1

1 •.

Final Report for 1985-86

to

National Aeronautics and Space Administration

RESEARCH ON ENHANCING THE UTILIZATION OF DIGITAL

MDLTISPECTRAL DATA AND GEOGRAPHIC

INFORMATION SYSTEMS IN

GLOBAL HABITABILITY STUDIES

NASA Grant NGL 17-004-024

(NASA-CB-177294) RESEARCH CB ENHANCING THE N86-26669

UTILIZATION OF DIGITAL MULTISFECTRAL DATA

AND GEOGRAPHIC INFORMATION SYSTEMS IN GLOBAL

HABITABILITY STUDIES Final Report, Dnclas

1985-1986 (Kansas Univ, Center for Research, G3/43 43590

Edward A. Martinko, Ph.D.

Principal Investigator

Assistant Scientist, Space Technology Center

James W. Merchant, Ph.D.

Co-Investigator

Senior Remote Sensing Specialist

Space Technology Center

February 1986

ThE UNivERsiry of KANSAS CENTER FOR RESEARCH, INC.

2291 Irving Hill Drive-Campus West Lawrence, Kansas 66045
TABLE OF CONTENTS

Introduction 1

The Kansas Applied Remote Sensing Program (KARS) 2

Principal KARS Program Personnel 6

Facilities 9

Development of Automated Strategies for Landscape Regionalization:

Employing Geographic Reasoning and Spatial Logic in Analysis of

Digital Multispectral Data 16

Development of Models for Assessing Biological Productivity of the Land:

The Use of Digital Multispectral Data and Spatial

Modeling Techniques 22

Merging Remotely Sensed Data with Ancillary Data:

A Geographic Information Systems Approach to Resources Management.... 30

Appendix I - KARS Program Brochures and Space Technology Center Brochure

Appendix II - Kansas Commission on Applied Remote Sensing

Appendix III - "Employing Geographic Reasoning in Landscape Mapping,"

Proceedings of the Pecora X Symposium; Remote Sensing

in Forest and Range Resource Management, Falls Church, VA:

American Society for Photogrammetry and Remote Sensing,

pp. 557-566.

Appendix IV - "Using Landsat TM Imagery and Spatial Modeling in Automatic

Habitat Evaluation and Release Site Selection for the Ruffed

Grouse (Galliformes: Tetraonidae)*" by Jorge M. Palmeirim
„ V

RESEARCH ON ENHANCING THE UTILIZATION OF DIGITAL MULTISPECTRAL

DATA AND GEOGRAPHIC INFORMATION SYSTEMS IN GLOBAL HABITABILITY STUDIES

INTRODUCTION^

The University of Kansas Applied Remote Sensing (KARS) Program is engaged

in a continuing long term research and development effort designed to reveal

and facilitate new applications of remote sensing technology for decisionmakers in governmental agencies and private firms. Now in its twelfth year,

this program is developing the concepts and procedures for utilization of

products prepared from the data gathered by various remote sensors (e.g.,

multispectral scanners, cameras) mounted in low and high altitude aircraft and

spacecraft. Increasingly, KARS staff are developing mechanisms for

integrating remotely sensed data with other data in automated geographic

information systems (CIS). KARS staff conduct both basic research and applied

research projects. Applied research projects are designed to address specific

problems faced by officials in governmental agencies (municipal, county,

multi-county, state and federal) and in business and industry. The interaction between the KARS Program and agency and industry personnel as they

jointly work on cooperative projects insures the continued relevance of the

program and maximizes the extent to which remote sensing/CIS technologies

address actual issues, problems and needs.

The key objectives of the KARS Program may be summarized as follows:

• ^Research and develop new modes of analyzing Multispectral Scanner,

Aerial Camera, Thermal Scanner, and Radar data, singly or in concert,

in order that more effective use can be made of such systems.

• Merge data derived from remote sensing with data derived from

conventional sources in geographic information systems to facilitate

better environmental planning and resources management.

• Stimulate the application of the products of remote sensing systems

to significant problems of resource management and environmental

quality now being addressed in NASA's Global Habitability directive.

° Apply remote sensing techniques and analysis and geographic

information systems technology to the solution of significant

concerns of state and local officials and private industry.

o Participate cooperatively on remote sensing projects with public

agencies and private firms.

1
* Effect the transfer of applicable remote sensing technology to

governmental agencies and private firms at all levels as a by-product

of projects conducted in the KARS Program.

* Assist personnel within public agencies and industry in the

evaluation of the capabilities of the rapidly changing remote sensing

systems and the benefits which might be achieved through their

utilization.

* Guide, assist and stimulate faculty, staff and students in the utilization of information from the Earth Resources Satellite (Landsat)

and Aircraft Programs of NASA in research, education and public

service activities carried out at the University of Kansas.

THE KANSAS APPLIED REMOTE SENSING (KARS) PROGRAM

The Kansas Applied Remote Sensing (KARS) Program was established by the

National Aeronautics and Space Administration (NASA) in 1972 to conduct

applied research on techniques which will enable public agencies and private

industry to better utilize available satellite and airborne remote sensing

systems. The KARS Program is a research program of the University of Kansas

Space Technology Center administered through the University of Kansas Center

for Research, Inc. The Program draws upon the remote sensing expertise and

facilities of the University of Kansas accumulated as a result of over 19

years of research in remote sensing conducted at the University (Appendix I).

The Space Technology Center (STC) was founded in 1972 by the National

Aeronautics and Space Administration and the State of Kansas to enhance

research and education in space-related science and technology through multidisciplinary research efforts. STC was established as part of a NASA plan to

set up a network of advanced facilities across the nation. Recognizing that

important University research is sometimes impeded because specialized

researchers do not have the facilities to work together, NASA sought to build

centers where such interaction not only would be possible, but would be

encouraged.

The goal of the Space Technology Center is to enhance research and education in space science and technology and contribute to the economic growth of

the nation. To achieve this goal, the Center fosters multidisciplinary

research in the sciences, humanities, engineering and business and transfers

the results to the public.
The KU Space Technology Center is the last of 27 interdisciplinary centers

that were built across the nation as part of NASA's $44 million investment

program. Its 77,000 square-foot design is planned to encourage communication

between researchers and to adapt easily to the growing interests of faculty

and students and the changing priorities of the space program. More than 30

KU faculty and 105 staff and students, representing every school of the

University, work at the Center to explore areas that are related to the space

program.

The KARS Program possesses a full range of remote sensing, mapping,

geographic information system, and related capabilities (Table 1). Projects

undertaken by the KARS Program with public agencies or private clients are

designed to identify and facilitate the manner in which remote sensing/

geographic information systems technology can be employed to aid in decisionmaking, policy formulation, planning and in meeting other responsibilities and

objectives. The KARS Program has provided assistance and services to more

than 40 agencies and firms in Kansas, Missouri and other states in the Great

Plains/Rocky Mountain region. Contractual applied remote sensing projects

have been carried out for the National Aeronautics and Space Administration,

U.S. Fish and Wildlife Service, U.S. Office of Surface Mining, USDA/Soil

Conservation Service, U.S. Environmental Protection Agency, U.S. National Park

Service, Missouri River Basin Commission, Kansas Fish and Game Commission,

Mid-America Regional Council and Farmland Industries, Inc. (Appendix VII).

Projects have involved research designed to enhance utilization of remotely

sensed data in land use/land cover inventory, monitoring land use change,

wildlife habitat evaluation, mapping of irrigated lands, surface mined lands

inventory, recreational area planning, soil conservation needs assessment,

aquatic vegetation mapping, rangeland condition evaluation, urban area

analysis, and education and training (Table 2). In addition, KARS staff have

provided remote sensing consulting services to the Government of India under

the auspices of UNESCO, the State of Wyoming, the State of Tennessee, and the

State of Chihuahua, Mexico.

One measure of the KARS Program's success in working with state agencies

and other users in Kansas is the establishment of the Kansas Commission on

Applied Remote Sensing. The Commission, comprised of representatives of all

state agencies that have utilized Landsat or other remote sensing data, is
Table 1. CAPABILITIES AND SERVICES OF THE KARS PROGRAM

« Interpretation of remotely sensed data (in digital or image format) in

support of land use/land cover, environmental, planning, agricultural and

natural resources inventories and analyses.

« Geocoding, geographic informaation system design and production;

statistical analysis, design of sampling surveys, areal statistical data

summaries;

• Field investigation either in support of remote sensing data collection or

independently designed to meet specific agency or client requirements;

o Aerial photography in support of KARS research and applications projects;

® Map production using state-of-the-art cartographic techniques including

negative scribing, color separation and computer graphics. Production of

printed maps in color or black and white, transparent overlays, precision

scale matching;

o Analysis of trends, projections, spatial modeling, monitoring of change on

a seasonal (e.g., range burning, harvesting) or annual basis (e.g., land

use, wildlife habitat);

o Location and acquisition of remote sensing data, flight mission design;

o Instruction in remote sensing techniques, interpretation and applications;

short courses, workshops, seminars; technology transfer.
Table 2. MAJOR KARS PROGRAM RESEARCH AND APPLICATIONS AREAS

Land use/land cover inventory, change detection and mapping

Irrigated lands inventories

Water resources management

Wildlife habitat evaluation

Strip mined lands assessment

Crop and rangeland resource inventory and evaluation

Integrated natural resources inventories

Geographic information system design, construction, and application

Thematic mapping

Technology transfer/remote sensing education
actively engaged in examining alternatives for making greater operational use

of the KARS Program to serve Kansas State government (Appendix II). The Kansas

Legislature, in 1983, awarded the KARS Program funds to work more

operationally with Kansas agencies and established a fee fund to facilitate

such work.

The KARS Program has published the quarterly KARS Newsletter since 1972.

Now funded by the Kansas Commission on Applied Remote Sensing, the Newsletter

is designed to foster the application of remote sensing data and to provide a

forum for communication on remote sensing related matters. Current

circulation is approximately 2,000. Readers include employees of local,

state, regional, and federal agencies, research centers, colleges and

universities, and private firms. Most readers reside in the Midwest and

Western U.S., but Newsletters are mailed throughout the United States, and to

several other nations. Many new projects have developed from this medium.

As a means of facilitating KARS' responses to continual requests for

information about the Program, a descriptive brochure has been designed,

printed and distributed to all recipients of the KARS Newsletter

(Appendix I). The brochure summarizes the facilities, equipment and staff of

the Program and describes the capabilities and services of the KARS Program.

The brochure has proved to be an invaluable introduction to the Program that

provides an attractive "calling card" for distribution at meetings, workshops,

agency visits and use in answering phone and mail queries.

The KARS Program has sponsored over 25 workshops, conferences, short

courses and seminars on applied remote sensing. Since 1972 the KARS Program

has provided training for over 900 agency personnel and other users from

throughout the U.S. Training, briefings and technology transfer activities

have been conducted both in Lawrence and at other locations. KARS staff have

provided briefings, workshops, and seminars for legislators, public agencies,

professional organizations, Industry and other users in Kansas, Missouri,

Wyoming, Tennessee, Mexico, and India.

PRINCIPAL KARS PROGRAM PERSONNEL

The KARS Program has assembled a unique staff of individuals who have over

40 years combined experience, broad contacts, and specialized expertise in

applied remote sensing, mapping, and natural resources. The KARS Program is

administered by Dr. Edward A. Martinko. Dr. James Merchant and Ms. Loyola
Caron are senior personnel of the Program. Other faculty in the University

participate in specific projects. The KARS staff is comprised of specialists

having expertise in ecology, forestry, wildlife biology, geography, computer

science, environmental studies and natural resources management. Three of the

principals have worked for state government and, therefore, have first-hand

knowledge of the needs and problems experienced by such agencies.

The Director of the KARS Program is Dr. Edward A. Martinko. Dr. Martinko

is an Assistant Scientist in the Space Technology Center and is also Director

and Assistant Professor of Environmental Studies at the University of

Kansas. He has over 15 years' experience in the areas of ecology and remote

sensing of biological resources, nine years of which he has been associated

with the KARS Program. He is also Director of the Kansas Biological Survey,

an agency of the State of Kansas responsible for the inventory and study of

plants and animals in Kansas. He is a co-founder of the Kansas Interagency

Task Force on Applied Remote Sensing and is intimately familiar with the

functions and needs of state government and industry. He has first-hand

familiarity with public agencies and other users in the states of Kansas,

Colorado, Missouri and Texas, and has been a consultant to the government of

Chihuahua, Mexico. Dr. Martinko's recent work has focused on remote sensing

applications in integrated pest management, noxious weed inventory and

wildlife habitat assessment.

James W. Merchant, Senior Remote Sensing Applications Specialist with the

KARS Program, has been engaged in basic and applied research in remote sensing

since 1971, ten years of which he has been associated with the KARS Program.

He has also served as a Natural Resources Planner with the Baltimore

(Maryland) Regional Planning Council and Research Assistant in microwave

remote sensing with the University of Kansas' Remote Sensing Laboratory. He

holds a B.A. in Geography from Towson State University, Towson, Maryland, an

M.A. and Ph.D. in Geography from the University of Kansas. He is currently

working with Kansas and federal agencies on projects involving the application

of remote sensing and geographic information systems to resources problems in

the areas of water resources management, rangeland inventory and evaluation

and land use analysis. He is Executive Director of the Kansas Commission on

Applied Remote Sensing. Dr. Merchant is personally acquainted with users and

remote sensing specialists throughout the U.S. He has participated in the
National Conference of State Legislatures' Natural Resource Information System

(NCSL/NRIS) Task Force meetings and NCSL briefings for state government

officials in Kansas, Wyoming and Tennessee, and has been a consultant to the

government of Chihuahua, Mexico.

Loyola M. Caron, Remote Sensing Specialist with the KARS Program, has a

unique background in natural resource information systems (NRIS) technology,

remote sensing, wildlife management and forestry. She holds a B.S. in

wildlife biology and an M.S. in forestry, with emphasis on remote sensing of

natural resources,.from the University of Minnesota. Prior to joining the

KARS Program staff, she was Staff Associate with the National Conference of

State Legislatures' Natural Resource Information Systems Project in Denver.

This program provided technical assistance to state legislators on Landsat and

NRIS technology. During her two years with the Conference, she participated

in state legislative committee briefings throughout the U.S. designed to

inform legislators about Landsat and NRIS capabilities and limitations; represented state needs in the national "Five Agency" effort to develop a national

land classification system for vegetation, landforms, soils and water;

assisted in a performance audit for the Arizona Resource Information System,

with special emphasis on examining state agency information needs; and was

responsible for preparation of a bi-monthly newsletter and various other

publications about Landsat/NRIS technologies and their use by state legislators and agencies. She has also worked for North Dakota's Regional

Evnironmental Assessment Program (REAP), an experimental effort by the State

of North Dakota to implement NRIS technology in an attempt to better plan for

and manage development of its resources. In this capacity she served as the

earth sciences research coordinator responsible for collecting natural science

data for a statewide automated data base. Ms. Caron also acted as liaison

between state agencies and REAP computer personnel to determine optimum

strategies for meeting agency needs.

Projects requiring specialized scientific expertise are staffed by

graduate students and faculty from the specific academic disciplines

involved. Personnel from the various firms and agencies are involved in their

own projects at no cost to NASA.
FACILITIES

KARS Program offices and laboratories are located in the University of

Kansas Space Technology Center. The program has complete facilities for

processing and interpretation of remote sensing data In both image and digital

formats, state-of-the-art cartographic production, statistical analysis, and

geographic data base production. Graphic arts, photographic processing and

support services are provided within the Space Technology Center.

The KARS Program's Image Interpretation Laboratory is furnished with a

complete range of equipment for viewing and analyzing imagery, and for

transferring image data to base maps of various scales. Included are a Bausch

and Lomb Zoom Transfer Scope, five Richards Light Tables with Bausch and Lomb

Zoom 240 stereoscopes, a Saltzraan Reducing/Enlarging Projector, a MacBeth

Color Spot Densitometer, an Old Delft Scanning Stereoscope, and a complete

assemblage of other manual image interpretation aids.

Aerial photography in support of KARS projects is acquired from a Cessna

180 Skywagon accessible to KARS staff. Both a multispectral cluster of four

Hasselblad 500EL 70mm format cameras and a Fairchild nine-inch format

cartographic camera are available for photographic missions.

Custom designed cartographic and graphic products are prepared by KARS

staff using negative scribing and photo-mechanical techniques. Production of

color graphic and color separations are standard procedures. Printing

services are available. KARS staff also have access to Tektronix computer

graphics systems, computer mapping software, and both flatbed and drum

plotters.

A current file of Landsat, Skylab and aerial imagery is maintained by the

KARS Program for the use of project personnel and user agencies. The Landsat

file contains a combination of selected black and white and FCC imagery for

various dates since the earliest Landsat in mid-1972. Over 130 prints and

1,100 transparencies are included in the file. The imagery is catalogued by

path and row and date and includes complete coverage of Kansas. Aerial

photography holdings include over 170 rolls of film.

An extensive map collection is maintained by the KARS Program. This

collection contains a variety of maps of Kansas and surrounding areas.

Included are state base maps, topographic sheets, county maps, general

regional maps, thematic maps and image mosaics.
The KARS Program also maintains a substantial reference library for both

in-house and agency use. This material includes reports, articles,

periodicals, manuals and textbooks pertinent to remote sensing and selected

applications areas.

Analysis of digital remote sensing data, digitizing and other computerassisted data processing operations are supported by facilities of the KARS

Digital Data Analysis Laboratory. The KARS stand-alone Digital Image

Processing System provides KARS with a full range of capabilities in computer

enhancement and classification of digital Landsat data, as well as other

remotely sensed data. The system also supports computer graphics, geographic

information system, statistical, cartographic, and integrated natural

resources analysis.

The KARS Digital Image Processing System has as its host a Digital

Equipment Corporation LSI-11/23+ central processing unit, a microcomputer

implementation of minicomputer technology. The computer has one million bytes

of main memory, a Sky Computers SKYMNK arithmetic pipeline processor, an 80-

raegabyte CDC 9762 disc pack drive with Emulex SC01B2 controller, a Kennedy

9100 75-inch-per-second vacuum-column tape drive, and serial input/output

ports for as many as eight user terminals.

There are two color image displays connected to the system. The first, a

Digital Graphic CBX-400 display, is used with the ERDAS software package as

described below. The CBX has a spatial resolution of 256 by 240 pixels in a

4-bit-per-gun RGB mode, and a spatial resolution of 512 by 480 in a 16-color

lookup-table mode. The ERDAS software uses the low-resolution mode for

imagery and the high-resolution mode for GIS applications. KARS hopes to

upgrade the device to a CBX-800, which can display 256 colors in highresolution-mode—normally adequate for imagery. The CBX is actually a selfcontained Cromemco Z-80 microcomputer. It includes a joystick for cursor

control and a high-speed parallel interface to the 11/23. The second display,

a Terak 8510/a-8600 HDX microcomputer, is used with other KARS software. The

8510/a includes a DEC 11/2 processor, 64K bytes of memory, an 8-inch dual

density floppy disc drive, keyboard and monochrome CRT display with graphics

capability at a 320-by-240 pixel resolution. It can function as a- stand-alone

computer running DEC's RT-11 operating system. The 8600 HDX display

integrated with the 8510/a includes an Intel 8086 processor and video refresh

10
memory and display circuitry capable of 640-by-480 pixel resolution. The 8600

HDX can display 64 simultaneous colors from a palette of 512.

Graphics output can be performed on a Versatec 8222-F 22-inch

electrostatic printer/plotter with 200 point-per-inch raster resolution. An

Anadex 9501A dot-matrix graphic printer is dedicated to ERDAS applications.

Spatial input is provided by an Altec AC90SM intelligent digitizer with a 60

by 42-inch backlit tablet.

Other peripheral devices include two desktop personal computers. .An

Intertec Superbrain is used as an intelligent terminal and as an operator

console and data preprocessor for the Altec digitizer. A Zenith Z-100

personal computer is used for word processing and small—scale data base

applications. Various CRT and hardcopy terminals are also connected to the

KARS system.

KARS also has access to the University of Kansas central computer system,

a Honeywell Level 66 DPS-3E computer. The KARS instructional image processing

package, developed for special short courses but also used in regular

University classes, runs on the central system.

The KARS 11/23+ runs under the RSX-11M operating system (version 4.1).

KARS programming has been done using the DEC FORTRAN-77 (version 5.0)

compiler.

The software capabilities of the KARS program have been substantially

enhanced by the recent acquisition of a second ERDAS software package from the

Earth Resources Data Analysis Systems organization in Atlanta, GA. The

software runs on the IBM-PC AT computer system. This system provides KARS

staff with a capability to conduct off-site data processing and analysis.

The ERDAS software is a powerful and general package of image processing

and CIS programs. Capabilities include a grid-cell oriented CIS system

adapted from the Harvard IMGRID package, a set of programs for analysis of

Landsat or other image data, programs for modification or annotation of a

displayed image or map, programs for various sorts of hard copy output,

utility programs including magnetic tape input and output routines, a

demonstration facility and X-Y tablet digitizing capabilities.

PROPOSED PROJECTS FOR 1986-87

During 1986-87, the KARS program will continue to build upon long-term

research efforts oriented towards enhancement and development of new

11
technologies for using remote sensing in the inventory and evaluation of land

use and renewable resources (both natural and agricultural). These research

efforts will directly address needs and objectives of NASA's Land-Related

Global Habitability Program as well as the needs of and interests of public

agencies and private firms. The KARS Program will place particular emphasis

on two major areas:

1. Development of Intelligent Algorithms to Improve Automated

Classification of Digital Multispectral Data; and

2. Integrating and Merging Digital Multispectral Data with Ancillary

Data in Spatial Models.

These areas and the specific contexts in which they will be addressed are

discussed in the following pages. Our 1986-87 research will build upon

ongoing work of the KARS Program through the following projects:

1. Employing Geographic Reasoning and Spatial Logic in Analysis of

Digital Multispectral Data.

The study of global habitability will require the preparation of a myriad

of small scale (i.e., 1:250,000 - 1:5,000,000) thematic maps. Initial

assessment of science issues which need to be addressed in the study of global

habitability indicates that among others, maps depicting land use, biotic

communities and ecological regions will be required (NASA, 1983). Such maps

will need to be prepared for large areas (perhaps the entire Earth), and will

need to be periodically updated in order to monitor and evaluate change in the

environment, its causes and effects.

Landsat provides both the spatial and multi-temporal coverage required

for such mapping. Most remote sensing specialists would agree that

computer-based techniques have been rather successfully employed to classify

and map land cover from Landsat Multispectral Scanner (MSS) and, more

recently, Thematic Mapper (TM) digital data. In a rather typical outcome of

computer classification, on a pixel by pixel basis, land cover can be

identified quite accurately where such distinctions can be made based upon

spectral characteristics of the cover.

Production of the types of thematic maps cited above, however, will

require the development of new techniques of data analysis, ones that employ,

not only spectral characteristics of the data, but also spatial, structural

and contextual attributes. The importance of this task has been recognized in

NASA's review of research needs related to the study of land-related global

habitability (NASA, 1983, p II.5-11).
Spatial/contextual algorithms are required for several reasons:

1. to help distinguish between land cover types which have similar

spectral characteristics, but which may, in fact, be quite

different. Such cover types (e.g., irrigated alfalfa and watered

urban lawns) would be indistinguishable using conventional

spectrally-based algorithms;

2. to aid in the generalization of classifications in order that smallscale maps can accurately represent the full resolution (i.e., each

pixel visible) products from which they were derived; and

3. to aid in the preparation of maps which portray integrated or

syntetic regions (e.g., regions of land use or eco-regions which are

comprised of complexes of land cover).

"Employing Geographic Reasoning and Spatial Logic in Analysis of Digital

Multispectral Data," is a KARS project designed to develop new techniques to

improve classifications through the analysis of context (i.e., relationships

between and among classified pixels). These techniques en be important for

the production of maps depicting land use, biotic communities and ecological

regions required for the study of global habitability.

2. Development of Models for Assessing Biological Productivity of the

Land: The Use of Digital Multispectral Data and Spatial Modeling

Techniques.

Fundamental to the study of global habitability is an understanding of

biological productivity and its relationships to global energy balance and to

biogeochemical and hydrological cycles. Biological productivity, however, not

only requires the study of the primary productivity of plant communities but

also the secondary productivity provided by animal communities. Knowledge of

the composition, and the spatial and temporal distribution of plant and animal

communities associated with land surface cover changes is therefore of central

importance for developing models of biological productivity.

Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) digital data

in conjunction with computer-based techniques have been utilized successfully

in the classification and mapping of changes in land use and land cover. The

ability to employ digital multispectral data to portray the spatial and

temporal distribution of land surface features is essential for establishing a

basis for modeling and assessing biological productivity on a regional or

ecosystem basis.

13
NASA's Global Habitability Program reviews the importance of these and

other related research needs (NASA, 1983, p II, 5-7) as a basis for an

understanding of the complex processes that offer habitability. Specifically,

studies of the biological productivity of land require:

1. investigation of integrated ecosystems in order to understand the

interactions of land surface cover with life support processes;

2. the assessment of the extent and spatial distributions of biomass and

productivity associated with land surface features;

3. the collection and analysis of remote sensing and ground data to

determine normal variations within and among years;

4. the development of models which characterize natural vs. man—induced

surface cover variation to predict spatial and temporal changes in

productivities.

"Development of Models for Assessing the Biological Productivity of the

Land" is a KARS project designed to utilize digital multispectral data to

classify land surface cover as a basis for developing models of biological

productivity. Ground data on plant and animal community composition and

distribution will be utilized in the biological productivity models for

assessment of spatial and temporal productivity changes.

3. Merging Remotely Sensed Data with Ancillary Data: A Geographic

Information Systems Approach to Resources Management.

Environmental management and policy decisions must, almost always, be

based upon examination and analysis of the interplay of many different factors

which may bear upon a particular issue. Decisions concerning conservation of

high quality groundwater, for example, must be based upon evaluation of a

spectrum of institutional, political, economic and environmental data. These

data are usually geographically-referenced (i.e., data tied to specific

locations on the Earth's surface). Geographically-referenced data may be

considered data that can be mapped. Automated geographic information systems

(GIS) enable one to rapidly store, manipulate, compare and display

geographically-referenced data. Once stored, such data can be automatically

extracted, reconfigured, updated, analyzed, mapped in a format and at a scale

designed to meet a specific need, and used for many types of decisionmaking. Geographic information systems provide decision-making with a

capability to analyze complex spatial interrelationships between variables.

14
Approximately two-thirds of Kansas water supplies are derived from

groundwater sources. The aquifers containing these waters vary appreciably in

lateral extent, saturated thickness, specific yield, depth to water and

geological composition. The present quality of the groundwaters also varies

markedly. About 8 percent of public water supplies derived from groundwater

contain concentrations of hazardous substances that exceed State primary

drinking water standards. The Kansas Department of Health and Environment

(KDHE) believes that most of these excesses are due to natual causes, but that

some could possibly have been caused by man. The potential exists for

additional contamination of groundwater supplies as municipal, industrial, and

agricultural activities become more intense and cover a greater portion of the

area overlying aquifers. The purpose of this project will be to develop

techniques for using remote sensing and CIS technology to assess, monitor,

manage, regulate and forecase groundwater conditions. The project will be

funded 75% by the Kansas Department of Health and Environment and 25% by

NASA. It is expected that system use will generate suggestions for refinement

and augmentation, unforeseen applications, administrative support for the

technology and demand for additional capabilities related to other areas of

global habitability.

15
EMPLOYING GEOGRAPHIC REASONING AND SPATIAL LOGIC IN

ANALYSIS OF DIGITAL MULTISPECTRAL DATA

James W. Merchant

Introduction

Remotely sensed data are commonly used to aid in classification and

mapping of landscape regions (e.g., ecoregions, land use regions). Visual

image interpreters routinely employ spatial cues evident in a scene to aid in

data stratification, classification and mapping. Such cues may include parcel

size, parcel shape, pattern, texture, context and associations of cover

types. Optimal visual image interpretation requires the use of geographic

reasoning and spatial logic. "Geographic reasoning" is defined as the

systematic application of geographic knowledge, understanding and expectations

in the analysis of data acquired via remote sensing for the purpose of

accomplishing a specific objective or set of objectives. "Spatial logic" is

defined as the formal expression and systematic application of decision rules

which are formulated to assist in the attainment of a particular analytic

objective (e.g., preparation of a 1:250,000 scale land use map portraying

classes X, Y, and Z) and are based upon characteristics and relationships

evident in a particular landscape as depicted in a particular set of remotely

sensed data. Spatial logic is developed as the image analyst transforms

observations, understandings and geographic knowledge of the landscape into

decision rules which can be applied in data analysis to segment that landscape

into regions of interest. Such rules typically require consideration of both

spectral and spatial properties of the landscape, and may require inference

and deduction.

Algorithms currently used to classify multispectral scanner data are

founded, almost exclusively, upon spectral pattern recognition techniques.

Spectrally-based classification algorithms have been found to be rather

effective in distinguishing many types of "land cover", classes (e.g., corn,

wheat, water) which are more or less spectrally homogeneous. They have been

less successfully employed to identify classes which, although of vital

interest to (map) users, are not separable on the basis of spectral

characteristics alone (e.g., land use classes). Most work on digital land

cover/land use classification and mapping has focused on the Landsat MSS.

Previous research has often been mathematically sophisticated, but appears

16
less profound in its application of "geographic reasoning" and "spatial

logic." This research is directed towards defining and testing algorithms and

strategies, founded upon geographic reasoning and spatial logic, which will

enable production of better thematic maps from digital multispectral data.

Production of many types of thematic (e.g., land use, ecoregion) maps

will require the development of new techniques of data analysis, ones that

employ not only spectral characteristics of the data, but also spatial,

structural and contextual attributes. Spatial/contextual algorithms are

required:

1. To help distinguish between land cover types which have similar

spectral characteristics, but which may, in fact, be quite

different. Such cover types (e.g., irrigated alfalfa and watered

urban lawns) would be inseparable using conventional spectrally-based

algorithms;

2. To aid in the cartographic generalization of classifications in order

that small-scale maps can accurately represent the full resolution

(i.e., each pixel visible) products from which they were derived; and

3. To aid in the preparation of maps which portray integrated or

synthetic regions (e.g., regions of land use or ecoregions which are

comprised of complexes of land cover). The definition of such

regions depends upon analysis of the spatial distributions and

characteristics of cover types.

It is proposed that significant improvement in automated detection and

interpretation of landscape regions could take place if software were to

emulate important aspects of visual image analysis. Such "intelligent"

software would employ "geographic reasoning." In work completed to date, a

strategy for employing geographic reasoning in classification of Landsat

Thematic Mapper digital data has been proposed. Thematic Mapper data are

initially stratified into water, vegetated and non-vegetated pixels. A

region-growing algorithm is then used to define "fields" of similar land cover

composition. Fields are characterized by size and neighborhood attributes.

Hierarchical clustering has been employed to test the hypothesis that the

diversity, field sizes and spatial interspersion of land cover types are

useful discriminants of landscape regions. The hypothesis has been shown to

be valid (Appendix III).

17
A major focus of future research will be upon implementation of these

results. A geographic information system (CIS) will be used to provide a

spatial "frame of reference" for guiding digital image segmentation and

classification. The CIS, which includes elements of the local transportation

network, digital terrain data and hydrology, will be employed during the

classification process rather than, as is often the case, in postprocessing. Successful application of a CIS in this mode demands explicit

expression of the logic of image interpretation. Artificial intelligence

concepts will direct the formulation of image analysis strategies.

In this research, a layered classification strategy will be employed. In

the layered technique a classifier logically filters the unknown through a

sequence of decisions, each of which may be dependent on the outcome of

previous decisions. Classification will be guided by artificial intelligence

concepts. Landscape regions will be defined via both analysis of spectral

data and via reasoning based upon antecedent conditions, expert knowledge and

ancillary (e.g., field) data.

The "intelligent" classifier will be capable of geographic calibration.

In other words, the data analyst will be able to "tune" it to a particular

environment, thus allowing regional interpretation of locally derived

quantitative relationships between surface phenomena and indices (e.g.,

greenness and brightness indices) computed from satellite data. Calibration

will be accomplished by establishing, for specific geographic regions, a known

"baseline" from which change can be measured and against which surface

conditions can be calibrated. The "baseline" will include both data (e.g.,

digital elevation data) and geographic expert knowledge expressed as logical

rules.

Ancillary data and "a priori" knowledge, it is suggested, could be useful

in at least two ways:

(a) Existing maps, both digital (e.g., USGS Digital Line Graphs) and

conventional, known to be accurate, could provide a spatial

"starting point" to begin structural analysis of the landscape

(e.g., to "guide" region-growing). Existing road and railroad

networks and drainage systems might provide a structural

framework within which to grow "fields." Knowledge of former

land use might be employed to help assign probabilities of

likely current land use in digital classification (e.g., a CBD

18
is unlikely to change to bare cropland, a golf course is

unlikely to change to pasture).

(b) Ancillary data will be used, as well, to assign probabilities of

correct classification. Digital terrain data, for example,

could be employed to derive slope and local relief. It might be

determined that cropland is not likely to occur on steep slopes

or in hilly areas. Floodplains, known to be almost all cropland, might be defined topographically.

The geographic window (Figure 1) concept will also require further

consideration. Statistics in the hierarchical clustering study were computed

over such a window. The hierarchical clustering experiments demonstrated that

a set of statistics which characterized land cover composition and spatial

structure of the landscape can be used to discriminate regions of land use and

land cover (Appendix III). These statistics were computed, however, for

geographic windows (sample sites) demarcated visually. The sites were of

(variable) sizes known to capture the "character" of each respective land use

class sampled.

In order to implement any of the positive results found in the

hierarchical clustering experiment, statistics must be computed for an area -

a window. Intuitively, it is believed that the geographic window is a better

concept than the geometric window. If, for example, it is desirable to

measure field size or shape, then one does not want to use a window that

"cuts" fields into sectors, as a geometric window would.

The key to successful implementation of the geographic window may lie in

development of a technique to "focus" the field-of-view of the window. That

is, rather than let the window vary in size according to the character of a

single field, the dimension of the window might be allowed to vary by

evaluation of the characteristics of several neighboring fields. Such

statistics (e.g., mean field size, spatial complexity index) might be

generated and used to select a window size that would likely generate

meaningful statistics.

The window problem is related to the outcome of region-growing. Further

research on region-growing (using a CIS to guide segmentation as outlined

above) is required. It may be that different decisions made in this regard

would enable the geographic window, even as it stands, to perform better.

19
A. Geometric B. Geographic

3x3

Figure 1. A geometric window is comprised of a pixel and

its neighboring pixels. A geographic window is

comprised of a field and its neighboring fields.

20
Summary

Future research will be concentrated in six areas:

1. Experiments directed towards better definition of optimal spatial and

contextual measures/variables and weighting schemes;

2. Experiments directed towards better definition of regiongrowing/geographic window relationships and the effects these may

have on the use of a geographic window in a classification algorithm;

3. Efforts to improve the internal logic of and implement an operational

classification algorithm;

4. Examination of additional spatial, textural and contextual measures;

5. Examination of the potential applicability of alternative means of

image segmentation and classification (e.g., conventional per-pixel

classification); and

6. Comparative analyses of other airborne and spacecraft-borne

multispectral systems having different spatial resolutions (e.g.,

AVRIS, SPOT).

21
DEVELOPMENT OF MODELS FOR ASSESSING BIOLOGICAL PRODUCTIVITY OF THE LAND:

THE USE OF DIGITAL MULTISPECTRAL DATA AND SPATIAL MODELING TECHNIQUES

Introduction

Assessing the biological productivity of the land is of central

importance in understanding habitability. Productivity, however, changes both

spatially and temporally and therefore must be monitored on a continuing

basis. The synoptic view produced by remote sensing devices provides the

mechanism for monitoring and assessing changes in biological productivity on a

regional or ecosystem basis. The focus of this research effort is to

determine those factors that influence the long term ability of the land to

support given levels of biological productivity.

While the major biological variable of interest in biological

productivity is the primary productivity of an ecological system, the

secondary productivity of animals is also Important for the habitability of a

region or an ecosystem. Since animals are an important factor in shaping

plant communities which are responsible for primary production, knowledge of

the composition of animal communities is therefore important. Mapping areas

which are suitable for the occurrence of a species but where It is not known

to exist can, for example, be useful to: (1) choose suitable sites for

introduction or reintroduction of species (KARS successfully chose release

sites for pronghorn antelop.e in Kansas using MSS imagery (Martinko, 1981));

(2) predict the expansion of exotic species; (3) suggest areas where search

for a species might be fruitful; and (4) uncover species distribution problems

in need of study.

The potential for existing satellite imagery in mapping and evaluating

animal habitat is relatively limited. However the usefulness of digital MSS

imagery has already been demonstrated in various projects (e.g. Thompson et

al., 1980; Isaacson and Leckenby, 1981). The rapidly improving capabilities

of CIS technology in spatial analysis and the use of ancillary data are,

however, far from being fully explored. Furthermore, other types of

multispectral data will almost certainly prove to be better than the MSS in

habitat studies, due to higher resolution, broader spectral ranges, and higher

band specificity. The use of multispectral data and CIS technology in this

area seems to be quite promising.

22
Because most estimates of biological productivity are estimates of

potential rather than actual biological productivity, a basic prerequisite for

accurate models of actual productivity is the development of a suitable land

cover classification. We will compare various approaches to this problem and

experiment with different linear data transformations, spectral classification

algorithms, and spatial reclassification filters. Since we are using this

project as a model for future large scale studies, the cost of the various

techniques used will be considered along with accuracy. In spite of the

intensity of research in this area during the past decade, the development of

processing algorithms and methodologies for extracting land cover information

from remotely sensed data, and the study of associated costs, are still major

goals of NASA's global habitability program.

Cost and availability considerations make the use of remotely sensed data

in operational projects still very appealing. With this in mind, we will

compare the results obtainable with various types of multispectral data in

land cover mapping.

In summary, the major goals of this project are to:

(1) Experiment with various approaches to spectral/spatial land surface

classification in order to implement an accurate and cost efficient

model.

(2) Compare the performance of various types of multispectral data in

land cover mapping as a basis for studies of biological productivity.

(3) Develop and evaluate spectral/spatial/temporal models of biological

productivity of the land based on Landsat imagery and ancillary

ground data on plant and animal communities.

(4) Develop a model to classify and map animal communities based on the

results of (3).

In work completed to date an initial study area located in northeastern

Kansas was utilized. The area is dominated by forests, rangeland and

cropland. One TM scene obtained on September 3, 1982,. and a MSS scene

obtained by Landsat-4 on the same date, as well as a MSS winter image, were

used in this project. Large scale aerial photography were used along with

ground surveys to check the results of the analysis. A mixed

spectral/contextual land cover classification approach was used along with a

geographic information system to improve the final land cover map. This

spatial/spectral approach to land cover classifications resulted in an

accurate and effective model for habitat evaluation (Appendix IV).

23
The approach described above was designed to map areas that are suitable

for the occurrence of a particular species where it does not now exist, namely

the location of potential release sites for the ruffed grouse (Bonasa

umbellus). Future research will concentrate on the generalization of the

results of this initial effort to other species and experimentation with

different approaches to mapping communities and species distributions based on

point samples of community composition and species abundance.

On completion of the land cover mapping and animal community mapping,

temporal and spatial models of biological productivity will be developed

utilizing a synthesis of the remote sensing data and procedures from

theprevious research along with ground based measurements or primary and

secondary productivity. These models will integrate the ground based

measurements, the remote sensing data and ancillary data into a regional

estimate of biological productivity. It should be emphasized that the

development of these models is an end product of the research efforts

described herein and will incorporate the findings of each element. The

outcome of this effort should provide a basis for an improved understanding of

the spatial and temporal dynamics of biological productivity in terrestrial

ecosystems.

In the sections that follow, we will give a short description of the

research plan, along with a summary of the background information on which we

are basing this work.

Research Plan

During the last few years research on land cover classification techniques

has been very intensive. However, we are still far from being able to select

the optimal approach to follow in a specific application. Actually,

comparisons among the numerous available techniques are relatively scarce, and

the situation is considerably complicated by the lack of standardization at

all levels in this new area. In order to select the land cover classification

approach best suited to a particular community, species or ecosystem, we will

have to experiment with various techniques. Accuracy, cost, and time

considerations will be the basis for our comparisons and selection criteria.

The multitude of combinations of methods available make it impossible to

explore all them in the time available for this project. We have selected the

ones we are using based on the nature of the area in which we will be working,

software availability, and the published results of previous work.

24
In a project in which various classification approaches are tried and

compared, it is of special importance to use a practical and precise method to

evaluate the accuracy of the obtained products. Various recent papers discuss

the problems associated with accuracy assessment (e.g. Hay, 1979; Rosenfield

et al., 1982; Dozier and Strahler, 1983). A number of ground truth points

will be selected by stratified random sampling (Cochran 1977) in order to get

a good representation of the various zones of the study area. The ground

cover at this site will be identified using large scale aerial photographs

and, when necessary, field checked. The identity of this cover will be kept

on file for automatic comparison with the results of the various analyses.

Percentage confusion tables including confidence limits will be generated. An

arcsin transformation will be performed to correct for non-normality of the

percentile data (Sokal and Rohlf, 1981). To compare statistically the

obtained confusion tables, Retelling's T~ will be used for global comparisons,

and Bomferroni's multivariate tests for multiple comparisons, as suggested by

Merembeck and Turner (1979).

Mapping Animal Habitat and Communities

The importance of digital multispectral imagery in wild species habitat

studies has already been proven by work done in the last few years (for an

overview see Best, 1981). However, most of these studies are simply involved

in the identification of habitat elements, such as vegetation. This is a

necessary step, but it is unfortunate that so few works try to quantitatively

relate these elements to the actual needs or preferences of the individual

animal species (but see Thompson et al., 1980). In this part of our work we

will try to develop models to allow us to map the habitats of species in an

area, based on Landsat imagery and ancillary data. These maps should also

include an evaluation of the habitat "quality" for each species. This model

will only be successful in cases where the habitat limiting variables can be

directly or indirectly detected and measured on the imagery and/or easily

included as digital ancillary data.

Because of the amount of data available for the study area, initial effort

will focus on the passerine bird community. Ground data on the breeding bird

fauna of the study area will be collected using a point count technique

(Blondel, 1981). About one hundred sampling sites will be randomly

distributed throughout the various habitat types present. Data will consist

of a relative measure of bird abundance. Only the species that are relatively

common will be included in the analysis, to make the samples more

25
representative. The counts will be carried out early during the breeding

season (late May and June) in order to increase the count accuracy. To reduce

temporal bias only early morning counts made on days with good weather will be

included (Verner, in publication).

Two approaches to evaluate and map the habitat of the individual bird

spectes will be followed:

(1) Each habitat type identified in the previous land cover

classification will be rated in accordance with the pooled information of all

the sampling points within its boundaries. This method assumes that the

habitat types selected in the classification process are relevant in the

distribution of the sampled species.

(2) The second approach searches for a direct relationship between the

species distribution and abundance and the spectral image values. In order to

establish this relationship we will experiment with three methods: (1) a

stepwise multiple linear regression will be performed using various bands and

ancillary digital information as dependent variables; (2) a maximum likelihood

classifier will be employed to separate habitat from non-habitat using the

bird survey points as training sites; (3) a K-nearest neighbor classifier will

also be used with the same training sites.

The results of habitat mapping for individual bird species is examplified

by the work done on ruffed grouse (Appendix IV). This approach to habitat

mapping will provide the framework for the evaluation of the habitat of other

species and the development of a model to predict the distribution of the

various communities present in an area based on Landsat imagery and ancillary

digital information. We have not found a bibliography dedicated to this

subject. Community classification and mapping has great scientific interest

and can be an important tool in planning and managing protected areas. Two

different approaches to the design of the model will be tried:

(1) The first approach will be based on the potential distributions

predicted by the most successful of the species distribution predictive models

described in the previous section. Each pixel will be characterized by the

predicted presence/absence of each of the studied bird species. Several

clustering algorithms will be used to identify faunistically distinct units,

which will then be mapped. This approach is similar to the one used by

various authors (e.g., Simpson, 1964; Schall and Pianka, 1977) to identify

zoogeographical units in large scale studies. An extension of this approach

will be the inclusion of information on the predicted species abundances in

26
the cluster analysis. Obviously, the successfulness of this community mapping

is dependent on the accuracy of the distribution/abundance digital maps

constructed. The use of many species may compensate for part of the noise

introduced by incorrect predictions of the distribution and/or abundance of

some of the species. In order to reduce computing time, pixels with similar

faunistical composition will be grouped in homogeneous units, which will then

be submitted to the clustering algorithms.

(2) The second method will be initiated by clustering the sampled points

based on the presence and/or abundance of the studied species. Each cluster

of sampled points will then be considered as a training set in a land cover

classification based on the Landsat imagery and ancillary digital maps, such

as "habitat homogeneity," "habitat unit size," etc. The same classification

methods referred to previously for land cover mapping will be tried here.

Another clustering operation will group the bird species according to their

habitat preferences.

The spatial relationship between the bird communities mapped and the land

cover may suggest which habitats are recognized as distinct by the birds and

even what variables are most important in controlling their distribution.

Future work will concentrate on further refining the models and habitat

mapping procedures as a basis for the development of spatial and temporal

models of biological productivity on a regional basis.

27
Selected Bibliography

Best, R.G., 1981. Handbook of Remote Sensing in Fish and Wildlife

Management. Remote Sensing Institute, South Dakota State University,

Brookings, South Dakota. 6+125+23+44 pp.

Bracken, P.A., W.B. Green, and R.M. Haralick, 1983. Remote Sensing Software

Systems, in R.N. Colwell (ed.), Manner of Remote Sensing, 2nd Ed.,

American Society of Photogrammetry, Falls Church, Virginia, pp. 807-830.

Cochran, W.G., 1977. Sampling Techniques, Third Ed., Wiley, New York, New

York.

Dozier, J. and A.M. Strahler, 1983. Ground Investigations in Support of

Remote Sensing, in R.N. Colwell (ed.), Manual of Remote Sensing, 2nd Ed.,

American Society of Photogrammetry, Falls Church, Virginia, pp. 959-986.

Fukunaga, K. and P.M. Narendra, 1975. A Branch and Bound Algorithm for

Computing K-Nearest Neighbors. IEEE Transactions on Computers, C-24:750-

753.

Gessaman, M.P. and P.H. Gessaman, 1972. A Comparison of Some Multivariate

Discrimination Procedures. Journal of the American Statistical

Association, 67:468-472.

Goldstein, M., 1975. Comparison of Some Density Estimate Classification

Procedures. Journal of the American Statistical Association, 70:666-669.

Gurney, C.M. and J.R.G. Townshend, 1983. The Use of Contextual Information in

the Classification of Remotely Sensed Data. Photogrammetric Engineering

and Remote Sensing, 49:55-64.

Hand, D.J., 1981. Discrimination and Classification. Wiley, New York, New

York, 218 pp.

Hand, D.J. and B.C. Batchelor, 1978. An Edited Condensed Nearest Neighbor

Rule. Information Sciences, 14:171-180.

Hart, P.E., 1968. The Condensed Nearest Neighbor Rule. IEEE Transactions on

Information Theory. 11-14:515-516.

Hay, A.M., 1979. Sampling Designs to Test Land-Use Map Accuracy.

Photogrammetric Engineering and Remote Sensing. 45:529-533.

Isaacson, D.L. and D.A. Leckenby, 1981. Remote Sensing Inventory of Rocky

Mountain Elk Habitat in the Blue Mountains. ASP-ACSM Fall Technical

Meeting, San Francisco and Honolulu, pp. 282-291.

Ittera, K.I. and F. Fasler, 1978. Thematic Adaptative Spatial Filtering of

Landsat Land Use Classification Results. Proceedings. 13th International^

Symposium on Remote Sensing of Environment. Ann Arbor, Michigan, pp. 1035-

1042.

Martinko, E.A., 1981. Monitoring Agricultural Growth in Pronghorn Antelope

Habitat. Proceedings. Pecora IV. Sioux Falls, South Dakota, pp. 210-216.

28
Merembeck, B.F. and B.J. Turner, 1979. Directed Canonical Analysis and the

Performance of Classifiers under its Associated Linear Transformations.

Proceedings, Machine Processing of Remotely Sensed Data, Purdue

University, LARS, West Lafayette, Indiana, pp. 314-322.

Nedelman, K.S., R.B. Gate, and R.M. Bizzell, 1983. Automated Vegetation

Classification using Thematic Mapper Simulation Data. Technical Papers,

49th Annual Meeting, ASP, Washington, D.C., pp. 367-375.

Nelson, R.F. and R.M. Hoffer, 1979. Computer-Aided Processing of Landsat MSS

Data for Classification of Forestlands, LARS Technical Report 102679,

Purdue University, West Lafayette, Indiana, 95 pp.

Rosenfield, G.H., K. Fitzpatric-Lin, and H.S. Ling, 1982. Sampling for

Thematic Map Accuracy Testing. Photogrammetric Engineering and Remote

Sensing. 48*: 131-137.

Schall, J.J. and E.R. Pianka, 1977. Species Densities of Reptiles and

Amphibians on the lerian Peninsula. Donana, Acta Vertebrata, 4:27-34.

Scholz, D., N. Fuhs, and M. Hixson, 1979. An Evaluation of Several Different

Classification Schemes: Their Parameters and Performance. Proceedings,

13th International Symposium on Remote Sensing of Environment, Ann Arbor,

Michigan, pp. 1143-1149.

Simpson, G.G., 1964. Species Density of North American Recent Mammals.

Systematic Zoology, 13:57-73.

Sokal, R.R. and F.J. Rohlf, 1981. Biometry, 2nd Ed., Freeman and Company, San

Francisco, 859 pp.

Swain, P.H., H.J. Siegel, and B.W. Smith, 1980. Contextual Classification of

Multispectral Remote Sensing Data Using a Multiprocessor System. IEEE

Transactions on Geoscience and Remote Sensing, GE-18:197-203.

Thompson, D.C., G.H. Klassen, and J. Cihlar, 1980. Caribou Habitat Mapping in

the Southern District of Keewatin, N.W.T.: An Application of Digital

Landsat Data. Journal of Applied Ecology. 17:125-138.

Tilton, J.C. and P.H. Swain, 1981. Incorporating Spatial Context into

Statistical Classification of Multidimensional Image Data. LARS Technical

Report 072981, Purdue University, West Lafayette, Indiana, 92 pp.

Ton, J.T. and R.C. Gonzalez, 1974. Pattern Recognition Principles^. AddisonWesley, London, 377 pp.

Verner, J., in publication. Assessment of Counting Techniques, in R.F.

Johnston (ed.), Current Ornithology.

29
MERGING REMOTELY SENSED DATA WITH ANCILLARY DATA:

A GEOGRAPHIC INFORMATION SYSTEMS APPROACH

TO RESOURCES MANAGEMENT

Environmental management and policy decisions must, almost always, be

based upon examination and analysis of the interplay of many different factors

which may bear upon a particular issue. Decisions concerning conservation of

high quality groundwater, for example, must be based upon evaluation of a

spectrum of institutional, political, economic and environmental data. These

data are usually geographically-referenced (i.e., data tied to specific

locations on the Earth's surface). Geographically-referenced data may be

considered data that can be mapped. Automated geographic information systems

enable one to rapidly store, manipulate, compare and display geographicallyreferenced data. Once stored, such data can be automatically extracted,

reconfigured, updated, analyzed, mapped in a format and at a scale designed to

meet a specific need, and used for many types of decision-making. Geographic

information systems provide decision-makers with a capability to analyze

complex spatial interrelationships between variables.

The objective of this project is to design optimal techniques for

interfacing remotely sensed data with other geographically-referenced data in

automated geographic information systems. This prototype, funded chiefly

through the Kansas Department of Health and Environment, will focus on

development of GIS techniques for assessing, monitoring, managing, regulating

and forecasting groundwater conditions.

Approximately two-thirds of Kansas water supplies are derived from

groundwater sources. The aquifers containing these waters vary appreciably in

lateral extent, saturated thickness, specific yield, depth to water and

geological composition. The present quality of the groundwaters also varies

markedly. Temporal variations in quality can occur in response to changes in

recharge, amount of pumping and the introduction of substances by man's

activities. About 8 percent of public water supplies derived from groundwater

contain concentrations'of hazardous substances that exceed State primary

drinking water standards. The Kansas Department of Health and Environment

(KDHE) believes that most of these excesses are due to natural causes, but

that some could possibly have been caused by agricultural practices. In a few

cases, past pollution by oilfield brines has adversely affected public water

supplies. The potential exists for additional contamination of groundwater

30
supplies as municipal, industrial, and agricultural activities become more

intense and cover a greater portion of the area overlying aquifers.

Water resources scientists will be involved in identifying the

information components of the geographic information system, will aid in the

design of strategies, models and algorithms for analyzing date (e.g., the

National Water Well Association's DRASTIC), and will have access to the system

in order to gain experience in using the CIS for routine decision-making.

Such utilization is essential if water managers are to better define

mechanisms and methods for employing CIS capabilities in management

activities. The feedback stemming from "hands on" experience will aid in

"fine-tuning" system design, in more accurately establishing cost-benefit

relationships and in better defining applications. A geographic information

system, employing the ERDAS IBM PC-AT CIS, will be established to enable water

resources managers to better address resources management issues pertaining to

groundwater and surface water quality, irrigation, and municipal water supply.

The study area selected for the prototype CIS includes all of Harvey

County, the easternmost townships of Reno County, and the northern-most

townships of Sedgwick County, Kansas. This region was chosen because it

includes a portion of major aquifer (the Equus Beds) that, in terras of

groundwater sources, probably supplies the largest number of people in Kansas

for an area equivalent to that of an average county. The Wichita well field

(municipal water supply) and a major portion of the Equus Beds Groundwater

Management Destrict (irrigation water district) is included within the study

boundary. A region of sand dunes probably receiving greater than normal

surface recharge is in the northwestern part of the study area. A section of

the alluvial aquifer of the Arkansas River is in the southwest, while limestone aquifers yielding supplies of water adequate for only domestic or stock

purposes are in the east. Oil has been produced for many years from the

Burrton and other smaller oil fields. Oilfield brine associated with this

activity has polluted the surface and intermediate levels of the aquifer

around Burrton; this pollution is being monitored by the Groundwater

Management District and the Department of Health and Environment.

Additional support for selection of this area for the system prototype is

the relatively large amount of remotely-sensed groundwater-related and landuse data that exists. An important example is that the soils survey of Harvey

County has been digitized by the Kansas Applied Remote Sensing Program.

31
The proposed project will encompass 10 major Tasks, though Task 10 is not

covered by funds currently available. The Tasks are as follows:

1. Define KDHE needs for a geographic information system (CIS);

2. Identify existing sources and files of geohydrology, water quality,

water supply, waste sources, soils and land use data;

3. Identify existing digital data/computer capability which could be

employed in a CIS (work through Kansas Commission on Applied Remote

Sensing, Kansas Geological Survey, and Kansas Water Data Control

Committee);

4. Identify hardware/software and data that would need to be acquired;

5. Identify types of information needing calculation and associations

among different types of data necessary for decisions concerning

groundwater quality protection (e.g., environmental performance

zoning);

6. Identify existing data analysis models that could be used to process

and display desired information;

7. Modify existing models or develop new models to address KDHE

requirements;

8. Develop and test prototype CIS for an area representative of

groundwater supplies important to large numbers of individuals and

that has different existing and potential water pollution problems

(Harvey, eastern Reno, and northern Sedgwick counties, within

Groundwater Management District No. 2 and including the water well

field of Wichita);

9. Recommend mode of system implementation, integration, and institutionalization.

10. Provide on-site training (workshop).

Although designed for water resouces management, it is important to note

that other users may access the GIS for applications ranging from urban

planning and land appraisal to soil erosion hazard assessment and wildlife

habitat evaluation. It is expected that system use will generate suggestions

for refinement and augmentation, unforeseen applications, administrative

support for the technology and demand for additional capabilities.

32
APPENDIX I
APPENDIX II
APPENDIX II: The Kansas Commission Applied Remote Sensing

In July 1982 the Kansas Legislature established the Kansas Interagency

Task Force on Applied Remote Sensing. Major objectives of that Task Force

were to provide policy direction for the KARS Program, the enhance interagency

communication, and to assess alternatives for greater and more operational

utilization of remote sensing/geographic information systems technologies on a

statewide basis. The Task Force presented a Final Report on its

accomplishments, studies and deliberations to the Governor and the Kansas

Legislature in December 1983. A major recommendation of the Task Force was

that a permanent Commission on Applied Remote Sensing be formed to foster the

use of remote sensing and related geographic information systems

technologies. House Bill 2670 (now KSA 74-7701) establishing the Kansas

Commission on Applied Remote Sensing was signed into law by Governor John

Carlin in April 1984. The duties of the Commission are to:

o Assist users in assessing the capabilities, costs, and alternatives

for employing remote sensing or related geographic information

systems technologies;

9 Serve as a forum and mechanism for interagency communication,

coordination and cooperation for the use of remote sensing and

geographic information systems technologies;

o Advise the KARS Program regarding the data and informational needs of

Commission members, and aid the KARS Program in identifying and

prioritizing projects which are of greatest import to the State;

o Disseminate information regarding new developments and capabilities

pertaining to remote sensing and geographic information systems;

« Prepare and present to the Governor and Legislature on or before

May 31, 1986, a report and any recommendations regarding the need for

an integrated, comprehensive Kansas resources information center; and

o Prepare and present annual reports to the Governor and Legislature,

and recommend funding levels for the KARS Program and the Commission

in the subsequent fiscal year; and make recommendations to each

regular session of the Legislature and to the Governor concerning

necessary or advisable legislation relating to issues of statewide

importance concerning remote sensing or geographic information

systems technologies.

Twelve state agencies, the Governor's Office, both houses of the Legislature,

county governments, and the groundwater management districts are represented
on Che Commission. Federal and local agencies and private firms are invited

and encouraged to participate in Commission activities.

The work of the Kansas Commission on Applied Remote Sensing is currently

supported by a state allocation to the KARS Program of approximately

$55,000. This funding was originally appropriated to KARS to help it support

the work of the Interagency Task Force and to provide a fundamental level of

services to state agencies. The National Aeronautics and Space Administration

(NASA) also contributed funds which helped subsidize these activities; NASA

funds are no longer available. The Commission has requested funding for FY 87

in the amount of $98,000, this amount to supplement KARS' current $50,000

allocation.

The Commission has recommended augmentation of KARS funds to support

three major programs. These are:

1. Support for the Kansas Commission on Applied Remote Sensing. Funds

would provide administrative and technical support for quarterly

meetings of the Commission. This support would include both staff

and material expenses such as:

1. Preparation, duplication and mailing of the agenda, reports,

handouts, minutes for quarterly meetings;

2. Technical and secretarial support for the chairperson and

executive director;

3. Travel for KARS staff to Commission meetings;

4. Preparation of materials for legislative and agency briefings;

5. Publication and distribution of a newsletter designed to enhance

awareness of the services and technologies available;

6. Preparation of the annual report of the Commission; and

7. An Executive Director (.25 EFT) to carry out day-to-day

administrative and technical tasks of the Commission.

2. Services to Kansas. Funds would cover the following services:

1. Consulting with Kansas agencies, Kansas firms and individuals

requesting information;

2. Proposal preparation for agencies;

3. Training, workshops, shortcourses for Kansans;

4. Affiliation with the National Cartographic Information Center to

foster the availability of data on maps and remote sensing data

needed by Kansas agencies; and
5. Maintenance of KARS image collections, maps and digital data for

use by Kansas agencies, firms and individuals.

Initiation of a land information system. A land information system

would contain information regarding vegetation, agricultural land

use, and urbanization. Such data do not exist, but are needed for

water resources management environmental pollution assessment,

conservation needs evaluation, wildlife management and other

purposes. Kansas agencies, the Legislature and private individuals

would find such data invaluable.

The Commission has requested funds for KARS to initiate

production of such a data base. The data base would be capable of

producing maps and statistical data formatted to the needs of a

specific user. KARS would assist agencies in using these data to

make management and policy decisions more effectively and at lower

overall cost.

A Study of the Need for a Kansas Resources Information Center. The

Commission is charged, under KSA 74-7701, to prepare and present to

the Governor and Legislature on or before May 31, 1986,

recommendations regarding the need for an integrated, comprehensive

Kansas resources Information Center. In 1985 the Commission began

this evaluation.

The Kansas Interagency Task Force on Applied Remote Sensing had

considered, in a preliminary fashion, the need for a broadly focused

state information center. Such a center would retain and expand all

of the current capabilities of the KARS Program. In addition, it

could be charged with inventorying, cataloging and coordinating data

about Kansas maintained by state, local and regional agencies,

federal agencies, some private firms and institutions of higher

education. The center could provide clearinghouse and referral

services; spatial data analysis capabilities;.geographic data base

development for state users; remote sensing data/imagery

interpretation; training and briefings; and development and/or

implementation of new high technologies. A resources information

center could facilitate enormous tasks such as a statewide

reappraisal, water resources planning, soil errosion assessment, and

monitoring of prime agricultural land use change.
APPENDIX III
EMPLOYING GEOGRAPHIC REASONING IN LANDSCAPE MAPPING

James W. Merchant

Kansas Applied Remote Sensing Program

University of Kansas

Space Technology Center

Lawrence, Kansas 66045

ABSTRACT

"Geographic reasoning" is defined as the systematic application of

geographic knowledge and understandings in the analysis of data acquired

via remote sensing. Visual image interpreters routinely use geographic

reasoning in preparing maps of land use and ecoregions. A strategy for

employing such reasoning in classification of Landsat Thematic Mapper

digital data is proposed. An experiment is designed to test the proposed strategy on a Kansas site. Thematic Mapper data are initially

stratified into water, vegetated and non-vegetated pixels. A regiongrowing algorithm is then used to define "fields" of similar land cover

composition. Fields are characterized by size and neighborhood attributes. Hierarchical clustering is employed to test the hypothesis that

the diversity, field sizes and spatial interspersion of land cover types

are useful discriminants of landscape regions. The hypothesis is shown

valid. A major focus of current research is upon implementation of

these results.

INTRODUCTION

Classification of landscapes and mapping of landscape regions (e.g.,

ecoregions, land use regions) are important foci of study in geography

and other earth sciences (Spence and Taylor, 1970; Gardiner and Gregory,

1977). The concept of the geographic region is based upon the premise

that there are identifiable physical and/or cultural landscape elements

which serve to characterize an area and differentiate it from surrounding areas. Remotely sensed data are commonly used to aid in classification and mapping of landscape regions.

Conventional computer-assisted classification of digital multispectral

scanner data may be considered monothetic in that it is usually based

upon essentially one type of variable - spectral reflectance (though

this variable may be comprised of individual measurements taken in

several spectral bands). Spectrally-based classification algorithms

have been found to be effective in distinguishing many types of "land

cover" (e.g., grass, coniferous forest, water). They have been less

successfully employed to identify and map landscape regions (Merchant,

1984a, b). Classification of ecoregions and land use requires a polythetic procedure (Spence and Taylor, 1970). Land cover composition,

which can often be estimated via multispectral classification, is an

important variable, but it alone does not permit differentiation among,

or demarcation of, such regions. . Other variables must be employed.

Bailey, ejt aJU, (1978) observe that "the relationship between components

of landscape and physical and biological process is almost always

through spatial pattern or structure rather than through composition

alone." :.

Landscape regions are comprised of mosaics of individual parcels or

"patches" of land cover. Forman and Godron (1981) propose that the

structure of a landscape is primarily a series of patches surrounded by
a matrix . . . Che numbers of patches of each patch origin [natural or

cultural], biotic patch type, size and shape determine in part the

landscape structure. However, the spatial configuration among the

patches present may be just as important as the numbers." Landscape

structure is infrequently random (Grigg, 1965; Forman and Godron, 1981).

Randomness implies that each event has an equal probability of occurrence. Landscapes, particularly those resulting from human decisions,

are likely to reflect some spatial order.

The spatial character of the landscape may' arise from a variety of

phenomena and processes. In the "natural" environment where man is not

a significant factor, spatial structure may reflect the pattern of

vegetation communities and/or soils which tend to develop in a particular mosaic pattern under a given set of geologic, topographic (e.g.,

elevation, slope, aspect, drainage) and climatic circumstances. Where

man is a significant organism in the environment, the spatial character

of the surface will be related to segmentation of the landscape resulting from land ownership, land use (including settlement, cropping,

grazing, and mineral extraction practices), transportation and energy

resource development, and urbanization. In most instances, these phenomena will still reflect, to a substantial extent, the natural environment (e.g., soils, slope, drainage, relief, climate), since the environment imposes some constraints (at least economic) on the practical use

of the land.

Visual image interpreters routinely employ spatial cues evident in a

scene to aid in data stratification, classification and mapping. Such

cues may include parcel size, parcel shape, pattern, texture, context

and associations of cover types. Optimal visual image interpretation

requires a systematic, logical approach to data analysis, and usually

employs deductive reasoning and inference. Visual image interpretation

requires the use of geographic reasoning and spatial logic. "Geographic

reasoning" is defined as the systematic application of geographic knowledge, understandings and expectations in the analysis of data acquired

via remote sensing for the purpose of accomplishing a specific objective

or set of objectives. "Spatial logic" is defined as the formal expression and systematic application of decision rules which are formulated

to assist in the attainment of a particular analytic objective (e.g.,

preparation of a 1:250,000 scale land use map portraying classes X, Y

and Z) and are based upon characteristics and relationships evident in a

particular landscape as depicted in a particular set of remotely sensed

data. Spatial logic is developed as the image analyst transforms observations, understandings and geographic knowledge of the landscape into

decision rules which can be applied in data analysis to segment that

landscape into regions of interest. Such rules typically require consideration of both spectral and spatial properties of the landscape, and

may require inference and deduction.

This research is directed towards examining means for applying such

reasoning in the classification of digital multispectral data. The

research has two specific objectives:

1. To formulate and test a data analysis strategy, founded upon spatial

logic and emulating some aspe.cts of visual image interpretation,

which can be employed to prepare maps of landscape regions from

digital multispectral data; and

2. To evaluate the utility of Landsat Thematic Mapper (TM) data for

this application.
DEVELOPMENT OF A DATA ANALYSIS STRATEGY

A data analysis strategy, founded upon geographic reasoning and spatial

logic, was formulated. The strategy, described at length previously

(Merchant 1984 a, b), is here only summarized.

It was observed that visual image interpreters creating land use maps

appear to rely on analysis of the spatial distributions of rather generalized land cover types in order to demarcate regions of land use.

For example, an image interpreter can identify "cropland" without first

having to identify corn, wheat, oats and soybeans. Cropland can be

defined on the basis of the size, shape and spatial arrangement of

"fields" (parcels, "patches") of "crop-like" stuff, bare soil and other

less frequently occurring cover types (e.g., water, trees).

It was proposed that a greenness/brightness data transformation be used

initially to stratify the data into, what might be termed, "least common

denominator" cover classes - water, organic (vegetated) and inorganic

(non-water, non-vegetation) pixels. A "region-growing" technique would

then be employed to define "fields" of similar cover composition within

each stratum. Fields would be subsequently characterized in a more

refined manner (e.g., crop-like, grass-like). Such cover classes should

comprise the fundamental constituents of all land use classes, should

have "physical significance," and should be readily and consistently

identifiable by any data analyst.

Having "mapped" the distribution of fundamental cover types, the task

would then be to "classify" land use on the basis of differences in the

spatial structure of the landscape so depicted. Three properties of the

landscape would be employed, initially, in an attempt to differentiate

landscape regions:

1. Fundamental cover composition;

2. Cover diversity (i.e., the number and types of cover per unit area);

and

3. Cover interspersion (i.e., parcel size, arrangement and context).

These three variables, selected from a broad range of possible measures

of landscape structure, were thought to characterize important elements

of spatial structure employed by visual image interpreters in the definition of land' use regions. A complex supervised iterative contextual

classification algorithm was developed to assign final land use/land

cover labels to each field.

INITIAL EXPERIMENTS

The strategy outlined above was tested on a 640 x 480-pixel scene selected from a Landsat TM image acquired September 3, 1982 (ID 40049-

16273). The study area includes the-City of Topeka, Kansas, and vicinity. The success of classification was evaluated with respect to (1)

contemporary aerial photography and (2) a conventional supervised multispectral classification of the same data set carried out by other researchers.

Initial classification results were unsatisfactory. Several adjustments

and retrials resulted in little-.improvement. Consequently, it was

decided to examine class statistics to determine if problems lay within

the logic of the proposed strategy or were within the complex final

classification algorithm employed.
HIERARCHICAL CLUSTERING OF SAMPLE DATA

It was hypothesized that the problems experienced in classification were

related to the complex manner in which the classification was implemented, rather than to the logic of the overall proposed strategy. An

experiment was conducted to test this hypothesis. The experiment had

two major objectives:

1. To determine if the proposed measures of cover composition, field

.size and interspersion, computed for the landscape defined by the

data processing carried out heretofore, could be used to discriminate between land use classes; and

2. To determine if all measures were of equal value and to identify

useful measures.

If the measures were shown to be valid and useful, then one might assume

that the problems experienced with the classification program were

likely due to the complexity of implementation rather than to basic

flaws in spatial logic.

Forty samples of twelve land use/land cover classes were selected (Table

1). Each of the forty samples was characterized by a set of eleven

statistical variables which were designed to characterize land cover

composition, field size attributes and land cover interspersion (Table

2).

Various combinations of the eleven variable sets were entered into a

hierarchical clustering program. Hierarchical clustering (HC) is a tool

used to cluster together (or link) objects of interest according to

selected measures of their similarity (or, if desired, their dissimilarity) (Davis, 1973; Mather, 1976). The intent of clustering was to

determine whether any of the statistics computed served as useful discriminants for separating samples of different classes, and, if so, which

served as the best discriminants.

RESULTS

A 40-sample x 40-sample error matrix was constructed for each HC run.

The error matrix portrays linkages between all samples at the twelvecluster level. A perfectly accurate twelve-cluster linkage would have

resulted in all samples of a single land use class being linked with one

another and no linkages between different land use classes (Figure 1).*

A relative accuracy (RA) score was computed for each of the twelve

classes and for each run. The within-class relative accuracy score was

computed as:

actual within-class links achieved

maximum within-class links possible

The overall score for the entire run was computed as:

actual "correct" within-class links

achieved over all classes

maximum within-class links possible

*Note that, since the error matrices are symmetrical, only one-half of

the matrix is shown.
TABLE 1. LAND USE/LAND COVER CLASSES

Number of Sample

Class Sites (Sample Numbers)

1. Residential - Urban Core A (1-4)

2. Residential - Older A (5-8)

3. Residential - Newer A (9-12)

A. Central Business District 3 (13-15)

5. Secondary/Neighborhood Business Districts A (16-19)

6. Industrial 2 (20-21)

7. Institutional 3 (22-24)

8. Urban Open Land 3 (25-27)

9. Cropland and Pasture - Upland 4 (28-31)

10. Cropland and Pasture - Floodplain 4 (32-35)

11. Rangeland 3 (36-38)

12. Woodland 2 (39-40)

TABLE 2. VARIABLES COMPUTED FOR EACH SAMPLE

1. The relative frequency of occurrence (RF) of each of the nine cover

types of which any sample is comprised (expressed as the percent of

the total pixels in the sample):

(1) Water

(2) Dark Inorganic Material

(3) Medium-Dark Inorganic Material

(A) Medium Inorganic Material

(5) Medium-Bright Inorganic Material

(6) Bright Inorganic Material

(7) Crop-like Material

(8) Tree-like Material

(9) Grass-like Material;

2. The relative frequency of occurrence of Water, Inorganic Material

(the sum of cover classes 2-6) and Organic Material (the sum of

cover classes 7-9);

3. The mean field size of each cover type present in a sample;

A. The mean field sizes of Water, Inorganic and Organic Material;

5. The standard deviation of field sizes of each cover type present;

6. The standard deviation of field sizes of Water. Inorganic and

Organic Material;

7. The nodal field size of each cover type present;

8. The modal field size of Water, Inorganic and Organic Material;

9. The percent of the sample In each of nine field size categories:

10. The mean, standard deviation* and nodal field size for all fields in

the sample; and

11. An index of spatial complexity (interspersion) for the entire sample

(SCI), where '-••

fields

SCI • 100

pixels
PAGE-IS

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Figure 1. Example of an error matrix portraying perfect linkage between

all samples within each land use class and no confusion

between classes. Linkage indicated by «.

In either case, the score ranges from 0 (indicating no "correct" links)

to 1.0 (indicating that all samples of a given class were linked perfectly). Note, however, that this score cannot be interpreted as percent correct. A class that has a score of 1.0 may still have links with

other different classes. That is, all samples of a given class may have

been linked together (giving an RA score of 1.0) but all may have also

been linked, in a particular run, to samples of a completely different

class. Such "confusion" is graphically portrayed on the error matrices.

The RA scores and error matrices provided a guide to interpretation and

comparison of the results of each run.

Because of limited space, only the best outcome of hierarchical clustering is discussed in detail below. Complete results are presented in

Merchant (1984b). .

The most satisfactory result (Run 7) was obtained by clustering on four

variables: . . . .

(1) The relative frequency of occurrence (RF) of each of the nine cover

types of which any sample could be comprised;

(2) The mean field size of each cover type present in a sample;

(3) The standard deviation of field sizes of each cover type present;

and

(4) The modal field size of each cover type present.
Analysis of the error matrix for Run 7 produced an overall RA score of

0.56, a substantial improvement over other runs (Figure 2). This was

attributed to the addition of one or more of the field size descriptors.

Subsequent addition of the spatial complexity index (SCI) and percent of

the sample in each of nine field size categories (PCF) to the variable

set produced no discernible increase in success of clustering.

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FIGURE 2. Error matrix for best HC run.

The result of Run 7 was compared with the result of the next best run,

Run 2 (which was tased on the RF alone). The success of within-class

linkage was noted to have improved in Classes 2, 5 and 10, but decreased

in Classes 3 and A.

Rural classes were generally quite well-defined. All samples of Classes

10, 11 and 12 (Cropland and Pasture-Floodplain, Rangeland and Woodland,

respectively) were linked with one another, and were linked with no

other classes. The confusion between Class 10 and Sample 18, Class 5

(White Lakes Mall), which was present in Run 2, was absent in Run 7.

Field size apparently aided in sorting these classes. It was noted that

confusion between Classes 9 and 10, observed in other HC runs, was also

resolved favorably. , . .. .

Class 6 (Industrial) was perfectly defined in Run 2, but was confused in

Run 7 with Sample 3 (Class 1) and Samples 10 and 11 (Class 3). This

confusion of industrial and residential land uses was believed to be an

artifact of the 3-to-l weighting applied to field size attributes. It
was noted that the SCI, properly weighted, might have been used to help

segregate these samples at least to the extent of separating industrial

from residential land uses (Table 3).

TABLE 3. RELATIONSHIPS BETWEEN REGIONAL SPATIAL COMPLEXITY

INDEX (SCI) (NUMBER OF SAMPLES) AND LAND USE/LAND COVER

Land Use/Land Cover

Classes

1. Residential - Urban

Core

2. Residential - Older

3.. Residential - Newer

4. Central Business

District

5. Secondary/Neighborhood

Business Districts

6. Industrial

7. Institutional

8. Urban Open Land

9. Cropland and Pasture

- Upland

10. Cropland and Pasture

- Floodplain

11. Rangeland

12. Woodland

Spatial Complexity Index (SCI)

1-10

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Note: SCI - » 100

pixels

SCI - 100 • maximum intersperston; os SCI approaches 0 It

indicates decreasing intersperslon

Overall, urban classes (Classes 1-8) and rural classes (Classes 9-12)

were well distinguished from one another. Only Classes 8 and 9 (Urban

Open Land and Cropland and Pasture-Upland, respectively) were confused^

In terms of RF and field size, these might be considered "transitional1

classes. It was observed, again, that even this confusion might have

been reduced by use of a properly weighted SCI.

The results of the hierarchical clustering experiments may be summarized

as follows:

1. The results showed that the relative frequency of occurrence of each

of the nine cover types of which a sample could be comprised and

field size statistics could ie profitably employed to separate land

use/land cover samples into a logical classification.

2. Two other variables tested were shown to have little value. These

were:

(a) the percent of the sample in each of nine field size

categories; and

(b) the spatial complexity index (SCI) of the sample site.
3. Classification wherein cover was labeled only as Water, Inorganic

and Organic material was not as successful as classification wherein

cover was broken into nine types.

4. Rural and urban land use/land cover classes were well-separated from

one another in the best run. Most confusion between classes was

logical and explainable.

5. There was evidence that results might be improved if field size, RF

and SCI variables were more equitably weighted.

6. Comparison of the results of these experiments with conventional

spectral classifications of the study, area produced from TM and MSS

data shows that the addition of spatial information aids in definition of land use and in separation of certain land use classes.

It should be noted that all of these findings reflect the manner "in

which fields were defined (i.e., through greenness-brightness transformations and region-growing) and labeled, the decisions that were made

during these steps of analysis (e.g., thresholds selected), and the way

in which spatial structure and cover composition were measured (e.g.,

RF, x field size, SCI). They also reflect the manner in which hierarchical clustering was conducted. Nevertheless, it seems clear that the

type and diversity of cover and field size are important variables that

can be employed to help classify and identify landscape regions in

digital TM data.

CONCLUSIONS

A case has been made for the more rigorous application of geographic

reasoning and spatial logic in analysis of digital remotely sensed data.

A strategy for mapping landscape regions, founded upon spatial logic,

has been proposed and tested. The strategy was judged to be valid, but

full implementation will require additional effort.

The major conclusions of this research to date are as follows:

1. Spatial logic and geographic reasoning provide useful frameworks for

formulation of digital data analysis strategies;

2. Greenness and brightness data transformations and region-growing are

useful for defining landscape structure in a cogent manner;

3. Several measures of the landscape structure so-defined are effective

for classification of land use. These include:

(a) the relative frequency of occurrence of land cover types;

(b) the mean field size for each cover type that occurs; and

(c) the standard deviation of the field sizes of each cover type

that occurs;

4. Other measures of landscape structure, such as the spatial complexity index, may be useful discriminants if weighted appropriately; and

5. The results of the hierarchical clustering experiments suggest that

the overall approach to data analysis proposed is valid. The failure of the contextual classification algorithm to perform successfully is believed to stem from the internal complexity and logic of

the program and not from the spatial logic which guided the overall

strategy. Further research is warranted.

. ACKNOWLEDGMENTS

The constructive critiques and helpful suggestions of T. H. Lee Williams

and George F. Jenks have been important contributions to this research.

Tshow Chu and Chien-Chung Chan provided expert and innovative software

development. The financial support of the National Aeronautics and

Space Administration (Grant NGL 17-004-024) is gratefully acknowledged.
REFERENCES

Bailey, R. G., R. D. Pfister and J. A. Henderson, 1978, Nature of Land

and Resource Classification - A Review: Journal of Forestry, Vol. 76,

pp. 650-655.

Davis, J. C.t 1973, Statistics and Data Analysis in Geology, J. Wiley,

New York.

Forman, R. T. T. and M. Godron, 1981, Patches and Structural Components

for a Landscape Ecology: Bioscience, Vol. 31, pp. 733-739.

Gardiner, V. and K. J. Gregory, 1977, Progress in Portraying the Physical Landscape: Progress in Physical Geography, Vol. 1, , Edward Arnold,

London, pp. 1-22.

Grigg, D.", 1965, The Logic of Regional Systems: Annals of the Association of American Geographers, Vol. 44, pp. 465-491.

Mather, P. M., 1976, Computational Methods of Multivariate Analysis in

Physical Geography, J. Wiley, London.

Merchant, J. W., 1984a, Using Spatial Logic in Classification of Landsat

TM Data: Proceedings of the Pecora IX Symposium. Institute of Electrical and Electronic Engineers, Piscataway, NJ, pp. 378-385.

Merchant, J. W., 1984b, Employing Spatial Logic in Classification of

Landsat Thematic Mapper Data, Ph.D. Dissertation, Department of Geography, University of Kansas, Lawrence, KS.

Spence, N. A. and P. J. Taylor, 1970, Quantitative Methods in Regional

Taxonomy: Progress in Geography, Vol. 2, Edward Arnold, London, pp. 1-

64.
APPENDIX IV
USING LANDSAT TM IMAGERY AND SPATIAL MOpJELING IN

AUTOMATIC HABITAT EVALUATION AND RELEASE SITE SELECTION

FOR THE RUFFED GROUSE (GALLIFORMES: TETRAONIDAE)*

Jorge M. Palmeirim

Kansas Applied Remote Sensing Program,

and Museum of Natural History

University of Kansas, Lawrence, Kansas 660^5, U.S.A.

and

Departamento de Zoologia, Faculdade de Ciencias

Universidade de Lisboa, 1200 Lisboa, Portugal

ABSTRACT

Thematic Mapper (TM) imagery was integrated in a

Geographic Information System (CIS) to develop

automated models to evaluate and map ruffed grouse

habitat, and select potential release sites for

that species in northeastern Kansas (U.S.A.). Forest cover was mapped from a TM image using a mixed

spectral/contextual approach. The high resolution

of the TM imagery allowed a detailed mapping of

forest edge, a critical element of potential grouse

habitat in this region. Forest areas were then

evaluated as potential ruffed grouse habitat by a

spatial model using the following elements: (1)

woodlot size, (2) woodlot interconnection, (3)

amount of forest edge, (4.) distance to woodlot

edge, and (5) forest type. Another model generated

a map of release site suitability based on the

regional amount and quality of appropriate habitat.

The results obtained were evaluated by wildlife

management professionals familiar with the grouse

and the local habitat conditions. It was concluded

that the techniques employed have substantial potential as tools in grouse management.

1. INTRODUCTION

The low ground resolution of the Multispectral Scanner (MSS) (79x?9m)

has been a major factor limiting the use of its imagery in habitat studies.

Nevertheless many papers have been published on. the subject in the last few

years showing a wide range of applications when a high ground resolution is

not critical (for an overview see Palmeirim, in prep.)- In particular the

usefulness of MSS Landsat imagery in the selection of sites for reintroduction of locally extinct species has been demonstrated by various projects.

Wild turkeys (Meleagris ^allopavo) were released at sites selected using an

MSS-based land cover classification (Katibah and Graves, 1978); monitoring

the rates of conversion of rangeland to agricultural land using MSS imagery

"Presented at the Nineteenth International Symposium on Remote Sensing of

Environment, Ann Arbor, Michigan, October 21-25, 1985
assisted in the choice of release sites of pronghorn antelope (Antilocapra

americana) in Kansas (Martinko, 1978). The higher resolution of the Thematic

Mapper ("TM) (30x30m) considerably increased the potential to use satelliteborne sensors in wildlife studies.

Most of the previous work focussed upon simple identification of habitat

elements, such as vegetation. In spite of the usefulness of that work, it is

unfortunate that so little effort has been expended in relating these elements to the actual needs or preferences of the individual animal species

(but see Thompson, e_t a_l. , 1980; Lyon, 1983). In this project I attempted to

do this using TM imagery to generate a vegetation map, and GIS models based

on the biology of the. ruffed grouse (Bonasa umbellus) were used to manipulate

the vegetation map. An automated model was developed to evaluate the quality

of the identified vegetation types, as habitat for the ruffed grouse. This

model generated a habitat suitability map which was then used in another

model that selected potential sites for reintroduction of the grouse.

The ruffed grouse is a forest species with a wide range in North America. Due to its importance as a game bird various agencies have been reestablishing the grouse in areas where it became extinct in historical times, or

even introducing it outside its historic range (Gullion, 1984). The Kansas

Fish and Game Commission is currently attempting to restore this species in

wooded areas in northeastern Kansas. Throughout most of its range the grouse

depends heavily on aspen (Populus tremuloides, P_. grandidentata) for food and

cover (Gullion and Svoboda, 1972~)THowever, in the southern part of its

range, in the absence of aspen, the grouse seems to be mostly dependent on

understory growth of shrubs, vines, and herbs for those requirements (Hale et

al., 1982). Not surprisingly it is believed that in these regions it was,

prior to settlement by Europeans and widespread extinctions, limited to

ecotones and early successional areas (Hunyadi, 1984)- Since Kansas is oh

the southern edge of the historical range of ruffed grouse these are the

habitats where it is most likely to thrive in the state.

The study area is located around the intersection of Douglas, Leavenworth and Jefferson counties, just north of Lawrence, Kansas. It includes 52

km (240x240 TM pixels) of intermixed rangeland, cropland, deciduous forest

and old fields.

2. MATERIALS AND METHODS

Since many wildlife biologists have little knowledge of image processing

methods, the objectives of the various operational methods carried out in

this project are explained in general terms. A textbook on image processing

( — •&.' Schowengerdt, 1983) may be consulted for additional details regarding

some" of the techniques used.

All the image processing in this project was done on a Honeywell 66 DPS3E computer. The photographic image products were generated using a Video

Display Interface (VDI), installed on a Digital Equipment Corporation PDP11 /23, and a Terak 8510/23 Graphics Computer System.

2.1 SPECTRAL LAND COVER CLASSIFICATION

An automatic supervised spectral classification was first performed.

The image was subdivided into various cover types, according to their spectral characteristics. _A series of training areas of known cover was select-
Image Processing System (KUTIPS) program package (Williams _et _al. , 1983).

Two separate land cover maps were generated using the same training

statistics but different classification algorithms. The Maximum Likelihood

Classifier produced the map with the highest overall accuracy, but the Minimum Distance Classifier was more successful in mapping wooded areas. To take

advantage of this situation the two forest classes of the Minimum Distance

map were digitally overlayed onto the Maximum Likelihood image. The Map

Analysis Package (MAP) (Tomlin, 1980) was used to merge the two classified

images. The final land cover map used was therefore a combination of the

results of both classifications.

2.2 CONTEXTUAL LAND COVER CLASSIFICATION

The contextual information in an image can be used to improve the accuracy of pixel identification (e_.g_^_ Gurney and Townshend, 1983). Contextual

information was incorporated in the land cover classification following a

simple and computationally inexpensive approach. Pixels that could not be

assigned with acceptable certainty to any of the cover classes based on their

spectral characteristics alone were submitted to the spatial classifier; each

of these pixels was assigned to the majority cover in its immediate neighborhood.

Two approaches were used to spatially classify pixels, depending on the

homogeneity of its neighborhood: (1) if at least five of the eight neighbors

in a 3x3 pixel neighborhood of a particular pixel belonged to a single cover

class the pixel was assigned to that class; (2) the identity of the remaining unclassified pixels was determined by a larger (5x5 pixel) neighborhood.

However, since a pixel adjacent to an unclassified pixel provides a better

clue to its identity than a pixel located further away, the individual contributions were proportional to the distance from the pixel being classified.

This contextual classification was performed using a modified version of

neighborhood functions available in the MAP package.

2.3 HABITAT AND RELEASE SITE EVALUATION

Extensive processing of the land cover map was needed to generate the

habitat and release site maps. All the operations were performed using

unmodified functions available in the MAP software. Although MAP is an

interactive package, the large number of operations needed for each model

made it advantageous to lay out sequences of commands in files; the commands

were then executed automatically in the set sequence. This approach resulted

in considerable time savings due'to the need to make modifications in the

models during their development. Implementing the same model in other areas

and/or including minor modifications also becomes simpler and more accurate.

Flowcharts and printouts of these command files are available from the author.

3. RESULTS AND DISCUSSION

3.1 LAND COVER CLASSIFICATION

The seven class land cover roap generated included rangeland, old fields,

standing crop, bare soil, water, and two types of deciduous forest. Since

the ruffed grouse is a forest species I made a particular effort to optimize

the mapping of these latter cover types. A comparison of the results obtained with aerial photography and field checks showed that the generated map

(Fig. 1) accurately represents the distribution of forest in the study area.

Small clumps of trees, the wider tree rows, and the intricate contours of the

irregular woodlots characteristic of this area were evident. The gray area
on the classified image (Fig. 1) indicates forest appearing younger, mostly

on south-facing slopes, and black represents the remaining forest. The

composition and structure of the forest on the south-facing slopes differs

considerably from that of other exposures. This seems to be at least partially related to their age (Fitch and McGregor, 1956, include a description

and history of some of the forest lots in the study area). The degree of

confusion between the two types of forest on the classified map is hard to

estimate, because no absolute criteria were established to separate them on

the ground.

3.2 HABITAT EVALUATION

When visually classifying ruffed grouse habitat suitability using aerial

photographs, the experienced wildlife biologist would make a series of more

or less subjective decisions. The models used attempt to process the imagery

making similar judgements. This is a particularly hard task because it is

often not possible to state absolute rules for those judgements. In the

following pages I will illustrate the various "decision steps" made, and

explain their biological significance in the context of ruffed grouse habitat.

Woodlot size and interconnection

The wooded areas were first isolated from the land cover map; other

habitats were considered as not fulfilling the minimal ruffed grouse habitat

requirements. The analysis will not be seriously affected if grouse use

these habitats since this will only happen along forest edges. Although all

forested areas can potentially be grouse habitat the high resolution of the

TM imagery (30x30m) allows the mapping of very small clumps of trees, many of

which are too small to be used by the grouse. Isolated forest pixels were

therefore eliminated. Of the remaining woodlots some are still too small,

unless they are located close to other woodlots. Forest patches separated

from other forest patches by more than 300 meters were eliminated unless they

had an area of 4 ha or more (Fig. 2). This area was chosen because one pair

of grouse per 4. ha is about the highest possible density under most conditions (Gullion and Svoboda, 1972). Fig. 2 still includes some woodlots

smaller than 4 ha; this is because they are close enough to other forest

patches to be considered connected to them. This figure also shows that

there is a high level of interconnection among the woodlots and that small

patches of forest are important in the interconnection of the larger woodlots .

Edge effects

The most important limitation of a habitat evaluation model based on

Landsat imagery is that it can only include variables that can^ be directly

detected and measured on the imagery or easily included as digital ancillary

data. In the case of ruffed grouse habitat in Kansas, the inability of

Landsat images to detect the structure of the forest understory is a serious

limitation because in the southern part of its range the grouse seems to be

mostly dependent on the understory (Hale et_ al". , 1982). The imagery is,

however, particularly suited for modeling using spatial components of the

habitat and closely correlated variables. Forest edges are a good example -

they can be accurately mapped and are closely associated with a denser understory. Overgrazing has destroyed much of the forest undergrowth in northeastern Kansas. This factor is likely to have been a major cause for the

disappearance of the ruffed grouse in the state (Goss, 1891)- Presently,

dense undergrowth occurs mostly along edges, where growing conditions are

more favorable and regeneration faster. Forest edges are therefore the most

important single element in this habitat evaluation. TM imagery is well

suited for this project because it allows an accurate mapping of the forested

areas, and its comparatively high resolution portrays the detail of the

woodlot edges. Using imagery with a poorer resolution, such as MSS, would
result in a considerable loss of detail in these edges, which are so critical

for the grouse. Forest clearings are often good habitat for the ruffed

grouse in this area since they usually create an edge effect that generates a

denser understory in the forest in their vicinity. However, very small

clearings are probably not important habitat, and the likelihood that they

are the result of a misclassification of the Landsat data is comparatively

high. To avoid a major influence on the final habitat evaluation the smallest clearings were eliminated. Fig. 3 shows the results of this operation.

The appropriateness of this step is however quite debatable because, while it

eliminates undesirable image noise, it also causes the loss of some habitat

information. Since the final result of the model was acceptable, this step

was retained in the analysis.

Since forest edges are most likely the best habitat for the grouse in

this area, the edge pixels were given the highest ratings on a scale of 0 (no

habitat, lightest on Fig. 4) through 5 ("best" habitat, darkest on Fig. 4).

In the map on Fig. 4 the relative quality of each pixel is proportional to

its location in relation to the forest edge. Rating 5 was given to the

actual edge pixels, 3 to pixels between 30 and 90 meters away from the edge,

2 to the pixels between 90 and 180 meters, and 1 to the innermost forest.

Non-forest pixels were assigned a rating of 0. The scale is arbitrary and is

designed to reflect relative probability of habitat quality: the probability

of a pixel being suitable as habitat for the grouse is higher when located

near an edge than in the innermost part of a woodlot. Further studies are

needed to select an optimal rating scale.

Forest type

The preferred types of forest for the ruffed grouse seem to vary seasonally, even though grouse seem to use all types of hardwood forest (Bump, et

al. , 1947; Gudlin and Dimmick, 1984). Overall, the grouse seems to prefer

second growth hardwood forest over climax forest, particularly for brood

cover (Bump e_t al. , 1947). Although preferences shown in other areas may not

hold in the study area, the ratings of the areas of younger forest (Fig. 1)

were increased slightly, by adding one unit to the previously assigned ratings (Fig. 5). The suitability scale now varied between 0 and 6, the highest,

value corresponding to second growth forest edge pixels.

Final habitat suitability map

Habitat suitability is better measured by an average of quality over an

area than by the ratings assigned to individual pixels as in Fig. 5. The

quality of each pixel as potential habitat for the grouse is not only a

function of its characteristics but also of the forest pixels in its neighborhood. The rating of each pixel was, consequently, substituted by the

average rating of the forest located within a circle of about 30 ha centered

on the pixel (Fig. 6). Although the size of the scanning area is not critical, (because the technique is deriving a relative not absolute quality

rating), the choice of 30 ha as the scanning area is based on the fact that

this is a reasonable size for the home range of a male grouse (Gudlin and

Dimmick, 1984; Woolf, et^ al., 1984). The cell ratings on this image can,

thus, be considered a measure of the relative quality of home range of a bird

that used the area within a circle, of 30 ha centered on each cell. However,

since nonfcrested pixels were excluded from these calculations, the actual

averaged area may be much less than the 30 ha scanning circle. Notice on

this figure that the highest ratings have been assigned to small and/or

irregularly shaped woodlots due to the greater importance of edges in those

lots. The lowest probability habitat suitability ratings were assigned tc

the innermost areas of the larger lots.

The road network was digitized and superimposed on the habitat suitability and following maps (Figs. 6, 7, 8, and 9) to make them easier to use.

The signal of the roads on the original imagery was deleted to avoid confusion with the road overlay.

The suitability map on Fig. 6 is needed for the process of selection oi

release sites, but it can also have other applications in a grouse management
"project. For example, it can be used to help in the choice of sites to place

man made drumming logs, since the areas with darker tones are more likely to

become grouse territories.

3.3 RELEASE SITE SELECTION

Despite its usefulness the haMtat suitability map generated (Fig. 6)

still leaves open the question of where the release points should be located.

One could select these points using the habitat suitability map by visually

choosing areas including large amounts of good habitat. To locate the best

candidate sites automatically the rating of each forest cell was made proportional to the amount and quality of the forest habitat within a circle having

a diameter of 1800 meters centered on the pixel (Fig. 7). The choice of

diameter is a compromise between the need to sample a considerable amount of

habitat and the advantages of keeping a small scanning radius. The use of a

larger radius could result in suggested release sites located far from patches of good habitat; •the spatial correlation between the generated "release

sites" map and the "habitat suitability" map is inversely proportional to the

length of the radius used. The 1800 meter diameter was chosen to reflect

known dispersion patterns of grouse away from release sites (Gudlin and

Dimmick, 1984 ).

Before performing the scanning operation the values on the scanned map

(Fig. 6) were squared. The squaring operation was designed to increase the

relative contribution of the best habitat types in generating the "release

sites" map (Fig. 7). The rating scale ranged then from 0 (lowest, no habitat) to 3o (highest, best habitat). The values of all the pixels within the

scanned area were then averaged; the value obtained was assigned to the

center pixel.

The "release sites" map is intended to be an aid in the location of

sites to release grouse. The suitability of the areas having the highest

ranks should be field checked first. Grouse can then be released either on

these high ranking areas or in neighboring areas of good habitat (as suggested by the "habitat suitability" map and field checks).

In the choice of release sites there are usually two main considerations: (1) site quality, which was discussed above and is dependent on the

habitat suitability around the potential site (Fig. 7), and (2) convenience,

a component most often dependent on the accessibility of the site. A very

good site may not be used because it is too costly to reach. In this study

area the dense road network and the absence of common obstacles such as large

water bodies and rough terrain make the access to all areas fairly easy.

However I include here, as an example, a simple extension of the release site

selection model that takes in consideration convenience.

The convenience of a release site decreases with the distance from a

road. Since it is much easier to move in some land cover types than in

others, it would not be appropriate, to evaluate convenience with a simple

distance measure; the difficulty of moving in the various cover types should

also be included. I estimated that it is about three times harder to walk

through the forest than through the other major cover types. Fig. 8 shows

four levels of relative difficulty in reaching the various parts of the study

area, taking into consideration the.differences in mobility. Fig. 9 is a

combination of Fig. 8 with the release sites map (Fig. 7); it combines the

quality of release sites with their convenience. For sake of clarity only

the higher ranking areas are shown in Fig. 7. The darkest tones _represent

very good quality and very good convenience (accessibility) ratings; the

lighter tone also represents very good quality but lower convenience rating.

3.4. VALIDITY OF THE MODELS

A serious problem with the models presented here is the difficulty in

tootinc their validity. Even if grouse are released and. the species becomes?
"establisued in the study area, it will take several years before its distribution and density will become a useful indication of habitat suitability.

However, the experimental results obtained were evaluated by wildlife management professionals familiar with both the grouse and the local habitat conditions. It was concluded that the techniques employed have substantial potential as tools in grouse management. Field checks also support the utility

of these TM imagery based models and they will soon be implemented operationally in a much larger area, which will help in their evaluation.

4. CONCLUSIONS

TM imagery proved to be appropriate for this project, in which accurate

forest classification and detailed woodlo"t edge mapping were needed. The MAP

package was an invaluable CIS tool. The many available general use commands

when combined allow the implementation of complex image operations. MAP was,

used in this project not only to implement the models, but also to improve

the spectral land cover classification, introducing contextual information in

the final land cover map. The package also allowed the inclusion of roads as

a reference system on the final maps generated. Various versions of the MAP

package are now available, including one for microcomputers (pMAP, Spatial

Information Systems) making this technology quite accessible.

The models implemented in this paper were designed to be used in northeastern Kansas. It was assumed that most good ruffed grouse habitat lies

along the forest edges. The models are, therefore, applicable only where

this assumption is met, as in the many areas lacking aspen along much of the

southern range of the species. Model adjustments can be made to adapt the

model to different areas. For example, the suitability rating of the nonedge forest, very low in this study area, can be increased if the model is

applied to areas where the forest has a more dense understory.

This is one of the first attempts to combine Landsat imagery and spatial

modeling in wildlife biology. However, the need for habitat models is great

and many models based on habitat variables obtained in the field are now

available (for the ruffed grouse see Cade and Sousa, 1985). These models

have the advantage of basing their predictions on variables that describe

habitat with more detail than TM imagery. The limited capability of satellite-borne sensors to provide information on certain important habitat components restricts the range of situations were the approach described in this

paper can be successfully implemented. However, when applicable, models

based on digital satellite imagery can be implemented quickly and inexpensively over large geographical areas. Because some of the variables employed

(£._g. density of understory) are not directly detectable but are inferred

froni other variables (e.j*. distance to edge), the various suitability maps

generated in this project cannot be called absolute maps. The rating assigned to a pixel on these maps is intended to be correlated with the probability of that pixel being good habitat. The maps produced might therefore

be better described as "probability" maps than "suitability" maps, as they

are refered to for convenience in this paper. The results obtained support

the belief that, when their limitations are not underestimated, these and

similar models can be very useful tools in decision making for wildlife

management.

••_ " • #i

5. ACKNOWLEDGEMENTS

I am grateful to Drs. R.S. Hoffmann, E. Martinko, J. Merchant, N.

Slade, T.H.L. Williams and to S. Thorsteinsdottir for critically reading ar.

earlier version of the manuscript. R. Rodgers and R. Wells generously sharec

their knowledge of the biology of the ruffed grouse. T. Chu provided valuable discussions and helped with programming. This research was supported b;,

grants from NASA (NGL 17-004-024) and the Kansas Fish and Game Commission tc

the Kansas Applied Remote Sensing (KARS) Program.
6. REFERENCES

Bump, G., R.W. Darrow, F.C. Edminster and W.F. Crissey. 1947. The ruffed

grouse; life history, propagation, management. New York State Conservation Department, Rolling Press, Buffalo, New York, xxxvi + 915 pp.

Cade, B.S. and P.J. Sousa. 1985. Habitat suitability index models; ruffed

grouse. U.S. Fish Wildlife S.erv. Biol. Rep. 82(10.86) , 31 pp.

Fitch, H.S. and R.L. McGregor. 1956. The forest habitat of the University

of Kansas Natural History Reservation. Univ. of Kansas Publ., Museum of

Natural History, 10:77-127.

Gossl N.S. 1891 . History of the birds of Kansas. Geo. W. Crane & Co. ,

Topeka, Kansas, 692 pp.

Gudlin, M.J. and R.W. Dimmick. 1984.. Habitat utilization by ruffed grouse

transplanted from Wisconsin to west Tennessee. Pp. 75-88 in Ruffed

grouse management; grtate of the art in the early 1980 ' s (W. L. Robinson,

ed.) . BookCrafters, Chelsea, Michigan, 181 pp.

Gullion, G.W. 1984.; . Ruffed grouse management - where do we stand in the

eighties? Pp. 169-181 in Ruffed grouse management; state of the art in

the early 1980's (W.L. Robinson, ed.). BookCrafters, Chelsea, Michigan,

181 pp.

Gullion, G.W. and F.J. Svoboda. 1972. The basic habitat resource for ruffed

grouse. Aspen Symp. Proc., U.S. Forest Service Gen. Tech. Rep. NC-1, pp

113-119.

Gurney, C.M. and J.R.G. Townshend. 1983. The use of contextual information

in the classification of remotely sensed data. Photogrammetric Engineering and Remote Sensing, 49:55-64.

Hale, P.E., A.S. Johnson and J.L. Landers. 1982. Characteristics of ruffed

grouse drumming sites in Georgia. Journal of Wildlife Management,

46:115-123.

Hunyadi, B.W. 1984. Ruffed grouse restoration in Missouri. Pp. 21-35 in

Ruffed grouse management; state of the art in the early 1980's (W.L.

Robinson, ed. ). BookCrafters, Chelsea, Michigan, 181 pp.

Katibah, E.F. and W.C. Graves. 1978. Remote sensing-aided assessment of

wild turkey habitat. Proc. Pecora IV Symp. , Sioux Falls, South Dakota",

pp. 78-81.

Lyon, J.G. 1983. Landsat-derived land-cover classifications for locating

potential kestrel nesting habitat. Photogrammetric Engineering and Remote

Sensing, 49:245-250.

Martinko, E.A. 1978. Monitoring agricultural growth in pronghorn antelope

habitat. Proc. Pecora IV Symp., Sioux Falls, South Dakota, pp. 210-216.

Palmeirim, J.M^In prep. Remote sensing in wildlife habitat studies: a

summary.

Schowengerdt, R.A. 1983. Techniques for image processing and classification

in remote sensing. Academic Press, New York, New York, 249 pp.

Thompson, D.C., G.H. Klassen and J. Cihlar. 1980. Caribou habitat mapping

in the Southern District of Keewatin, N.W.T.: an application of digital

Landsat data. Journal of Applied Ecology. 17:125-138.

Tomlin, C.D. 1980. The^ Map_ Analysis Package (draft). Yale Univ., New Haven,

Connecticut, 81 pp.

Williams, T.H.L., C. Gunn and : J;.. Siebert. 1983- Instructional use of a

mainframe interactive image analysis system. Photogrammetric Engineering and Remote Sensing, 49:1159-1165.

Woolf, A., R. Norris and J. Kube. 1984. Evaluation of ruffed grouse reintroduction in southern Illinois. Pp. 59-74 in fluffed grouse management; state of the art in the early 1980's. BookCrafters, Chelsea.

Michigan, 181 pp.
.

PAGE rs

POOR QUALITY

1 km

Fig. 1. (Left) IR aerial photo (NHAP) and (right) classified forest cover of study area.

(On classified image gray is forest appearing younger, mostly on south-facing slopes; other

forest is black.)

Fig. 2. Usable woodlots.

(SW corner of study area. Single forest cells

nnd small isolated woodlots were eliminated.)

Fig. 4. Distance from edge.

(SW corner of study area. Darker areas have a

hig!ii.-r probability of being good habitat.)

Fig. 3. Forest minus small clearings.

(SW corner of study area.)

Fig. 5. Distance from edge and forest type.

(SW corner of study area. Darker areas have

a higher probability of being good habitat.)
OIF POOR

Fig. 6. Habitat suitability map.

(Darker areas have higher probability of being good habitat. Black are roads.)

Fig. 7. Suggested release sites.

(Darker areas are more likely to be good release sites. Black are roads.)

Fig. 8. Difficulty of access.

(Darker areas are harder to reach from roads.

Roads are in black.)

Fig. 9. Good and accessible potential release sites.
D KANSAS

APPLIED

REMOTE

SENSING

PROGRAM

: n a

The University of Kansas

Space Technology Center

Lawrence, Kansas 66045

Land use/land cover map of Lawrence, Kansas and vicinity prepared

by computer processing of Landsat 4

Thematic Mapper digital data.

MAJOR KARS PROGRAM

RESEARCH AND

APPLICATION AREAS

Geographic information system

design, construction and application

Integrated natural resources inventories

Land use/land cover inventory,

change detection and mapping

Irrigated lands inventories

Wildlife habitat evaluation

Strip mined lands assessment

Crop and rangeland evaluation

Thematic mapping

Technology transfer/remote sensing education
FACILITIES

KARS Program offices and laboratories are located in the University of

Kansas Space Technology Center.

The Program has complete facilities

for processing and interpreting remote sensing data in both image and

digital formats, state-of-the-art cartographic production, statistical analysis and geographic data processing.

Graphic arts, photographic and other

support services are provided within

the Space Technology Center.

The KARS Program's Image Interpretation Laboratory is furnished with

a complete range of equipment for

viewing and analyzing imagery, and

for transferring image data to base

maps of various scales. Included are

a Bausch and Lomb Zoom Transfer

Scope, an Itek Color Additive Viewer,

a Variscan Rear Projection Viewer,

five Richards Light Tables with

Bausch and Lomb Zoom 240 stereoscopes, a Saltzman Reducing/Enlarging Projector, a MacBeth Color

Spot Densitometer and a complete

assemblage of other visual image interpretation aids.

Analysis of digital remote sensing

data, digitizing and other computerassisted data processing operations

are supported by facilities of the

KARS Digital Data Analysis Laboratory. KARS' digital data analysis system is based upon a DEC POP 11/23

computer. Support equipment includes an 80-megabyte Control Data

Corporation disc drive, a Kennedy

9100 tape drive, a Sky Computers

SKYMNK array processor, and a Versatec 8222-F 22-inch electrostatic

printer/plotter. Color video display

functions are provided by a Terak

8510/a-8600HDX microcomputer

having a 19-inch RGB monitor capable of 640 x 480-pixel resolution. Digitizing is accomplished with an Altek

AC90SM microprocessor-controlled

digitizer having a 42 x 60-inch backlighted digitizing tablet.

KARS' computer system supports a

comprehensive array of software for

interactive digital image processing

and classification, geographic information system operations, statistical

analysis and computer mapping. Remote terminals also enable KARS

staff to draw upon the substantial

facilities of the University of Kansas

Honeywell Level 66 Computer System.

Aerial photography in support of

KARS projects is acquired from a

Cessna 180 Skywagon accessible to

KARS staff. Both a multispectral cluster of four Hasselblad 500EL 70mmformat cameras and a Fairchild nineinch format cartographic camera are

available for photographic missions.

Custom designed cartographic and

graphic products are prepared by

KARS staff using negative scribing

and photo-mechanical techniques.

Production of color graphics and

color separations are standard procedures. Printing services are available. KARS staff also have access to

Tektronix computer graphics systems, computer mapping software,

and both flatbed and drum plotters.

THE UNIVERSITY OF KANSAS APPLIED

REMOTE SENSING (KARS) PROGRAM

The University of Kansas Applied Remote Sensing

(KARS) Program was established by the National

Aeronautics and Space Administration (NASA) in

1972 to conduct applied research on techniques

which will enable public agencies and private firms to

better utilize available satellite and airborne remote

sensing systems. The KARS Program is an applied

research program of the University of Kansas Space

Technology Center. The Space Technology Center

was established in 1972 by NASA and the State of

Kansas to enhance research and education in spacerelated science and technology through multi-disciplinary research efforts. The KARS staff is comprised of

specialists having backgrounds in ecology, geography, forestry, wildlife biology, engineering, cartography, computer science, environmental studies and

natural resources management.

Projects undertaken by the KARS Program with local,

regional, state and federal agencies and private industry are designed to identify and enhance the

manner in which remote sensing technology can aid

in decision-making, policy formulation, planning and

in meeting other needs and responsibilities. The

KARS Program and Kansas agencies have established the Kansas Interagency Task Force on Applied

Remote Sensing to foster the utilization of remote

sensing and related spatial data analysis techniques

by Kansas state government. All KARS services are

provided at large to public agencies and private firms,

both within and outside of Kansas, on a contractual

basis.

The KARS Program has provided assistance and

services to more than forty agencies in Kansas,

Missouri and other states in the Great Plains/Rocky

Mountain region. Contractual applied remote sensing

projects have been carried out for the NASA Earth

Resources Laboratory, NASA Ames Research Center, U.S. Fish and Wildlife Service, U.S. Office of

Surface Mining, USDA/Soil Conservation Service,

U.S. Environmental Protection Agency, U.S. National

Park Service, Kansas Fish and Game Commission,

Missouri River Basin Commission, Mid-America Regional Council, and Farmland Industries, Inc. Projects

have involved land use/land cover inventory, monitoring land use change, wildlife habitat evaluation, mapping of irrigated lands, surface mined lands inventory,

recreational area planning, soil conservation needs

assessment, aquatic vegetation mapping, rangeland

condition evaluation, urban area analysis and education and training. In addition, KARS staff have provided remote sensing consulting services in India,

Mexico and several states.

For additional information contact:

Kansas Applied Remote Sensing

(KARS) Program

University of Kansas

Space Technology Center

2291 Irving Hill Drive

Lawrence, Kansas 66045-2969

Telephone: 913/864-4775

KANS-A-N 564-4775

KARS NEWSLETTER

The KARS Program publishes the

quarterly KARS NEWSLETTER which

is designed to foster the application

of remote sensing data and to provide

a forum for communication on remote

sensing-related matters. Current circulation is approximately 2,000.

Readers include employees of local,

state, regional and federal agencies,

research centers, colleges and universities, and private firms. Most

readers reside in the Midwest and

Western U.S., but Newsletters are

mailed throughout the United States

and to several other nations. Subscriptions are available upon request.

WUTHfH RfcORDS

Landsat4

REMOTE SENSING

Remote Sensing is the science of

acquiring information about an object

or area in the absence of physical

contact with the entity of interest.

Remote sensing systems, such as

cameras, scanners and radars,

mounted aboard aircraft and spacecraft are increasingly being used to

inventory, evaluate and monitor the

extent and condition of phenomena

such as land use, water resources,

crop and rangeland, conservation

practices and urbanization. The Kansas Applied Remote Sensing (KARS)

Program was established to assist

public agencies and private firms

concerned with natural resources

management, agriculture, regional

planning and related issues in employing remote sensing technology.

Data acquired by remote sensing,

especially when used in concert with

information obtained in traditional

ways, can often enable such agencies and firms to make better, more

rapid, and/or more cost effective decisions regarding problems with

which they must deal.

Computer-based Geographic Information Systems aid in decision-making by

facilitating the analysis of data acquired from many different sources.
SERVICE

S OF THE KARS

PROGRAM

The KARS Program provides the fol-

lowing services:

Interpretation of remote sensing

data in support of land use/land

cover, environmental, planning, ag-

ricultural and natural resources in-

ventories and analyses;

Research in the analysis of remote

sensing data and in applications of

remote sensing/geographic infor-

mation systems technologies;

Geocoding, geographic informa-

tion system design and production;

statistical analysis, design of sam-

pling surveys, areal statistical data

summaries;

Analysis of trends, projections,

spatial modeling, monitoring of

change on a seasonal (e.g., range

burning, harvesting) or annual

basis (e.g., land use, wildlife hab-

itat);

Map production using state-of-the-

art cartographic techniques includ-

ing negative scribing, color separa-

tion and computer graphics;

Field investigation either in sup-

port of remote sensing data collec-

tion or independently designed to

meet specific agency or client re-

quirements;

Aerial photography in support of

KARS research and applications

projects;

Location and acquisition of re-

mote sensing data; flight mission

design;

Instruction in remote sensing tech-

niques, interpretation and applica-

tions; short courses, workshops,

seminars; technology transfer.

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