disk’s table of contents

The Data are Out There, Now What?

With the data files archived, indexed, and bundled 

into distinct datasets, and metadata written, it may be

tempting to think that the job is done. At this point, however, certain aspects of the project are becoming relevant.

For example, project managers and geologists must review the final layout of the publication page and datasets 

before they are officially posted to the Web, despite any 

previous quality assurance testing.

We are describing a major change in the functionality

of the DGGS Web site. These changes will affect users

and cooperators, which warrants some sort of notification. 

It would be beneficial to identify key end-user groups and 

notify them via the Web site itself, e-mail lists, monthly

reports, meetings, or phone calls. Once end-users are

aware of the new data-distribution service, it is imperative

to provide effortless feedback methods with which these

users can comment on data quality and ease of use, and

submit suggestions. Similar to the open-source software

community, the multitude of end-users are relied upon to

find any remaining “bugs” in the system. Moreover, the 

DGGS will utilize database log files and web statistics 

to identify the most “popular” datasets and get a better

understanding for what information is in demand.

LESSONS LEARNED

It was imperative when designing the D3 Project that

the data distribution methods for DGGS staff were consistent and clearly stated. The D3 Project designers met with

geologists and project leaders to discuss the distribution

work-flow, user interface, responsibility assignments, and 

Table 2.	Types	of	digital	data	formats.

Examples of digital Digital Data Files Native Data Set Files Native Data Set

data types (DGGS Standard) Environment Files

Tabular data ASCII comma, tab Excel, Lotus 123, or NA

delimited other spreadsheets

Vector data ESRI shape files ESRI files, MapInfo workspace,

geodatabase, MapInfo ESRI Map document,

tab files fonts, symbol sets,

shade sets, etc.

Raster data TIFF and world file TIFF and MapInfo tab

files

Grid data ASCII comma or tab ESRI grid files, MapInfo

delimited, Geosoft grid or vertical files, ER Mapper

ESRI grid (size of ASCII files grid files

may be prohibitive)

Relational databases Native formats accepted here Access, MySQL, or Report, query or data

(i.e. MS Access), otherwise FileMakerPro database entry documents

ASCII comma, tab delimited (HTML, MSWord, Java,

PSP, or ASP)
THE	CHALLENGES	AND	BENEFTS	OF	DSTRBUTNG	DGTAL	DATA:	LESSONS	LEARNED 111

details	of	particular	types	of	datasets	and	archival	strategies. The data distribution process should also be flexible to 

meet	changing	expectations	and	technical	requirements	of	

end-users.	For	example,	breaking	up	the	publications	into	

several on- and off-line datasets provides flexibility and 

benefits those with small bandwidth or no Internet access.

Prior	to	distributing	data	to	the	public,	an	in-house	

inventory	of	existing	data	serves	to	identify	which	data	

are at risk. This process benefits both the distributor and 

the	end-user	by	ensuring	that	the	data	adheres	to	current	

documentation	standards,	and	by	securing	the	data	on	

more	reliable	media.	Many	agencies	take	the	risk	of	storing	and	distributing	data	in	proprietary	data	formats	that	

may	soon	become	obsolete	or	unreadable.	With	regard	to	

such	a	risk,	one	has	to	ask,	“Which,	if	any,	software	will	

be	available	5,	10,	or	20	years	from	now	that	can	read	the	

data?”,	and	“When	might	the	data	become	legacy	data?”	

n	theory,	data	are	always	becoming	legacy	in	status	

when	software	vendors	upgrade	their	program	packages,	hardware	becomes	obsolete,	and	geologic	maps	are	

updated.	Many	agencies	invest	a	large	amount	of	time	

programming	and	creating	scripts	in	the	current	software	

version, only to find that those scripts are worthless in the 

next	program	release.	Similarly,	storing	precious	data	on	

only	one	type	of	archival	media	can	be	a	terrible	mistake.	

t	is,	therefore,	up	to	project	managers	and	authors	to	

know	when	valuable	data	may	be	at	risk	and	establish	

a	legacy	data	recovery	plan	to	prevent	future	data	loss.	

mplementing	a	project	such	as	this	forces	the	agency	to	

“clean	house”	and	index	valuable	data.

Everyone	involved	with	these	kinds	of	projects	must	

understand	that	documentation	and	data-quality	information	for	every	dataset	are	required.	As	a	result,	end-users	

will	get	consistent,	quality	data	that	are	well-documented,	

which	will	allow	them	to	have	access	to	the	information	

they	need	to	use	the	dataset.	f	a	user	of	a	given	dataset	

cannot find its documentation, he or she will more than 

likely	(1)	not	use	it,	(2)	attempt	to	use	the	dataset	without	

proper	guidance	and	understanding,	or	(3)	use	the	dataset	

incorrectly	or	inappropriately.	f	project	managers	and	

authors	take	the	time	to	document	their	data	soon	after	it	

is	created,	the	painstaking	process	of	going	back	through	

tens	or	hundreds	of	datasets	(some	20	years	old),	contacting	retired	staff	members,	and	guessing	about	the	details	

of	a	publication	can	be	avoided.	Moreover,	by	automating	

distribution	methods	to	the	greatest	extent	possible,	the	

data	can	be	delivered	on	demand.	Since	the	freely	provided	data	are	already	in	digital	form,	easily	searchable,	well	

documented,	and	organized	by	dataset,	users	can	focus	on	

merging	the	data	into	their	own	projects	and	spend	more	

time	on	analysis	and	understanding	the	implications	of	

their scientific data and observations.

REFERENCES

Browne,	C.L.,	Freeman,	L.K.,	and	Graham,	G.R.C.,	2003,	The	

Alaska	Division	of	Geological	&	Geophysical	Survey’s	

Metadata	Policy	Development	and	mplementation,	in	

Soller,	D.R.,	ed.,	Digital	Mapping	Techniques	2003—

Workshop	Proceedings:	U.S.	Geological	Survey,	Open-File	

Report	03–471,	p.	201–208,	available	at	http://pubs.usgs.

gov/of/2003/of03-471/browne/index.html.

Davidson,	Gail,	1998,	Can	we	get	there	from	here?	Experiences	

of	the	Alaska	Division	of	Geological	and	Geophysical	

Surveys,	in	Soller,	D.R.,	ed.,	Digital	Mapping	Techniques	

1998—Workshop	Proceedings:	U.S.	Geological	Survey,	

Open-File	Report	98-487,	p.	13–15,	available	at	http://pubs.

usgs.gov/of/of98-487/davidson.html.

DGGS	Staff,	2005,	Alaska	GeoSurvey	News:	Alaska	Division	

of	Geological	&	Geophysical	Surveys	Newsletter	2005-2,	8	

p.,	available	at	http://www.dggs.dnr.state.ak.us/pubs/pubs?	

reqtype=citation&D=14595.

Freeman,	L.K.,	2001a,	The	DGGS	Geologic	Database:	Putting	

Geologic	Data	Modeling	into	Practice:	Alaska	Division	

of	Geological	&	Geophysical	Surveys,	Alaska	GeoSurvey	

News,	v.	5,	no.	3,	3	p.,	available	at	http://www.dggs.dnr.

state.ak.us/pubs/pubs?reqtype=citation&D=14588.

Freeman,	L.K.,	2001b,	A	Case	Study	in	Database	Design:	The	

Alaska	Geologic	Database,	in	Soller,	D.R.,	ed.,	Digital	

Mapping	Techniques	2001—Workshop	Proceedings:	U.S.	

Geological	Survey	Open-File	Report	01-223,	p.	31–34,	

available	at	http://pubs.usgs.gov/of/2001/of01-223/	

freeman.html.

Freeman,	L.K.,	and	Sturmann,	A.,	2004,	Progress	Towards	an	

Agency-Wide	Geologic	Map	Database	at	Alaska	Division	

of	Geological	&	Geophysical	Surveys,	in	Soller,	D.R.,	ed.,	

Digital	Mapping	Techniques	2004—Workshop	Proceedings:	U.S.	Geological	Survey	Open-File	Report	04-1451,	p.	

9–14,	available	at	http://pubs.usgs.gov/of/2004/1451/	

freeman/index.html.

Steinmetz,	J.C.,	Hill,	R.T.,	and	Sowder,	K.H.,	2002,	Digital	

Archives	and	Metadata	as	Mechanisms	to	Preserve	nstitutional	Memory,	in	Soller,	D.R.,	ed.,	Digital	Mapping	

Techniques	2002—Workshop	Proceedings:	U.S.	Geological	

Survey	Open-File	Report	02-370,	p.	171–176,	available	at	

http://pubs.usgs.gov/of/2002/of02-370/steinmetz.html.
112	 DGTAL	MAPPNG	TECHNQUES	‘06

APPENDIX A

(Description	of	Terms)

the	DGGS	metadata	extension,	entity_and_Attribute_Layer_name	(See	Steps	4	and	5,	Figure	1).	f	

no	layers	exist	in	the	metadata,	the	author	may	have	

to	create	layer	names	for	their	dataset	within	the	application for the purpose of indexing their files.

Metadata:	Metadata	consist	of	information	that	characterizes	data.	Metadata	are	used	to	provide	documentation	for	data	products.	n	essence,	metadata	answer	who,	what,	when,	where,	why,	and	how	about	

every	facet	of	the	data	that	are	being	documented.	

Metadata	written	by	the	DGGS	must	conform	to	

FGDC	standards	(http://www.fgdc.gov/metadata/

geospatial-metadata-standards).	Metadata	will	be	

distributed in three file formats to allow maximum 

readability	and	usability:	Frequently	Asked	Question	(FAQ)	HTML,	ASC	plain	text,	and	Extensible	

Markup	Language	(XML).

Native	dataset: Digital data in file formats that were 

produced	by	the	software	that	was	used	to	generate	

and	process	the	digital	data;	the	dataset	does	not	

include supporting native environment files (See 

Table	2).	The	user	of	these	datasets	may	need	access	to	the	same	software	version	that	was	used	to	

produce	the	data.

Native	dataset	environment:	The	software	operating	

system, hardware, and supporting files used by the 

producer	to	create,	view,	and	process	the	dataset	(See	

Table 2); it may be specific enough that it could be 

very difficult to replicate.

On-line	distribution:	Provides	the	e-mail	and	web	browser	

customers	with	digital	data	in	the	form	of	compressed	

downloadable	data.

Over-the-counter	distribution:	Provides	the	phone,	mail,	

and	walk-in	traditional	customers	with	digital	data	on	

some	media	(e.g.,	CD-ROM).

Project file: Any file found within the publication or project	directory	located	in	the	DGGS	directory	structure	

on the central fileserver.

Custom distribution: A custom distribution is a combination of data or data derivative that has not already

been generated via the publication process. This may

include requests for data reprojections, file format 

conversions, combining GIS layers from multiple

projects or publications, statistical or spatial analyses,

and excessively large amounts of data.

Dataset: A unique group of data that acts as a component

of the publication. Examples include vector geologic features (i.e. bedrock, surficial, hazard polygons/lines/points), geochronology (i.e. spreadsheets,

ASCII .csv), DEM data, electromagnetic anomalies,

and grid data.

Digital data: Information that is ready for numeric or geographic manipulation with a minimum of conversion

or preparation by the customer (e.g. Excel spreadsheets, formatted ASCII files, relational databases, 

geo-referenced raster files, geo-referenced vector 

graphics files).

Digital dataset: A logical, thematic, and geographic grouping of data, including any code sets (required to interpret the data). There may be one or more datasets per

publication; a metadata document describes a digital

dataset. Examples include GIS bedrock geology and

spreadsheets that contain geochemical data related to

a single publication.

Digital data file: Digital data in a file format that can be 

used across a wide variety of computing systems and

meets the needs of most data consumers (See Table

1). These should be the standard formats that DGGS

uses to distribute digital data.

Digital data product: Provides data and supporting information required to view the data in the native dataset

environment (See Table 1). An example includes an

ESRI Geodatabase and all supporting information

like symbols, fonts, workspace files, base maps, etc.

Layer name: The name of the GIS layer, coverage, TAB

file, or table name as defined in the metadata by 
113

113

The Alabama Metadata Portal: http://portal.gsa.state.al.us

By Philip T. Patterson

Geological Survey of Alabama

420 Hackberry Lane

P.O. Box 869999

Tuscaloosa, AL 35468-6999

Telephone: (205) 247-3611

Fax: (205) 349-2861

e-mail: PPatterson@gsa.state.al.us

INTRODUCTION

n	recent	years	federal,	state,	and	local	government	

entities	in	Alabama	have	made	substantial	investments	in	

the	collection,	management,	and	use	of	geospatial	data.	

However,	there	has	been	no	large	scale	effort	to	share	data	

effectively and efficiently. The result was unnecessary 

expenditures	in	redundant	data	creation.

Most	Alabama	Geographic	nformation	Systems	

(GS)	users	currently	have	broadband	internet	access.	The	

increased	network	connectivity	and	high	data-transmission	

rates	have	produced	the	expectation	that	large	amounts	of	

data	can	be	accessed	instantly.	This	demand	for	data	access	has	motivated	the	Alabama	Emergency	Management	

Agency	(AEMA)	and	the	Geological	Survey	of	Alabama	

(GSA)	to	collaborate	in	developing	the	geospatial	data	

portal,	which	allows	cooperators	and	users	to	search	for,	

discover,	and	access	geospatial	data	(GSA,	2006).

BACKGROUND

Before	starting	the	project,	extensive	research	on	

a	variety	of	data	delivery	options	was	performed.	The	

majority	of	the	options	were	related	to	data	clearinghouses, which are mainly useful for specific types of 

static	data	like	imagery,	civic	boundaries,	center	lines,	etc.	

However,	the	data	delivery	website	to	be	built	would	not	

be	intended	for	static	data	alone.	The	need	was	to	build	

a	robust	compilation	of	all	different	types	of	vector	and	

raster	data,	ranging	from	general	datasets	to	obscure	data	

specific to individual projects. Also long-term administration	responsibilities	for	this	type	of	complex	compilation	

site	were	a	concern	for	GSA.	Eventually,	the	grant	for	site	

development	would	end,	and	GSA	would	have	to	support	

managing	and	updating	the	site	from	internal	resources.	

With	support	from	Environmental	Systems	Research	

nstitute	nc.	(ESR),	we	addressed	this	concern	with	a	

modified out-of-the-box application using open-source 

web	applications	in	conjunction	with	ArcMS,	ArcSDE,	

and	an	underlying	database	management	system	(DMS).	

The resulting site provides the functions of a clearinghouse

for general data and a search engine for unique data. It also

offers semi-automated administration, which allows users,

as well the administrator, to manage the site. This solution

is ideal in addressing the data delivery goals and the longterm administration concerns posed by this project.

Connection

This search engine and download site provide the

framework for a mutual geospatial user community of

organizations and stakeholders that facilitates discovery,

sharing, and delivery of GIS content and services. The

portal also facilitates the organization of content and

services such as directories, search tools, community

information, and support resources applications.

The underlying structure of the portal is a threepart generalized connection as follows (Figure 1): (1)

the portal connects to a data provider’s metadata library,

which grants users the rights to publish specified metadata 

records to the portal’s online catalog; (2) the data user

connects to the portal’s search option to locate data using

the portal’s search engine without physically browsing

through the stakeholder’s data; and (3) the data users will

connect to the data provider for download, data captures,

or the identification of the data resource.

By storing only metadata records in our catalog, we

have the ability to index a large amount of virtual data,

and more importantly, the GSA and AEMA will not

have to store the physical data. Our goal is to automate

the tasks of data discovery and distribution so that once

portal connections are complete, minimal maintenance is

required from the hosting agency.

Architecture

A portal is essentially a master web site, which is

connected to a web server and contains a database of

metadata information about geographic data and services.

The services are exposed as web applications using open
114	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 1. Generalized data partnership and user connection concept (modified from ESRI, 2004).

source environments (Tomcat, Java, html, http, xslt, xml,

and jsp) to provide a user-friendly and visually appealing

interface.

The architecture of the metadata server, which connects to all indexed metadata records, relies on three

existing ESRI products: ArcIMS, ArcGIS, and ArcSDE.

The ArcIMS provides the framework and architecture on

which the metadata server runs. The ArcGIS ArcCatalog

application serves as an authoring and publishing tool.

The ArcSDE stores published metadata in records inside

a relational database (ESRI, 2004). ArcIMS introduces a

new approach to serving map products over the internet

through a Java-based application management environment that includes mapping services and map design tools

to support a variety of internet map services (ESRI, 2004).

Main components associated with the ArcIMS communication architecture and web applications are identified in 

Figure 2.

Figure 2. GIS software environment (Modified from 

ESR,	2004).

THE Portal’s online interface

components

Home Page

The home page shown in Figure 3 is the access point

for all online components, and it provides quick access to

the most popular data applications. From the home page,

a user can do a basic keyword search, navigate to the map

viewer, find help information, and access the quick links 

to downloadable data, GIS projects and services, and GIS

resources.

The home page is also where users login to their

accounts. A user account is not necessary to access the

portal, but it increases user capability and enhances functionality. There are five distinct user levels of the portal 

based on a top-down hierarchy; that is, higher level users

can do everything a lower level user can do. From lowest

to highest, these include:

	1.	Anonymous users can be anyone. These users have

the ability to browse the site and use three basic

Figure 3. Example of the portal homepage.
THE	ALABAMA	METADATA	PORTAL:	HTTP://PORTAL.GSA.STATE.AL.US 115

online components: home page, map viewer, and

search page.

	2.	Public Users have the ability to save their created

maps from the map viewer and save their data

searches, which will be available on the users’

home page.

	3.	Publisher Users have the ability to create, publish,

and manage their metadata online.

	4.	Channel Managers have the ability to create and

publish a quick link on the home page.

	5.	Administrators check metadata for accuracy, batchupload metadata, harvest publisher metadata, and

manage users.

Map Viewer

The portal map viewer shown in Figure 4 is a

mapping application that allows users to view one or

multiple internet map services at the same time in their

web browser. Access to selected federal, state, and local

Web Map Services (WMS) using the “add service” menu

is provided, but this limited number of services can be

expanded by entering other map server URL addresses to

access other WMS available online. Viewing internet map

services through the portal map viewer allows users to:

•	 add map services from the portal and other map

servers

•	 display one or multiple map services in a single

map view

•	 set the transparency of map services for overlaying

multiple images

•	 turn map layers on or off within a map service

• find latitude/longitude anywhere in the state for 

automatic navigation of the map

• find street addresses in the state for automatic navigation of the map

•	 identify attribute information about features in a

map service.

The portal is not limited to just ArcIMS WMS; it also

supports several specifications and services of the Open 

Geospatial Consortium (OGC). The OGC is a non-profit, 

international, voluntary consensus standards organization

that is leading the development of standards for geospatial

and location-based services (OGC, 2006). The portal supports the following specifications from the OGC:

•	 Web Mapping Services versions 1.0, 1.1, and 1.1.1

•	 Web Feature Services version 1.0.0

•	 Web Coverage Services version 1.0.0

Figure 4. The portal’s map viewer.
116	 DGTAL	MAPPNG	TECHNQUES	‘06

•	 Web Map Context Documents version 1.0.0

•	 Geographic Markup Language versions 2.0 and 3.0

(when approved)

•	 Open GIS Location Services version 1.0.

Search Function

The search page shown in Figure 5 is the tool for

searching and discovering the metadata of content offered

by many publishers of the Alabama Metadata Portal. The

search page allows users to specify the geographic extent,

keywords, content type, or content theme criteria to find 

matching metadata of map services, data, maps, web services, activities, or documents published in the Alabama

Metadata Portal. Users can search the portal by defining 

“where” they would like to search, “what” in the state

they would like to search, and “when” they would like the

content they are searching for to have been created or updated. Users only need one parameter for a simple search;

however, each additional parameter helps to narrow or

retrieve a search.

THE METADATA PUBLISHING

FUNCTION

The importance of writing good metadata is difficult 

to communicate to potential publishers of the portal. The

success of the connection in Figure 1, however, is based

on accurate and current metadata. Metadata describes

the who, what, when, where, why, and how questions

about the data, which gives users the knowledge to decide

whether the data is appropriate for their desired application. Writing good metadata also mitigates the overall

burdens and cost of data maintenance. The standards for

including metadata records in the portal are the Federal

Geographic Data Committee’s (FGDC) Content Standard

for Digital Geospatial Metadata (CSDGM).

There are three user levels that have metadata administration: Publisher, Channel creator, and Administrator.

The administration of metadata includes the ability to

create, manage, and add metadata to the portal. There

are three options to make metadata records available

for search in the portal. The first option is to publish a 

Figure 5. The portal’s advanced search page.
THE	ALABAMA	METADATA	PORTAL:	HTTP://PORTAL.GSA.STATE.AL.US 117

metadata collection to a metadata repository where the

portal can harvest it. The second option is to upload an

individual or batch Extensible Markup Language (XML)

formatted metadata record to the portal. The third option

is to create a metadata record online using the portal’s

metadata creation tool.

Metadata Harvesting

Metadata harvesting is a self-regulated, scheduled

process for collecting new and updated metadata from

various metadata collection libraries. The process of harvesting allows the portal to synchronize its metadata repository with the publisher’s metadata catalog. If publishers participate in metadata harvesting, any updates made

to their local metadata collection will be updated in the

portal during the next harvesting session. Currently, the

portal can harvest FGDC-compliant metadata from three

different types of harvesting protocols: Z39.50 metadata

clearinghouse node, ArcIMS metadata service, and Web

Accessible Folder.

Metadata harvesting in the Alabama Metadata Portal

is performed in three steps as shown in Figure 6:

1. Harvesting: Based on harvesting protocol specified 

at the time of registration, the portal will connect to

the user’s local metadata repository and retrieve all

new and updated metadata records.

	2.	Validation: During validation, the portal administrator examines each metadata record to confirm 

that minimum portal requirements are met. Records

that are rejected are sent back via e-mail with a list

of invalid fields that need to be added. The records 

will	not	be	added	until	the	metadata	record	is	corrected	and	revalidated.

	3.	Publishing:	All	successfully	validated	and	accepted	

metadata	is	published	in	the	portal	database.	Once	

the	metadata	is	published,	it	is	searchable	through	

the	portal’s	search	interface	by	all	users.

Direct Metadata Upload

f	users	do	not	have	access	to	any	of	the	metadata	distribution	server	protocols	as	described	above,	they	can	upload	

their	XML-formatted	metadata	records	directly	to	the	portal.	

A	metadata	publisher	can,	through	the	online	administration	

tool,	add	and	manage	metadata	on	their	homepage.	Selecting	the	“Upload	Metadata”	button,	users	can	upload	individual	metadata	records	saved	on	their	local	computer.	These	

records	will	be	validated	and	either	rejected	or	published	in	

the	same	process	as	metadata	harvesting.	A	drawback	to	the	

direct	upload	option	is	that	uploaded	published	metadata	is	

not	linked	to	the	local	metadata	repository.	That	is,	updates	

to	a	local	metadata	record	must	be	uploaded	or	manually	

changed	because	they	are	not	automatically	updated	by	the	

portal	when	the	user	updates	local	records.

ArcCatalog Direct Metadata Upload

Batch	uploading	of	metadata	records	directly	to	the	

portal’s	metadata	MS	service	is	possible	if	the	user	is	

using	ESR’s	ArcGS	suite.	Through	ArcCatalog,	the	user	

will	directly	connect	to	the	portal’s	ArcMS	metadata	

server;	a	metadata	publisher	account	name	and	password	

must be specified. With this connection to the portal in 

place,	the	users	can	drag	and	drop	their	folder	of	metaFigure 6. Diagram of the harvesting process(Modified from ESRI, 2004).
118	 DGTAL	MAPPNG	TECHNQUES	‘06

data records into the Publish Metadata Service. An added

benefit to this drag-and-drop method is that the metadata 

record is validated automatically and displays an error

message for all incorrect field values. The drawback of 

this method is the same as the direct upload option where

uploaded published metadata is not linked to the local

metadata repository as they would be with harvesting.

Updates to a local metadata record must be uploaded or

manually changed because they are not automatically

updated by the portal when the user updates local records.

Metadata Direct Entry

The user might not have access to metadata creation

or editing software, or may have very few records to contribute to the portal. If this is the case, the user can utilize

the metadata creation tool provided on the home page.

Users will login to their account and find the “publish 

online form” button under the “My Function” section.

This button will take users to an online form designed to

assist users in the development and production of FGDC

metadata quickly and efficiently. The form provides the 

users with drop menus, fields that are required (indicated 

by *), as well as help definitions and suggestions for each 

of the requested metadata fields.

The minimal compliance of the direct entry method

provides only the elements necessary for data discovery

and is only moderately functional to users searching for

data. The direct entry is a means by which to encourage

users to write metadata in the hope that they will see its

importance and progress toward creating a comprehensive

FGDC-compliant record in the future. By using the online

creation tool, the metadata will be stored only in the portal, and all updates must be made through the portal.

Conclusion

Data download sites and web applications have dramatically improved the GIS productivity. To complete jobs faster,

it is critical that the GIS community share data effectively

and efficiently: the portal is a powerful tool that benefits 

all users and addresses these needs. Faster discovery of

specific datasets and projects, data access to download sites 

and use in the online Map Viewer, lowering of data costs

by reducing the redundancy of data, comparison of multiple

providers to find data that suits their needs, and improvement of data quality and coverage with a constant updating

of agency metadata are a few benefits available through the 

portal. More importantly, the portal heightens the visibility

of participating organizations by displaying the quality and

quantity of their data offerings, which is an indication of their

GIS capabilities. This allows a better understanding of how

an organization could partner for future projects or initiatives.

The first 18 months since the activation of the Alabama Metadata Portal, there were 378,225 total domain

hits, which represent 16,197 visits by 5,544 unique users

(unique IP addresses) shown in Figure 7. We estimate

that each return user has viewed an average of 68 pages.

This current assessment shows the effectiveness of the

Alabama Metadata Portal and the public’s interest in

accessing the data provided. It is important to note that

the portal initiative is by no means the sole solution in

producing an integrated GIS community; the portal represents a fundamental step moving Alabama into the next

generation of GIS productivity.

References

Environmental Systems Research Institute, Inc. (ESRI), 2004,

GIS portal technology, an Environmental Systems Research

Institute, Inc. White Paper: Redlands, California, 9 p.

Geological Survey of Alabama (GSA), 2006, Geological Survey

of Alabama Portal Project: Alabama Metadata Portal, accessed 01 Sept. 2006 at <http://portal.gsa.state.al.us>.

Open Geospatial Consortium, Inc. (OGC), 2006, Welcome to

the OGC website: accessed 01 Sept. 2006 at <http://www.

opengeospatial.org/>.

Figure 7. Daily hits on the portal from 07/01/2005 – 10/01/2006.
119

119

3D Geological Modeling: Solving as a Classification

Problem with the Support Vector Machine

By Alex Smirnoff, Eric Boisvert, and Serge J. Paradis

Geological Survey of Canada

GSC-Quebec

490 de la Couronne

Quebec, Canada G1K 9A9

Telephone: (418) 654-3716

Fax: (418) 654-2615

e-mail: alsmirno@nrcan.gc.ca

ABSTRACT

The process of creating multi-unit 3D geological

models by successive unit interpolation may be tedious

and time-consuming. Here, we propose to automate this

procedure through presenting the problem as a classification task and solving it simultaneously with the Support

Vector Machine (SVM), a method known from the field of 

artificial intelligence. Experiments with various input data 

and kernel parameters demonstrated that the SVM has

great potential in 3D reconstructions from sparse geological information. An extended version of this paper has

been accepted for publication in “Computers and Geosciences” (Smirnoff et al., 2008).

INTRODUCTION

Often, geologists are faced with a variety of diverse

information that requires generalization and analysis.

3D modeling software packages such as Gocad of

Earth Decision Sciences have proven an excellent means

for data presentation and interpretation. The procedure

normally requires reconstruction of individual geological

units using surfaces interpolated from control points with

subsequent fusion of these units into a single model. The

popular interpolation techniques include Inverse Distance

Weighting (IDW), Discrete Smooth Interpolation (DSI),

and various flavors of kriging preceded by semi-variogram analysis.

The above procedure can easily become a tedious

and time-consuming task when a complex geomodel

is considered. In addition, the traditional interpolation

techniques assume reasonable areal coverage of the input

data. Therefore, there is a strong need for an algorithm

that would automate the process of model creation even in

cases when only a few pieces of information on regional

geology, (e.g., a few sparse cross-sections) are available.

Finding such an algorithm and testing its performance on

available data sets was the objective of this study.

Here, we propose the use of the Support Vector Machine (SVM), a tool routinely applied in the field of image 

analysis and pattern recognition. The SVM is becoming

increasingly popular and has been successfully used to

solve classification and regression problems in biology (e.g., Noble et al., 2005), hydrology (e.g., Yu et al.,

2004), medicine (e.g., El-Naqa et al., 2002), and environmental science (e.g., Gilardi et al., 1999). In this study,

we demonstrate that the application of SVM in geology

allows sparse data to be efficiently combined in order to 

reconstruct shape, area, and volume of multiple geological units.

METHODOLOGY

The SVM Algorithm

The SVM algorithm is based on the Statistical Learning Theory developed by V. Vapnik (Vapnik, 1995). It

uses a set of examples with known class information

to build a hyperplane that separates samples of different classes. In machine learning theory, this is known as

supervised learning as opposed to unsupervised learning when no a priori class information is available. This

initial dataset is known as a training set, and every sample

within it is characterized by features upon which classification is based. Figure 1A demonstrates this for the onedimensional (single-feature) case. The samples closest to

the hyperplane are termed support vectors (filled marks in 

Figure 1).

In more complicated, non-linear cases, the task of

discovering the separator is turned into a linear task by

transferring input data into a higher-dimensional space

known as the feature space. Figure 1B shows a non-separable one-dimensional data set in the input space. The

problem is easily solved through re-mapping data to a

higher, two-dimensional space where a linear solution is

found (Figure 1C). Functions satisfying certain conditions

and known as kernels are normally employed for this
120	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 1. Building the separating hyperplane, in separable

and non-separable one-dimensional case. Filled marks

represent support vectors. (A) Linearly separable case and

decision function in input space; (B) non-separable case in

input space. (C) training data re-mapped into two-dimensional feature space using φ(x) = (x, x2) and linear solution

in this space; (D) solution re-plotted back in input space.

transfer (e.g., Abe, 2005). The solution becomes non-linear when shown in the original data space (Figure 1D).

Once the equation for the optimal classifier is found, 

new data with unknown class information (test samples)

can be classified based on the value of this decision function. Unlike most interpolation methods based on the principle that values at points closer in space are more similar,

the SVM is a boundary classification method where the 

boundary is built based on the initial training set among

which only a small number of samples (support vectors)

are involved in the final decision making.

The classical SVM task is a binary (two-class) classification. However, a number of methods have been 

developed to support multi-class classification through 

various combinations of binary methods such as “oneagainst-all”, “one-against-one”, etc. (see Hsu and Lin,

2002 for references). Therefore, the SVM approach is also

applicable for models with more than two classes. More

detailed descriptions of the SVM algorithm are available

from a number of sources (e.g., Cristianini and ShaweTaylor, 2000; Abe, 2005).

SVM Application to 3D Modeling

To apply the SVM algorithm to our geological reconstructions, we defined the 3D space-partitioning task as 

a pure spatial classification problem. Three coordinates 

uniquely describe every point in the 3D reconstruction

space. However, only a limited number of those points

possess descriptions or class information that can be identified through well drilling, surficial geology mapping, 

and seismic profiling. The class information describes the 

geological unit to which each particular point belongs.

Therefore, the points with known class labels become

samples in the SVM training set, and point coordinates in

the three-dimensional space are used as sample features.

Once a classification model based on this training set is 

built, the rest of the points in the reconstruction space can

be classified based on their coordinates (features).

We employed one of the many SVM implementations

freely available over the internet, namely LIBSVM developed at the National Taiwan University (see Chang and

Lin, 2001 for detailed description). As recommended in

Hsu et al. (2004), we used LIBSVM with the radial basis

function (RBF) kernel, the most general form of kernel

resulting in a prediction model controlled by only two hyperparameters, C and γ. A single solution is obtained for

every pair of parameters, and it is sensitive to the choice

of their values. However, selecting the appropriate values

is a dark art normally done on a try-and-see basis.

For multi-class classification, LIBSVM uses “oneagainst-one” approach, which was shown to be advantageous to other methods for practical use (Hsu and Lin,

2002). In addition, a set of in-house Java utilities has been

developed for scaling, validation, and format conversion

purposes.
3D	GEOLOGCAL	MODELNG 121

Data

A 3D geomodel created at the Geological Survey of

Canada, Quebec, in the course of the Esker/Abitibi project

(Bolduc et al., 2005) was used as the reference dataset

in all of the experiments. This model is based on surface

geology, well, and cross-section data. Six geological units

were sequentially interpolated from the above control

points using the Discrete Smooth Interpolation (DSI)

technique (Mallet, 1989) in the Gocad GIS.

Experimental Work

The experimental work was designed to perform the

following tasks: (1) investigate whether the SVM can be

efficiently used in binary geological reconstructions, (2) 

test the SVM for multi-unit modeling, and (3) examine

how the resulting model depends on the RBF kernel parameters used in the reconstruction.

General Approach

The general approach taken in all of the experiments

was as follows:

Prediction

•	 In Gocad, create a reconstruction space as a set of

volume elements (voxet) of the shape representative of study area geometry. The reconstruction

space was defined by a voxet with the following 

number of volume elements (voxels) in each direction: X-110, Y-240, Z-24.

•	 Add available data to the reconstruction space.

The unit type property for each of the six geological units (SVM classes) was transferred from the

stratigraphic grid (SGrid) structure representing the

reference model.

•	 In Gocad, using a DEM, define all voxet nodes 

above the surface as air or no-data points.

• Define a training set for the experiment. For the remaining ground points, set the unit type property to

zero; these are the points that will be later classified 

by trained SVM.

•	 Scale coordinate values for the training set between

0 and 1 as recommended in Hsu et al. (2004).

•	 Using LIBSVM, build a prediction model based

on the training set. A single reference set of kernel

parameters (C = 104 and γ=102) previously determined from 2D experiments was used in all reconstructions except the parameter sensitivity tests.

•	 Scale coordinate values for the points to be classified and classify them using the prediction model 

created in the previous step.

•	 Import the point set with predicted class information

back into Gocad, for visualization and analysis.

Validation

• Based on the available reference data, define the 

validation set and extract it as a set of points with

attached class property.

•	 Test predicted class labels against the validation

set to determine how many original points in each

class and overall were adequately classified, a measure also known as the recall rate.

Binary Reconstruction

The training set was composed of the Esker/Abitibi

model points located on 11 arbitrarily chosen parallel

sections oriented along axis X. Points were grouped into

two classes as shown in Table 1. The input data statistics

are given in Column 4 of Table 1. As seen from the table,

the training set was dominated by points representative of

Class 2, which combined all model units except the Esker

Unit. The validation set was composed of all the remaining model points (not included in the training set). The

number of points used for validation in each of the two

classes is shown in Column 1a of Table 3.

Table 1.	Geological	units,	SVM	classes,	and	training	set	statistics	for	Esker/Abitibi	Binary	Model.	Total	number	of	

points to be classified is 371783.

 1. Geological Unit 2. SVM Class 3. All Points (#/%)b 4. Training Points (#/%)c

Esker	 1	 20300/5.22	 995/0.26

Non-Eskera 2	 368935/94.78	 16457/4.23

All	Units	 -	 389235/100	 17452/4.48

a

Non-Esker	unit	included	Rock,	Till,	Clay,	Littoral	and	Organic	units

bNumber	of	all	class	points	and	their	percentage	of	all	model	points

c

Number	of	training	points	per	class	and	their	percentage	of	all	model	points
122	 DGTAL	MAPPNG	TECHNQUES	‘06

Multi-Class reconstruction

The	same	training	points,	arranged	into	six	classes	

corresponding	to	the	six	geological	units	found	in	the	

original	model,	were	used	to	test	the	SVM	capabilities	

in multi-class classification (Table 2). For training data 

statistics,	see	Column	4	of	Table	2.	This	time,	bedrock	

(Class	6)	entirely	dominated	the	training	set,	with	organics	(Class	1)	being	the	least	representative.	The	validation	

set	also	contained	information	about	all	six	geological	

units	as	shown	in	Column	2a	of	Table	3.

Hyperparameters Sensitivity tests and Multiple 

Parameter-Set reconstructions

To	analyze	the	sensitivity	of	prediction	results	to	the	

values	of	hyperparameters,	C	and	γ,	we	used	a	simple	grid	

search	procedure	as	proposed	in	Hsu	et	al.	(2004).	The	

grid search was run for the above training set configurations,	and	the	range	of	parameters	scanned	by	every	

search was from 2-8 to 215 for C and from 2-15 to 212 for

γ incrementing parameter values by a power of 2. As in

previous experiments, the success rate was determined

through direct comparison with the validation set extracted from the reference model. We also examined the dependency of success rate on parameter values for the class

with the least number of training points (Class 1–Organics). Finally, binary models were built with combinations

of parameters drawn from the margins of the reasonable

working range. These included low C (2-2) – high γ (28),

low C (2-2) – low γ (25), average C (27) – average γ (26),

high C (214) – high γ (28) and high C (214) – low γ (25).

Results and Discussion

Binary Reconstruction

The original esker body, training sections, and the results of binary reconstruction with the reference parameter

set are shown in Figure 2. Column 1b of Table 3 describes

Table 2. Geological	units,	SVM	classes,	and	training	set	statistics	for	Esker/Abitibi	Multi-Class	Model.	Total	number	of	

points to be classified is 371783.

 1. Geological Unit 2. SVM Class 3. All Points (#/%)a 4. Training Points (#/%)b

Organics	 1	 1210/0.31	 48/0.01

Littoral	 2	 3819/0.97	 193/0.05

Clay	 3	 13295/3.42	 628/0.16

Esker	 4	 20300/5.22	 995/0.26

Till	 5	 15865/4.08	 747/0.19

Bedrock	 6	 334746/86.00	 14841/3.81

All	Units	 -	 389235/100389235/100389235/100	 17452/4.4817452/4.4817452/4.48

a

Number	of	class	points	and	their	percentage	of	all	model	points

bNumber	of	training	points	per	class	and	their	percentage	of	all	model	points

Table 3.	Validation	data	and	results	for	Esker/Abitibi	binary	and	multi-class	model.	Number	of	validation	points	in	original model and percentage of points properly classified by SVM.

SVM Class 1. Binary 2. Multi-Class

a. Validation Points (#/%)a b. Success (%) a. Validation Points (#/%)a b. Success (%)

1	 19305/5.19	 71.50	 1162/0.31	 18.76

2	 352478/94.81	 98.87	 3626/0.98	 37.20

3	 -	 -	 12667/3.41	 57.10

4	 -	 -	 19305/5.19	 67.65

5	 -	 -	 15118/4.07	 45.72

6	 -	 -	 319905/86.05	 95.45

All	 371783/100	 97.34	 371783/100	 89.87

a

Number	of	validation	points	per	class	and	their	percentage	of	all	model	points
3D	GEOLOGCAL	MODELNG 123

Figure 2. Binary esker reconstruction. (A) Original Esker

Unit; (B) training set; (C) reconstructed Esker Unit.

the validation results. As seen from Table 3, the success

rate of SVM prediction is exceedingly high. Especially

remarkable results, 98.87%, are achieved in Class 2. In

part, this can be attributed to the fact that points of this

class entirely dominate the training set. When the SVM

cannot classify a point in binary classification, it tends 

to attribute it to the predominant class. Considering that

bedrock points constitute 94.81% of all points that need

to be classified (352478 of 371783 as shown in Column 

1 of Table 3), it is no surprise that the overall success of

prediction achieves 97.34%.

With the above explanation in mind, the classification success in Class 1, which represents only about 6%

of the training set, is still as high as 71.50%. This, in our

opinion, proves that the SVM can be effectively used for

binary (single-unit) reconstructions even with training sets

substantially skewed toward one of the classes.

We further analyzed success rate in Class 1 on all

model sections where the Esker Unit was present (234

sections). The results are presented in Figure 3. The

figure clearly demonstrates that the success of prediction 

decreases as the distance from a training section increases.

As training section # 1 did not intersect the esker body,

the success rate on the first 18 sections drops to 0%. 

Therefore, as could be expected, the overall reliability of

prediction is directly proportional to the density of sections with training data.

Multi-Class Reconstruction

The results of this experiment are found in Figure 4

and Column 2b of Table 3. The overall success score is

89.87%, which is mainly controlled by the predominant

bedrock class (Class 6). Two other classes, esker and

marine clay, demonstrate success rates over 50%. These

units are somewhat better represented in the SVM training

set than the remaining classes. Figure 5 shows that the

success of reconstruction for a particular class in this experiment is almost directly proportional to the number of

those class points in the training set, exceeding 75% when

the number of training points exceeds 1% of the total.

We also compared area and volume of geological

units in the original model and its reconstructed counterpart. The results presented in Table 4 and Figure 6 show

that, for both area and volume calculations, the reconstructed and original values for any unit are the same

order of magnitude. This suggests that along with single

unit modeling, the SVM can efficiently be applied in 

multi-unit volumetric reconstructions.

Hyperparameter Sensitivity Tests and

Multiple Parameter-Set Reconstructions

Figure 7 summarizes the results of grid search for

the best pair of hyperparameters in binary and multi-class

reconstructions. The best overall success rates, 97.79%

and 92.03%, were achieved at [C=21, γ=26] and [C=2-1,

γ=26], respectively. The analysis of success rates in the

class with the least number of training points yielded

77.38% and 22.46% at [C=22, γ=26] and [C=29, γ=25],

correspondingly. As seen from the results, parameters are

fairly stable, and a single range for C [2-3 - 215] and γ [24 -
124	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 3. Validation results for binary esker reconstruction from 11 parallel sections. Success in

Class 1 (Esker) against section number. Vertical lines indicate training sections. Total length of

horizontal axis is 24km and sections are spaced at 100m.

Figure 4. Multi-class esker reconstruction from 11 parallel

sections. (A) Original model; (B) training set; (C) reconstructed

model.
3D	GEOLOGCAL	MODELNG 125

Figure 5. Validation results for multi-class esker reconstruction from 11 parallel sections. Success

per class vs. number of training points per class.

Figure 6. Surface area and volume comparison for original (reference

model) and reconstructed geological units. (A) Surface area; (B) volume.
126	 DGTAL	MAPPNG	TECHNQUES	‘06

Table 4.	Results	of	SVM	reconstruction	from	11	parallel	sections	for	Esker/Abitibi.	Unit	area	and	volume	comparison	

(original	model	vs.	reconstructed).	See	Table	2	for	training	set	statistics.

Geological Unit 1. Area (m2) 2. Volume (m 2. Volume (m3)

a. Original b. Reconstructed a. Original b. Reconstructed

Organics	 2.55E+07	 2.99E+07	 8.06E+07	 1.05E+08	

Littoral	 7.56E+07	 7.34E+07	 2.54E+08	 3.30E+08

Clay	 1.99E+08	 1.97E+08	 8.86E+08	 1.01E+09

Esker	 1.48E+08	 1.33E+08	 1.35E+09	 1.16E+09

Till	 2.15E+08	 1.99E+08	 1.06E+09	 1.07E+09

Bedrock	 2.70E+08	 3.06E+08	 2.23E+10	 2.23E+10

Figure 7. Summary of best results from parameter sensitivity tests for binary and multi-class

reconstructions and proposed range for RBF kernel parameters (C [2-3, 215] and γ [24, 29]).

29]	can	be	recommended.	Within	this	range,	higher	overall	

scores	and	higher	scores	for	over-represented	classes	are	

achieved	at	somewhat	lower	C	values.	On	the	other	hand,	

proper classification of points in the least represented 

classes	requires	higher	C	values.	Visual	examination	of	

binary	models	built	with	combinations	of	parameters	

drawn	from	different	corners	of	the	above	range	also	

show	that	a	more	generalized	picture	can	be	achieved	

at	lower	C’s	(Figure	8a,	8b)	while	higher	values	of	this	

parameter	promote	more	detailed	interpretation	(Figure	

8d,	8e).	Average	C	values	result	in	well-balanced	models	

(Figure 8c). The influence of the second parameter (γ)	is	

not	as	obvious.	

CONCLUSIONS

Our experiments clearly showed that the SVM with

RBF kernel can be efficiently used for both single- and 

multi-unit 3D reconstructions. The procedure is performed in a single step, which eliminates the need for

unit-by-unit interpolation. Even from a limited training

set (e.g., several cross-sections sparsely distributed across

the study area) reasonable reconstruction results can be

achieved.

It is important, however, that all classes to be reconstructed are reasonably represented in the training set.

In the multi-class case, the reconstruction success was
3D	GEOLOGCAL	MODELNG 127

shown	to	be	directly	proportional	to	the	number	of	unit	

samples	in	the	training	data.	The	reliability	of	prediction	is	greater	in	the	vicinity	of	the	training	data,	and	

therefore,	the	density	of	training	sections	and	spatial	

continuity	of	lithological	units	may	directly	affect	the	

reconstruction	results.

The	kernel	parameters	should	be	chosen	from	the	

range	2-3-	215	for	C	and	24-	29	for	γ.	When	more	model	

details	are	required	or	classes	with	a	small	number	of	

training	points	are	involved,	higher	C	values	should	be	

considered.	Lower	C	values	result	in	more	generalized	

models	with	fewer	details.	This	favors	classes	that	dominate	the	training	set.

Finally,	our	results	indicate	that	when	appropriate	parameters	are	chosen,	not	only	the	general	shape	

of	a	geological	body,	but	also	such	characteristics	as	its	

surface	area	and	volume	can	be	reconstructed	with	results	

close	to	those	obtained	from	the	application	of	classical	

GS	methods.

Figure 8. Binary reconstructions with parameters drawn from different corners of the range

presented in Figure 7. (A) low C (2-2) – high γ (28); (B) low C (2-2) – low γ (25); (C) average C

(27) – average γ (26); (D) high C (214) – high γ (28); (E) high C (214) – low γ (25).

ACKNOWLEDGEMENTS

The	research	was	conducted	at	the	Quebec	Division	of	the	Geological	Survey	of	Canada	(GSC),	and	was	

funded	by	the	Groundwater	Program	of	the	Earth	Sciences	Sector	of	Natural	Resources	Canada	(contribution	

number	20060335).	We	are	especially	grateful	to	Andrée	

Bolduc	(GSC)	for	her	involvement	in	this	work	and	for	

having	made	the	Gocad files with initial data and 3D Esker/Abitibi	model	used	in	experiments	readily	available.

REFERENCES

Abe, S., 2005, Support Vector Machines for Pattern Classification:	Springer-Verlag,	London,	343	p.

Bolduc	A.M.,	Paradis	S.J.,	Riverin	M.N.,	Lefebvre	R.,	and	Michaud	Y.,	2005,	A	3D	esker	geomodel	for	groundwater	research:	The	case	of	the	Saint-Mathieu–Berry	esker,	Abitibi,	

Quebec,	Canada,	in	Russell,	H.,	Berg,	R.C.,	and	Thorleif-
128	 DGTAL	MAPPNG	TECHNQUES	‘06

son,	L.H.	eds.,	Three-Dimensional	Geological	Mapping	for	

Groundwater	Applications,	Geological	Survey	of	Canada,	

Ottawa,	Ontario,	Open	File	5048,	p.17-20.

Chang,	C-C.,	and	Lin,	C-J.,	2001,	LBSVM:	a	Library	for	Support	Vector	Machines,	accessed	at	http://www.csie.ntu.edu.

tw/~cjlin/papers/libsvm.pdf.

Cristianini,	N.,	and	Shawe-Taylor,	J.,	2000,	Support	Vector	

Machines:	Cambridge	University	Press,	189	p.

El-Naqa,	.,	Yang,	Y.,	Wernik,	M.N.,	Galatsanos,	N.P.,	and	

Nishikawa,	R.,	2002,	Support	vector	machine	learning	for	

the detection of microcalcifications in mammograms: IEEE 

Transactions	on	Medical	maging	21,	p.	1552-1563.

Gilardi,	N.,	Kanevski,	M.,	Maignan,	M.,	and	Mayoraz,	E.,	1999,	

Environmental and Pollution Spatial Data Classification 

with	Support	Vector	Machines	and	Geostatistics:	Workshop	

W07	“ntellegent	techniques	for	Spatio-Temporal	Data	

Analysis	in	Environmental	Applications”,	ACA99,	July,	

Greece,	p.	43-51.

Hsu,	C-W.,	Chang,	C-C.,	and	Lin,	C-J.,	2004,	A	Practical	Guide	

to Support Vector Classification, accessed at http://www.

csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

Hsu,	C-W.,	and	Lin,	C-J.,	2002,	A	comparison	of	methods	for	

multi-class	support	vector	machines:	EEE	Transactions	on	

Neural	Networks	13(2),	p.	415-425.

Mallet,	J-L.,	1989,	Discrete	Smooth	nterpolation:	ACM	Transactions	on	Graphics	8(2),	p.	121-144.

Noble,	W.S,	Kuehn,	S.,	Thurman,	R.,	Yu,	M.,	and	Stamatoyannopoulos,	J.,	2005,	Predicting	the	in	vivo	signature	of	human	

gene	regulatory	sequences:	Bioinformatics	21(1),	p.	338-343.

Smirnoff,	A.,	Boisvert,	E.,	and	Paradis,	S.J.,	2008,	Support	Vector	

Machine	for	3D	modelling	from	sparse	geological	information	of	various	origins:	Computers	and	Geosciences	34,	p.	

127-143.

Vapnik,	V.,	1995,	The	Nature	of	Statistical	Learning	Theory:	

Springer-Verlag,	New	York,	311	p.

Yu,	X.,	Liong.,	S-Y.,	and	Babovic,	V.,	2004,	EC-SVM	approach	

for	real-time	hydrologic	forecasting:	Journal	of	Hydroinformatics	06(3),	p.	209-223.
129

129

Qualitative and Quantitative 3D Modeling

of Surficial Materials at Multiple Scales

By Erik R. Venteris

Ohio Geological Survey

2045 Morse Rd., Bldg. C

Columbus, OH 43229

Telephone: (614) 265-6459

Fax: (614) 447-1918

e-mail: erik.venteris@dnr.state.oh.us

INTRODUCTION

An understanding of the distribution of surficial materials is key to many problems in geological engineering,

mineral-resource inventory, and environmental remediation. However, for most states the zone between the surface (usually represented by surface glacial geology and

the cooperative soil survey) and bedrock is unmapped. In

Ohio, regional-scale maps of soil, surface glacial geology, and bedrock have been available for over 100 years,

but concentrated mapping of the full sequence of surficial 

materials did not begin until 1998. The Ohio Division of

Geological Survey (ODGS) is conducting three-dimensional (3D) mapping and modeling of surficial materials 

at the 1:100,000 scale. Currently, over one-third of the

state has been mapped (concentrating on glaciated areas)

at the 1:100,000 scale using qualitative methods based

on geologic interpretation and drafting on Mylar (for an

example of a completed product, see Swinford et al., this

volume). Surficial materials are represented by two-dimensional (2D) polygons, which are assigned alphanumeric sequences describing sediment type, thickness,

and lateral distribution (“stack” maps, Kempton, 1981),

providing information in the third dimension. Mapping is

conducted from the surface to the bedrock interface using soil maps, legacy geologic maps, water wells, bridge

borings, and detailed site studies (mainly from environmental remediation). Envisioned applications for the GIS

data and maps include surface/ground water simulations,

mineral-resource inventory, and geologic engineering

(seismic hazards, landslides, etc.).

As part of this mapping work, quantitative methods

based on geostatistics are also being investigated. Lithology (clay, silt, sand, gravel) is modeled using sequential

indicator simulation (Journel, 1983; Deutsch and Journel,

1998) to investigate methods for modeling stratigraphic

and facies-scale variability at the 1:24,000-scale. Simulation is based on the same principles as kriging, but Monte

Carlo techniques are used to develop multiple models

(realizations) or configurations from one data set rather 

than obtaining a single, optimized estimate of lithology.

Geostatistic simulation provides a range of statistically

possible configurations of the subsurface that are faithful 

to the well data and statistical structure. While useful in

themselves as 3D models, geostatistical models can give

guidance to stack mapping by exposing consistent configurations of lithology. The amount of horizontal continuity

in the models can give some measure of the uncertainty or

appropriateness of assigning a stack sequence to potentially complex sediments of buried glacial valleys.

The goal of this paper is to illustrate the application of both methodologies being pursued at the ODGS

to map unconsolidated sediments. Results are compared

to enhance the understanding of the strengths and weaknesses of both approaches, and to discern how information from one technique can be used to improve the other.

Geostatistical simulation has much to offer as a technique

for 3D modeling. Output is in a 3D grid format (“voxel”)

and ready for volumetric (inventory) calculations or

input into numerical models such as flow simulations 

for groundwater. There are scientific advantages as well; 

model parameters and procedures are completely traceable, so the maps are more “scientific” in that they meet 

the requirement of repeatability. While subjectivity is

reduced, such modeling still requires much interpretation and “trade craft.” A key advantage of simulation is

the generation of multiple versions (“realizations”) of the

model that obey the data and the spatial structure. This

aspect of simulation provides myriad possibilities for the

assessment of uncertainty, ranging in complexity from

generating basic statistics to describe variations between

simulation runs to full analysis of the effect of uncertainty

on all model parameters. Uncertainty assessment is key

to evaluating risks when using the models for real world

decisions. However, simulation techniques are not a way

to generate maps quickly (good geostatistical practice

requires careful, time-consuming investigation), nor map

large areas (computation demands are limiting). Reconnaissance mapping over large areas is still the realm of

traditional geologic mapping due to the limitations of
130	 DGTAL	MAPPNG	TECHNQUES	‘06

the data (interpretation is needed over interpolation) and

computation limits of large 3D voxel grids. This study

proposes that both traditional mapping and geostatistical

simulation have important and complementary roles in

surficial mapping and characterization.

Mapping work for the 2006 fiscal year National 

Cooperative Geologic Mapping Program-STATEMAP

component project is located in the Ashtabula and

Youngstown 1:100,000 scale quadrangles located in

northeast Ohio. This project area is the focus of this

contribution. The area is heavily glaciated with extensive deposits of Wisconsinan- and Illinoian-age drift.

Near Lake Erie, ice proximal (till, kames, outwash) and

lake deposits (lacustrine, beach ridges) cover the Portage escarpment (Brockman, 1998). Further inland, the

depositional environment changes to till plains and buried

valleys. A key feature of most of the buried valleys in this

region (Bagley, 1953) is that they were ice-dammed (to

the north), which resulted in a greater portion of fine sediments (lacustrine deposits) than is found in buried valleys

that drain to the south (Ritzi et al., 2000). A buried valley

in the southwest corner of the 1:24,000 Ashtabula South

quadrangle is the subject of both qualitative stack mapping and geostatistical modeling (Figure 1).

METHODOLOGY

A key component of both mapping techniques is the

collection of base maps and boring data. A GIS/ digital

database approach (based on ESRI ArcGIS and Microsoft Access) to data compilation is adopted for efficient 

distribution and storage. Detailed discussion of specific 

software modules and file formats used in the GIS data 

management is beyond the scope of this paper. Software

used for geostatistical modeling is given a more thorough

treatment.

The 1:100,000-scale “stack” maps (for this study, the

USGS Ashtabula and Youngstown quadrangles) are initially drawn on 1:24,000-scale Mylar maps using (underlying) several different paper base maps and a light table.

Interpretations are based on maps of soil parent materials, drift thickness, bedrock geology, and legacy geology

maps. In addition, boring data from water wells (Ohio

Division of Water, ODW), bridge borings (Ohio Department of Transportation, ODOT), and environmental

studies (Ohio Environmental Protection Agency, OEPA)

provided key information for mapping below the surface.

The general data sources and procedures for making stack

maps are described in the following sections.

Soils Maps

Whereas a major innovation of these maps is the inclusion of information in the subsurface, surface information remains critical and has a large impact on the appearance of the final map. Surface mapping units (lithologies) 

are largely derived from county-scale soil surveys. Soil

survey information (1:15,840 scale) is used to make maps

of parent material. Teams of pedologists use field investigations, soil sampling, and air photo interpretation to

divide the landscape into polygons of like soils. The suite

of soil mapping units and the rules for delineation on the

landscape are based on a mutually agreed upon conceptual

model. Mapping units are organized on major transitions

in soil type, often those of significance to the management 

of the land. At the county scale, these transitions between

soil units are usually due to changes in geomorphology

and, therefore, provide a potentially high-resolution data

source for surface lithology.

The primary source of digital soil data is the Natural Resource Conservation Service (NRCS) Soil Survey

Geographic, commonly known as SSURGO (Soil Survey

Staff, 2006). SSURGO GIS databases provide the mapping polygons as GIS files and extensive tabular data that 

describe soil horizonation, chemical/ physical properties,

and descriptions of soil suitability for a wide range of

land uses. The tables do not, however, explicitly define 

parent materials for each soil type. They are assigned to

the polygons by creating a lookup table of interpreted

(by this author) parent materials for each mapping unit.

The tables are based on the detailed soil profiles and 

interpretive descriptions found in the written soil survey

report. 1:24,000-scale maps of parent material are generated for each quadrangle (Figure 2). The stack model is

much more accurate for layers near land surface than at

depth because the level of detail in the soil survey is far

greater than available well and boring data. However, the

main intended use for soil mapping is land management.

Therefore, there are often discrepancies between parent

materials determined from soil polygons and the actual

parent material (verification is conducted from boring data 

and by geomorphic interpretation). Parent material maps

created in this fashion must be used with caution and

interpreted with care.

Drift-Thickness Map

A second piece of mapping information is drift thickness (DT) (Powers and Swinford, 2004). DT maps are calculated from the surface digital elevation model (DEM)

and the bedrock-topography map (Figure 3) (the bedrock

topography map is usually updated and revised during the

stack mapping process, past versions available as Mylar

basemaps, contour shapefiles (vector), and grids (ODGS, 

2003)). A key issue for DT maps is “flying outcrops” 

where the elevation of the bedrock topography (BT) exceeds that of the DEM. Such areas are not unexpected, as

there are significant inaccuracies in both datasets. The accuracy of USGS DEMs is in the range of 5 to 10 feet Root

Mean Squared Error (RMSE) (Venteris and Slater, 2005;

Smith and Sandwell, 2003). The accuracy of BT within

the Ashtabula and Trumbull quadrangles was estimated as
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 131

Figure 1. Map of drift thickness for part of the Ashtabula South 1:24,000-scale quadrangle, Ohio.

part of the current project. External validation was conducted for BT based on new bridge borings that were not

used to create the map. Bedrock elevations from bridge

borings were compared with interpolated elevations based

on a TOPOGRID model of BT contours. RMSE error was

found to be 22 feet. Assuming zero covariance between

the error in the DEM and BT, the total error DT is

E2 Dt = σ2

DeM + σ2Bt (1)

where σ2

DEM is the error (expressed as a variance) in the

DEM and σ2

BT is the error in the BT. Assuming an error of

7 ft RMSE for the DEM, the total RMSE error in DT for

this area is 23 feet. Hence, the error in DT is dominated

by the error in the BT. Because of this uncertainty, drift

thickness less than this value may in reality be areas of

bedrock outcrop. For the current mapping project, areas

of negative drift thickness are corrected to a DT value of

zero feet. Areas of thin drift (less than 5 feet) are usu-
132	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 2. Parent material map based on SSURGO.

ally also identified in the parent-material (soil survey) 

maps, providing another means to confirm the presence of 

outcrop. Surficial materials at such locations are marked 

with	parentheses	on	the	“stack”	maps	to	indicate	that	drift	

coverage	is	discontinuous	in	the	area.

Other Base Maps

Legacy	geologic	maps	are	also	used	in	mapping.	Digital versions of the bedrock geology map (Ohio Division

of Geological Survey, 2003) are used to map the bedrock

base of each stack unit (the entire area of interest in the

present mapping is underlain by shales of Devonian age).

Existing maps of surface glacial deposits are compared to

the parent material maps (from the soil survey) to aid in

the assignment of surface units, especially in interpreting

depositional environment. Often, lithology information is

obtained from the well data and the soils maps without a
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 133

Figure 3. Glacial-geologic map of the study area taken from White and Totten (1979).

geomorphic	interpretation.	However,	such	interpretations	

are	critical	to	the	use	of	this	data	for	its	intended	applications. For example, identification of sand and gravel (a 

lithology)	as	either	an	outwash	or	ice	contact	deposit	(a	

geomorphic	interpretation)	is	key	to	groundwater	modeling,	as	there	will	be	marked	differences	in	facies	hydraulic	

structure	and	conductivity	between	the	materials.

County-scale	glacial-geology	maps	(Figure	3)	were	

available	for	the	three	counties	of	the	2006	STATEMAP	

project area (White, 1971; White and Totten, 1979; Totten

and White, 1987). These maps were scanned and rectified 

(not digitized in a vector format) and used in the GIS as

an additional layer to aid in the mapping of surface units

(assignment of lithology, etc).

Water-Well and Other Boring Data

Mapping in the subsurface is based on lithology and
134	 DGTAL	MAPPNG	TECHNQUES	‘06

other information available from borings drilled for water

wells and engineering studies. The most spatially dense

data set are water well records from Ohio Division of

Water (ODOW). It is a legal requirement that the records

(logs) from the drilling of water wells be filed with the 

state. The lithology (texture), thickness, and occasionally color of layers encountered while drilling the well

are contained in the records. The records are obtained as

Excel spreadsheets (ODOW, 2005) and converted to a

geodatabase for use in ArcGIS. An ArcGIS Visual Basic

application was built to display the well location and

lithology on paper base maps (Figure 4). The water well

data provide critical information on lithologic sequences

with depth, but the unit “clay” requires careful interpretation. It is likely that the “clay” unit of the water-well

records contains lithologies that range from clay to silt.

The identification of silt units is strongly underrepresented 

compared to the proportion indicated in more detailed and

reliable texture data (textures based on laboratory work)

such as those from bridge borings (Table 1). The lithology

“clay,” therefore, is reinterpreted in this modeling to mean

clay and silt.

More accurate and detailed depth information is

available from the Ohio Department of Transportation

in the form of detailed records from geotechnical borings drilled to support bridge construction. These data

are available as paper records from ODOT (Figure 5). A

digital relational database (Figure 6) is in development

Table 1.	Comparison	between	sediment	textures	in	

water-well	and	bridge-borings	in	Ashtabula	County.	The	

comparison	contains	many	sources	of	serious	bias,	as	the	

water-well	data	generally	extends	to	greater	depths	than	

the	ODOT	bridge	boring	data,	and	the	spatial	distribution	

of	bridge	borings	is	seriously	biased.	However,	it	is	clear	

the	silt	is	grossly	underrepresented	in	water	wells.	The	

column	“Bridge	Boring”	gives	the	textural	percentage	

used to define each lithlogic class in the bridge borings.

 Texture 

Class 

Water 

Well 

Bridge 

Boring 

Cutoff for

Bridge Boring

Clay	

Silt	

Sand	

Gravel	

0.6	

0.004	

0.18	

0.21	

0.37	

0.26	

0.07	

0.07	

>40%

>40%

>40%

>30%	sand,

>10%	gravel

Figure 4. Close-up example of water-well postings.
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 135

Figure 5. Scan of a portion of an ODOT bridge boring record.

Figure 6. Schematic showing fields, tables and database relationships for the bridge boring/ OEPA 

database,	built	by	the	Ohio	Division	of	Geological	Survey.
136	 DGTAL	MAPPNG	TECHNQUES	‘06

by	ODGS	to	facilitate	computer-based	analysis	of	these	

data.	ODOT	records	provide	detailed	engineering	data	on	

texture,	mechanical	strength	(blow	counts,	plasticity	indexes,	liquid	limit,	wetness),	and	lithology	(color,	texture,	

texture	class).	ODOT	bridge	borings	provide	excellent	site	

information,	but	have	limited	spatial	density	and	a	strong	

locational	bias.	The	borings	are	mainly	collected	where	

roads	intersect	major	streams	and	the	depth	is	usually	limited to approximately 50 ft, which is insufficient in typical 

buried	valleys	where	drift	thickness	can	exceed	300	feet.

Further	depth	information	is	available	from	OEPA	

in the form of detailed site studies. Engineering firms 

with projects such as sanitary landfills and industrial 

facilities file with OEPA their detailed studies of the 

subsurface.	These	reports	typically	contain	several	well	

borings	(typically	10	to	30	wells)	with	a	wide	range	of	

engineering	data.	The	types	of	information	are	usually	

similar	to	ODOT	records,	but	with	inconsistent	coverage	(one	well	may	contain	good	textural	information,	

but	lack	blow	counts,	etc.).	Digital	capture	of	all	wells	in	

these	sites	is	beyond	the	mapping	goals	(site	studies	vs.	

regional	mapping)	and	resources	(time/	labor	constraints)	

of	the	ODGS.	Typically,	the	best	well	of	the	group	(with	

represtative	geology	and	data	compatible	with	the	ODOT	

database)	is	chosen	from	each	site	and	entered	into	the	

same	database	as	the	bridge	borings.

Qualitative Mapping—Ashtabula South 

Quadrangle Case Study

The	idea	for	mapping	in	three	dimensions	using	stack	

sequences and 2D polygons is based on previous surficial 

mapping approaches (Kempton, 1981), with refinements 

unique	to	ODGS	(Brockman	et	al.,	2004).	The	emphasis	

in	ODGS	work	is	on	lithologic	characterization,	so	stack	

sequences	describe	both	layer	lithology	and	thickness	

(estimated	to	within	50%)	from	the	surface	to	bedrock.	

Little	attempt	is	made	in	this	mapping	work	to	assign	time	

units	to	the	layers,	except	where	distinctions	between	

the	Wisconsinan	and	prior	glaciations	are	well	known	or	

obvious.	Tills	are	not	mapped	by	traditional	stratigraphic	

units	(for	example,	the	Hiram	and	Waverly	tills	of	White	

and Totten, 1979) but are divided where there are significant	changes	in	texture	or	chemistry	(carbonate	content).

Base	data	are	compiled	into	a	stack	model	using	

traditional	geologic	mapping	methods	(drafting)	followed	

by	GS	digitization.	Experience	has	shown	that	accurate	

and efficient generalization and interpretation require the 

geologist	to	utilize	much	information	in	a	spatial	context.	

Large	scale	(1:24:000)	mapping	conducted	using	transparent	Mylar	and	paper	base	maps	can	display	much	more	

information	at	a	legible	scale	at	one	time	than	any	practical	(inexpensive)	computer	screen.	After	compilation,	

hand-drawn	maps	are	digitized	and	attributed	to	make	

GS	coverages	using	standard	GS	techniques.

The first step in mapping is to delineate the major 

lithologic	and	geomorphic	units	present	at	the	surface.	

Polygons	of	surface	features	are	drawn	initially	by	generalizing	(as	appropriate	for	a	1:100,000-scale	map)	the	

parent	material	polygons	from	the	interpreted	soil-survey	

map.	Elevation	contours	and	DEMs	are	often	used	as	an	

additional	guide	to	generalization	(breaks	in	slope,	or	

stream	and	erosion	patterns).	The	surface	model	is	further	

refined by adding geomorphic interpretations based on the 

mapper’s	own	knowledge,	aided	by	legacy	geologic	maps	

such	as	the	glacial-geology	series.	The	surface	model	

(Figure 7) is also checked and verified using information 

from	the	various	water-well	logs	and	other	boring	data.

The difficulty and need for geologic interpretation	increases	greatly	when	mapping	in	the	subsurface.	

Subsurface	transitions	are	delineated	on	the	maps	using	

a	contrasting	line	color,	which	is	expressed	as	a	different	

line style on the final map (solid lines for changes in surface	materials	and	dashed	lines	for	subsurface	transitions	

(Brockman	et	al.,	2004;	Swinford	et	al.,	this	volume)).	

Subsurface	polygons	denote	large	changes	in	thickness	

and lithologic sequence. The first step in subsurface mapping	is	inspection	of	the	drift	thickness	map.	The	geologist	looks	for	major	geomorphic	features,	which	provide	

a	rough	idea	of	where	the	major	transitions	will	be	drawn.	

n	general,	breaks	in	thickness	that	delineate	buried	valleys	and	end	moraines	are	the	most	common	and	critical	

to	communicating	the	geology	of	the	area.	Once	major	

thickness	transitions	are	denoted,	a	stratigraphic	model	

is	developed	to	assign	stack	sequences	to	each	mapping	

polygon.	This	model	is	based	on	inspection	and	analysis	

of	the	well	data	in	the	area.	n	general,	detailed	information	(mainly	texture,	but	penetration	and	plasticity	tests	

are	also	useful)	from	bridge	borings	and	environmental	

study	sites	are	used	to	develop	an	initial	conceptual	

model. The model is then verified and extended spatially 

using	the	more	general	water	well	data	(which	are	usually	posted	to	the	parent	material	and	drift	thickness	base	

maps) and an understanding of the expected configuration 

of	sediments	for	the	given	geomorphologic	environment.

An	illustrative	example	of	mapping	at	depth	is	provided	for	the	SW	corner	of	the	Ashtabula	South	Quadrangle.	This	area	is	unusually	complex,	as	it	contains	the	

Painesville	end	moraine	superimposed	on	the	buried	valley	associated	with	the	Grand	River.	The	area	surrounding	

these	features	contains	end	moraines,	beach	ridges,	and	

lacustrine	sediments.	The	main	subsurface	feature,	a	major	north-south	bedrock	valley,	was	dammed	(to	the	north)	

when	glacial	ice	occupied	the	Lake	Erie	basin.	The	feature	

is	approximately	four	miles	wide	and	contains	drift	with	

a	thickness	up	to	300	feet	(Figure	1).	When	mapping	this	

buried	valley,	we	have	to	make	two	major	decisions:
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 137

Figure 7. Preliminary model of the subsurface geology at head of the Grand River. This is a portion of the “stack” map for the 1:100,000 scale Ashtabula Quadrangle. The map is currently under

review. Some key abbreviations: TG = Wisconsinan till unit high in silt content, TE = Wisconsinan

till unit high in clay, SG = sand and gravel, LC = silt and clay (generally lacustrine), Sh = shale

bedrock, S = Sand, CG = buried-valley deposit with undifferentiated lithology.
138	 DGTAL	MAPPNG	TECHNQUES	‘06

	1.	What is the best way to draw polygons to communicate the important transitions in thickness and

lithology?

	2.	How do we best generalize available well data into

a stratigraphic model to assign stack sequences to

these polygons?

Maps of drift thickness are used to provide an initial

impression of the subsurface configuration. The DT map 

(Figure 1) shows the superposition of the north-trending Grand River buried valley and the SW-NE trending

Painesville moraine. Some “fingers” of increasing drift 

thickness off the main valley are due to the Painesville

moraine, whereas others are due to the presence of buried

side valleys. The bedrock-topography map must be used

in conjunction with the drift thickness map for proper interpretation. These maps were used to develop basic ideas

on mapping the subsurface, such as the locations and extents of major subsurface polygons, total depth needed for

stack sequences, boundaries of major subsurface morphologic units (in this case delineating a reasonable extent of

lake sediments when the buried valley was flooded), and 

cartographic concerns such as minimizing small and sliver

polygons caused by the interaction of surface features and

subsurface polygons.

Borehole information is used to develop a generalized stratigraphic sequence or “stack” for each polygon.

Each stack contains the lithology (and geomorphology, where applicable) and thickness estimate (within

±50%). The goal is to generalize information from many

wells into vertical sequences and identify horizontal

transitions and breaks in the sequences (overall depth

or lithology) that warrant drawing additional polygon

boundaries.

Stack sequences for the study area were mainly

based on water-well lithology logs. Detailed (bridge)

boring data were not available for this geomorphic feature within the quadrangle boundary. Only one detailed

site with limited depth (~100 ft) existed to the southwest

(in the Jeffersonville 1:24,000-scale quadrangle), so

stratigraphic models were developed mainly from the

water-well database. The first attempt at a stratigraphic 

model was based on interpretation of common patterns

in lithology. An attempt to correlate first clays, first gravels, second clays, second gravels and so on was done.

Some patterns emerged, but it was clear there was much

variability between the data. Differences in vertical

resolution and quality between loggers, complex spatial

variations in geology, and blunders all made it difficult 

to create a generalized stack. The preliminary subsurface

model and stratigraphic sequence (Figure 7) was subject

to a more rigorous review, aided by results of geostatistical and statistical analysis.

Quantitative Mapping—Geostatistical

Modeling (Sequential Indicator Simulation)

Geostatistical simulation techniques exist to model

and simulate categorical variables such as lithology. Such

methods are common in oil and gas exploration (Deutsch,

2000) and have been used to characterize surficial deposits for ground-water modeling (Carle and Fogg, 1996;

Ritzi et al., 2000). The various forms of geostatistical

simulation are generally preferred over indicator kriging

for modeling of surficial deposits because buried-valley 

and other surficial deposits have high spatial complexity. In addition, the sample spacings of typical well data

sets contain gaps or average spacings that greatly exceed

the scale of autocorrelation. In this context, the rigorous

techniques of uncertainty analysis and superior ability to

extrapolate results beyond the well data (due to sequential

approach used in simulation algorithms) of simulation

techniques are valuable. While model results in sparsely

sampled regions are not reliable for predicting the position

of lithologies (say for drill planning), such extrapolation is

useful for groundwater simulation, provided many realizations are used to understand the range of possible results

in poorly constrained areas. There are also many theoretical reasons (missing variance and inherent smoothing of

kriging, etc.) to choose simulation techniques over kriging

(Deutsch, 2000).

The goals and output results of geostatisical simulation are different than those of indicator kriging. Kriging

provides the best estimate of values at unsampled locations. The goals of simulation are to use kriging in conjunction with Monte Carlo techniques to produce many

different realizations faithful to the data locations and

reproduce global statistics (histogram) and local spatial

structure (variogram). Each realization represents a statistically valid, potential configuration of the subsurface. 

However, interpolated values in each model are not optimal estimates and can vary widely between realizations.

The variation between realizations is the strength of the

method, as it is the basis for evaluating the uncertainty of

the model. The more tightly constrained the model (large

amount of well control, predictable spatial structure with

strong (repeated) patterns), the less variability between

runs. Summary statistics that characterize the differences

between realizations provide a rigorous and convenient

way to assess model uncertainty.

A full development of sequential indicator simulation

(SISIM) (Journel, 1983) is not presented here. However,

the basics of the algorithm are described to provide the

reader with insight as to how the technique works and the

configuration of the data and spatial autocorrelation will 

affect results. First, a regular three-dimensional grid is

specified for the volume of interest (for efficiency) and a 
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 139

random interpolation order is chosen for the cells. This is

important, as simulated values are treated as data points

and used to calculate kriging weights for subsequent

cells. SISIM is based on the calculation of a conditional

distribution for each cell, which is randomly drawn from

to assign a lithology. The conditional distribution is based

on the kriging estimate (local information) and the global

probability. For each lithological type (k) at location (u),

the conditional probability is

u ( ) u (u (2) k

n n

pk = ∑ ki −+ ∑ p =1 =1

* )( 1; α

α α

α

λα λ )

(2

( )

where λα

tor	code	(a	binary	variable	for	each	lithology,	i.e.,	sand	

(1)	or	not	sand	(0))	for	a	neighboring	data	point	(either	

a	“real”	data	point,	or	a	simulated	cell)	and	pk(u)	is	the	

global	probability	for	the	respective	lithology.	Hence,	

the	conditional	probability	is	a	function	of	the	values	

of neighboring cells (whose influence with distance is 

defined by the variogram and kriging) and a global probability.	Next,	p*

k	results	for	each	lithology	are	combined	

to define the cumulative conditional distribution function 

(ccdf).	A	random	number	between	0	and	1	is	drawn,	and	

the	ccdf	is	used	to	assign	a	lithology	for	that	cell.

Some	key	aspects	of	the	algorithm	should	be	considered	when	evaluating	an	individual	realization:

 1. Global probabilities have decreasing influence with 

increasing	density	of	neighboring	data	(due	to	the	

presence of well control or cells that are filled in 

later	in	the	grid	order).	n	data-rich	regions,	results	

are mainly influenced by the data values and the 

variogram (the distance of influence increases 

with	range;	the	nugget	effect	(if	used)	decreases	

the kriging weights and increases the influence of 

global	proportions).	n	sparsely	sampled	areas,	initial	cells	are	controlled	by	the	global	probabilities.

	2.	Randomness	and	subsequent	differences	between	

realizations	arises	from	two	sources:	the	random	

path	for	assigning	cell	values	and	the	random	draw	

to	assign	a	lithology	from	the	conditional	distribution	function.	Once	a	lithology	is	assigned	to	

an empty cell, it becomes a data point and influences	the	neighboring	results.	This	is	generally	a	

good	feature	of	sequential	simulation,	as	it	tends	

to	produce	geologic	bodies	even	in	areas	poorly	

constrained	by	wells	(for	example,	once	sand	is	

assigned	to	a	cell,	it	is	more	likely	that	neighboring	

cells	also	will	be	assigned	as	sand,	creating	a	sand	

layer).	The	end	result	is	that,	even	in	areas	of	little	

or	no	data,	global	statistics	and	spatial	structure	are	

preserved.

is the simple-kriging weight, i is the indicaThe sequential indicator simulation algorithm in

GSLIB (Deutsch and Journel, 1998) was used to model

lithology (clay, silt, sand, or gravel) for the southwest portion of the Ashtabula South Quadrangle described above.

Workflow

The work presented here was a preliminary exploration, mainly intended to create 3D models for comparison

with the stack maps. A far more rigorous modeling of

the area (conducted in elevation space, using simulations nested by stratigraphic units, and models of spatial

structure based on well statistics (Ritzi et. al, 2000)) is

presented in Venteris, 2007. The preliminary investigations presented here were conducted to test whether lithologic information from the water wells could be extended

(interpolated) into continuous models using geostatistical

simulation techniques. The amount of similarity between

models created by the two techniques could provide insight into the geology of the study area and the quality of

information contained in water wells.

The first step in modeling was to define the spatial 

domain of the model. The area for study was chosen for

geological interest and the presence of sufficient water-well data to support simulation. The lack of detailed

ODOT borings was a significant disadvantage to this 

study area. The models were created in depth-space rather

than using real world elevations. Such an approach was

advantageous for preserving lateral continuity for layers

that follow topography such as till sheets and for comparison with the stack model, which were also modeled

as depth and thickness. A three dimensional grid with cell

dimensions [x=200 ft, y=200 ft, z=2 ft] (data continuity is

much higher in the z direction) was defined for modeling. 

This domain contained both unconsolidated sediments

and bedrock, the boundary between the two defined by the 

DT grid. A FORTRAN program was written to convert

the 2D ASCII format DT grid into a 3D GSLIB format

voxel, which was then used to clip the model results. Geostatistical simulation was only conducted for the portion

of the data containing unconsolidated sediments. Bedrock

and unconsolidated sediments were not modeled together

because the probability of either lithology group is not

spatially constant over the domain (stationarity).

Well data were converted to a data format appropriate

for geostatistical modeling. Water well data from ODOW

were converted to a simple set of indicator codes. The

conversion required some interpretation and generalization. For example, a water well record described as C/R

or “Clay and Rock” was coded as clay, assuming that the

driller was describing a till with rock fragments. There

were many lithologies that require interpretation in the

water-well database, but their overall proportion in the
140	 DGTAL	MAPPNG	TECHNQUES	‘06

dataset	was	small.	Most	descriptions	were	of	common	

and easily classified lithologies. Unconsolidated lithology 

classes from the water wells were reclassified into four 

indicator	variables	[clay=1,	silt=2,	sand=3,	gravel=4].	

n	addition,	the	wells	were	discretized	in	the	vertical	

direction.	Lithologies	for	wells	in	the	ODOW	database	

were	given	a	range	of	depth	(upper	and	lower	values).	

The	water	wells	were	discretized	at	one-foot	increments	

to provide sufficient continuity of lithology values for the 

intended	vertical	resolution	of	the	voxel	model.

SSM	required	the	assignment	of	global	probabilities	

for	each	lithology.	For	this	data	set,	clay	was	the	dominant	

lithology	(Table	2).	Silt	is	grossly	under	represented	in	

the	water-well	dataset	as	discussed	above,	and	so	cells	

modeled	as	clay	include	both	clay	and	silt.	The	data	were	

checked	for	clustering	bias	using	the	DECLUS	routine	

in	GSLB.	Bias	due	to	clustering	is	less	than	5%	for	this	

dataset and is not considered a significant source of error.

The next step was to define the spatial structure 

(variogram)	for	use	in	assigning	kriging	weights.	First,	

experimental	variograms	were	calculated	from	the	data.	

Experimental	variograms	were	used	to	create	model	variograms	for	input	into	the	simulation	(kriging)	procedure.	

Model	variograms	can	be	created	by	visual	estimation,	

trial and error modeling, and automatic fitting routines. 

Key	information	to	obtain	from	the	experimental	variograms	was	the	overall	shape	(expressed	as	a	function,	

usually	spherical),	the	range	of	autocorrelation	(where	

the	variogram	intercepts	the	sill),	and	the	magnitude	of	

the	nugget	effect	(non-zero	intercept,	caused	by	smallscale	variability	below	the	distance	of	the	lag	spacing	and	

measurement	error).

Experimental	variograms	were	calculated	(using	

GAMV	routine	in	GSLB)	for	each	lithology	(except	silt,	

for which there are insufficient data points) in the vertical	and	horizontal	directions.	A	range	of	experimental	

variograms	were	calculated	to	explore	many	possible	

scales	of	spatial	structure	(by	adjusting	lag	spacings	and	

the	number	of	lags)	and	to	check	for	anisotropy.	There	

was	some	indication	of	anisotropy	(semi-variance	values	

exceeding	sill),	but	the	noise	in	the	data	set	precluded	an	

accurate	estimate	of	directionality.	ndicator	variograms	

in	the	vertical	direction	(Figure	8)	were	generally	smooth,	

Table 2.	Proportions	of	each	lithologic	unit	for	the	simulated	area.

 Texture Class Water Well

Clay	 0.816

Silt	 0.002

Sand	 0.137

Gravel	 0.045

Figure 8. Indicator variograms in the vertical direction.

Dark blue is clay, light blue is silt, yellow is sand, and red

is gravel.

had a small nugget effect, and were fit with a basic spherical model (Table 3). Experimental variograms were much

less stable in the horizontal direction. Smooth variograms

were possible using large lag spacings (Figure 9) but a

more revealing picture was obtained by using small lag

spacings (Figure 10). The smooth experimental variograms suggested a very large nugget effect and a range

of around 3,000 feet. The gravel lithology never fully

approached the sill, which suggested an anisotropic structure. Experimental variograms using a smaller lag spacing

showed a more complex picture. For small lag spacings

the nugget was much reduced, but the semi-variance oscillated widely, making range selection ambiguous. This

“hole effect” could have been due to the natural spatial

structure of the glacial sediments or a result of incomplete

and noisy sample data. Determining the range (point of

intercept with the sill) was essential for kriging and simulation. The variograms showed an initial structure (local

maxima) at about 1,500 feet. All variograms intercepted

the sill several times over the range of 1,500 to 5,000 feet.

Several variograms were used in the simulations,

as the experimental variography did not provide clear

guidance for the horizontal variogram model (a different,

more rigorous approach to characterizing spatial structure for SISIM modeling is presented in Venteris, 2007).

Three example variogram models were used to demonstrate a reasonable range of results. No attempt was made

to choose the best model from the range of possibilities.

Rather, multiple scenarios were run to illustrate the effect

of variogram parameters on results. Model variograms

for simulation were loosely based on the experimental

horizontal variograms (vertical models are held constant

(Table 3)). For the first experiment, the horizontal range 

for all lithologies was set to 2,000 feet (a compromise
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 141

Table 3.	Model	variogram	parameters	used	in	the	three	indicator	simulation	runs.	The	

nugget	effect	is	zero	for	the	short	range	and	long	range	models.	The	nugget	is	0.5	for	the	

short-range	with	nugget	model.

 Indicator Horiz. Range (Anisotropic) Vertical Range Contribution

Nugget

1	

2	

3	

4	

Short	Range

1	

2	

3	

4	

Long	Range

1	

2	

3	

4	

2000	

2000	

2000	

2000	

2000	

2000	

2000	

2000	

5000	

5000	

5000	

5000	

50	

15	

40	

80	

50	

15	

40	

80	

50	

15	

40	

80	

0.5

0.5

0.5

0.5

1

1

1

1

1

1

1

1

Figure 9. Indicator variograms in the horizontal direction

(isotropic) using a lag separation of 1500 feet and a lag

tolerance of 800 feet. Colors and lithologies are the same

as in Figure 8.

Figure 10.	ndicator	variograms	in	the	horizontal	direction	(isotropic)	using	a	lag	separation	of	300	feet	and	a	lag	

tolerance	of	150	feet.	Colors	and	lithologies	are	the	same	

as	in	Figure	8.

between	the	ranges	of	clay	and	sand	(intercept	at	1,500	

feet)	and	gravel	(around	2,500	feet))	with	a	large	nugget	

contribution	(0.5,	or	50%	of	the	variance	due	to	measurement	error	and	spatial	variation	below	the	scale	of	

the	lag	distance).	For	the	second	model,	the	range	was	

held	to	2,000	feet,	but	the	nugget	effect	was	set	to	zero	

(assuming	nugget	due	to	inadequate	sampling	rather	than	

geologic variability). For the final model, it was assumed 

that	both	early	oscillations	and	large	nugget	effects	were	

spurious. The variograms were modeled with a range of

5,000 feet and a nugget effect of zero. This model represented the maximum amount of spatial continuity that

could reasonably be interpreted from the experimental

variogram results.

Lithologies were simulated using the SISIM algorithm as implemented in GSLIB. 16 individual realizations were produced for each variogram model. As a rule

of thumb, the number of realizations should be around
142	 DGTAL	MAPPNG	TECHNQUES	‘06

100. Running this model in GSLIB resulted in a stack

overflow and program discontinuation after 16 runs. 

The solution was to run multiple batches and then write

software to recombine them for final products. Modified 

software to process 100 runs was not complete at the writing of this contribution. The goals of this study are mainly

exploratory and illustrative, so the limitation to 16 realizations had little meaningful impact.

To find the most common value and assess the variability of the realizations for each cell, the realizations

were post-processed. Firstly, the portion of the model at or

below bedrock was removed (clipped). FORTRAN programs were written to post-process the SISIM runs. The

mode value was used as the most common value between

runs. The algorithm did not break ties between lithologies

(a very rare occurrence), and cells with an ambiguous

mode were written to “no value.” Cells that did not contain a well data point could take any value from realization to realization. The variety of lithologies written to

each cell was of interest. Variation between runs for each

voxel cell was evaluated using Shannon’s (1948) entropy

where pi was the proportion of each lithology within the

∑=

−=

i

ppH ii

1

ln (3)

n

set of realizations. Values close to zero indicated consistent values between runs, and values near 1 represented

high variation between runs.

RESULTS

Qualitative Mapping

A wide range of surface features of mappable size

exist in the study area. The preliminary stack map is

presented in Figure 7. The major surface geomorphic

feature is the Painesville end moraine, which trends from

the southeast to the northwest. On the lake ward side (to

the northwest), sand and gravel beach ridges are superimposed. Most alluvial deposits are too small to be mapped

at 1:100,000 scale. Behind the end moraine is a small

southeast/ northwest trending lacustrine deposit. In the far

southwest corner is a large sand and gravel deposit, which

was previously interpreted as outwash (White and Totten,

1979). The far southeast corner is occupied by till in the

form of small end moraines and ground moraine. There

is also a major alluvial valley deposit in this part of the

study area. For the stack maps, tills are not differentiated

into end and ground moraines, as on a traditional glacialgeology map. Instead, tills are divided on the basis of

broad textural class where the unit “TG” represents a siltrich unit found lake ward and “TE” indicates a clay-rich

till found inland.

The major subsurface feature in the study area was

the north-south trending buried valley, and it was the major challenge to mapping in the study area. A generalized

stratigraphic model was developed from a wide variety

of information, little of which could provide definitive 

guidance or insight. The first task toward modeling the 

stratigraphy was to use previous studies (White and

Totten, 1979), wells, and base maps to develop a general reconnaissance model of the subsurface. This initial

survey gave a basic sense of the sediments that might be

encountered in the subsurface and, in particular, indicated

that lacustrine sediments were an important component of

this buried valley.

The surface was dominated by tills of Wisconsinan

age that ranged in thickness from nearly 0 to 100 feet in

the study area (Figure 11). A rough estimate of the thickness of this unit was estimated from the wells using the

first gravel or sand as the boundary between the Wisconsinan tills and underlying sediments. However, the marker

was very thin, absent, or ambiguous in many of the wells.

The map was useful for estimating thickness within ±50%

for defining stack sequences, but was interpreted with 

caution.

The next issue was determining a generalized stratigraphy below the major till unit. The task was highly

interpretive. Water well records only provided basic

lithology (clay, silt, sand, etc.) and gave no information on

the geomorphic environment. Hence, a lithology of “clay”

could have referred to till or lacustrine deposits (perhaps

ice contact as well). The buried valley likely contained

till, lacustrine, sand, and gravel deposits. However, the

only direct evidence for the existence of lacustrine deposits was from an ODOT bridge boring south of the study

area (Figure 12). This well showed a 25-foot thick layer

that contained 0% aggregates at a depth of 40 feet, which

is likely a lacustrine deposit. Even with this high resolution and quality evidence, a low aggregate till could not

be completely ruled out for this layer, however.

Further information to aid interpretation was provided by a plot of the proportion of sand and gravel with

depth for all the wells of the study area (Figure 13). Sand

and gravel did not commonly occur in the upper 50 feet

of the surficial deposits (where Wisconsinan till predominated). Below 50 feet, the likelihood of encountering

sand and gravel deposits increased up to a maximum of

40% at a depth of 105 feet. Then the proportion of sand

and gravel dropped off again, to around 0.25 from 120

to 145 feet. Finally, the proportion increased again, but

was interpreted with caution, as a limited number of wells

penetrated to this depth. (The trends in the proportion of

sand and gravel had implications for geostatistical simulation and are discussed later). This plot was used to guide

the placement of sand and gravel layers within the stack

sequences.

This variety of information was interpreted, combined and simplified to create the “stack” for each of 

the polygons. A base stack was developed for the center

(thickest part) of the end moraine and buried valley, which
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 143

Figure 11. Estimated thickness of Wisconsinan till from kriging of water-well data.

was	correlated	outwards,	eliminating	lower	units	as	the	

bedrock	elevation	increased.	The	data	could	have	been	interpreted	and	generalized	in	many	ways,	so	several	stack	

models	were	possible	(Table	4).	A	range	of	models	was	

given	with	varying	degrees	of	complexity	and	interpretation.	The	layer	of	Wisconsinan	tills	(TG)	was	the	most	

certain	of	the	units	and	was	used	in	all	models.	Model	

1	was	the	most	detailed	and	heavily	interpreted	version.	

The sand and gravel unit noted at the base of the tills was

included in the second layer as (SG). Lacustrine deposits

identified in the detailed bridge boring occurred below 

this unit. This was followed by a sequence of sand and

gravel units estimated to be between 15 and 45 feet thick

(SG3). This unit was based on information from Figure 13

(sections where the proportion of sand and gravel exceeds

30%). This was followed by another lacustrine unit, inter-
144	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 12. Texture analysis from an ODOT bridge boring

close to the study area.

Figure 13. Proportion of sand, gravel and sand, and

gravel with depth for all the wells in the study area.

Table 4.	Potential	stack	models	for	the	part	of	the	study	

area	with	the	thickest	drift	(an	end	moraine	superimposed	

on	a	buried	valley).	The	complex	models	(e.g.,	#1)	might	

provide	more	information,	while	the	simpler	ones	(e.g.,	

#4)	may	provide	a	more	reasonable	picture	of	what	is	

known	about	the	geology	in	the	area.	The	numbers	represent	thickness	divided	by	10,	and	are	considered	accurate	

to	within	50%.	Parentheses	indicate	that	the	presence	of	

a	layer	is	discontinuous	between	wells.	Abbreviations	are	

as	follows,	TG-	Wisconsinan	till	with	high	silt	content,	

SG-	sand	and	gravel	deposits,	LC-	silt	and	clay	(generally	

lacustrine)	deposits,	CG-	undifferentiated	buried	valley	

deposits with insufficient well control or extreme complexity	that	prevents	differentiation	of	lithology.

 Model 1 Model 2 Model 3 Model 4

Layer	1	

Layer	2	

Layer	3	

Layer	4	

Layer	5	

Layer	6	

Layer	7	

TG9	

(SG)	

LC2	

SG3	

LC2	

(SG)

CG7

TG9	

(LC2)	

SG3	

LC2

CG9

TG9	

SG4	

CG11

TG9

CG16

preted	from	the	drop	in	the	proportion	of	sand	and	gravel.	

A	buried	till	could	was	a	possible	alternate	interpretation	

for	layer	5.	The	water	wells	indicated	a	dominance	of	clay	

for	this	depth,	but	there	is	no	information	on	aggregate	

content,	etc.,	to	show	how	this	clay	was	deposited.	Layer	

6	in	this	model	represented	deep	sand	and	gravels,	which	

were mainly identified from detailed EPA site studies and 

descriptions	in	previous	publications	(White	and	Totten,	

1979).	This	was	a	unit	of	pre-Wisconsinan	till	or	sand	and	

gravel	that	was	oxidized	and	probably	occurs	near	the	

bedrock	interface.	The	rest	of	the	sequence	(on	average,	

70	feet	of	material)	was	essentially	unknown	because	

there	were	few	borings	that	penetrated	to	this	depth	that	

contained	material	descriptions.	This	unit	was	designated	

as	CG,	which	denoted	buried-valley	lithologies	that	range	

from clay to gravel. Model 2 was simplified by eliminating two units. The first sand and gravel (layer 2) of Model 

1	was	eliminated	because	it	was	often	nonexistent	or	too	

thin	to	map.	Also,	the	bottom-most	sand	and	gravel	of	

Model 1 was eliminated because evidence for its existence

is questionable. Model 3 further simplified the model by 

removing the upper LC unit because it was confirmed 

at only one location. Likewise, the lower LC unit was

eliminated, as it was purely an interpreted unit. There was

little evidence to differentiate this interval between till or

lacustrine deposits, so inclusion of this interval with the

CG unit was justified. Model 4 represented the most conservative model. Here, everything below the Wisconsinan

till unit was considered unknown. The justification for this 

approach was that the depth of occurrence and lithology

of the buried-valley deposits below the till was essentially

unknown and unmappable.

The choice of final stack model for the map was 

arbitrary. Decisions must be based on the judgment of

the geologist, using a compromise between the limited

available information (what can be justified on the data or 

evidence) and the need to communicate what likely would

be encountered below the surface (based on geologic
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 145

knowledge	and	interpretation).	Model	4	was	considered	

too	simplistic.	From	Figure	13,	it	was	clear	that	there	was	

a	nearly	even	chance	of	encountering	a	sand	and	gravel	

layer	at	depths	ranging	from	70	to	110	feet.	The	increased	

likelihood	of	sand	and	gravel	layers	over	this	depth	range	

was	not	communicated	in	Model	4.	Model	3	was	also	

probably	too	simplistic.	Here,	the	presence	of	sand	and	

gravel	was	communicated,	but	no	lacustrine	deposits	

were	designated.	Model	2	contains	all	the	major	components that we expected to find in this buried valley. Two 

lacustrine	deposits	were	designated,	which	bracketed	the	

most	probable	stratigraphic	position	and	thickness	of	sand	

and	gravel.	The	stack	model	communicated	the	main	idea	

of	the	deposit:	tills	underlain	by	buried-valley	deposits	

that have more fine-grained materials than was typical 

for	Ohio	(due	to	the	ice	damming	to	the	north).	However,	

model	1	was	too	detailed	and	seems	over-interpreted	

compared	to	the	quality	of	the	data.	The	bottom	SG	unit	

was only identified in a few wells and did not provide the 

user	with	particularly	useful	new	information.	The	SG	

unit	of	Layer	2	was	much	more	common	in	the	well	data,	

but	thickness	and	depths	are	inconsistent.

In summary, it was difficult to correlate lithologies 

between wells with confidence. This was consistent with 

the	results	of	variogram	modeling,	which	indicated	that	

spatial	patterns	were	noisy	at	best	(large	nugget	effect).	

Finding meaningful and reliable patterns between data

points was a serious issue for both qualitative and quantitative mapping approaches.

Geostatistical Simulation

Example realizations, mode, and entropy are presented for each of the three variogram models to compare

results. The results demonstrate the range of possible configurations (individual realizations) and the amount of spatial continuity using long and short autocorrelation ranges

and the nugget effect. The results are presented as fence

diagrams for an overview, and cross sections are provided

for close inspection. An overview of the model domain,

well data, and bedrock surface is found in Figure 14.

Individual realizations are presented in Figure 15,

with two example realizations (of the 16 calculated)

provided for each variogram model. Each obeys the data

values, spatial structure, and histogram of the original

data. Each realization is one possible configuration of the 

subsurface from a range of possibilities. There are clear

differences between results. The 2000-foot range, large

nugget effect model (Figure 15-A) produces realizations

with a high amount of randomness. For example, simulated sand and gravel bodies contain many cells of clay,

and regions of clay are “speckled” with sand and gravel

Figure 14. Overview three-dimensional model showing wells, their lithology, and the bedrock surface (in

units of depth, not elevation).
146	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 15. Example realizations for models. A, short-range variogram and high nugget effect; B, short

range variogram with no nugget effect; C, long-range variogram with zero nugget effect. The spatial orientation is the same as for Figure 14 (north to the upper left corner).
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 147

cells. Such a model would have low flow continuity when 

modeling groundwater flow. Retaining the range (2000 

ft)	but	removing	the	nugget	effect	produces	a	lithologic	

model	with	more	continuous	bodies	(Figure	15-B).	There	

is	much	more	spatial	continuity,	and	“speckling”	is	minimized.	Extending	the	range	to	5000	feet	(Figure	15-C)	

produces	elongate	horizons	of	sand	and	gravel.	Such	results	are	visually	pleasing,	as	they	produce	a	layered	look	

to	the	geology,	which	is	compatible	with	stratigraphic	

concepts.	However,	this	model	is	the	least	faithful	of	the	

three	to	the	results	of	experimental	variography.	t	represents	the	maximum	extent	of	spatial	continuity	of	sand	

and	gravel	bodies	that	could	be	reasonably	interpreted	

from	the	data.

nter-run	variability	between	the	short-range/high	

nugget	and	long-range	models	was	investigated	more	

closely	by	looking	at	an	individual	slice	through	the	

models.	A	cross-section	(Figures	16,	17,	18	and	19)	was	

chosen	that	has	wells	proximal	to	guide	interpolation,	but	

not	within	the	displayed	cells.	This	location	was	chosen	

in	order	to	investigate	the	variation	in	simulation	results	

where there was some well guidance, but also significant	gaps.	There,	little	commonality	was	found	between	

single	realizations	of	the	short-range	model	and	those	of	

the	long-range	model	(Figures	16-A	and	18-A).	As	noted	

above,	the	short-range	model	had	more	interspersion	

between	lithologies,	and	less	contiguous	sand	and	gravel	

bodies (increased influence of marginal probabilities over 

kriging	weights).	For	the	short	range	and	nugget	model,	

the data had limited influence on the results. For example, 

silt	lithologies	were	found	throughout	the	model,	even	

though	they	were	not	present	in	any	nearby	wells.

The	mode	of	the	16	runs	(Figures	16-B	and	18-B)	

also	showed	the	increased	randomness	of	the	short-range	

model.	The	mode	for	the	short-range	model	showed	only	

one	stable	sand	body,	the	result	of	nearby	wells	constraining	the	results.	The	lithology	“clay”	was	the	mode	for	

most	of	the	area.	For	the	short-range	model,	the	global	

proportions heavily influenced the results. The mode for 

the	long-range	model	showed	more	sand	and	gravel	in	

laterally	extensive	bodies.	These	sand	and	gravel	bodies	

existed	from	simulation	to	simulation	because	surrounding	wells	contributed	to	the	ccdf,	so	that	each	randomdraw	was	constrained	by	data	(the	left	terms	of	equation	2	

had more influence than the global proportions).

The	entropy	results	(Figures	17	and	19)	further	

illustrated	the	differences	between	the	models.	For	the	

short-range	model,	entropy	results	were	mainly	“granular”	

with	little	pattern,	save	for	an	area	of	low	entropy	in	the	

upper right, where there was influence from a well. There 

were	clear	patterns	in	entropy	results	for	the	long-range	

model.	The	upper	60	feet	or	so	generally	had	low	entropy.	The	wells	in	the	area	were	consistently	clay,	except	

toward	the	center,	where	there	was	less	well	control	(allowing	for	more	variation	between	realizations).	The	area	

of	high	entropy	in	the	deep,	central	portion	was	produced	

Figure 16. East and west-oriented cross section. Cross

section is about 14,500 feet across and contains depths

ranging from 0 (top) to 300 feet. Cross section results for

long-range variogram with zero nugget effect. A, single

realization; B, mode.

Figure 17. Cross section showing entropy results for

long-range variogram. Orientation and dimensions are the

same as Figure 16.
148	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 18. Cross section results for short-range variogram

with nugget effect. A. single realization, B. mode. Orientation and dimensions are the same as Figure 16.

Figure 19. Cross section showing entropy results for

short-range variogram with nugget effect. Orientation and

dimensions are the same as Figure 16.

by alternation of sand and gravel between runs. True

distinction between sand and gravel was questionable; an

alternate modeling approach using hydrofacies (Ritzi et

al., 2000) where the domain is divided into high and low

conductivity units, which are modeled within stratigraphic

units, is a better approach (Venteris, 2007).

CONCLUSIONS

The results raise important questions about the

mapping of lithologies in buried valleys, particularly the

feasibility of mapping and spatial modeling at 1:24,000

and more detailed scales. Most issues can be corrected

with adequate well control coupled with geophysical studies, but this is not practical for the scales of interest due to

the resources involved. Are the data good enough and is

the geology predicable enough at this scale of interest to

support county and regional-scale mapping of buried-valley deposits?

Of primary concern is the low horizontal continuity

(lateral consistency) of lithologies between water wells

for the current data set. This creates difficulties for both 

stack mapping and geostatistical simulation. Assigning a

stack to a polygon implies that there is a predictable stratigraphy at that location. At simple locales (such as till

over bedrock), the meaning of the stack is clear, and it is

likely a reasonable prediction of the geology at that location. Tills can be correlated over large distances (Ehlers,

1996, chapter 9). Much of the surficial mapping work in 

Ohio is based on the correlation of tills. For this study

(Figure 11), the scale of autocorrelation for predicting

thickness of Wisconsinan till was on the order of 35,000

feet. The picture is less clear for mapping the lithologies of buried valleys. As seen in the water wells and

simulation results, the depth of occurrence and thickness

of sand and gravel bodies is highly variable. Some of

this variability is due to the fact that lithologic layers

encountered include both those that extend for long

distances (stratigraphic units) and short distances (facies

units). There are depth horizons where the occurrence

of gravel is more probable, but these cannot be reliably

traced from well to well. Such cases require careful consideration of what the stack sequence is predicting and

communicating about the geology. In well-constrained

situations, the stack can give accurate stratigraphic

information, i.e., it represents a typical configuration 

of sediments for that polygon. When the driller puts in

a new hole, he could expect a configuration and thickness of sediments similar to the stack sequence on the

map. For more variable systems, an alternative, more

probabilistic interpretation of the stack is warranted.

In this case, the stack is an “average” sequence, which

identifies the types of expected lithologies and their 

most likely vertical positions. For any location, however,

portions of the stack may be absent and extra layers

may be present. Wide ranges in thickness are possible
QUALTATVE	AND	QUANTTATVE	3D	MODELNG	OF	SURFCAL	MATERALS	AT	MULTPLE	SCALES 149

as well. Comparison of the chosen stack model with the

variability demonstrated in the geostatistical simulation

results shows the difficulty in applying a stack sequence 

to complex buried-valley sediments.

Another important issue is the reason behind the poor

horizontal correlation between wells. Several possible

interpretations exist, the end members being:

	1.	The geology of this buried valley is predictable at

this scale (well spacing), but the water well data

are noisy and provide inconsistent information on

stratigraphic and facies units. These complications

mask the prediction of stratigraphy.

	2.	The water-well data are accurate, but geological

variability occurs at scales well below the sample

spacing (perhaps on 100m scales). The sample

density is insufficient.

There is reason to believe that both case 1 and 2

are true. The water-well dataset is known to be noisy.

Drilling crews with a wide range of geological training

and experience produce these records as a legal requirement with the State of Ohio. Some water wells provide a

very good approximation of local geology, while others

contain serious errors in interpretation. Some of these

errors were detected and fixed through the processing 

of the water wells into indicator variables, but certainly

misidentifications and other blunders remain in the 

dataset. Past studies have suggested that case 2 may also

be a contributing factor. Ritzi et al (2000) found that

the range of autocorrelation for buried valley sediments

is less than 1,000 feet. Therefore, very dense sampling

is required (such as is conducted for site remediation

studies) to make accurate three-dimensional models

from experimental variography. An alternative is to use

sources of more detailed information (geophysical profiles, outcrops) to develop models of spatial structure for 

geostatistical simulation.

REFERENCES

Bagley, C.T., 1953, Subsurface study of glacial deposits at

Cleveland, Ohio: The Ohio Journal of Science, Vol. 53(2),

p.65-71.

Brockman, C.S., 1998, Physiographic Regions of Ohio: Ohio

Division of Geological Survey, Geologic Maps of Ohio

series, accessed at http://www.dnr.state.oh.us/geosurvey/

pdf/physio.pdf.

Brockman, C.S., Pavey, R.P., Schumacher, G.A., Shrake,

D.L., Swinford, E.M., and Vorbau, K.E., 2004, Surficial geology of the Ohio portions of the Cincinnati and

Falmouth 30 X 60-minute quadrangles: Ohio Division

of Geological Survey Map SG-2 Cincinnati-Falmouth,

scale 1:100,000.

Carle, S.F., and Fogg, G.E., 1996,Transition probability-based

indicator geostatistics: Math. Geol., 28(4), p. 453-476.

Deutsch, C.V., 2000, Geostatistical Reservoir Modeling. Oxford

University Press, 384 p.

Deutsch, C.V., and Journel, A.G., 1998, GSLIB: Geostatistical

Software Library and User’s Guide: Oxford University

Press, 369 p.

Ehlers, J., 1996, Quaternary and Glacial Geology: J.S. Wiley,

578 p.

Journel, A.G., 1983, Nonparametric estimation of spatial distributions: Mathematical Geology, 15(3), p. 445–468.

Kempton, J.P., 1981, Three-dimensional geologic mapping for

environmental studies in Illinois: Illinois State Geological

Survey Environmental Geology Notes 100.

Ohio Division of Geological Survey, 2003, Shaded bedrock

topography map of Ohio: Ohio Department of Natural

Resources, Division of Geological Survey, BG3, 1:500,000

scale, with text.

Ohio Division of Water, 2005, Potentiometric Surface Mapping

in Ohio: Ohio Division of Water Fact Sheet 05-65.

Powers, D.M., and Swinford, E.M., 2004, Shaded drift thickness

map of Ohio: Ohio Department of Natural Resources, Division of Geological Survey, SG3, scale 1:500,000.

Powers, D.M., Laine, J.F., and Pavey, R.R., 2002 (revised 2003),

Shaded elevation map of Ohio: Ohio Division of Geological Survey Map MG-1, scale 1:500,000.

Ritzi, R.W., 2000, Behavior of indicator variograms and transition probabilities in relation to the variance of lengths of

hydrofacies: Water Resources Research 36(11), p. 3375-

3381.

Ritzi, R.W., Dominic, D.F., Slesers, A.J., Greer, C.B., Reboulet,

E.C., Telford, J.A., Masters, R.W., Klohe, C.A., Bogle,

J.L., and Means, B. P., 2000, Comparing statistical models

of physical heterogeneity in buried-valley aquifers: Water

Resources Research 36(11), p. 3179-3192.

Shannon, C.E., 1948, A mathematical theory of communication: Bell System Technical Journal, v. 27, p. 379-423 and

623-656.

Smith, B., and Sandwell, D., 2003, Accuracy and resolution

of shuttle-radar topography-mission data: Geophysical

Research Letters 30(9), p. 1467.

Soil Survey Staff, Natural Resources Conservation Service,

United States Department of Agriculture. Soil Survey

Geographic (SSURGO) Database for Survey Area, State

[Online WWW], accessed at http://soildatamart.nrcs.

usda.gov.

Swinford, E.M., Pavey, R.R., and Larsen, G.E., this volume,

New map of the surficial geology of the Lorain and Put-InBay 30 x 60 minute quadrangles, Ohio.

Totten, S.M., and White, G.W.,1987, Glacial geology of Mahoning County, Ohio: Ohio Division of Geological Survey

Report of Investigations No. 133, 29 p., 21 figs., 3 tables, 

color map (1 inch equals about 1 mile), one sheet with text.

Venteris, E.R., 2007, Three-dimensional modeling of glacial

sediments using public water-well data: An integration of

interpretive and geostatistical approaches: Geosphere, v. 3.

Venteris, E.R., and Slater, B.K., 2005, A comparison between

contour elevation data sources for DEM creation and soil

carbon prediction, Coshocton, Ohio: Transactions in GIS,

9(2), p. 179-198.
150 DIGITAL MAPPING TECHNIQUES ‘06

White, G.W., 1971, Glacial geology of Trumbull County, Ohio: 

Ohio Division of Geological Survey Report of Investigations No. 80, color map (1 inch equals about 1 mile), one 

sheet with text.

White, G.W., and Totten, S.M., 1979, Glacial geology of 

Ashtabula County, Ohio: Ohio Division of Geological 

Survey Report of Investigations No. 112, 52 p., 22 figs., 2 

tables, color map (1 inch equals about 1 mile).
151

151

Arkansas Geological Commission Template

for 1:24,000 Scale Geologic Maps

By Jerry W. Clark and William D. Hanson

Arkansas Geological Commission

3815 West Roosevelt Road

Little Rock, AR 72204

Telephone: (501) 683-0152

Fax: (501) 663-7360

e-mail: jerry.clark@arkansas.gov, doug.hanson@arkansas.gov

This	template	(available	at	http://ngmdb.usgs.gov/

nfo/dmt/docs/hanson06a.pdf)	was	developed	by	the	staff	

at	the	Arkansas	Geological	Commission	for	use	in	the	

layout	of	1:24,000	scale	geologic	maps.	The	basic	framework	for	this	template	was	derived	from	various	layout	

styles	at	DMT	conferences,	published	U.S.	Geological	

Survey	geologic	maps,	and	geologic	maps	produced	by	

the	Arkansas	Geological	Commission.	Features	included	

with	this	geologic	map	are	the	correlation	chart,	stratigraphic	column,	map	unit	description,	cross	section,	joint	

frequency	diagrams,	symbols	legend,	state	or	agency	

seal,	references,	and	disclaimer.	Not	all	of	these	features	

occur	on	each	of	our	maps,	and	layout	size	ranges	from	a	

minimum	of	30	x	36	to	a	maximum	of	42	x	36	inches	for	

most	maps.

The	standardizing	of	fonts	and	creation	of	specialized	symbol	palettes	is	of	great	importance,	as	it	will	save	

much	time	and	effort.	Times	New	Roman	font	is	used	for	

text	and	symbols.	Text	is	12	point,	while	section	headers	

are	16	point.	The	title	block	has	various	font	sizes.	The	

title	itself	is	36	point,	the	authorship	and	date	published	

are	16	point,	and	the	Director’s	name	and	agency	are	

18	point.	The	series	and	reference	numbers	are	10	point	

and	located	in	the	northeast	corner	of	the	layout.	The	

disclaimer	is	8	point	and	placed	under	the	symbol	legend	

if	space	permits.	Revision	dates	are	located	in	the	lower	

right	corner	of	the	layout	and	are	10	point.	The	state	or	

agency	seal	is	placed	in	the	extreme	lower	right	corner	of	

the	layout	and	is	2	inches	square.	Formation	contact	lines	

are black and 1.0, and the formation symbols are 12 point.

Colors used to signify different formations were adopted

from the most current state geologic map, which was

published by the U.S. Geological Survey.

Symbols used on the map are variable, but the decision was made to make them large enough to be easily

found on the map. It must be understood that different

symbol palettes will require different symbol sizes. This

is another reason to create customized symbol palettes.

Symbols for mines, quarries, and pits are 30 point. 

Strike and dip symbols are 60, while overturned strike

and dip symbols are 30. The accompanying number is 8

point. Thrust fault, normal fault, syncline axis, anticline

axis, and monocline axis lines are 2 point. Symbols accompanying synclines, anticlines, and monoclines are 30

point. Daggers for the thrust fault lines are size 10. The

line that shows placement of the cross section is 1.5, and

lettering is 16 point. The border of the layout has a line

weight of 2.5.

In the future, we would like to incorporate digital

pictures of interesting geologic features associated with

the quadrangle. These images would be placed on the left

side of the layout, space permitting.

This template was put together by J.W. Clark and

W.D. Hanson for use in constructing 1:24,000 scale geologic maps at the Arkansas Geological Commission. By

no means is this template engraved in stone. Much leeway

is incorporated to give individual authors flexibility in 

designing their own layout
152	 DGTAL	MAPPNG	TECHNQUES	‘06
153

153

Geologic Map of the Ouachita Mountain Region and a

Portion of the Arkansas Valley Region in Arkansas

By William D. Hanson and Jerry W. Clark

Arkansas Geological Commission

3815 West Roosevelt Road

Little Rock, AR 72204

Telephone: (501) 683-0115

Fax: (501) 663-7360

e-mail: doug.hanson@arkansas.gov, jerry.clark@arkansas.gov

One	hundred	and	seventy-eight	7.5-minute	(1:24,000)	

quadrangles	in	the	Ouachita	Mountain	region and	a	

portion	of	the	Arkansas	Valley	region	in	Arkansas	were	

mapped	by	Charles	G.	Stone	(Arkansas	Geological	Commission)	and	Boyd	R.	Haley	(U.S.	Geological	Survey)	under	the	COGEO	Map	project	(a	precursor	to	the	National	

Cooperative	Geologic	Mapping	Program’s	STATEMAP	

component).	The	task	of	mapping	this	area	took	eight	

years	and	was	completed	in	1995.	The	maps	completed	

during	this	project	were	compiled	onto	1:100,000	USGS	

topographic	quadrangle	maps	and	then	digitized	and	published	(Haley	and	Stone,	2006).	The	map	includes	parts	of	

the	Fort	Smith,	Russellville,	Conway,	Searcy,	Mena,	Little	

Rock,	DeQueen,	Arkadelphia,	and	Malvern	1:100,000	

quadrangles,	and	all	of	the	Lake	Ouachita	quadrangle.	

Files	from	the	ten	digitized	1:100,000	quadrangles	were	

merged	in	such	a	way	as	to	produce	seamless	layers.	Editing	by	the	authors	and	additional	staff	members	of	the	Arkansas	Geological	Commission	was	performed	after	each	

individual	map	was	completed	and	after	the	compilation	

map	product	was	produced.	To	make	each	map,	heads-up	

digitizing	was	done	over	scanned	base	maps	using	ESR	

ArcGS	9.x	software.	Upon	completion	of	the	individual	

quadrangles, a new view was created and all files pulled 

together into a master file set.

Features	included	in	these	data	sets	are	the	surface	geology	(which	includes	formation	contacts,	strike	

and	dip	symbols,	thrust,	tear	and	normal	faults,	quarry,	

mine	and	pit	symbols,	igneous	intrusions	and	igneous	

dikes),	correlation	chart,	formation	descriptions,	and	a	

few	representative	pictures	of	various	geologic	features	

encountered	in	the	mapped	area.	The	digital	pictures	are	

relatively	new	to	our	maps,	and	we	hope	they	give	the	

viewer	a	better	understanding	of	rock	types,	structures,	

and	outcrop	appearances.

Formations	that	occur	on	this	map	from	oldest	to	

youngest	are	the	Paleozoic	Collier	Shale	(Oc);	Crystal	

Mountain	Sandstone	(Ocm);	Mazarn	Shale	(Om);	Blakely	

Sandstone	(Ob);	Womble	Shale	(Ow);	Big	Fork	Chert	

(Obf); Polk Creek Shale (Opc); Blaylock Sandstone (Sb);

Missouri Mountain Shale (Sm); Arkansas Novaculite

(MDa); Stanley Shale (Ms); Jackfork Sandstone (IPj);

Johns Valley Shale (IPjv); Atoka lower, middle, and upper (IPal, IPam,and IPau); Hartshorne Sandstone (IPhs);

McAlester (IPma); Savanna (IPsv); Mesozoic Cretaceous

igneous (Ki) and undifferentiated (Ku); Cenozoic Tertiary

undifferentiated (Tu); and Quaternary undifferentiated

(Qu). In some instances, the Polk Creek Shale, Missouri

Mountain Shale, and Blaylock Sandstone have been

grouped together. When this occurs, the units are grouped

under the SO symbol.

Economic resources that occur in this part of the

state are coal, natural gas, cinnabar, antimony, novaculite,

tripoli, crushed stone, dimension stone, shale, slatey shale,

sand and gravel, clay, quartz, barite, manganese, copper,

lead, zinc, vanadium, columbium, titanium, molybdenum, soapstone, and water. Landslides, most of which are

induced by human activities, are the main geohazard in

this area of the state. Ground subsidence can be a problem

where historic underground coal mining has occurred.

Controlled lakes built by the Corp of Engineers in this

part of the state have greatly reduced the potential for major flooding, though minor flooding can occur when heavy 

rains occur in smaller tributaries.

Users of this map are state, regional, and local

planners; local, state and federal government agencies;

explorers of economic minerals; risk assessors; and those

directly involved with earth sciences. Copies are available

from the Arkansas Geological Commission, Little Rock,

AR. A .pdf image can be accessed from the AGC website

(www.state.ar.us/agc/agc.htm), which is about 350 Mb.

REFERENCE

Haley, B.R., and Stone, C.G., 2006, Geologic map of the Ouachita Mountain region and a portion of the Arkansas Valley

region in Arkansas: Arkansas Geological Commission Map

DGM-OMR-001, scale 1:125,000.
154	 DGTAL	MAPPNG	TECHNQUES	‘06
155

155

ArcGIS Geodatabase Schema for Geologic Map Production

By Vic Dohar

Natural Resources Canada

601 Booth Street

Ottawa, ON K1A 0E8

Telephone: (613) 943-2693

Fax: (613) 952-0738

e-mail: vdohar@NRCan.gc.ca

PREFACE

A	general	understanding	of	the	ESR	geodatabase	

model would be beneficial when reading this document. 

Please	refer	to	the	accompanying	poster	at	(http://ngmdb.

usgs.gov/nfo/dmt/docs/dohar06.pdf)	for	discussion	below	

and	note	that	the	geodatabase	schema	that	is	represented	

is	a	DRAFT	version.

A GEODATABASE FOR MAP 

PRODUCTION

This	geodatabase	schema	has	been	designed	to	facilitate	and	manage	digital	data	for	the	publication	of	geologic	maps.	n	a	production	scenario,	a	personal	geodatabase	

is	created	by	each	cartographer	on	their	workstation.	Geologic	and	non-geologic	digital	data	are	stored	separately	

in	respective	feature	datasets.	All	feature	datasets	and	the	

feature classes contained within are prefixed by a sevencharacter	string	that	represents	the	publication	series	and	

number	(e.g.,	OF04780	represents	Open	File	#4780).	This	

ensures that all the files, when transferred from a personal 

geodatabase	to	an	SDE	enterprise	geodatabase,	will	be	

unique.	All	digital	base	data	are	preserved	in	the	original	

Shapefiles and stored outside of the geodatabase.

CARTOELEMENTS FEATURE DATASET

The	CartoElements	feature	dataset	contains	non-geologic	features	that	are	used	in	preparing	a	geologic	map.	

These	consist	of	features	such	as	the	map	border,	UTM	

grid,	leader	lines,	cross-section	lines,	base	annotation,	and	

masking	polygons	used	with	ArcMap’s	advanced	layer	

masking	option.	Eventually	over	time,	some	of	these	features	like	the	map	border	will	be	replaced	with	ArcObjects	

or	macros	used	in	ArcMap.

GEOLOGY FEATURE DATASET

The	Geology	feature	dataset	contains	all	the	geologic	

features on the map that are represented by points, lines,

and areas, as well as the bedrock and surficial geological units. Also included are topology rules that define the 

relationship between these features, a polygon feature that

represents the area of interest, and geologic annotation. At

a future date, raster geologic datasets will be a part of this

dataset.

The geodatabase provides many options for managing these datasets efficiently, such as incorporating a 

subtype field and topology and domain validation. The 

three features classes that represent geologic area, point,

and line features all contain a geodatabase subtype field 

named CATEGORY. Each unique value or subtype code

in this subtype field can be viewed as a separate individual feature class. This provides better data management because common attribute fields can be shared by 

all features, with the ability to assign a different domain

to each of these attribute fields for subtype code in the 

subtype field CATEGORY. Therefore, the attribute field 

FEATURE is assigned to a different domain for each

unique subtype code. For example, the faults and folds

subtype has the domain Faults and Folds assigned to the

FEATURE attribute field, respectively (see Figure 1). The 

Faults domain contains the valid fault types that can be

assigned to this field for any given feature, and the same 

can be said for the Folds domain. This method of managing data exists in the GeologyPoints, GeologyLines,

and GeologyAreas feature classes since most features,

depending on map scale, can be represented in any of

these states.

The GeologyUnits feature class contains the bedrock

and surficial geological polygons. In this feature class, the 

field MAPUNIT contains the unique value for each polygon feature. Several related tables are used to define each 

of these unique values. The first is the UnitComposition 

table, where each unique MAPUNIT value is defined as a 

composition of one or more geological units represented

in the fields VENEER (used mostly for surficial geology 

maps), UNIT_1, UNIT_2, and UNIT_3. The geological

units in these four fields are then related to the BedrockGeology or the SurficialGeology tables, each of which 
156	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 1. The CATEGORY field in the feature class is set as the subtype field, and its subtype codes are listed below. 

The subtype codes group geologic features in a common theme (e.g. faults, folds). For each subtype code, a separate

domain listing all possible features of that subtype is assigned to the FEATURE field (e.g., the Faults domain is assigned 

to the FEATURE field for the faults subtype code).

further defines each geological unit and is comparable to 

the	description	that	appears	in	the	legend	on	the	map.	n	

addition, ID values in the field RELATION in the UnitComposition	table	are	related	to	the	UnitRelation	table	that	

describes	the	relationship	between	one	or	more	geological	

units	(e.g.,	First	unit	is	predominantly	60-80%	coverage).

The	MappedArea	feature	class	simply	stores	one	or	

more polygons that define the area of interest or geological	study.	The	primary	purpose	of	this	feature	is	to	ensure	

that	all	geological	features	exist	within	its	boundaries,	as	

part of the established topology rules. Also, the polygon

features are used to define the FGDC element Data_Set_

G-Polygon and for display in an index map on the final 

hardcopy version of the map.

The topology rules are basically those provided by

the ESRI geodatabase model. As noted above, all geological features must exist within the boundaries of the

MappedAreas feature class. In addition, it is also important to maintain coincidence of geological contacts, faults,

and dykes to the outline of the geological units.
ARCGS	GEODATABASE	SCHEMA	FOR	GEOLOGC	MAP	PRODUCTON 157

DOMAINS

Domains are used to constrain the values allowed in

any particular field in a feature class or table. As mentioned above, each subtype value has a separate coded

value domain that is assigned to the FEATURE field. 

Each of these domains contains the respective geologic

features that have been compiled from the legend descriptions in GSC publications of the past ten years. Each

geologic feature or description is assigned a code or a

unique integer value in the 000’s that corresponds to the

subtype value (e.g., the subtype fault has a value of 3, and

the Faults domain consists of coded values from 3000-

3999). The use of domains aids in validating the feature

attributes, ensures consistency and quality, and guarantees

a homogenous dataset when combining the digital data

from one or more geodatabases/publications.

CONCLUSION

The purpose of this geodatabase schema is to create a

foundation for managing geological digital data in a map

production environment. The ESRI geodatabase model

provides the tools and validation methods to ensure a high

quality output of data for geologic maps and the dissemination of digital data. These practices will continue to

evolve as more datasets are incorporated.

REFERENCES

ArcCatalog and ArcMap, ESRI Inc., http://www.esri.com.

Microsoft Office Visio Standard 2003, Microsoft Corporation.

Zeiler, Michael, 2002, revised by Nichols, Greg, 2005, Geodatabase Diagrammer: an ArcScript available as a download

from ESRI Support Center, http://support.esri.com.
158	 DGTAL	MAPPNG	TECHNQUES	‘06
159

159

Compression of Digital Orthophotography Collections:

Factors to Consider in the Compression of Large

Data Sets of Geospatial Imagery

By Deette M. Lund

Illinois State Geological Survey

615 E. Peabody Drive

Champaign, IL 61820

Telephone: (217) 265-5642

Fax: (217) 333-2830

e-mail: lund@isgs.uiuc.edu

INTRODUCTION

In early 2006 the Illinois State Geological Survey

(ISGS) was scheduled to receive several large digital

orthophotography collections. As part of the contract requirements, the individual images were to be compressed

and made available to the public via the Internet. In December 2005 and January 2006, research was conducted

regarding compression formats, compression software,

and target compression ratios. During the course of our research, it became clear that the technology and standards

involved with the compression of geospatial imagery were

fast changing.

Image compression techniques have improved in the

past few years. It seemed that whichever compression

format we chose, it would yield visibly better results than

those available five or even two years ago. The visible 

results of image compression are only part of what needs

to be considered when making decisions regarding the

compression of geospatial imagery. Metadata is also very

important to geospatial imagery. Information such as pixel

size, geographic location, and the coordinate reference

system are just a few of the critical pieces of metadata

embedded in a compressed geospatial image file that GIS 

applications need to properly display the image. A metadata standard is necessary for the variety of compressed

file formats to interact with the GIS applications appropriately. Metadata standards1 for compressed geospatial

imagery, in some compression file formats, were not fully 

established as of January 2006.

FORMATS

Two popular compression formats were included

in the research: JPEG 2000 (non proprietary), and

LizardTech’s MrSID. A third compression format, Earth

Resource Mapper’s ECW format, was included in the

initial stage of our research but was excluded due primarily to our long established relationship with LizardTech.

The time constraints on our project did not allow time for

building a new relationship with a different company.

JPEG 2000 Format

In 2004 & 2005 the JPEG 2000 compression format

had become accepted as a standard by the International

Organization for Standardization (ISO) and the International Electro-technical Commission (IEC). It became

apparent during our study that JPEG 2000 was being

developed in phases and that it was not fully developed

(Morris, 2005). Several aspects of JPEG 2000 had been

accepted as a standard by the ISO/IEC, but as of January, 2006 the geospatial aspects of the JPEG 2000 format

were still in development and had not yet been approved

as a standard. Another factor to consider with JPEG 2000

is that in 2003, according to Stuart Nixon, founder and

CEO of Earth Resource Mapping (ER Mapper), there

are at least three competing ways to store map projection

information within a JPEG 2000 file, and our software 

developers use different methods (Thurston, 2003).

MrSID Format

LizardTech offers several compression algorithms

within its latest upgrade of GeoExpress 6.0. Three that the

ISGS considered were MrSID Generation 2 (MG2), MrSID Generation 3 (MG3), and JPEG 2000 (JP2). MG3 has

improved compression capabilities. MG3 can compress

1By June 2006, the metadata standards issue involving JPEG 2000

had been resolved. According to the Open Geospatial Consortium the

“GML in JPEG 2000 Inter-operability Experiment (GMLJP2)” initiative

has been completed. Currently it appears that all of the phases have been

fully developed. Time constraints have prohibited any further research

into these latest developments. Further research will be needed to determine what that means.
160	 DGTAL	MAPPNG	TECHNQUES	‘06

in	lossless	format,	2:1	for	black	and	white	imagery	and	

up	to	6:1	in	color	imagery	(ratios	will	vary	from	image	to	

image).	The	lossy	compression	for	MG3	is	also	improved,	

generating	up	to	50%	better	compression	ratios	(depending	on	the	image)	than	MG2.	Unfortunately	not	all	GS	

software	packages	have	caught	up	with	the	MrSD	technology,	including	software	developed	by	Environmental	

Systems	Research	nstitute	(ESR).	Some	of	ESR’s	GS	

software	packages	are	still	not	fully	capable	of	using	the	

MG3	format	and	the	majority	of	the	GS	user	community	

in	llinois	uses	ESR	technology.	For	this	comparison	only	

MG2	and	JP2	were	tested.

ECW Format

ER	Mapper’s	ECW	format	was	not	considered	for	

this comparison due to a number of factors. The first was 

due	to	time	constraints	on	our	project	which	did	not	allow	time	to	establish	a	new	relationship	with	a	different	

company.	Secondly	there	were	patent	litigation2	issues	at	

the	time	of	our	research.	Earth	Resource	Mapping	(ERM),	

the	parent	company	of	ER	Mapper,	was	in	litigation	with	

Galdos,	the	parent	company	of	LizardTech,	over	issues	

involving	patent	infringements	(Thurston,	2003).	LizardTech	started	the	litigation	and	claimed	their	patent	had	

been	infringed.	The	companies	have	been	in	litigation	

since	October	1999	and	although	it	appeared	it	would	be	

resolved	soon,	the	SGS	could	not	wait	for	an	outcome.	

A	third	factor	included	several	documents	available	on	

the	nternet	that	report	comparisons	between	ECW	and	

MrSD	formats	(GS	Services,	2005;	Warmath,	2004).	

Those	comparisons	did	not	promote	ECW	as	the	better	

format.	n	contrast	to	those	comparisons,	we	did	get	some	

positive	feedback	about	ER	Mapper	and	the	ECW	format	

from	the	Digital	Mapping	Techniques	2005	forum	about	

image	compression.

SOFTWARE

Two	popular	software	packages	were	included	in	

our	research:	Leica’s	Erdas	magine,	and	LizardTech’s	

GeoExpress.	A	third	software	package,	GeoJasPer,	was	

initially	included	in	the	research	but	was	excluded	early	

on	in	the	research	due	primarily	to	its	lack	of	technical	

support	services.

2As	of	January	31,	2006	the	litigation	between	Earth	Resource	Mapping	and	Galdos	was	settled	(http://www.ermapper.com/company/news_

view.aspx?PRESS_RELEASE_D=398	).	Earth	Resource	Mapping	won	

its	claims	against	Galdos,	but	too	late	to	be	considered	by	the	SGS.

Leica’s Erdas Imagine

Erdas Imagine provides free image compression

within its software application. One limitation that is that

it only provides compression for files up to 50 MB for the 

MrSID format files. The 2005 USGS NAPP-DOQ files 

exceed 170 MB in size and the 2005 USGS Urban Area

files exceed 70 MB in size. The size of the files ruled it 

out as an option before another, not so obvious, factor

came into play, which concerned the fact that Erdas used

LizardTech’s Software Development Kit (SDK) in setting

up its compression capabilities. The developer has already

made some encoding decisions for the user. Erdas only

allows the user to change some of the multiple encoding

options that are available with GeoExpress.

LizardTech’s GeoExpress

Prior to December 2005, the ISGS had used LizardTech’s MrSID Geospatial Encoder to compress all

existing ISGS orthophotography collections. We needed

to factor in the cost of an upgrade if we were going to use

LizardTech’s software again. The ISGS hadn’t kept pace

with LizardTech’s software upgrades. This was primarily

due to fiscal constraints and low usage of the software by 

staff after the initial purchase to compress the 1998-2000

NAPP DOQ collection. The upgrades at the ISGS had

stopped just short of LizardTech’s decision to use “data

cartridges” (a file that keeps track of the amount of imagery that has been compressed) as its new way to charge

customers for compression. LizardTech’s new GeoExpress 6.0 would be able to compress imagery using either

MrSID or JPEG 2000 formats and offered an unlimited

“data cartridge” at a set price.

GeoJasPer

Before the ISGS started the actual compression tests

of the two formats it was determined that the project team

would need to use a software application that had a technical support system. Then, if there were trouble with the

software itself or how it was handling compressions, the

ISGS staff could use the support service to troubleshoot

and fix any problems. Through this decision it was decided that the ISGS would not use GeoJasPer since there

was no technical support system.

COMPARISON CHART

A comparison chart between the two major compression formats was developed in an effort to organize the
COMPRESSON	OF	DGTAL	ORTHOPHOTOGRAPHY	COLLECTONS 161

facts related to each factor in the decision-making process

(Figure 1). This chart shows the factors an institution or

agency should consider when making decisions about

which compression format and software to choose. Some

of the facts within the chart are time sensitive and may no

longer be relevant.

End User

Another factor in our decision-making process that

was not added to the chart was the end user. The ISGS

had already “trained” its Clearinghouse user base to use

MrSID compressed imagery. Using GeoExpress to comMrSID and JPEG 2000 Comparison*

FACTOR MrSID (sid format) JPEG 2000 (GeoJP2 format)

Software

Choices

GeoExpress (LizardTech -- PC, LINUX, SOLARIS* options)

 * note - have experienced trouble with Solaris installation.

 Also, instruction manual for command line encoding

 could include better examples.

Erdas (LizardTech SDK - only useful on files under 50 MB)

GeoExpress (LizardTech)

Erdas Imagine (software extension created from LizardTech

 Software Development Kit (SDK))

GeoJasPer (created from LizardTech SDK)

ECW JPEG 2000 (ER Mapper)

Cost GeoExpress 6.0 - Unlimited version = >$3000

 or Data Cartridge Version = >$2000 per TB

Erdas - different pricing available to each institution or agency

Erdas - different pricing available to each institution or agency

GeoExpress - same as MrSID format costs

GeoJasPer - free

Geography

Markup

Language

(GML) standard

Follows the GML standard Has GML in some cases. Still working on standardizations. Current

 status of future standardization is not clear. Currently there are

 at least 3 competing ways to store map projection information. 

ISO Standard No - because it is proprietary Yes - but all phases not fully developed yet

ESRI Compatible MrSID Generation 2 - Yes - but need to define projection or

 provide an .AUX file

MrSID Generation 3 - Not in all cases 

Yes - but potential issues with geospatial info

 - depends on code writers choice of where to store geospatial

 metadata (couldn't find any problems during limited testing)

Compatibility

with other

GIS Software

Packages

MG2 - Majority of cases (with Plug-ins for a few)

MG3 - Not in all cases

http://www.gisservices.net/downloads/NYProgram.pdf

(As of May 2004)

ExpressView Browser Plug-in

Not in all cases

 - and even then it might have problems with geospatial info 

Web Browser

(Free Viewer)

Yes - the ExpressView and a few others

 (some viewers are better than others) 

Compatibility

with Adobe CS2 

Yes

 - by using MrSID Decode (free)

 - by using “Save as” in the ExpressView browser (be careful it

 only saves the image visible on the screen at the time but it

 will kick out a .TFW file if you save it to .TIFF format)

Yes

 - can place the image in Illustrator

 - can also use same “save as” method described in sid format

No - can not open in Photoshop 

Compatibility

with Other

Graphics

Software

Many third party plug-in's available

 - some are free

 - some are free for the "lite" version and then you pay extra for

 more bells and whistles

Generates

log file

(for metadata and

statistics puposes)

GeoExpress

 UNIX - Yes

 PC - Yes

Erdas - Yes

Erdas - No

GeoExpress - Yes

GeoJasPer - Software not tested; No on-line information provided

 about log file generation

Target -vsActual

Compression

Ratio

GeoExpress - can be much different 12:1 can result in 9.64:1

Erdas - same as GeoExpress 

Erdas - No log file to list actual compression ratio information

GeoExpress - stays more on target (from existing tests)

 12:1 is 11.94:1

GeoJasPer - Software not tested; No on-line information provided

 about generating actual compression ratio information

Batch processing UNIX - Yes

PC

 GUI - multiple file (not “true” batch processing)

 CMD - batch processing (similar to UNIX - not tested)

Erdas - possible according to help documents (not tested)

GeoExpress

 GUI - multiple file (not “true” batch processing)

 CMD - batch processing

GeoJasPer - possible according to on-line instructions (not tested)

Control over

encode

settings

UNIX - full (command driven)

PC - full (can save established profiles), "pre-tuned" but user

 can alter all settings

Erdas - not as many options as GeoExpress

GeoExpress - more control than Erdas, "not pre-tuned" like MrSID

 (can be good or bad thing), can’t control gamma or weight

GeoJasPer - only controls target compression - no other settings

Customizable

Metadata

UNIX - Yes

PC - Yes

Erdas - No

GeoExpress - Yes

GeoJasPer - No

Generates

world file

UNIX - Yes

PC - Yes

Erdas - No instructions available about generating a world file

GeoExpress - Yes

GeoJasPer - No instructions available about generating a world file

*All costs and statistics current as of January 2006

Yes

 - by using MrSID Decode (free)

 - by using “Save as” in the ExpressView browser (be careful it

 only saves the image visible on the screen at the time but it

 will kick out a .TFW file if you save it to .TIFF)

Figure 1. Factors considered in comparison of MrSID and JPEG 2000 Compression formats.
162	 DGTAL	MAPPNG	TECHNQUES	‘06

press	the	images	into	either	MrSD	or	JPEG	2000	format	

would	result	in	no	changes	to	user	instructions	and	or	

viewer	downloads.	Researching	how	well	other	software	

would	provide	a	compressed	image	that	would	be	able	to	

use	the	established	viewer	and	whether	the	compressed	

images	from	that	software	would	load	properly	into	

ArcSDE	was	beyond	the	time	frame	of	the	project.	These	

considerations	would	need	to	be	re-evaluated	under	different	funding	sources	and	time	constraints.

CONCLUSION

n	a	technically	challenging	process,	we	considered	

the	pros	and	cons	of	each	compression	format	and	each	

software	option.	We	chose	GeoExpress	and	the	MrSD	

Generation	2	compression	format	for	compressing	the	

large	data	sets	that	would	begin	arriving	in	spring	2006.

The	SGS	chose	8:1	for	the	target	compression	ratio	

for	the	2005	USGS	Chicago	Urban	Area	color	orthophotography	collection.	Differences	between	the	original	and	

the	compressed	imagery	at	actual	size	are	not	detectable.	

Zooming	in	beyond	the	reasonable	usefulness	of	the	image,	at	pixel	level,	the	user	can	see	a	few	changes.	Those	

changes	appear	to	be	slight	shifts	in	color	on	a	few	of	

the	pixel	groupings,	but	they	are	not	easily	detected.	The	

average size of the uncompressed file is 71.5 MB. To keep 

the	download	time	to	a	minimum,	an	8:1	target	compression ratio produced files under 10 MB in size. The average actual compression ratio for the 4527 files in this data 

set	was	8.3:1.

The	SGS	chose	10:1	for	the	target	compression	ratio	

for	the	2005	USGS	NAPP-DOQQ	grayscale	orthophotography	collection.	There	are	little-to-no	differences	

between	the	original	and	the	compressed	imagery	at	

actual size. If users zoom in to 200%, “compression

artifacts” (loss of edge detail and slight fuzziness) are

visible. For the most part the “compression artifacts” in

the compressed images do not affect the use of the images

for research. The average size of uncompressed file is 177 

MB (State Plane version). To keep the download time

to a minimum, a 10:1 target compression ratio produced

files around 20 MB in size. To date, we have compressed 

nearly one thousand of these State Plane version files. The 

average actual compression ratio for the ~900 State Plane

version files that have been delivered is 9.6:1. 

Due to rapid advances in standards and technology the

facts are frequently changing in regards to image compression. Each institution or agency has its own particular factors to consider when dealing with image compression. The

factors listed in Figure 1 should be used as a starting point

or guide but the facts within the chart must be re-examined

before deciding which formats and software to adopt.

REFERENCES

GIS Services, 2005, New York State Program - GeoExpress with

MrSID: GIS Services, accessed at http://www.gisservices.

net/downloads/NYProgram.pdf.

Morris, Steve, 2005, GML Content of JPEG 2000 format: North

Carolina State University Library, December 30, 2005,

personal communication (email).

Thurston, Jeff, 2003, Unlocking Data to Expanded Potentials:

Interview with Stuart Nixon, Founder and CEO of Earth

Resource Mapping, GEO Informatics Magazine, accessed

at http://www.ermapper.com/document/doc.aspx?doc_

id=79.

Warmath, Eric, 2004, State Mapping Advisory Committee Meeting Notes: Report on Image File Compression Software,

April 15, 2004, accessed at http://www.nbmg.unr.edu/smac/

apr2004.pdf

ADDITIONAL ONLINE RESOURCES

Brislawn, Christopher M., 2002, The FBI Fingerprint Image Compression Standard, accessed at http://www.c3.lanl.

gov/~brislawn/FBI/FBI.html.

Digital Preservation Formats - http://www.digitalpreservation.gov/formats/index.shtml

	 Jpeg 2000 - http://www.digitalpreservation.gov/formats/fdd/fdd000143.shtml

	 Jpeg 2000 - http://www.digitalpreservation.gov/formats/fdd/fdd000140.shtml

	 MrSID Generation 2 - http://www.digitalpreservation.gov/formats/fdd/fdd000031.shtml

	 MrSID Generation 3 - http://www.digitalpreservation.gov/formats/fdd/fdd000184.shtml
COMPRESSON	OF	DGTAL	ORTHOPHOTOGRAPHY	COLLECTONS 163

Erdas Imagine Software Website

http://gi.leica-geosystems.com/default.aspx

ER Mapper Software Website

http://www.ermapper.com/

GeoJasPer Software Website (software function has changed since January 2006)

http://www.dimin.net/software/geojasper/

GIS Monitor - Newsletter

	 April 1, 2004

	 	 LizardTech and Galdos Take on JPEG 2000.

	 	 LizardTech/Earth Resources Mapping Lawsuit Judgment

	 January 20, 2005

	 	 LizardTech Introduces GeoExpress 5.0 with MrSID

	 December 15, 2005

Industry Survey: What was big news this year and what do you wish for next year?

Jakulin, Aleks, 2002-2004, Baseline JPEG and JPEG2000 Artifacts Illustrated, accessed at http://ai.fri.uni-lj.si/~aleks/

jpeg/artifacts.htm.

Joint Photographic Experts Group - http://www.jpeg.org

	 Jpeg 2000 - http://www.jpeg.org/jpeg2000/index.html

Library of Congress, How to View - The American Memory Collections, accessed at http://memory.loc.gov/ammem/

help/view.html

LizardTech - Press Room

	 March 23, 2004

	 LizardTech, Galdos Systems Collaborate to Develop ISO Standard for JPEG 2000.

	 May 3, 2004

	 	 LizardTech Unveils MrSID Software Developer Kit with JPEG 2000.

LizardTech – GeoExpress Software

http://www.lizardtech.com/

Morley, Karen, 2006, Avoid Pitfalls When Using JPEG 2000: GeoPlace.com, Tech Time Article, April 11, 2005, accessed

at http://www.geoplace.com/uploads/FeatureArticle/0411tt.asp.

Pew Internet - American Life Project Demographics about internet use in America, accessed at http://www.pewinternet.org.

Wallace, Steve, 1999, Image Compression Software, accessed at http://www.directionsmag.com/features.php?feature_

id=27.

Wikipedia

	 Jpeg 2000 - http://en.wikipedia.org/wiki/Jpeg_2000

	 MrSID - http://en.wikipedia.org/wiki/MrSID
164	 DGTAL	MAPPNG	TECHNQUES	‘06
165

165

Converting Adobe Illustrator Maps to ArcMap Format

By Jennifer Mauldin

Nevada Bureau of Mines and Geology

University of Nevada

Mail Stop 178

Reno, NV 89557

Telephone: (775) 682-8759

Fax: (775) 784-1709

e-mail: mauldin@unr.edu

BACKGROUND

The Nevada Bureau of Mines and Geology has

developed a new cartographic production system for

completion of geologic maps. Under this new system, all

new maps will be completed using ESRI’s (Environmental Systems Research Institute) ArcGIS Desktop software

package––from initial digitization of lines through final 

layout design. Previously, a combination of software

applications were used, which included Adobe Illustrator, ESRI ArcView 3.x, ESRI ArcInfo, Canvas, Microsoft

Excel, and Avenza Map Publisher. Since the new cartographic production system is used for new maps, most

geologic maps presently available at the Nevada Bureau

of Mines and Geology are in Adobe Illustrator format and

need to be converted to ArcMap format.

Various factors help us to determine whether a map

will be converted to ArcMap format. We take into consideration time, effort, difficulty, and cost. For example, if an 

Illustrator map has been released as an Open-File Report

(and therefore does not have a full office or field review), 

and a geologist decides to finalize the map for publication (full review) with minimal changes to the geology,

we will opt to finalize in Illustrator rather than make the 

conversion. In instances, however, where funding for

conversion is available or a request for conversion is submitted by geologists mapping in a particular area where

a GIS (geographic information system) version will be

beneficial, conversion of those maps under consideration 

will need to be implemented.

After a map is converted to ArcMap, final file types 

include an ArcMap document (.mxd), a geodatabase

(.mdb), a topographic base map (.tif), and the digital file 

used for publication (.pdf). With the exception of the

publication digital file, which is used for web and sales 

purposes at our agency, all those listed above are common

GIS file formats for use with current versions of ArcMap. 

Other file types can be exported from these formats, 

which allows us to meet specific project requirements and 

provide digital data to those whose software applications

are not compatible with the formats we typically provide.

There are substantial benefits to using the geodatabase	format	because	all	information	in	that	format	is	

bundled	together	as	opposed	to	comprising	a	collection	of	

shapefiles, coverages, dxfs, linked text documents, various projection files, etc., that make up the final map files. 

Although some of these files are generated as intermediate 

steps	during	the	conversion	process,	they	no	longer	need	

to be included in our final files after a map has been fully 

converted. The final digital files are few in number, organized, and pre-defined, which results in easy data transfer 

and	viewing	between	colleagues,	clients,	and	customers.

HOW THE ILLUSTRATOR MAPS 

COMPARE WITH THE ARCMAP MAPS

We	have	worked	hard	to	make	our	ArcMap	maps	

match	the	cartographic	quality	of	our	llustrator	maps.	

mprovements	over	the	years	to	the	ESR	software	suite’s	

cartographic	functionality	and	presentation	have	made	it	

possible	to	complete	high-end	cartographic	products	entirely in ArcGIS. Specific tools such as the Endpoint-Arc 

tool	in	ArcMap	allow	smooth	digitizing	of	lines,	while	

general	improvements	in	the	symbology	options	help	to	

produce	much	more	visually	appealing	products	using	

ESR	software.

Specific cartographic differences we have observed 

between	llustrator	and	ArcMap	maps	include	color	display, font use, labeling methods, and difficulty in formatting	the	map	layout	in	ArcMap.	We	have	also	dealt	with	

differences	between	printer	drivers	after	upgrading	our	

large-format	printer	during	transition	to	our	new	cartographic	production	system.

Color: RGB vs. CMYK

ArcMap	and	llustrator	maps	differ	in	color	display	

both	onscreen	and	in	print.	ArcMap	displays	colors	in	

RGB	(red,	green,	blue)	even	though	CMYK	(cyan,	magenta,	yellow,	black)	color	sliders	may	be	used	to	enter	

the	same	percentages	for	each	color	value	as	used	in	the	

llustrator	CMYK	color	palette.	The	ArcMap	CMYK	
166	 DGTAL	MAPPNG	TECHNQUES	‘06

color	sliders	simply	allow	a	user	to	specify	color	values	

using	the	CMYK	method,	but	do	not	actually	display	the	

map	in	CMYK	color.	When	comparing	a	map	printed	

from	llustrator	with	a	version	of	that	same	map	printed	

from	ArcMap,	it	may	appear	that	completely	different	

colors	were	chosen	to	produce	the	two	maps,	even	though	

llustrator	and	ArcMap	use	the	same	CMYK	color	values.	

This	is	because	the	ArcMap	map	is	actually	displaying	in	

RGB	and,	therefore,	is	printed	in	RGB	even	though	the	

printer	may	be	set	to	print	using	CMYK.

Fonts: Arial vs. Helvetica

Since	the	Helvetica	font	is	not	automatically	installed	

on	the	majority	of	our	geologists’	computers,	we	decided	

to	use	Arial	as	the	default	font	which	does	come	automatically installed. This is due to the frequent file sharing with 

geologists	and	other	cartographers	at	our	agency	who	

work	directly	from	our	map	documents.	We	use	Helvetica,	

however,	on	maps	we	decide	to	leave	in	llustrator	and	

especially	those	that	cartographically	began	on	a	Macintosh.	Often,	llustrator	maps	at	our	agency	that	have	been	

started	in	previous	years	and	are	now	nearing	publication are Macintosh-based and were first created using the 

Helvetica	font.

Labeling: Floating Text vs. Annotation

Labeling	geologic	units	in	llustrator	is	simple;	

however,	the	labels	are	not	georeferenced	and	are	standalone	text	elements	that	are	not	linked	to	a	GS	attribute	

table.	To	add	a	label,	one	needs	only	to	type	new	text,	or	

copy	and	paste	an	existing	label	and	move	it	on	top	of	its	

corresponding	geologic	unit.	Labeling	in	ArcMap	is	not	

that	easy.	First	of	all,	the	labels	are	generated	from	the	

geologic	unit	attribute	table,	rather	than	being	typed	as	

“floating” text labels directly onto the map. After defining a label field in the symbology and turning on the label 

features	option,	the	map	is	labeled	but	the	labels	are	static	

and	unselectable.	To	maintain	control	over	label	placements,	we	convert	our	labels	to	annotation	after	turning	

on	the	label	features	option.	This	creates	a	feature	class	

that	is	added	to	the	map	document	as	a	layer.	The	feature	

class	has	its	own	set	of	attributes	in	a	table,	and	the	features	can	be	moved	around	on	the	map	in	editing	mode.

Dealing	with	superscripts	and	subscripts	within	labels	

is	also	a	challenge.	Before	converting	labels	to	annotation,	

label	classes	are	set	up	in	the	label	properties,	which	allow	

VB	Script	expressions	to	be	used	for	specifying	superscripts	and	subscripts.	Setting	these	expressions	can	be	

time	consuming	when	many	label	classes	contain	superscripts	and	subscripts.	Once	label	classes	are	set	up	and	

labels	have	been	converted	to	annotation,	proper	placement	of	each	label	is	needed.	Due	to	the	irregular-shaped	

geological units on a geologic map, annotation labels are

not always placed in the best cartographic location and

need to be manually moved to a better location. Additionally, some labels require leader lines and also need to be

manually moved with a leader line assigned.

Layout: Graphical Interface vs. Technical 

Interface

To avoid using multiple applications for layout finalization, we now use ArcMap’s Layout View rather than

Illustrator. This not only allows the map to be completed

in one document, but also enables an interactive view of

the georeferenced data in the document’s Data View by a

click of a button. When Illustrator was used for final map 

layout, a separate application, for example ArcView 3.x,

had to be opened in order to view the data interactively

while displayed in its proper coordinate system.

The graphical interface of Illustrator contributed

to the efficiency of layout finalization in our previous 

cartographic production system. However, the benefits 

of viewing spatial data in one map document and the efficiency and accuracy of making revisions to geology led 

us to finalize the map production in ArcMap. Even though 

ArcMap’s Layout View is not as graphically oriented

as Illustrator and often requires more steps to perform

similar tasks, the overall map finalization process is much 

easier and organized using one application that provides

all desired functionality.

Print Drivers: PS vs. RTL

As our agency moved toward our new conversion

system, we purchased a 42-inch HP 5500 DesignJet

PS3 large-format printer to be used as our draft plotter.

After using the 42-inch plotter for our draft printouts

and comparing various settings used, as well as previous drafts from old plotters, we discovered that using

the RTL (raster transfer language) driver combined with

the appropriate settings, instead of the Post Script driver,

produced crisper lines, higher quality base images, and

more accurate color.

CURRENT CHALLENGES DURING

CONVERSION

Clean Up of Illustrator Layers

Illustrator documents are not always clean before

converting to ArcMap. It is necessary to go through each

layer and make sure map elements are on correct layers. We usually have to move misplaced elements back

to their proper layers. Often, small elements such as unit

labels, leader lines, strike and dip symbols and other small
CONVERTNG	ADOBE	LLUSTRATOR	MAPS	TO	ARCMAP	FORMAT 167

symbols	are	accidentally	added	to	polygon,	line,	and	other	

layers.	Having	map	elements	on	incorrect	layers	can	create	problems	when	bringing	the	layers	into	GS,	such	as	

causing	polygon	topology	to	fail	to	build	and	features	to	

attribute	incorrectly.

Preliminary Setup before Exporting

There	are	many	steps	involved	in	preliminary	setup	

for conversion. Before an Illustrator file is ready to be 

imported	into	ArcMap,	the	user	must	add	anchorpoints	

to	the	lines,	simplify	the	anchorpoints	on	the	lines	using	

the straight lines option, and convert the file to a Drawing	Exchange	File	(.dxf)	before	converting	to	coverage	or	

shapefile. If these steps are not done properly, the coverage, shapefile or even DXF file will appear broken apart 

when	viewed	in	ArcMap	and	be	useless.	Additionally,	the	

user	must	make	sure	to	join	the	llustrator	attributes	(the	

layer names) with the coverage or shapefile so he or she 

can	properly	symbolize	our	ArcMap	layers	based	on	their	

attributes.

Line Clean Up, Building Polygons

Since	we	rebuild	the	polygons	during	conversion	

rather	than	converting	the	existing	llustrator	polygons,	

we	must	clean	the	lines	and	check	for	potential	problems	

that	would	cause	the	polygons	to	build	incorrectly.	Unfortunately, as an Illustrator map moves closer to finalization, 

it	is	much	easier	to	make	edits	directly	in	the	llustrator	

document rather than going back to the original shapefiles 

or	coverages	that	were	used	to	import	into	the	llustrator	

document. This means that those original shapefiles and 

coverages	become	obsolete.	Rebuilding	polygons	from	

our	llustrator	line	layer	during	conversion	ensures	that	

any edits made to the lines are reflected in the polygons, 

and	our	GS	data	are	accurate.	However,	this	also	means	

that	we	must	perform	clean	up	of	overshoots,	undershoots,	and	other	errors	that	could	cause	the	polygons	to	

build	incorrectly.	To	do	this,	we	either	use	the	ET	GeoWizards	tools	(http://www.ian-ko.com/),	Topology	Rules	

in	ArcMap,	or	the	Advanced	Editing	tools	in	ArcMap	to	

clean	up	our	lines.	Often	a	combination	of	methods	is	

used	during	map	conversion.	This	cleanup	can	require	

multiple	iterations	until	all	linework	is	properly	closed	

and	all	polygons	have	properly	built.

Re-labeling the Map

Although	we	found	a	quick	way	to	convert	llustrator	labels	to	ArcMap,	it	still	requires	clean	up	of	

duplicate	labels	or	incorrect	labels,	which	can	be	time	

consuming.	The	process	involves	exporting	the	llustrator	text	labels	to	a	point	feature	class	that	may	be	used	to	

attribute polygons, and then further converting the point

feature class to an annotation feature class, which requires

manual cartographic placement of labels and leaders. The

alternative to the quick method of generating labels is to

manually select polygons on the map, attribute them, and

generate labels, which are then converted to annotation.

This method is ideal for maps that have simple geology;

however, for very detailed maps, a judgment call should

be made as to which method is more efficient.

Redigitizing Strike and Dip Symbols as 

Points

Since we use strike and dip symbols as point feature

classes, which are symbolized and rotated within the map

document, rather than graphical floating symbols as in 

Illustrator, the strike and dips are redigitized as a point

feature class. They are then symbolized as a strike and dip

cartographic marker symbol in the ArcMap document and

rotated using the rotation tool with the geographic rotation

option in the symbology window linked to the field in the 

attribute table that will store the rotation. As an alternative

to redigitizing points, we are currently experimenting with

exporting strike and dips to points to increase productivity

of the strike and dip symbol conversion.

However, symbolizing the strike and dip point feature

class in the ArcMap document leads to another challenge.

When people request the files but are not capable of supporting our ArcGIS file types, they will only see the point 

feature class as a point, and not the symbolized strike and

dip symbol, when they bring the point layer into their map

documents. This is because the symbolization is stored in

the ArcMap document that we supply. As a cartographic

solution, we convert strike and dips to lines when using

alternative file formats.

Although an entire new map can be completed from

start to finish in ArcMap, the conversion process of an 

existing map from Illustrator to ArcMap is not as straightforward. As far as we are aware, ArcGIS simply does not

have the capabilities to import a raw Illustrator file and 

generate a completely attributed map that is cartographically high in quality. In addition to an experienced cartographer, the conversion process requires multiple software

applications and file formats to get the job done.

OUR SUCCESS WITH CONVERSION

Although we have only converted a handful of maps

since developing our system for conversion, we have

been successful in generating accurate and cartographically pleasing products that closely match the Illustrator

versions. The balance between the extra steps involved in

maintaining our high quality cartographic products when

using ArcMap, and the ability to have georeferenced,
168	 DGTAL	MAPPNG	TECHNQUES	‘06

attributed	data	all	in	one	document,	is	reasonable.	As	the	

software	continues	to	improve,	our	lives	as	cartographers	

will	only	become	easier,	which	will	allow	us	to	produce	

more appealing maps, more accurately. The positives far

outweigh the challenges that we have encountered, and

we will continue to use ArcMap for map production.

APPENDIX A

Software used for conversion: 

Adobe	llustrator	8,	10

Adobe	Photoshop	(minimal	use)	7,	CS

Canvas	(for	label	conversion	only)	

Arcnfo	Workstation	9.0,	9.1

ArcGS	Desktop	9.0,	9.1

Output devices currently used:

HP	5500	DesignJet	PS3	(42-inch,	dye	ink)

HP	5500	DesignJet	PS3	(60-inch,	dye	ink)

HP	5000	DesignJet	PS3	(42-inch,	dye	ink)

Output devices previously used:

HP	2500	DesignJet

HP	755	Design	Jet

Note: Avenza Map Publisher and ESRI ArcView 3.x not used during current conversion process.
169

169

Using GIS to Create and Analyze Potentiometric-Surface Maps

By Paul N. Spahr1, A. Wayne Jones1, Kelly A. Barrett1,

Michael P. Angle2, and James M. Raab1

1Ohio Department of Natural Resources

Division of Water

2045 Morse Rd., B-2

Columbus, OH 43229-6693

Telephone: (614) 265-6895

Fax: (614) 265-6767

2Ohio Department of Natural Resources

Division of Geological Survey

2045 Morse Rd., C-2

Columbus, OH 43229-6693

Telephone: (614) 265-6602

Fax: (614) 447-1918

e-mail: mike.angle@dnr.state.oh.us

INTRODUCTION

This	paper	describes	the	content	of	a	poster	display	

that	is	directly	related	to	the	oral	presentation	and	paper	

of	Jones	and	Barrett	(this	volume).	Jones	and	Barrett	

discussed	at	length	the	GS	used	to	construct	this	poster	

as	well	as	the	database	used	to	create	the	GS	layers.	[GS	

applications	were	based	on	Environmental	Systems	Research	nstitute	(ESR)	software,	including	Arc	Map,	Arc	

Catalog,	Arc	Toolbox	(Spatial	Analyst,	3-D	Analyst)	and	

Arc	Workstation	(9.1).]	Here,	we	will	reintroduce	their	

discussion	and	add	some	further	explanation	and	details	

regarding	the	poster	itself.

DISCUSSION OF THE POSTER

The	poster	(Figure	1)	is	available	for	download	at	

http://ngmdb.usgs.gov/nfo/dmt/docs/angle06.pdf,	and	

contains	a	legend	with	a	brief	explanation	and	three	panels.	

Each	of	the	three	panels	depicts	an	identical	base	map	of	

the	study	area	overlain	by	various	layers	created	by	the	

process	described	in	Jones	and	Barrett	(this	volume).	The	

study	area	is	a	section	of	Darby	Creek	in	western	Franklin	

County,	Ohio.	A	National	and	State	Scenic	River,	Darby	

Creek	provides	an	area	of	unique	habitat	to	many	endangered	species	of	mussels	and	minnows.	t	is	also	an	area	

that	is	undergoing	rapid	development	pressure.	Numerous	

parties,	including	planners,	geologists,	surface	water	ecologists, and fish biologists, were interested in determining 

the full impact of ground water flow on Darby Creek and 

a	major	tributary,	Hellbranch	Creek.	There	was	particular	

interest	in	determining	the	gradient	of	the	water	table	for	

the	bedrock	and	unconsolidated	(sand	and	gravel)	aquifers,	which	are	possible	areas	of	ground	water	discharge	to	

Darby	Creek	and	Hellbranch	Creek.	Areas	of	recharge	to	

the	aquifers	were	of	importance	as	well.	t	should	be	noted	

that	part	of	the	purpose	was	to	create	maps	that	would	be	

easily	understood	by	those	with	little	geologic	background.

The	panel	on	the	left	shows	the	bedrock	aquifer	

potentiometric-surface	contours	(arcs)	superimposed	over	

a	topographic	DRG	basemap.	This	map	helped	portray	the	

elevation, rough gradient, and direction of flow of the potentiometric-surface	of	the	bedrock	(limestone)	aquifers.

The	center	panel	shows	the	sand	and	gravel	aquifer	

potentiometric-	surface	contours	(arcs)	superimposed	over	

the	same	topographic	DRG	basemap.	This	map	helped	

display the elevation, gradient, and direction of flow of 

the	potentiometric-surface	of	the	sand	and	gravel	aquifers.

n	most	of	the	study	area,	the	sand	and	gravel	aquifer	

overlies	the	bedrock	aquifer.	There	was	an	even	distribution	of	water	well	log	data	points	over	the	area	that	

derived	water	either	from	the	limestone	aquifer	or	the	

sand	and	gravel	aquifer.	There	was	interest	in	the	relative contribution of each aquifer to streamflow, and in the 

recharge	areas	of	the	aquifers.

The	right	panel	shows	a	difference	map	that	was	used	

to	compare	potentiometric-surface	maps.	For	each	map,	

the shapefile was converted into a TIN model, which was 

then	converted	into	a	grid.	Arc	Map’s	Spatial	Analyst	was	

used	to	perform	a	subtraction	between	the	two	grids.	A	ras-
170	 DGTAL	MAPPNG	TECHNQUES	‘06

ter difference map was then generated; this showed a value

for the potentiometric head that indicates whether there

was a relative movement of ground water from the sand

and gravel aquifer into the bedrock aquifer or vice versa.

On the difference map, negative values (blues)

show areas where water has a net downward movement

from the sand and gravel aquifers to the bedrock aquifers. The blue areas are found on the uplands between

Darby Creek and Hellbranch Creek. These areas tend

to be recharge areas for both aquifers. Positive values

(reds) show areas where the bedrock aquifer has a net

upward effect, so water moves from the bedrock into

sand and gravel units. These areas tend to be along the

steep valley sides of Darby Creek and, commonly, are

where the aquifers discharge. These small, steep tributaries of Darby Creek provide baseflow to the stream.

The	information	in	this	poster	was	widely	accepted	

by	the	planners,	biologists,	and	other	stakeholders	interested	in	the	project.	Readers	are	encouraged	to	view	the	

poster	in	color	at	http://ngmdb.usgs.gov/nfo/dmt/docs/

angle06.pdf.

REFERENCE

Building	a	Water	Well	Database	for	GS	Analysis,	by	A.	Wayne	

Jones	and	Kelly	A.	Barrett	(Ohio	Department	of	Natural	

Resources,	Division	of	Water)	DMT	2006,	The	Ohio	State	

University,	125	South	Oval	Dr.,	Columbus,	Ohio	43210.	

http://ngmdb.usgs.gov/nfo/dmt/docs/jones06.ppt.

Figure 1. Poster “Using GIS to create and analyze potentiometric - surface maps”.
171 171

171 171

Updates to the Known and Probable Karst Map of Ohio

By Donovan M. Powers and Dennis Hull

Ohio Geological Survey

2045 Morse Rd. Bldg C-1

Columbus, OH 43229-6693

Telephone: (614) 265-6591

Fax: (614) 447-1918

e-mail: donovan.powers@dnr.state.oh.us

INTRODUCTION

The data shown in this map were created to establish 

a comprehensive statewide coverage of the known and 

probable karst features in Ohio (Hull, 2006). They show 

the subsurface locations of karsts and karst terrain. The 

project was conceived to help facilitate the location of a 

low-level radioactive-waste disposal site. This investigation was initially funded through a grant administered by 

the Ohio Low-Level Radioactive Waste Disposal facility 

Development Authority in 1997. The  ndings would be 

used to show the vulnerable karst areas and prompt the 

Development Authority to locate the waste disposal facility elsewhere. The primary source for the karst locations 

was the Ohio Cave Inventory provided by Dr. Horton H. 

Hobbs III of Wittenberg University. In addition, some 

investigation was conducted by utilizing geologic and 

soils maps as well as aerial photographs to  nd indications of karsts.

Thousands of sinkholes were inventoried but, for 

clarity, are not shown on the map. Field inspection of 

many indicated karst features allowed for documentation 

of more known karst locations and veri ed the mapping 

methods.

Probable karst areas were de ned as those that: (1) 

lie within a half mile of a known or indicated karst location, and (2) are underlain by carbonate or gypsiferous 

bedrock with overburden generally of less than 20 feet 

of noncarbonate bedrock or unconsolidated material as 

shown by comparison of 7.5-minute bedrock-topography 

and bedrock-geology maps to surface topography. Known 

karst locations and probable karst areas on the work maps 

were digitized and are shown on this 1:500,000-scale map 

(Hull, 2006).

The map was updated by utilizing some of the 

datasets that the Ohio Division of Geological Survey has 

recently published as Geographic Information System 

(GIS)  les. The criteria for carbonate or gypsiferous 

bedrock and for noncarbonate bedrock were queried out 

of the 2006 Bedrock Geology dataset, and like units were 

merged for simpli cation. The overburden of 20 feet or 

less criteria was derived from the Drift-Thickness 2004 

dataset. This raster dataset was generalized and reclassi-

 ed into greater than or less than 20-foot units. These two 

sets of data were then combined and added to the map.

PROCESS

The Known and Probable Karst in Ohio Map has 

gone through three iterations since its creation in 1999. 

The  rst version was created via utilization of Cadastral 

software (Figure 1). The next version was converted to a 

GIS format by way of Environmental Systems Research 

Institute’s (ESRI) ArcInfo Coverage format (Figure 2). 

This conversion was also accompanied with updates 

and additions to the dataset. In this third version, we use 

newly created data layers and a more graphically oriented 

software package, the result of which is a more illustrative 

product (Figure 3).

The latest version incorporates data from three other 

products: Bedrock Geology, Drift Thickness, and the Glacial Margin extents from the Quaternary geology datasets. 

All of these layers were derived from sources within the 

Division of Geological Survey and are available as GIS 

datasets.

The incorporation of the bedrock geology units was a 

simple, straight-forward approach (Figure 4). The data are 

in a polygon vector format, so simply performing an attribute query on the bedrock units yielded those that meet 

the criteria conducive to karsting terrain.

The drift thickness data required a more extensive 

process (Figure 5). The data initially were in a raster 

format. For quality and illustrative purposes, the desired 

output needed to be vector. The process was to classify 

the raster into thickness of 20-feet or less and thickness 

over 20 feet. Next, the reclassi ed raster was converted 

to vector. This process caused stair stepping effect in the 

polygons and sporadic errors. A smoothing algorithm 

was used to better represent the areas. The smaller polygons were removed at a speci c tolerance. The classi ed 

overburden layer was then merged with the carbonate 

or gypsiferous bedrock data. The dataset was closely 
172	 172 DGTAL	MAPPNG	TECHNQUES	‘06 DIGITAL MAPPING TECHNIQUES ‘06

checked against the original source layers and found to 

be topologically correct. The most time consuming portion of this maps creation was the clean-up and editing 

process. 

A  nal addition used for assistance in visualizing 

the pattern to karsting was the glacial margin lines that 

were adapted from the 1999 Quaternary Geology dataset 

(Pavey and others, 1999) (Figure 6). Adding more basemap

data helped make the  nal map more cartographically 

appealing. The cities, road networks, and hydrography 

were derived from the Ohio Department of Transportation 

datasets.

REFERENCES

Hull, D.N., 1999 (revised 2006), Known and probable karst in 

Ohio: Ohio Division of Geological Survey, Map EG-1 1999 

(revised 2006).

Pavey, R.R., Hull, D.N., Brockman, C.S., and others with GIS 

compilation by Powers, D.M., 1999 (revised 2006), Known 

and probable karst in Ohio: Ohio Division of Geological 

Survey, Map EG-1 1999 (revised 2006).

 Pavey, R.R., Goldthwait, R.P., Brockman, C.S., Hull, D.N., 

Swinford, E.M., and Van Horn, R.G., 1999, Quaternary 

geology of Ohio: Ohio Division of Geological Survey Map 

M-2, 1:500,000-scale map and 1:250,000-scale GIS  les.
Updates to the Known and probable Karst Map of ohio 173

Figure 1. Version 1.0 (DCMS 24) of the Known and 

Probable Karst in Ohio, 1999.

Figure 2. Version 2.0 of the Known and Probable Karst in 

Ohio, 1999.

Figure 3. Version 3.0 of the Known and Probable Karst in 

Ohio, 1999.

Figure 4. Bedrock Geologic Ma0p of Ohio, 2006.
174 diGital MappinG teChniQUes ‘06

Figure 5. Shaded Drift Thickness Map of Ohio, 2004.

Figure 6. Quaternary Geology Map of Ohio, 1999.
Updates to the Known and probable Karst Map of ohio 175 Figure 7. Poster of Known and Probable Karst in Ohio 2006 (as presented at the DMT meeting; see http://ngmdb.usgs.gov/Info/dmt/docs/powers06.pdf).
176	 DGTAL	MAPPNG	TECHNQUES	‘06
177

177

New Map of the Surficial Geology of the Lorain and

Put-in-Bay 30 x 60 Minute Quadrangles, Ohio

By Edward M. Swinford, Richard R. Pavey, and Glenn E. Larsen

Ohio Department of Natural Resources

Division of Geological Survey

2045 Morse Road Bldg. C-1

Columbus, OH 43229

Telephone: (614) 265-6473

e-mail: mac.swinford@dnr.state.oh.us

ABSTRACT

A map depicting the surficial geology of the Lorain	and	Put-in-Bay	30	x	60	minute	(1:100,000-scale)	

quadrangles	has	been	produced	by	the	Ohio	Department	

of	Natural	Resources,	Division	of	Geological	Survey.	

Existing surficial maps at various scales document the 

uppermost surficial lithology of the area. The new map 

depicts	underlying	lithologies	from	the	surface	down	to	

bedrock	for	use	in	geotechnical	studies,	land-use	planning,	and	mineral	exploration.	To	produce	the	new	map,	

surficial deposits were mapped at 1:24,000 scale to create 

thirty-six	7.5-minute	quadrangles,	which	were	compiled	

digitally	using	GS	technology	and	converted	into	a	

full-color, print-on-demand, 1:100,000-scale, surficialgeology	map.	The	map	includes	all	or	portions	of	Erie,	

Huron,	Lorain,	Lucas,	Sandusky,	and	Seneca	Counties	in	

north-central Ohio. Data sources include field mapping, 

county	soil	surveys,	Ohio	Department	of	Transportation	

and	Ohio	Environmental	Protection	Agency	boring	logs,	

engineering	logs,	ODNR	water-well	logs,	theses,	and	

published	and	unpublished	geologic	and	hydrogeologic	

reports.	Map	polygons	were	attributed	using	a	stack-unit	

designator	that	indicates	the	thickness	and	stratigraphic	

sequence	of	major	material	units	(i.e.,	till,	gravel,	sand,	

silt,	and	clay),	from	the	surface	down	to	and	including	the	

uppermost	bedrock	unit.	Several	regional	material	trends	

are	apparent	on	the	map,	including	large	areas	of	lacustrine	clay	and	silt	landward	of	Lake	Erie,	the	prominence	

of	shallow	bedrock	that	parallels	the	Lake	Erie	shoreline,	a	deltaic	sequence	deposited	during	higher	levels	

of	water	of	ancestral	Lake	Erie,	the	locally	widespread	

and	thick	organic	and	marl	deposits,	and	the	expanse	of	

Wisconsinan-age	till	that	mantles	the	surface	in	most	of	

the	quadrangles.	The	text	explains	how	to	read	the	map,	

provides	lithologic	descriptions	of	mapped	glacial	and	

bedrock	units,	and	offers	other	explanatory	information.	

A	GS	geodatabase	contains	spatial	information	on	each	

polygon	and	data	attributes	of	the	stack	units,	all	of	which	

can be queried on the basis of material types and thicknesses for rapid generation of derivative maps. Potential

queries for derivative maps might include isolating clay

and silt deposits for the identification of potential geohazards, identifying sand and gravel deposits for aggregate

exploration, or depicting areas of thick glacial till for the

identification of potentially favorable solid-waste disposal 

sites. Mapping was partially funded by the U.S. Geological Survey, National Cooperative Geological Mapping

Program, STATEMAP component. Digital compilation

was made possible by funding from the Central Great

Lakes Geologic Mapping Coalition.

INTRODUCTION

In 1997, the Ohio Department of Natural Resources

(ODNR), Division of Geological Survey (OGS) began

work on a long-range goal to produce reconnaissance-style

three-dimensional surficial-geology maps for all of Ohio. 

The plan to attain this goal focuses on completing surficial 

mapping of major urban areas and highly populated corridors first in order that a majority of Ohio’s 11.4 million 

citizens (2003 Census estimate) can benefit from modern 

surficial-geologic maps for land-use planning, resource 

exploration, hydrogeologic investigations, and geohazard

identification. Less populated glaciated portions of Ohio 

will then be mapped followed by all of unglaciated Ohio.

To date, this effort has resulted in the completion of threedimensional surficial geology maps for 45% of the state’s 

land area (354 of 788 7.5-minute quadrangles).

Existing Maps

Prior to recent mapping efforts, glacial-geology maps

of Ohio were generalized, two-dimensional, geomorphically oriented products such as the 1:500,000-scale Quaternary geology of Ohio (Pavey and others, 1999) (Figure

1) and other published/open-file glacial-geology maps 

(generally at 1:62,500-scale) for 27 counties in north-
178	 DGTAL	MAPPNG	TECHNQUES	‘06

eastern,	central,	and	southwestern	Ohio	(Figure	2).	These	

older maps were constructed using a combination of field 

investigations,	geomorphic	analysis,	and	existing	soils	

maps. Describing the entire surficial lithologic interval 

from	surface	down	to	top	of	bedrock	was	not	attempted	

in this map set, as only the topmost unit was defined. 

However,	existing	glacial	maps	were	used	as	a	basis	for	

selecting	boundaries	of	the	uppermost	units	during	the	

remapping	effort.	While	such	maps	can	provide	general	

information	on	the	distribution	of	materials	deposited	by	

Pleistocene	glacial	and	postglacial	events,	the	maps	are	

wholly	inadequate	for	the	characterization	and	assessment	

of unconsolidated materials at depth. The new surficial 

mapping	effort	adds	the	third-dimension	component	of	

variable	lithologies	at	depth	and	their	thicknesses.

New Glacial Mapping Program

In 1996, OGS conducted a survey of surficial-geology	map	users	in	Ohio	to	determine	the	kinds	of	map	

information	they	require	for	their	needs.	The	majority	

of	respondents	to	the	survey	questionnaire	indicated	

a	strong	need	for	comprehensive,	three-dimensional,	

surficial-geology maps that depict all deposits and their 

thicknesses	down	to	and	including	the	uppermost	bedrock	

unit.	n	recognition	of	this	need	for	more	comprehensive	

surficial-geology data, OGS implemented a program to 

produce reconnaissance-style, three-dimensional, surficial-geology	maps	at	1:100,000	scale	for	the	entire	state.	

The mapping effort is based on a three-dimensional

mapping method first implemented by the Illinois State 

Geological Survey (e.g., Berg and Kempton, 1988). The

Illinois surficial mapping model used established glacial 

stratigraphic names (abbreviated) that are “stacked” as

stratigraphic units would appear within the polygon they

are defining. Ohio’s surficial mapping effort modified the 

stack-unit concept to reflect lithologies of materials rather 

than glacial stratigraphic names and introduced additional

constraints on unit thickness, allowing each area to be

mapped down to the bedrock surface.

To date, reconnaissance-style, 1:100,000-scale surficial-geology maps have been completed for fifteen of the 

thirty-four 30 x 60 minute quadrangle areas of the state.

An estimated 8.9 million Ohio citizens now have threedimensional surficial mapping for various societal needs, 

including mineral-resource exploration, land-use planning, geohazard identification, and environmental protection. A database that contains the surficial-unit lithology, 

thickness, and distribution information on thousands of

polygons shown on the map can be queried to produce

derivative maps that identify geology of societal interest

such as mineral resources or geohazards.

The new mapping program is largely funded by a tax

on the mineral industries of Ohio, including oil and gas,

with additional funding by the U.S. Geological Survey

(USGS) STATEMAP program, the Central Great Lakes

Geologic Mapping Coalition, and the U.S. Environmental

Protection Agency Nonpoint-Source Pollution Program.

Figure 1. Map showing the glacial deposits of Ohio.
SURFCAL	GEOLOGY	MAP	OF	THE	LORAN	AND	PUT-N-BAY	30	X	60	MNUTE	QUADRANGLES,	OHO 179

Figure 2. Map showing counties for which glacial-geology maps are available as published or open-file reports 

from the Ohio Department of Natural Resources, Division of Geological Survey or as part of Division of Water

bulletins.

Purpose and Justification

Three-dimensional	mapping	of	Ohio’s	glacial	geology	in	urbanized	or	rapidly	urbanizing	areas	(Figure	3)	

is	a	high	priority	for	OGS.	To	date,	OGS	has	completed	

1:100,000-scale three-dimensional surficial-geology maps 

for	the	Akron,	Canton,	Cincinnati,	Cleveland,	Columbus,	

Dayton, Lancaster, Springfield, Toledo, and Youngstown 

metropolitan	areas.	These	maps	have	been	used	by

	1.	private	sand	and	gravel	explorationists,

	2.	the	Ohio	EPA	for	waste-facility	siting	analysis	and	

contaminated-site	evaluations,	

	3.	regional	planning	commissions	for	land-use	planning,

	4.	colleges	and	universities	as	teaching	tools,	

 5. private geotechnical firms for site evaluations, and

	6.		the	Ohio	Department	of	Transportation	(ODOT)	

for	shallow	subsurface	evaluations.

Major	metropolitan	areas	and	their	surrounding	interstate	highway	corridors	(especially	in	glaciated	areas	of	

the	state)	are	experiencing	major	economic	development	

and	related	population	growth.	Land-use	planning	and	industrial development in these corridors will benefit greatly 

from	the	three-dimensional	mapping	this	overall	effort	

will provide. OGS’s long-range plan for surficial mapping 

in	Ohio	is	to	complete	statewide	mapping	of	the	densely	

populated	major	metropolitan	areas	where	most	Ohioans	

live and work; surficial mapping of densely populated 

areas	in	glaciated	Ohio	is	complete	or	in	progress.	When	

this phase of mapping is complete, the OGS mapping

effort will focus on rapidly developing interstate highway

corridors in glaciated portions of northern and western

Ohio, such as the Interstate-71 corridor in northern Ohio,

and the Interstate-75 corridor in western Ohio. After

completion of the major portions of glaciated Ohio, mapping efforts will focus on the largely unglaciated terrain of

southeastern Ohio, where thick deposits of outwash and

landslide-prone glacio-lacustrine sediments occupy large

portions of former and present-day river valleys.

METHODS

Construction of the Lorain/Put-in-Bay Map

OGS has compiled a map that shows the three-dimensional framework of the surficial geology, from the 

surface down to and including the uppermost bedrock

unit, for the Lorain/Put-in-Bay 30 X 60 minute quadrangles (Pavey and others, 2005) located in north-central

Ohio (Figure 4). Geologists at the OGS developed an

easy-to-read format, described below, that depicts 1) the

type of deposit, 2) the thickness range of the deposit, 3)

the vertical sequence of deposits in the map area, and 4)

the bedrock lying beneath the deposit.

Map Format Guidelines

•	 Map colors depict the uppermost continuous unit

and are intended to assist users in visualizing the

surface geology of the area (e.g., greens = till,

reds and oranges = sand and/or gravel, blues and

purples = silt and clay).

•	 Polygons or map-unit-areas define boundaries 

of the vertical sequence indicated by stack-unit

descriptions that are composed of letters, numbers,

and modifiers.

•	 Letters, numbers, and modifiers are arranged in

stacks to depict the vertical sequence of lithologic

units for a polygon. Simple abbreviations are used

for ease of reading.

° Letter abbreviations indicate lithology (e.g.,

SG = sand and gravel, T = till, L = silt).

° Numbers indicate average thickness in tens of

feet (e.g., 2 = 20 ft thick, + or - 50%).

° Modifiers indicate aerial extent. A minus {-}

sign following a number indicates the maximum thickness for that unit in areas like buried

valleys or ridges. Parentheses () indicate that a

unit has a patchy or discontinuous distribution

in that map-unit area.

Data used to create the map were collected from

numerous sources. The concentration of surficial data is 

greatest near the surface and decreases with depth. U.S.
180	 DGTAL	MAPPNG	TECHNQUES	‘06

Department	of	Agriculture,	Soil	Conservation	Service	

maps, which describe the top 5 ft of surficial materials, 

provided	an	initial	guide	to	map-unit	area	delineation.	

These areas were modified through interpretation of 

local	geomorphic	settings	and	other	data	that	indicated	

a	change	in	the	type	of	deposit	at	depth,	such	as	ODNR	

water-well	logs,	ODOT	and	Ohio	EPA	test-boring	logs,	

engineering-boring	logs,	theses,	and	published	and	unpublished geologic reports, maps, field notes, and seismicrefraction profiles. These data also provided the basis for 

lithologic	unit	descriptions,	which	summarize,	as	accurately	as	possible,	recognized	associations	of	genetically	

related materials. The total thickness of surficial deposits 

was	calculated	by	subtracting	from	land-surface	elevation	

the bedrock elevation found on OGS open-file bedrocktopography	maps,	which	are	available	for	each	7.5-minute	

quadrangle	in	the	map	area.	The	bedrock	units	were	summarized	from	OGS	bedrock-geology	maps,	which	are	also	

available	for	each	7.5-minute	quadrangle.	Land-surface	

topography	shown	on	the	base	map	was	prepared	largely	

from	data	derived	from	the	U.S.	Geological	Survey’s	

National	Elevation	Dataset.

The	polygon	and	stack-unit	information	were	handdrawn	at	a	scale	of	1:24,000	on	Mylar	overlays	registered	

to	1:24,000-scale	7.5-minute	quadrangles.	These	Mylar	

maps	were	scanned,	the	line	work	was	captured,	and	polygons were created. Stack-unit information that identifies 

the surficial geology from surface down to and including 

bedrock	for	each	polygon	was	input	into	a	geodatabase.	

Several	iterations	of	quality	control	took	place	to	ensure	

that	line	work	between	quadrangles	and	stack-unit	assignments	were	edge	matched.	The	color	map	consisting	

of	polygons	and	stack-unit	indicators	was	generated	and	

included	base	map	information	and	shaded	elevation	for	

the final map product. Other map elements include an explanation	of	how	to	read	the	map	along	with	a	schematic	

cross-section,	detailed	lithologic	unit	descriptions,	references	of	sources	used,	a	location	map	of	the	quadrangle,	

an	index	map	that	shows	mapping	responsibility,	and	a	

map	color	key.

RESULTS

Several regional surficial-material and bedrock-geology	trends	appear	on	the	map.	Large	areas	of	lacustrine	

clay	and	silt	deposited	during	higher	levels	of	ancestral	

Lake	Erie	were	mapped;	they	dominate	the	surface	materials	landward	of	Lake	Erie	in	the	central	and	western	

Figure 3. Map showing the population of Ohio by census tract and the outlines of areas

with completed three-dimensional surficial mapping. Sources: the U.S. Bureau of the 

Census and the Ohio Department of Development.
portions	of	the	map.	Shallow	bedrock	parallels	portions	

of	the	Lake	Erie	shoreline	and	ranges	from	economic	

deposits	of	limestone	in	the	west	to	shale	and	economic	

deposits	of	sandstone	in	the	east.	A	deltaic	sequence	of	

sand	and	silt	deposited	during	higher	levels	of	water	of	

ancestral	Lake	Erie	covers	a	large	area	in	the	central	portion	of	the	map.	Locally	widespread	and	thick	organic	and	

marl	deposits,	formed	from	the	precipitation	of	calcium	

carbonate	from	local	springs,	were	mapped	in	the	northwestern	portion	of	the	map.	Wisconsinan-age	till,	present	

as	ridge	and	ground	moraine	deposits	up	to	120	ft	thick,	

mantles	most	of	the	southern	portion	of	the	map.

Map Products

The final Lorain and Put-in-Bay 30 x 60 minute map 

will	be	released	to	the	public	in	three	formats:	1)	a	fullcolor	paper	format,	print-on-demand,	1:100,000-scale,	

surficial-geology map; 2) a digital format on CD-ROM 

disk, which includes database files, base-map files, 

metadata files, and a PDF file of the original map; and 

3)	an	nternet	Map	System	product	on	the	OGS	website	

(http://ohiodnr.com).

Derivitive Map Products

The Lorain/Put-in-Bay 30 X 60 minute map is a digital product that can be manipulated to isolate various geologic components. Polygons and stack-unit information

are in ArcGIS geodatabase file format and can be sorted 

by lithology and thickness to create derivative maps for a

variety of uses. Figure 5 is a derivative product showing

polygons that contain layers of sand, sand and gravel,

or gravel with a thickness greater than 20 ft. Mineral

companies could use this style of map to delineate areas

that contain economic deposits of natural aggregate (sand

and gravel) for potential exploration. Water-well drillers

could use this map to delineate areas of thick, coarse sand

and gravel deposits that may contain an abundant water

supply.

Other derivative map products can be extracted from

the digital data to suit many purposes. Examples include

derivative maps that show areas of thick till, for potential

placement of a solid-waste disposal facility, or areas of

surface silt, clay, or organic materials that could indicate

construction geohazards such as landslides and unstable

near-surface materials.

Figure 4. Surficial geology map of the Lorain and Put-in-Bay 30 x 60 minute quadrangles located 

in north central Ohio to be published as a full-color, print-on-demand paper map and released on

CD-ROM disk and as a web-based interactive map.

surficial geology 
182	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 5. Digitally derived map (extracted from the original stack-unit information) showing areas

that have sand, sand and gravel, or gravel with a thickness greater than 20 ft in the Lorain and Putin-Bay 30 x 60 minute quadrangles.

REFERENCES

Berg, R.C., and Kempton, J.P., 1988, Stack-Unit Mapping of

Geologic Materials in Illinois to a Depth of 15 Meters: Illinois State Geological Survey Circular 542, 23p., 4 pl.

Pavey, R.R., Goldthwait, R.P., Brockman, C.S., Hull, D.N.,

Swinford, E.M., and Van Horn R.G., 1999, Quaternary

Geology of Ohio: Ohio Division of Geological Survey Map

No.2.

Pavey, R.R., Larsen, G.E., Swinford, E.M., and Vorbau, K.E.,

2005, Surficial Geology of the Lorain and Put-in-Bay 30 

X 60-minute Quadrangles: Ohio Division of Geological

Survey Map SG-2 Lorain/Put-in-Bay, scale 1:100,000.
183

183

Rapid Acquisition of Ground Penetrating

Radar Enabled by LIDAR

By David Percy and Curt Peterson

Portland State University

Department of Geology

1721 SW Broadway

Portland, OR 97201

Telephone: (503) 725-3373

Fax: (503) 725-3325

email: percyd@pdx.edu

ABSTRACT

One of the most time-consuming aspects of collecting

a Ground Penetrating Radar (GPR) survey is the acquisition of topographic data. To be useful, GPR data must be

corrected for elevation, given that GPR “sees” only the

data directly below it. We have developed a new method

whereby LIDAR data can be used in conjunction with a

Global Positioning System (GPS) for rapid acquisition of

the elevation control that corrects GPR data for elevation

changes. By collecting real-time GPS data and GPR at the

same time, we can post-process these location data against

a high resolution LIDAR data set and develop an elevation

profile, which can then be used to offset the GPR data. This 

allows us to see features in the subsurface, such as groundwater surface trends or thickness of surficial deposits.

BACKGROUND

Ground Penetrating Radar (GPR) is a technique for

surveying data in the shallow subsurface of earth materials (up to 40 meters deep, depending on frequency

used, and characteristics of material) (Jol and Bristow,

2003). By generating an electromagnetic field and then 

recording reflections from subsurface materials with different transmissive properties, we are able to calculate

the depth to subsurface objects of interest, such as the

groundwater surface or the depth of surficial sediments 

(Peterson and others, in press). Each position along a line

is occupied with a transmitter and a receiver at a fixed 

ground distance apart, depending on the frequency used.

For example, a 100Mhz GPR survey will use an antenna

spacing of 1m, while a 200 Mhz survey will use antenna

separation of 0.5 m.

Every GPR survey that covers an area with varying topography requires a separate elevation survey that

will correct for changes in elevation. As seen in Figure

1, this elevation correction can significantly improve the 

interpretation	of	GPR	data.	Here,	we	see	how	subsurface	

groundwater	surfaces	become	horizontal,	as	expected,	

once	the	GPR	data	are	corrected.

When	collecting	GPR	data	in	the	past,	we	surveyed	

in	the	topographic	correction	data	using	various	methods	

that	take	nearly	as	long	to	collect	as	the	actual	GPR	data.	

n	2004,	our	department	purchased	a	cart	that	attached	

to	a	vehicle	and	allowed	for	transportation	of	the	GPR,	

and	data	acquisition	at	speeds	of	up	to	5	miles	per	hour.	

At	this	point,	we	needed	a	more	rapid	way	to	collect	

topographic	data.	Realizing	that	high	resolution	LDAR	

data	existed	for	our	study	area	on	the	coast	of	Oregon,	we	

decided	to	develop	a	methodology	for	topographic	correction	based	on	this.

LDAR	data	are	acquired	via	airborne	surveys	in	

which	laser	pulses	are	sent	to	the	ground	and	the	return	

times	are	collected,	thus	allowing	for	the	collection	of	a	

very	dense,	high	resolution	elevation	surface	(Daniels,	

2001).	n	some	surveys,	multiple	return	pulses	are	collected	(fourth	return)	and	can	thus	remove	returns	from	

higher	elevation	objects,	such	as	trees,	which	results	in	a	

“bare	earth”	elevation	surface.	Since	our	collection	typically occurs on roads, we can use “first return” data.

METHODS

A	Global	Positioning	System	(GPS)	receiver	is	set	

in	the	dashboard	of	the	collection	vehicle.	This	allows	

for	a	reasonably	good	view	of	the	satellites	and,	given	

that	the	GPS	can	receive	through	glass,	the	simultaneous	

collection	of	positional	and	GPR	data.	A	cart	is	attached	

to	the	rear	of	the	vehicle	on	which	the	GPR	antennas	are	

situated.	Fiber	optic	cables	connect	the	antennas	to	the	

interior	of	the	vehicle,	where	the	triggering	and	acquisition	equipment	are	operated.	An	odometer	wheel	located	

behind the antennas triggers the computer, again via fiber 

optics,	to	transmit	to	the	antennas	controlling	commands	

that	regulate	when	to	send	and	receive	data.
184	 DGTAL	MAPPNG	TECHNQUES	‘06

After the data acquisition, post-processing begins.

The GPS data are differentially corrected for standard

errors in GPS, such as atmospheric, ephemeris (satellite

positional error), and other errors that can be corrected

by differential correction. These data are then converted

into GIS coverages and named according to the GPR line.

We refer to each set of data as a line. Each line then needs

to be corrected by using heads up digitizing in the GIS

software. Even though it has been differentially corrected,

the GPS traverse will have “wiggles” in it due to errors

in collection. These errors will make the GPS line greater

in length than the GPR line and result in errors. Each

line is smoothed by the GIS operator such that it matches

the notes collected from the field and runs clearly down 

the road on which the GPR was collected. An overlay of

aerial photography assists greatly in this process.

Next, a set of “addresses” is generated. Each GPR

line is treated as if it were a street in the GIS software.

Thus, it has a beginning “address” of 0 and an ending

address of the length of the line. For example, a 1.5Km

line would have a beginning address of 0 and an ending

address of 1500. GIS software has a method for geocoding addresses against linear features based on the beginning and ending addresses of streets. This is often used

for delivering pizzas, for example. In our case, we use this

technique to determine positions along a line of data acquisition.	We	generate	a	set	of	addresses	every	10	meters	

to	retrieve	elevation	data	at	this	interval	from	the	LDAR	

(Figure	2	shows	addresses	and	corresponding	points).

These	addresses	are	then	geocoded	against	the	GPS	

lines,	which	generates	a	set	of	points	that	are	10	meters	

apart	on	the	GPS	line.	Next,	we	convert	these	points	to	

three-dimensional	points,	taking	the	Z-value	from	the	

LIDAR. After extracting these Z-values into a new field 

in	the	database,	these	values	are	reduced	to	an	array	of	xoffset	and	z	value	(Table	1),	which	can	be	used	to	correct	

the	GPR	data,	as	seen	previously	in	Figure	1.

CONCLUSION

This	methodology	has	enabled	us	to	collect	data	more	

rapidly	than	in	the	past.	There	are	obviously	some	fundamental	requirements	that	LDAR	data	and	aerial	photography	exist	for	the	study	area.	With	these	data	in	place,	

however,	we	can	now	collect	many	kilometers	of	data	per	

day without the expensive field collection of surveying elevation	points.	This	has	allowed	us	to	collect	and	process	

voluminous	data	on	groundwater	surfaces	in	the	coastal	

plains	of	Oregon.	As	more	LDAR	data	become	available,	

we anticipate using this method to acquire better surficial 

deposit	data	for	many	areas.

Figure 1. Topographically corrected Ground Penetrating Radar profile. Horizontal line at 350 ns 

time is groundwater surface.
RAPD	ACQUSTON	OF	GROUND	PENETRATNG	RADAR	ENABLED	BY	LDAR 185

Figure 2. GPR “addresses” overlaid on a hillshade of

LIDAR. Addresses are meters of offset from beginning of

lines, concatenated with the name of the GPR line. Northtrending linear features are shore parallel dune ridges.

Table 1.	Sample	elevation	offsets	calculated	from	LDAR	

and	geocoded	GPR	“addresses”.

 X location (m) Z elevation (m)

0	

10	

20	

30	

40	

50	

60	

70	

80	

90	

100	

110	

120	

130	

6.8

7

7.1

6.3

5.6

5.1

5.3

5.4

5.2

4.9

4.8

4.8

4.7

4.8

REFERENCES

Daniels, R.C., 2001, Datum conversion issues with LIDAR spot

elevation data: Photogrammetric Engineering and Remote

Sensing, v.67, p.735-740.

Jol, H.M., and Bristow, C.S., 2003, GPR in sediments: advice

on data collection, basic processing and interpretation, a

good practice guide, in Bristow, C.S., and Jol, H.M., eds.,

GPR in Sediments: Geological Society of London, Special

Publication 211, p.9-27.

Peterson, C.D., Jol, H.M., Percy, David, and Nielsen, E.L., in

press, Groundwater surface trends from ground penetrating

radar (GPR) profiles taken across late-Holocene barriers 

and beach plains of the Columbia River littoral system, Pacific Northwest coast, USA: Geological Society of America 

Special Publication.
186	 DGTAL	MAPPNG	TECHNQUES	‘06
187

187

GIS and GPS Utility in the Geologic Mapping of Complex

Geologic Terrane on the Mascot, Tennessee 7.5’ Quadrangle

By Barry W. Miller and Robert C. Price III

Tennessee Department of Environment and Conservation

Division of Geology

3711 Middlebrook Pike

Knoxville, TN 37921

Telephone: (865) 594-6200

Fax: (865) 594-6105

e-mail: Barry.Miller@state.tn.us, Bob.Price@state.tn.us

INTRODUCTION

The	bedrock	geology	of	the	Mascot,	Tennessee	7.5’	

Quadrangle	was	mapped	in	2003	under	a	STATEMAP	

cooperative	agreement	between	the	U.S.	Geological	Survey	and	the	Tennessee	Division	of	Geology.	The	Mascot	

Quadrangle	is	located	in	the	Valley	and	Ridge	Province	of	

east	Tennessee;	the	bedrock	consists	of	folded	and	faulted	

Cambrian	and	Ordovician	strata	(Figure	1).	The	Tennessee	Division	of	Geology	purchased	two	Trimble	GeoExplorer	3	Global	Positioning	Systems	(GPS),	which	were	

used	in	conjunction	with	ESR	ArcView	3.2	Geographic	

nformation	System	(GS)	software	to	record	the	geologic	

field data and present the results of the geologic mapping.

The	decision	to	utilize	GPS	technology	for	this	mapping	project	was	initially	based	on	the	geologic	complexity	of	the	Mascot	Quadrangle	and	the	need	to	improve	the	

accuracy	of	geologic	station	location.	The	Division	of	Geology	had	previously	used	GPS	units	to	locate	oil	and	gas	

wells	in	a	well	inventory	study,	but	the	majority	of	well	

data was input into a field book or datasheet. The Mascot 

project	would	help	determine	how	the	utility	of	GPS	and,	

specifically, the ability to input data directly into the GPS 

unit would affect the geologic mapping process.

METHODOLOGY AND RESULTS

Utilizing the Trimble GeoExplorer 3 GPS

and Data Dictionary for Geologic Field Data 

Collection

A GPS data dictionary contains a catalog of the

features and attributes pertinent to an endeavor or project.

It is used in the field to control the data collection of a 

feature (e.g., an object, geologic station, rock outcrop,

etc.) and its attributes (e.g., object information, soil type,

rock lithology, etc.). Using the Trimble GPS data dictionary, a geologic data spreadsheet was created to record the

important geologic aspects and their values relevant to the

mapping of the Mascot Quadrangle (Figure 2). Recording

geologic observations involved scrolling through attribute

windows (e.g., lithology) and choosing their values (e.g.,

shale, limestone, sandstone) from a predefined drop-down 

Figure 1. Location of the Mascot 7.5’ Quadrangle.
188	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 2. The GPS data dictionary (top) for the Mascot 7.5’ Quadrangle with expanded menu selections for the

geologic attributes (bottom).

menu. Attribute menu nomenclature was abbreviated to fit 

the	constraints	of	the	drop-down	menu	screen	on	the	GPS	

unit. Numerical fields for strike and dip of planar features 

such	as	bedding,	joints,	and	cleavage	required	direct	user	

input.	Strikes	were	measured	in	azimuth	degrees	(using	

the	right	hand	rule)	to	facilitate	the	proper	rotation	of	geologic	symbols	when	compiling	the	data	in	ArcView.	Two	

general geologic attribute fields containing drop-down 

menus	of	additional	pertinent	geologic	information	acquired	during	mapping	include	soil	character,	chert	type,	

karst features, and mining activities. A final comment field 

allows	the	mapper	to	input	directly	any	other	observations	

using a menu keypad. The data dictionary and accompanying spreadsheet were updated as needed when new

useful mapping criteria were observed.

The geologic data generally were input via the data

dictionary, while the GPS unit collected the satellitebased coordinate information. The latitude and longitude

coordinates were collected by the GPS unit in a decimal

degree format so that, later, field station locations could 

be plotted in ArcView (Figure 3). After returning to the

office, the data files were downloaded to a computer, and 

the coordinates were differentially corrected to a local

base station over the Internet to improve accuracy.
GS	AND	GPS	UTLTY	N	THE	GEOLOGC	MAPPNG	OF	COMPLEX	GEOLOGC	TERRANE 189

Utilizing ESRI ArcView 3.2 GIS Software 

and the GPS Database to Produce the Mascot 

Quadrangle Geologic Map

The differentially corrected GPS data file (.cor) was 

exported	in	dBase	format	and	transferred	into	a	compilation	

database	that	was	used	in	ArcView	to	compile	a	preliminary geologic map. Additional fields in the compilation 

database	are	included	to	plot	the	symbol	orientation	for	

planar	features	(SYMROT)	and	print	selected	geologic	

symbols	(PRNT_STK)	and	dip	numbers	(PRNT_DP#)	in	

ArcView (Figure 4). The SYMROT field was used to rotate 

the	geologic	symbols	by	using	the	formula	“270°	minus	

the	azimuth	strike.”	The	compilation	map	included	point	

themes	for	lithology,	bedding	attitudes,	formation	contact	

points,	and	other	geologic	features	recorded	in	the	database,	

all	of	which	are	shown	as	unique	points	or	symbols	on	the	

map	(Figure	5	and	6).	Dip	value	labels	were	added	to	the	

bedding	attitude	symbols.	These	point	themes	were	used	to	

interpret	the	location	of	stratigraphic	contacts	and	the	surface	trace	of	axial	planes	and	faults,	which	are	each	separate	

line	themes	on	the	geologic	compilation	map	(Figure	7).

To produce the “final” version of the geologic map 

in	ArcView,	stratigraphic	contacts	and	fault	lines	were	divided	into	solid	(exactlocation),	dashed	(approximatelocation),	and	dotted	(coveredlocation)	line	segments	based	on	

contact	location	certainty.	These	contact	line	themes	were	

converted	to	formation	polygon	themes.	The	formation	

polygons	were	colored	and	displayed	beneath	a	partially	

transparent	raster	topographic	base	map.	The	color	pallet	

of	the	raster	map	was	adjusted	such	that	the	underlying	

polygons	could	be	displayed	while	important	features	

such	as	roads,	streams,	and	contour	lines	also	were	visible.	Formation	labels	then	were	added	to	the	geologic	

map.	To	avoid	clutter,	the	lithologic	and	geologic	feature	

point themes were not displayed in the final version of the 

geologic	map	(Figure	8).	A	geologic	cross-section	location	line	was	placed	on	the	map.	n	the	ArcView	layout	

view,	titles,	labels,	and	a	geologic	symbol	explanation	

were	added	as	well	as	a	written	scale,	bar	scales,	and	a	

north	arrow	(Figure	9).	The	geologic	cross-section,	stratigraphic	column,	and	geologic	descriptions	were	drafted	

separately	using	Adobe	llustrator	8.0	software.

Figure 3. Example of part of the fieldwork database for the Mascot 7.5’ Quadrangle.
190	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 4. Example of part of the compilation database for the Mascot 7.5’ Quadrangle. Note SYMROT, PRNT_

STK, and PRNT_DIP columns near the right side of figure.

ADVANTAGES AND DISADVANTAGES 

ENCOUNTERED

Advantages of using GIS and GPS technology during

the geologic mapping process included the drastic reduction of office time to input field data into a spreadsheet 

format that can be used in ArcView. Prior to using GPS,

office time was required to determine field point coordinates and enter field book data into a spreadsheet. Also, the 

GPS data dictionary facilitated the compilation of data from

two mappers in that it listed basic attributes to record and

constrained how the attributes were described. The GPS

dictionary also provided a framework by which to set up

a consistent GIS compilation database between separate

mappers, and share data between mappers on a daily basis.

Disadvantages included the need for a paper copy of

the topographic map in the field for navigation, station 

point location, and plotting contact lines. The paper map

was also needed to plot the data in the field to determine 

where the geologic traverse needed to proceed. Areas that

contained observable geologic characteristics, but were

deemed inaccessible, were also plotted on the paper map.

Field books were used for lengthy geologic descriptions,

sketches, or other information. GPS satellite acquisition

was occasionally hampered under heavy forest canopy or

when satellites were not in proper array. On these occasions, station locations were plotted directly on the paper

map, and the correct coordinates were added later to the

compilation database. There were no independent checks

and balances in place designed to detect GPS user input

errors. The ArcView compilation map was also printed

on a periodic basis to guide future traverses and provide a

map for the geologists to use in the field.

This methodology may not be suitable when geologic

mapping involves long stays in the wilderness away from

the office and electricity. In this study, data files needed to be 

downloaded every day or two because of GPS memory limitations and the GPS batteries had to be recharged every day.
GS	AND	GPS	UTLTY	N	THE	GEOLOGC	MAPPNG	OF	COMPLEX	GEOLOGC	TERRANE 191

Figure 5. Part of the Mascot 7.5’ Quadrangle compilation map with lithologies, geologic contacts, and other geologic features shown as discrete points or symbols.

CONCLUSIONS

Despite minor problems in the field and office, the 

Trimble GeoExplorer 3 Global Positioning System and

the ESRI ArcView 3.2 Geographic Information System

worked well in unison to assist in the completion of the

geologic map of the Mascot Quadrangle. The GPS unit

was used to locate station points in geologically complex

areas where accurate plotting was crucial for constraining

the geologic interpretation. The GPS data dictionary permitted relatively rapid data entry into the database, while

the GPS unit collected coordinate information. The GIS

software had the versatility to import the GPS data files 

directly for rapid compilation of the geologic data into a

useable map. The Mascot 7.5’ Geologic Quadrangle map

can be printed on demand and is currently available from

the Tennessee Division of Geology as an open file map.

VENDOR CONTACT INFORMATION

Adobe Illustrator—Adobe Systems, Inc., 345 Park Ave., San

Jose, CA 95110-2704 USA, (800) 833-6687, <http://www.

adobe.com>.

ArcGIS, ArcPad, ArcView—Environmental Systems Research

Institute (ESRI), Inc., 380 New York St., Redlands, CA,

92373-8100 USA, (909) 793-2853, <http://www.esri.com>.

dBase—dataBased Intelligence, Inc., 2548 Vestal Parkway, East

Vestal, NY 13850, (877) 322-7340, <http://www.dbase.

com>.

Microsoft Excel—Microsoft Corp., One Microsoft Way, Redmond, WA 98052-6399 USA, (425) 882-8080, <http://

www.microsoft.com/office/excel>.

Trimble GeoExplorer—Trimble Navigation Limited, 645 N.

Mary Avenue, Sunnyvale, CA, 94088-3642, (408) 481-

8000, <http://www.trimble.com>.
192	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 6. Part of the Mascot 7.5’ Quadrangle compilation map with bedding strike and dip symbols for the point

features shown in Figure 5.
GS	AND	GPS	UTLTY	N	THE	GEOLOGC	MAPPNG	OF	COMPLEX	GEOLOGC	TERRANE 193

Figure 7. Part of the Mascot 7.5’ Quadrangle compilation map with geologic contact lines (black), fault lines (red),

and formation abbreviations. In the printed version, the fault lines appear as thick black lines. Map includes geologic point features (see Figure 5) and bedding attitude symbology (see Figure 6).
194	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 8. Part of the Mascot 7.5’ Quadrangle compilation map with polygons filled with appropriate geologic formation colors. Geologic point features are not shown. Bedding attitude symbology

shown.
GS	AND	GPS	UTLTY	N	THE	GEOLOGC	MAPPNG	OF	COMPLEX	GEOLOGC	TERRANE 195

Figure 9. Open file version of the Mascot Geologic Quadrangle Map.
196	 DGTAL	MAPPNG	TECHNQUES	‘06
197

197

Prototype GIS Database for the DNAG

Geologic Map of North America

By Christopher P. Garrity and David R. Soller

U.S. Geological Survey

National Center, MS 956

12201 Sunrise Valley Drive

Reston, VA 20192

Telephone: (703) 648-6426

Fax: (703) 648-6977

e-mail: {cgarrity, drsoller}@usgs.gov

ed by detection of compilation errors and by new regional 

mapping and interpretations. Further, the geologic unit 

descriptions shown on the printed map can be supplemented in the database by more detailed, richly attributed 

information derived from the many sources that were used 

to compile the map. This capability to revise the printed 

map and include additional descriptive information for 

map units is one of the primary reasons for building the 

database; the other reason is, of course, the analytical capabilities made possible by providing the map in a digital, 

Geographic Information System (GIS) compatible format.

The creation of this database and its enhancement to 

include new mapping and more richly attributed information is a daunting task that will take a signi cant amount 

of time and effort. Recognizing that a group of dedicated 

and knowledgeable scientists is essential to make this database useful and to keep its content up to date, GSA will 

develop a consortium of geological agencies to manage 

the database. With prototype development of the database, 

the National Geologic Map Database project provides a 

basis for this consortium to proceed.

PROTOTYPE AREA

The GMNA prototype spans an area of about 530,206 

square miles and includes both continental and sea oor 

geologic units in the United States and Canada (Figure 1). 

The area was chosen due to its relatively complex geology 

and abundance of both onshore and offshore map symbology displayed in the published GMNA. The prototype 

contains over 2,500 individual polygon features and about 

5,700 line features. Polygon features are symbolized by 

205 unique geologic unit values, each with custom color 

and pattern  lls designed to mimic those used in the published GMNA (Figure 2). The prototype contains customized line symbology which nearly duplicates the symbol 

sets used in the published map. All custom symbol sets are 

stored in ArcGIS layer  les. Line features are classi ed by 

PURPOSE1

When plans for the Geologic Map of North America 

(GMNA) were being made, the notion of geologic map 

databases was in its infancy. At that time, and for many 

years thereafter, few geologists were familiar with the 

design and use of databases to manage geologic map 

information. In 1998, the Geological Society of America 

(GSA) and the United States Geological Survey (USGS) 

National Geologic Map Database project agreed to costshare the digital preparation of this map. The plan was 

to digitize the hand-drawn, author-prepared geologic 

compilations for the four map quadrants to provide digital 

data for two purposes: (1) to allow GSA to print the map, 

and (2) to permit the National Geologic Map Database 

project to develop a prototype database for this map. With 

the map now printed, the National Geologic Map Database project has begun to design and create the prototype, 

based on certain assumptions regarding the anticipated 

content of, and uses for, the map database. The  rst version of this database will contain the descriptive information for geologic units shown on the map. It will serve as 

the fundamental entity from which products of the map 

can be derived. These products may be interpretive, or 

they may be future editions of the map.

In mid-2006, this prototype will be provided to the 

organizations principally responsible for map compilation 

(GSA, USGS, GSC, and WHOI) to initiate discussion 

and decisions on how the map database will be designed, 

managed, and distributed served to the public and cooperators. The prototype is shown here to generate technical 

discussion and guidance prior to formal discussion among 

those organizations.

To produce any future editions of the map, the database will incorporate all map revisions that are necessitat1Modi ed from Soller, in Reed et al. (2005)
198 DIGITAL MAPPING TECHNIQUES ‘06

Figure 1. The 2005 Geologic Map of North America (GMNA) is the first such map published in the past 

four decades. The map, which covers 15% of the Earth’s surface (shown in yellow box), depicts the geology of the seafloor in detail never before seen on a map of this scale (1:5,000,000). It is the first geologic 

map of North America to be compiled since the general acceptance of plate-tectonic theory and since 

radiometric dates for plutonic and volcanic rocks because widely available. This map distinguishes more 

than 900 rock unites, 100 of which are offshore. It depicts more than seven times as many terrestrial units 

as are shown on the previous 1965 map, as well as detailed features of the seafloor, such as spreading 

centers, seamont chains, and subduction zones (Reed, et. al., 2005). The GIS prototype area (shown in 

red box) was chosen due to its relatively complex geology and abundance of both on- and offshore map 

symbology displayed in the published GMNA. The release of the prototype is meant to serve as a forum 

for both general comments on the overall objectives of the GIS database design, and specific comment 

son elements such as cartographic symbolization style.
PROTOTyPE GIS DATAbASE fOR THE DNAG GEOLOGIC MAP Of NORTH AMERICA 199 Figure 2. Layout of the prototype area exported from ArcMap. The prototype, spanning an area of about 530,206 square miles, contains over 2,500 individual poly- gon features and about 5,700 line features. Polygon features are symbolized by 205 unique geologic unit values, each with custom color and pattern fills designed to mimic those used in the published GMNA.
200	 DGTAL	MAPPNG	TECHNQUES	‘06

geologic line type into 26 subtypes. Geologic line types 

include general geologic features (contacts, faults, etc.), 

special submarine features (slump scars, seamount chains, 

spreading centers, etc.), and lithologic/age de ned dikes 

and sills. Each subtype de nition matches the explanation 

of the corresponding map symbol used in the GMNA.

PROCESS DESCRIPTION

A secondary objective in creation of the prototype 

was to determine the most ef cient way to convert such 

an enormous digital map into a useable GIS. It was also 

important to determine a realistic time frame in which a 

project of this size could be completed. The two Adobe 

Illustrator  les that contain the source digital data were 

massive, with layer counts totaling over 1,500. When 

analyzed at scales much greater than the intended map 

scale, the  les showed areas where problems in topological relationships existed. Common topological problems 

in these areas included polygons that overlapped or had 

gaps between them, overlying line layers (contacts, faults, 

etc.) that were not coincident with polygon boundaries, 

and line features that self-overlapped. Although these 

areas were much too small to compromise the quality of 

the hard copy layouts, they did present problems when 

validating the topology in a GIS. Due to the sheer number 

of imported features, the errors reported after validating 

topology numbered in the tens of thousands. To avoid the 

time consuming process of correcting each error, it was 

decided that only the non-contact linework coincident 

with geologic unit boundaries would be imported directly 

from Adobe Illustrator. For attribution purposes, each line 

type (inferred thrust fault, concealed thrust fault, etc.) was 

imported to the GIS individually. The remaining linework (contact layer) was isolated in Adobe Illustrator and 

exported as a high-resolution raster image. The image was 

georeferenced using control points in the DNAG projection (Snyder, 1987), and auto-vectorized in ArcScan. 

By setting topology rules in ArcMap, line dangles in the 

vectorized layer were snapped to the nearest unit-bordering line feature, which resulted in a topologically clean 

layer. To build unit attributes quickly, individual geologic 

unit layers were batch exported from Adobe Illustrator 

and used to overlay the newly created layer. Through 

spatial querying, polygons in the new layer that had their 

center within a speci c overlay layer were attributed 

based on the overlay’s geologic unit abbreviation. Much 

of the remaining attribution was completed through 

simple VBA  eld calculator scripting based on the unit 

abbreviation  eld. The  nal step, which proved to be the 

most time consuming, involved recreation of feature class 

symbolization in ArcGIS. 26 symbolized line types, and 

205 unique color  lls/patterns had to be created to mimic 

those in the source Adobe Illustrator  le. The prototype 

was completed in about 10 days. GIS compatible  les for 

the southern map sheet, which comprises the conterminous United States, are scheduled to be completed and 

released in FY2007. Release of GIS compatible  les for 

the northern map sheet is scheduled to follow.

PROTOTYPE DATABASE

This prototype is intended to serve as a forum for 

comments on the overall objectives of the GIS database 

and the attribute information within. It is certain that as 

the database evolves, attribute information will be modi-

 ed to make the database more useful. For the prototype 

database, a preliminary set of attributes was chosen to 

serve as a foundation for an eventual GMNA data model. 

When that data model is formalized, we anticipate that 

it will incorporate elements of the North American Data 

Model (http://nadm-geo.org/) and the International 

Geological Map of Europe’s data model (http://www.bgr.

de/karten/IGME5000/igme5000.htm). The selected attributes attempt to capture the information depicted in the 

GMNA explanation sheet that accompanies the published 

map. The attribute list includes:

• ROCKTYPE – the “top level” rock classi cation 

(sedimentary, plutonic, volcanic, metamorphic)

• LITHOLOGY – the simpli ed description included 

for each geologic unit on the explanation sheet of 

the GMNA

• ROCK_UNIT_NOTE – special notes associated 

with certain units on the explanation sheet of the 

GMNA. For example, selected volcanic rocks are 

attributed “Basalt adjacent to active spreading 

centers,” selected metamorphic rocks are attributed 

“Granulate facies metamorphism,” and selected 

sedimentary rocks are attributed “Continental 

deposits”

• UNIT_UNCERTAINTY – a query following the 

map unit code indicates uncertainty about composition, or whether the rock is in situ

• MIN_AGE – minimum geologic age for the unit. 

Subdivisions of time-stratigraphic units are lower, 

middle, and upper (lower-case), and for plutonic 

rocks are Early, Middle, and Late
PROTOTYPE	GS	DATABASE	FOR	THE	DNAG	GEOLOGC	MAP	OF	NORTH	AMERCA 201

•	 MAX_AGE – see comments for MIN_AGE

•	 MIN_AGE_CODE – code derived from the geologic

age codes defined by the AAPG Committee on Standard Stratigraphic Coding (1967)

•	 MAX_AGE_CODE – see comments for MIN_AGE_

CODE

•	 AGE_UNCERTAINTY – a query preceding the map

unit label indicates uncertainty about the assigned age

•	 MAP_UNIT_CODE – the GMNA map unit code

•	 MIN_MAX_RELATE – the relationship (“and”, “or”,

“thru”) between the MIN?MAX ages of units bounded

by multiple ages

REFERENCES

American Association of Petroleum Geologists, Committee on Standard Stratigraphic Coding (George V.

Cohee, Chairman), 1967, Standard stratigraphic code

adopted by AAPG: American Assiciation of Petroleum

Geologists Bulletin, v. 51, no. 10, p. 2146-2150.

Reed, J.C. Jr., Wheeler, J.O., and Tucholke, B.E., compilers, 2004, Geologic Map of North America: Decade

of North American Geology Continental Scale Map

001, Boulder, Geological Society of America, scale

1:5,000,000.

Reed, J.C. Jr., Wheeler, J.O., and Tucholke, B.E., 2005,

Geologic Map of North America—Perspectives and

explanation: Boulder, Colorado, Geological Society of

America, Decade of North American Geology, 28 p.

Snyder, J.P., 1987, Map projections—a working manual:

U.S. Geological Survey Professional Paper 1395, 383

p.
202	 DGTAL	MAPPNG	TECHNQUES	‘06
203

203

USGS National Surveys and Analysis Projects:

Preliminary Compilation of Integrated Geological

Datasets for the United States

By Suzanne W. Nicholson1, Douglas B. Stoeser2, Frederic H. Wilson3,

Connie L. Dicken1, and Steve Ludington4

1U.S. Geological Survey

National Center, Mail Stop 954

Reston, VA 20192

Telephone: (703) 648-6344

Fax: (703) 648-6383

e-mail: swnich@usgs.gov

e-mail: cdicken@usgs.gov

2U.S. Geological Survey

Box 25046, Mail Stop 973

Denver, CO 80225

e-mail: dstoeser@usgs.gov

3U.S. Geological Survey

4200 University Dr.

Anchorage, AK 99508

e-mail: fwilson@usgs.gov

4U.S. Geological Survey

345 Middlefield Road, Mail Stop 901

Menlo Park, CA 94025

e-mail: slud@usgs.gov

INTRODUCTION

The	growth	in	the	use	of	Geographic	nformation	

Systems	(GS)	has	highlighted	the	need	for	regional	and	

national	digital	geologic	maps	attributed	with	age	and	

rock	type	information.	Such	spatial	data	can	be	conveniently	used	to	generate	derivative	maps	for	purposes	

that	include	mineral-resource	assessment,	metallogenic	

studies,	tectonic	studies,	human	health	and	environmental	

research.

n	1997,	the	United	States	Geological	Survey’s	

Mineral	Resources	Program	initiated	an	effort	to	develop	

national	digital	databases	for	use	in	mineral	resource	

and	environmental	assessments.	One	primary	activity	

of	this	effort	was	to	compile	a	national	digital	geologic	

map	database,	utilizing	state	geologic	maps,	to	support	

mineral	resource	studies	in	the	range	of	1:250,000-	to	

1:1,000,000-scale. Over the course of the past decade,

state databases were prepared using a common standard

for the database structure, fields, attributes, and data 

dictionaries. As of late 2006, standardized geological map

databases for all conterminous (CONUS) states have been

available on-line as USGS Open-File Reports. For Alaska

and Hawaii, new state maps are being prepared, and the

preliminary work for Alaska is being released as a series

of 1:500,000-scale regional compilations. See below for a

list of all published databases.

COMPILATION OF SPATIAL DATA FOR 

STATE GEOLOGIC MAPS

The first stage in developing state databases for the 

conterminous United States (CONUS) was to acquire

digital versions of all existing state geologic maps. Al-
204	 DGTAL	MAPPNG	TECHNQUES	‘06

though a significant number of digital state maps already 

existed, a number of states lacked them. For these states,

new digital compilations were prepared by digitizing existing printed maps either in cooperation with the respective state geologic survey (e.g. OH, SD, TX) or by the

USGS (e.g. KY, VT). In a few cases, we created digital

state maps by merging existing larger scale digital files 

(e.g. SC, OK). It is important to note that, for this first 

round of compilation, we focused on compiling bedrock

data for each state, although many state geological maps,

especially in the West, combine both bedrock and surficial 

units on a single map.

All CONUS state databases were fit to a state boundary Arc/Info coverage, which was derived from the

USGS 100k scale Digital Line Graphics (DLG) boundary

layer quadrangles and has a polygon for each state. The

purpose of fitting is so that adjoining state databases can 

be merged to form regional digital maps without slivers

or overlaps at the state boundaries. Fitting was done by

examining arcs along the boundary and extending or clipping them to the state boundary, depending on whether

the arcs under or overshoot the boundary arc. No “rubber

sheeting” was used. No attempt was made to reconcile

differences in mapped geology between contiguous states.

In the spatial tables, several fields were added in which 

a consistent set of terms was used for age and rock type,

so that multiple spatial databases could be queried at the

same time and allow generation of regional and national

derivative maps based on age and rock type.

SUPPLEMENTAL ATTRIBUTE TABLES

The second stage was to assign values to a standard

set of database fields in each state digital map database. 

Typically, state geologic maps contain more data than

just arcs and polygons. Unit descriptions as well as age,

lithologic, and bibliographic information present on the

original source map were captured in a series of additional

tables, including stratigraphic units, age, lithology, and

references. For some older state map databases, more

recent information was also captured. Figure 1 provides

a schematic illustration of the structure of the spatial

database tables and the supplemental tables. Figure 2

provides a more detailed look at the data entry format for

the supplemental tables.

Figure 1. Data model for conterminous U.S. databases. Solid lines show links between

tables through the unit_link field. Dashed lines indicate that the values in these fields have 

a one-to-one relationship to the ref_id field of the STref table. All values in the source, 

map_ref, and unit_ref fields are generated from the ref_id field in the STref table, though 

the definitions for each field are different. Fields marked with an asterisk are populated 

from a data dictionary. Figure is more legible in the web version.
USGS	NATONAL	SURVEYS	AND	ANALYSS	PROJECTS 205

Figure 2. Illustration of the five supplemental tables (STunits, STlith, STage, STref, and STreflink, where ST stands for the two-letter abbreviation for each state). FileMaker 5, 5.5, or 6.0 was

used to compile the supplemental tables, but it is not necessary to use this program in order to use

the tables. Figure is more legible and in color in the web version.

STANDARD FILE SET

Conus

The files supplied for each state consist of (1) one or 

more spatial databases (Figure 1), and (2) a set of related

supplemental tables (Figure 2). Each state database has

the same database structure and attribution fields, which 

use terminology from standardized data dictionaries. At

a minimum, the standard file set consists of a geology 

(polygon and arc, i.e. network coverage) spatial database,

metadata, and supplemental attribute tables; however,

additional spatial databases for other line or point features

present on the source map may also be included (e.g.,

faults (when presented on the published maps), dikes, fold

axes, volcanic vents, etc.). Detailed documentation of the

standards, procedures, data dictionaries, and formats used

accompanies each report.

The spatial databases are provided in ESRI export

(.e00) and shapefile (.shp) formats. All spatial databases are provided both in geographic coordinates and

a Lambert Conformal Conic projection for CONUS and

geographic coordinates and UTM projection in Alaska,

using a datum of NAD 27. The spatial database metadata

are provided in three formats: ASCII text (.txt), Microsoft	Word	(.doc),	and	HTML	(.htm).	The	supplemental	

data	consist	of	related	attribute	tables	(Figure	2):	units	

(UNTS),	age	(AGE),	lithology	(LTH),	and	bibliographic	references	(REF).	An	additional	table	(REFLNK)	links	spatial	data	and	attributes	to	bibliographic	

references.	The	tables	provide	standardized	attribution	

for	the	geologic	map	units	for	each	map.	These	tables	

are	available	in	comma-separated	value	(.csv),	dBASE	

(.dbf),	and	FileMaker	Pro	(.fp5)	formats,	and	for	Alaska	

datasets	as	a	runtime	Filemaker	Pro	application.	[Note,	

the .dbf format truncates all text fields at 256 characters, 

which	impacts	unit	descriptions	and,	potentially,	reference	citations.]

Alaska and Hawaii

For	Alaska	and	Hawaii,	new	state	map	compilations	are	being	prepared.	The	data	structure	for	Alaska	

is	very	similar,	but	not	identical,	to	the	data	structure	

for	the	lower	48	states.	The	preliminary	data	for	portions	of	Alaska	are	being	released	in	a	series	of	nominal	

1:500,000-scale	regional	compilations.	To	date,	ten	new	

geologic	compilations	that	cover	more	than	two-thirds	of	

the	state	of	Alaska	have	been	published	(Figure	3).	Additional	compilations	are	currently	being	prepared	for	the	
206	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 3. Map showing status of geologic map publication for quadrangles in Alaska and Hawaii.

Figure is more legible and in color in the web version.

remaining	portions	of	Alaska	and	for	the	state	of	Hawaii.	

Detailed	documentation	of	the	standards,	procedures,	data	

dictionaries,	and	formats	used	accompanies	each	report.

DERIVATIVE PRODUCTS OF THE STATE 

GEOLOGIC MAP COMPILATION

When	the	spatial	databases	are	merged,	these	standardized	tables	allow	development	of	derivative	maps	

based	on	stratigraphy,	lithology,	and	age.	Figure	4	shows	

a	map	of	the	dominant	rock	type	for	each	polygon,	which	

was	generated	by	plotting	the	controlled	vocabulary	

values that appear in the rocktype1 field. In Figure 5, the 

distribution of two rock types was generated by querying

the rocktype1 field in the state databases for shale and

granite. A generalized geologic age map (Figure 6) was

produced by generalizing the values in the free-form field 

unit_age in the spatial databases.

Another example of a use of these digital geologic

state map data is the preliminary mineral resource assessment of North America, which is now underway by the

USGS. The state geologic map datasets were used as base

layers along with mineral occurrence data to outline tracts

of favorable conditions for specific mineral deposit types.
USGS	NATONAL	SURVEYS	AND	ANALYSS	PROJECTS 207

Figure 4. Distribution of dominant rock type for each polygon. Map was generated by querying

the single-valued field rocktype1, which uses a controlled vocabulary. Figure is more legible and

in color in the web version.

Figure 5. Map of the distribution of two rock types––shale and granite––was generated by querying the rocktype1 field in the attribute table of the state databases for just those two rock types. 

Figure is more legible and in color in the web version.
208	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 6. Generalized geologic age map produced by generalizing the values in the free-form

unit_age field. Figure is more legible and in color in the web version.

BIBLIOGRAPHY OF DIGITAL STATE 

GEOLOGIC MAP COMPILATIONS

As of December 2006

Conterminous States:

Dicken, C.L., Nicholson, S.W., Horton, J.D., Foose, M.P., and

Mueller, J.A.L., 2005, Preliminary Integrated Geologic

Map Databases for the United States: Alabama, Florida,

Georgia, Mississippi, North Carolina, and South Carolina:

U.S. Geological Survey Open-File Report 2005-1323, online only, http://pubs.usgs.gov/of/2005/1323/.

Dicken, C.L., Nicholson, S.W., Horton, J.D., Kinney, S.A., Gunther, Gregory, Foose, M.P., and Mueller, J.A.L., 2005, Preliminary Integrated Geologic Map Databases for the United

States: Delaware, Maryland, New York, Pennsylvania, and

Virginia: U.S. Geological Survey Open-File Report 2005-

1325, on-line only, http://pubs.usgs.gov/of/2005/1325/.

Ludington, Steve, Moring, B.C., Miller, R.J., Stone, P.A, Bookstrom, A.A., Bedford, D.R., Evans, J.G., Haxel, G.A., Nutt,

C.J., Flyn, K.S., and Hopkins, M.J., 2005, Preliminary

Integrated Geologic Map Databases for the United States:

Western States: California, Nevada, Arizona, Washington,

Oregon, Idaho and Utah, Version 1.2 (updated 12/01/06):

U.S. Geological Survey Open-File Report 2005-1305, online only, http://pubs.usgs.gov/of/2005/1305/.

Nicholson, S.W., Dicken, C.L., Foose, M.P., and Mueller, Julie,

2004, Integrated Geologic Map Databases for the United

States: The Upper Midwest States: Minnesota, Wisconsin,

Michigan, Illinois, and Indiana: U.S. Geological Survey

Open-File Report 2004-1355 on-line only, http://pubs.usgs.

gov/of/2004/1355/.

Nicholson, S.W., Dicken, C.L., Horton, J.D., Labay, K.A.,

Foose, M.P. and Mueller, J.A.L., 2005, Preliminary

Integrated Geologic Map Databases for the United States:

Kentucky, Ohio, Tennessee, and West Virginia: U.S. Geological Survey Open-File Report 2005-1324, on-line only,

http://pubs.usgs.gov/of/2005/1324/.

Nicholson, S.W., Dicken, C.L., Horton, J.D., Foose, M.P., Mueller, J.A.L., and Hon, Rudi, 2006, Preliminary Integrated

Geologic Map Databases for the United States: Connecticut, Maine, Massachusetts, New Hampshire, New

Jersey, Rhode Island and Vermont: U.S. Geological Survey

Open-File Report 2006-1272, on-line only, http://pubs.usgs.

gov/of/2006/1272/.

Stoeser, D.B., Green, G.N., Morath, L.C., Heran, W.D., Wilson,

A.B., Moore, D.W., and Van Gosen, B.S., 2005, Preliminary

Integrated Geologic Map Databases for the United States:

Central States: Montana, Wyoming, Colorado, New Mexico,

North Dakota, South Dakota, Nebraska, Kansas, Oklahoma,

Texas, Iowa, Missouri, Arkansas, and Louisiana: U.S. Geological Survey Open-File Report 2005-1351, version 1.1 (updated

9/30/06) on-line only, http://pubs.usgs.gov/of/2005/1351/.
USGS	NATONAL	SURVEYS	AND	ANALYSS	PROJECTS 209

Alaska:

Gehrels, G.E. and Berg, H.C., 2006, Preliminary Integrated Geologic Map Databases for the United States: Digital Data for

the Geology of Southeast Alaska: U.S. Geological Survey

Open-File Report 2006-1290, on-line only, http://pubs.usgs.

gov/of/2006/1290/.

Labay, K.A., Crews, Jesse, Wilson, F.H., Shew, Nora, and

Hults, C.K., 2006, Preliminary Integrated Geologic Map

Databases for the United States: Digital Data for the

Generalized Bedrock Geologic Map, Yukon Flats region,

East-central Alaska : U.S. Geological Survey Open-File

Report 2006-1304, on-line only, http://pubs.usgs.gov/

of/2006/1304/.

Richter, D.H., Preller, C.C., Labay, K.A., and Shew, N.B., compilers, 2006, Geologic map of the Wrangell-Saint Elias National Park and Preserve: U.S. Geological Survey Scientific 

Investigations Map 2877, scale 1:350,000, available online

at http://pubs.usgs.gov/sim/2006/2877.

Wilson, F.H., Detterman, R.L., and Dubois, Gregory, 1999, Digital Data for the Geologic Framework of the Alaska Peninsula, Southwest Alaska, and the Alaska Peninsula Terrane:

U.S. Geological Survey Open-File Report 99-317 on-line

only, http://geopubs.wr.usgs.gov/open-file/of99-317/.

Wilson, F.H., Dover, J.H., Bradley, D.C., Weber, F.R., Bundtzen,

T.K., and Haeussler, P.J., 1998, Geologic Map of Central

(Interior) Alaska: U.S. Geological Survey Open-File Report

OF 98-133-A, Version 1.2, available as paper copy, on-line,

and CD, http://pubs.usgs.gov/of/1998/of98-133-a/.

Wilson, F.H., Labay, K.A., Mohadjer, Solmaz, and Shew, Nora,

2005, Preliminary Integrated Geologic Map Databases

for the United States: Digital Data for the Reconnaissance

Geologic Map of the Kodiak Islands, Alaska: U.S. Geological	Survey	Open-File	Report	2005-1340	on-line	only,	

http://pubs.usgs.gov/of/2005/1340/.

Wilson,	F.H.,	Labay,	K.A.,	Shew,	Nora,	and	Hults,	C.K.,	2006,	

Preliminary	ntegrated	Geologic	Map	Databases	for	the	

United	States:	Digital	Data	for	the	Reconnaissance	Geologic	Map	of	the	lower	Yukon	River	region,	Alaska:	U.S.	

Geological	Survey	Open-File	Report	2006-1292,	on-line	

only,	http://pubs.usgs.gov/of/2006/1292/.

Wilson,	F.H.,	Labay,	K.A.,	Shew,	Nora,	Mohadjer,	Solmaz,	and	

Patton,	W.W.,	Jr.,	2005,	Preliminary	ntegrated	Geologic	

Map	Databases	for	the	United	States:	Digital	Data	for	

the	Reconnaissance	Geologic	Map	of	the	Yukon-Koyukuk	Basin,	Alaska:	U.S.	Geological	Survey	Open-File	

Report	2005-1341	on-line	only,	http://pubs.usgs.gov/

of/2005/1341/.

Wilson,	F.H.,	Labay,	K.A.,	Shew,	Nora,	Preller,	C.C.,	Mohadjer,	

Solmaz,	and	Richter,	D.H.,	2005,	Preliminary	ntegrated	

Geologic	Map	Databases	for	the	United	States:	Digital	

Data	for	the	Geology	of	Wrangell-Saint	Elias	National	Park	

and	Preserve,	Alaska:	U.S.	Geological	Survey	Open-File	

Report	2005-1342	on-line	only,	http://pubs.usgs.gov/

of/2005/1342/.

Wilson,	F.H.,	Mohadjer,	Solmaz,	Labay,	K.A.,	and	Shew,	Nora,	

2006a,	Preliminary	ntegrated	Geologic	Map	Databases	

for	the	United	States:	Digital	Data	for	the	Reconnaissance	

Geologic	Map	of	the	Western	Aleutian	slands,	Alaska:	

U.S.	Geological	Survey	Open-File	Report	2006-1302,	online	only,	http://pubs.usgs.gov/of/2006/1302/.

Wilson,	F.H.,	Mohadjer,	Solmaz,	Labay,	K.A.,	and	Shew,	Nora,	

2006b,	Preliminary	ntegrated	Geologic	Map	Databases	

for	the	United	States:	Digital	Data	for	the	Reconnaissance	

Bedrock	Geologic	Map	for	the	Northern	Alaska	Peninsula	

area,	Southwest	Alaska:	U.S.	Geological	Survey	Open-File	

Report	2006-1303,	on-line	only,	http://pubs.usgs.gov/

of/2006/1303/.
210	 DGTAL	MAPPNG	TECHNQUES	‘06
211

211

Banding Birds with MapServer CGI

By Robert S. Wardwell and Kevin W. Laurent

U.S. Geological Survey

Cascades Volcano Observatory

1300 SE. Cardinal Court, Bldg. 10, Suite 100

Vancouver, WA 98683

Telephone: (360) 993-8908

Fax: (360) 993-8980

e-mail: rwardwell@usgs.gov

U.S. Geological Survey

Patuxent Wildlife Research Center

12100 Beech Forest Rd. Suite 4034

Laurel, MD 20708 - 4034

Telephone: (301) 497-5652

Fax: (301) 497-5826

e-mail: klaurent@usgs.gov

Since its establishment in 1936 as the nation’s first 

wildlife	experiment	station,	the	USGS	Patuxent	Wildlife	

Research	Center	(PWRC)	in	Patuxent,	Maryland,	has	

been	a	leading	international	research	institute	for	wildlife	

and	applied	environmental	research.	A	primary	goal	at	the	

PWRC	Bird	Banding	Laboratory	(BBL)	is	to	manage	the	

administration	of	bird	banding	permits,	coordinate	banding	

efforts,	and	collect	data	scientists	can	use	to	analyze	such	

things	as	species	behavior,	migratory	patterns,	and	the	

overall	health	of	a	species.	Recently,	computer	scientists	

at	the	BBL	have	developed	an	ORACLE	based	permitting	

and	banding	administration	system	focused	on	tracking	

a	wide	range	of	data	about	banded	birds	and	managing	

the	BBL	bird	banding	efforts	(Figure	1).	One	component	

of	this	system	relies	on	MapServer,	an	open	source	Web	

mapping	solution,	to	collect	an	absolutely	crucial	set	

of	data:	the	geographic	location	where	a	banded	bird	is	

encountered or recovered in the field. This paper describes 

how the BBL uses MapServer to collect scientific data and 

provides	some	insight	into	implementing	MapServer.

MapServer	was	originally	developed	by	the	University	of	Minnesota	ForNet	project	in	cooperation	with	

NASA	and	the	Minnesota	Department	of	Natural	Resources.	Presently,	the	MapServer	project	is	hosted	by	

the	TerraSP	project,	a	NASA	sponsored	project	between	

the	UMN	and	consortium	of	land	management	interests.	

MapServer	has	become	a	popular	tool	by	which	to	render	

spatial	data	such	as	vectors,	maps,	and	images	to	the	Web.	

The	MapServer	Website	http://mapserver.gis.umn.edu/	is	

a	valuable	resource	for	learning	more	about	MapServer.

MapServer’s	role	in	the	BBL	permitting	and	banding administration system is to generate interactive maps

used to plot and record a geographic location where a bird

was encountered or recovered. These data are currently

captured at USGS call centers in Patuxent, Maryland, and

Walla Walla, Washington, where reports are made when

a banded bird is encountered or recovered in the field. 

USGS call center employees ask the caller to describe the

bird’s location and then use an interactive map to find the 

location and record the approximate coordinates. First, the

caller describes a location using a common feature name,

such as the name of a town, landmark, park, or water

feature (Figure 2). Next, the BBL call center queries the

system to search for the location in a feature names gazetteer that contains millions of common feature names in

the US and abroad. Finally, MapServer is called upon to

render an interactive map of the selected area, with tools

that allow the call center employee to record the location

where the bird was encountered (Figure 3). Since every

caller is not equipped with a GPS or map, MapServer

makes it possible to utilize the caller’s relative proximity

to known geographic features and calculate an accurate

longitude and latitude values for their location.

Prior to using MapServer, these geographic data were

collected by the BBL as a pair of vectors showing a spatial relationship between a known geographic feature and

the location where a bird was encountered or recovered.

For example, a banding encounter may be described as

being “2 miles east and 3 miles north of Camas, Washington.” These descriptions could be intersected and plotted

within a mesh of 10 minute grid cells to add more quantitative measure to the data. This approach, however, led to
212	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 1. Screenshot of the banding management interface displays a data input form where

information is collected about birds that are encountered in the field. A geographic search is initiated in the middle frame, which opens the MapServer interface in the right hand frame of the Web

browser. The user identifies the location of the bird encountered by plotting it on the interactive 

map. Latitude and longitude coordinates are passed back to the main data entry form in the center

of the page. Submitting the Web form records the event.

Figure 2. A place name search generates a list of possible

name matches being returned in the Gazetteer Results list.

Selecting a place name from the list passes the feature

name coordinates to MapServer, which returns a map

centered on the selected location.

a large degree of inaccuracy. Now, with the aid of feature

rich maps rendered by MapServer, geographic location

may be communicated more effectively and more accurate data is stored as coordinate pairs in the BBL database.

The maps generated by MapServer contain a selection of global and national spatial data layers. These

layers include common landmarks, hydrology, urban

areas, transportation routes, wildlife refuges, and parks for

the United States along with Global political boundaries

and populated places for the entire globe. Also included

are transportation routes, hydrology, wildlife refuges,

and parks for Canada and Mexico. MapServer is capable

of serving both raster and vector data in a multitude of

formats. Vector data sources, for example, may include

ESRI shapfiles, PostGIS, ESRI ArcSDE, Oracle Spatial, 

MySQL. For the BBL banding application, base map data

were downloaded from the Web in ESRI shapefile format. 

Feature data from the USGS Geographic Names Information System (GNIS), Natural Resources of Canada Gazetteer, and the National Geospatial Agency’s Geographic

Names System (NGA GNS) were made into shape files, 

which made it possible to search for and label over 3 million features on the maps. Quadtree based spatial indexing
BANDNG	BRDS	WTH	MAPSERVER	CG 213

Figure 3. The interactive map created by MapServer

contains tools used to zoom in, zoom out, pan, and mark

a point on the map. In the above example, the user has

zoomed in and clicked on the map, which places an “x

marks the spot” symbol at the location where a bird was

encountered. MapServer calculates the coordinates of the

point which is then submitted to the database.

Figure 4.	The	map file (.map extension) is a text file 

created	manually	or	built	using	one	of	many	open	source	

tools. This file describes the data sources for your map 

and defines the map extent, data layers, symbols, and 

layer classification.

of each shape file was performed using the MapServer 

shptree	utility,	which	helps	speed	the	delivery	of	map	

data	over	the	Web.

	n	the	BBL	banding	application,	MapServer	operates	

as	a	CG	program	that	handles	requests	and	responses.	

The	map	and	everything	about	the	layers	within	the	map,	

such as cartographic symbols and the classification of the 

layers,	are	controlled	through	a	MapServer	map file. This 

file is a hierarchical text file with a .map extension that 

describes	each	of	the	data	layers	to	be	included	in	the	map,	

and	describes	how	each	layer	is	to	appear	(Figure	4).	The	

interactive	mapping	interface,	which	contains	tools	used	to	

zoom,	pan,	or	plot	the	location	of	a	banded	bird	encounter,	

is controlled by an HTML and JavaScript template file. 

The template file contains unique CGI variable tags, which 

are	handled	by	MapServer	each	time	the	user	makes	a	request	for	a	new	map	(Figure	5).	The	map	itself	is	rendered	

onto	the	Web	page	as	a	static	image,	such	as	a	.gif,	.png,	or	

.jpg	that	is	replaced	when	MapServer	processes	a	new	set	

of	input	parameters	from	the	HTML	template.

When	the	BBL	permitting	and	band	management	

application	went	to	production	use	in	March	2006,	the	

mapping component contained over 50 data layers, which

included data for 3.6 million named points in more than

50 countries. MapServer proved itself worthy by handling

these large datasets and displaying them efficiently over 

the Internet. Furthermore, we found MapServer to be a

simple, elegant solution for improving the data acquisition

at the BBL. The success of this project can be attributed

to the collaborative efforts of biologists, computer scientists, and GIS professionals, along with the open source

development community surrounding MapServer.

REFERENCES

Kropla, B., 2005, Beginning MapServer-Open Source GIS Development: Apress Publishing.

Lime, S., (from the MapServer Website), 2006, Welcome to

MapServer, accessed at http://mapserver.gis.umn.edu/.

Mitchell, T., 2005, Web Mapping Illustrated - Using Open

Source GIS Toolkits: O’Reilly Publishing.

Patuxent Wildlife Research Center Website, 2006, Patuxent

Wildlife Research Center – Mission: accessed at http://

www.pwrc.usgs.gov/aboutus/mission.cfm.
214	 DGTAL	MAPPNG	TECHNQUES	‘06

Figure 5. The HTML template file defines a map interface that can be customized and enhanced 

with JavaScript. The MapServer CGI program will process your map file and pass values to “substitution strings” that are enclosed in square brackets (‘[ ]’). The CGI variable examples in this

code snippet include map, mapext, zoomdir, zoomsize, layers, and img.
215

215

APPENDIX A

List of Workshop Attendees

[Grouped by affiliation]

Alaska Division of Geological and Geophysical Surveys

Kenneth	Papp

Arizona Geological Survey/USGS

Stephen	Richard

Arkansas Geological Commission

Jerry	Clark

Doug	Hanson

Avenza Software

David	Andrec

Doug	Smith

British Geological Survey

Jeremy	Giles

Colorado Geological Survey

Beth	Widmann

Colorado State University - national Park Service

James	Chappell

Ron	Karpilo

Stephanie	O’Meara

Trista	Thornberry-Ehrlich

Czech Geological Survey

Zuzana	Krejci

Robert	Tomas

eSrI

Jaynya	Richards

Geological Survey of Alabama

Philip	Patterson

Geological Survey of Canada

Eric	Boisvert

Boyan	Brodaric

Peter	Davenport

David	Viljoen

Hydrogeologic Services

Curtis	Coe

Idaho Geological Survey

Jane	Freed

Loudon	Stanford

Illinois State Geological Survey

Sheena	Beaverson

Jennifer	Carrell

Jane	Domier

Deette	Lund

Indiana Geological Survey

Rick	Hill

Kim	Sowder

Renee	Stubenrauch

Iowa Geological Survey

Jim	Giglierano

Kansas Geological Survey

John	Dunham

William	Harrison

Kentucky Geological Survey

Jerry Weisenfluh

Louisiana Geological Survey

R.	Hampton	Peele

Manitoba Geological Survey

Greg	Keller

Michigan Geological Survey

John	Esch

Minnesota Geological Survey

Harvey	Thorleifson

Missouri Geological Survey

Edith	Starbuck

national Park Service

Tim	Connors
216	 DGTAL	MAPPNG	TECHNQUES	‘06

N Xutong Niu

Karen Royce

Alan Saalfeld

Frank Schwartz

Mary Scott

Ontario Geological Survey

Zoran Madon

Optimal Geomatics, Inc.

Mark Brooks

Oregon Dept. of Geology & Mineral Industries

Paul Staub

Pennsylvania Geological Survey

Jay Parrish

Tom Whitfield

Portland State University

Dave Percy

Reserve Energy Exploration, Inc.

William Haas

South Carolina Geological Survey

Erin Hudson

State of Ohio/OIT/SDD/ESS

Stuart Davis

The Nature Conservancy

August Froehlich

U.S. Geological Survey

Stafford Binder

Luke Blair

Peter Chirico

Connie Dicken

Joseph East

Chris Garrity

Charles Hickman

Bruce Johnson

Victor Mossotti

Suzanne Nicholson

Dave Soller

Nancy Stamm

Rob Wardwell

Ric Wilson

University of Alabama

Doug Behm

University of Arizona/USGS

Jonathan Craigue
APPENDX	A 217

Harry McGregor

USDA Forest Service

Andy Rorick

Utah Geological Survey

Kent Brown

West Virginia Geological Survey

Gayle McColloch

Jane McColloch

Wisconsin Geological & Natural History Survey

Michael Czechanski

Deborah Patterson

Peter Schoephoester

Wyoming State Geological Survey

Allory Deiss

David Lucke

Phyllis Ranz