Remote Sensing Education & Training
Pam Lawhead
Dan Civco
James Campbell
Preparing Students for Careers
in Remote Sensing
Thursday, August 15, 2002
Remote Sensing Education & Training
•
•
•
•
Some History
The Remote Sensing Model Curriculum
Discussion
Summary
Preparing Students for Careers
in Remote Sensing
Remote Sensing Education & Training
An observation addressing education versus training
Knowing all the commands of ArcInfo will make
you no more of a GIS Analyst …
… than will knowing all the commands of
WordPerfect make you an author
Jay Morgan
Towson State University
Remote Sensing Education Timeline
1992
1994
1996
1970-80’S
Surveys
By Dahlberg
And Kiefer
Remote Sensing Education & Training
1998
2000
2002
2004
PE&RS 1992
• Civco, D.L., R.W. Kiefer,
and A. Maclean. 1992.
Perspectives on
earth resources
mapping education
in the United States.
Photogrammetric
Engineering and
Remote Sensing
63(8)1087-1092.
Remote Sensing Education & Training
Serie Geografica 1993
• Civco, D.L., R.W. Kiefer,
and A. Maclean. 1993.
La ensenanza de la
teledeteccion en las
actividade de la
American Society for
Photogrammetry and
Remote Sensing.
Invited paper in Serie
Geografica, Madrid,
Spain. 2:39-50.
Remote Sensing Education & Training
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
IGARSS ‘96
• Estes, J.E.and T. Foresman.
1996. Development of a
Remote Sensing Core
Curriculum. Geoscience and
Remote Sensing Symposium,
1996. IGARSS '96. 'Remote
Sensing for a Sustainable
Future.', Volume: 1 , 1996,
Pages 820 –822.
Actually preceded by ASPRS-EOSAT workshop
Remote Sensing Education & Training
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
RSCC
Remote Sensing Education & Training
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
Remote Sensing Industry 10 Year Forecast
• In August 1999, ASPRS and
NASA's Commercial Remote
Sensing Program (CRSP)
entered into a 5-year Space
Act Agreement (SAA),
combining resources and
expertise to:
–
Some slides from the
25 April 2002 ASPRS
Presentation follow
Remote Sensing Education & Training
Baseline the Remote
Sensing Industry (RSI)
– Develop a 10-Year RSI
market forecast
– Provide improved
information for decision
makers
– Develop attendant
processes
Students in RS/GIS Related Programs
• Based on survey results, the average number of
students involved in RS/GIS related programs
at Respondents’ universities/colleges is about 140
• Therefore, students involved in RS/GIS related
programs at these universities are slightly less
than 1% of the student body population
(Avg. 17,000)
• This small % of Student Population probably has a
negative effect on funding/resource availability
– A role for local industry? government?
Remote Sensing Education & Training
Level of Education by Sector
High School
Some College
Associates Degree (2 year or equivalent) Bachelor's Degree or equivalent
Master's Degree or equivalent
Doctoral Degree
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Academic
Commercial
Government
• Greater than 90% have a 4-year college degree or better.
• Over 60% have a Masters degree or better.
Based on Phase II 731 Survey Responses: Doctoral Degree 136, Master's Degree or equivalent 312, Bachelor's Degree or equivalent 227, Associates Degree (2 year or equivalent)
26, Some College 24, High School 6, Other 0
Degrees by Discipline by Sector
Geography & GIS Dominate
% of Respondents
60%
50%
40%
30%
Academic
Commercial
Government
20%
10%
0%
Discipline
• The “generalists” in remote sensing are degreed in Geography and
GIS and are probably very mobile in the Remote Sensing Industry
• Other disciplines are probably more transportable outside Remote
Sensing Industry
Formal Coursework in Remote Sensing
Regardless of discipline, about 60% have had course
work related to remote sensing
• Academic 75%
• Commercial slightly less than 50%
• Government nearly 60% of the respondents
The current community of managers/users is both
well educated and generally knowledgeable about
Remote Sensing
Based on Phase II Survey Reponses
Remote Sensing Training Other Than Formal Coursework
350
300
Responses
250
200
150
100
50
0
None
One Course
Several Courses
Certificate
Program
Other (s)
Training
• Most in the workforce get some formal coursework in Remote Sensing
 ~40% Certificate Programs; ~30% One Course; ~20% Several
Courses
•Certificates are important in workforce development strategies
Based on Phase II 733 Survey Responses: Manager/Supervisor 188, Manager/User 402, User 143
Employer Sponsored Training by Sector
50%
45%
% of Respondents
40%
Academic
Commercial
Government
35%
30%
25%
20%
15%
10%
5%
0%
monthly
quarterly semi-annually annually less often than never
annually
Training Frequency
Employer Sponsored Training is infrequent
Based on Phase II 734 Survey Responses: Academic 142, Commercial 247, Government 345
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
ASPRS Careers Brochure
• Disciplines
– Photogrammetry
– Remote Sensing
– Geographic Information Systems
• Education Requirements/Suggestions
– High School
– Community Colleges and Technical
Institutions
– Colleges and Universities
– Internships
– Continuing Education
• Careers in the Geospatial Sciences
Remote Sensing Education & Training
Remote Sensing Education & Training
•
•
•
•
Some History
The Remote Sensing Model Curriculum
Discussion
Summary
Preparing Students for Careers
in Remote Sensing
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
Dr. Jay Johnson
Dr. Pamela Lawhead
(662) 915-3500
geospat@olemiss.edu
http://geoworkforce.olemiss.edu
The University of Mississippi
The Project
• Located at the University of Mississippi
• Principal Investigators
• Pamela B. Lawhead – Computer Science
• Jay Johnson – Archaeology
• Courses created by content experts
• Multi-media intensive
• Goal: 50 courses in five years
Goals of the Project
To develop a highly skilled workforce educated and
equipped to lead the development of the geospatial
information technology industry by creating a library of
online courses reflecting a consistent curriculum in
remote sensing, GIS and other related disciplines.
To develop a state of the art course delivery system
and course creation process that will be selfsustaining.
To have 50 online courses in RS in five years
Our History
• Stennis, St. Petersburg, Washington
• ASPRS
• Request for Proposals
• Course Fellows Selection Symposium
• Course Fellows Award Workshop
• (Pecora)
National Advisory Panel
Ahmed Noor
Old Dominion
Paul Hopkins
SUNY
Stan Morain
U New Mexico
Randy Wynne
Virginia Tech
Lynn Usery
U of Georgia,
USGS
Chris Friel
GIS Solutions, Inc.
Allan Falconer
U of Miss/MSCI
Roger Hoffer
Colorado State U.
Tom Lillesand
U of Wisconsin
Dan Civco
U of Connecticut
John Jensen
U of S. Carolina
George Hepner U of Utah
Carolyn Merry
Ohio State U.
Vincent Tao
York University
National
Panel
National Advisory
Advisory Board
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1000 Miles
Asprs Participants : 18
Participants of Workshop
Non-Participating States
ASPRS
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Meeting in St. Petersburg
Model Curriculum Workshop
FIG 2002/ASPRS in D.C.
Educational Partnership, Announced in August
Request for Proposals
• Sent out in ASPRS newletter
• Appeared on our Web Site
• Sent as email to all ASPRS members
• 60 intents to present
• 30 proposals submitted
• 29 actual presenters
Geospatial Workforce Development
National Participation
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Participants : 30
Participating States : 14
Non-Participating States
Creation Process
• Course Fellows responsible for content only
• UM Course Creation Lab does technology
• Lesson ideas and text delivered:
•On-line, Video, Regular mail, Phone
•…
• Fellow responsible for ideas only
• UM does all technology
• Model = “Recreating the Expert”
Delivery Process
•
•
•
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Students enroll at UM
Students enroll at home inst.
Individual enrollment
Tuition paid to credit granting agency
Credit granting agency pays fee to UM
Current Status
• National Advisory Board in place
• Course creation lab under construction
• 2 Prototype courses under construction
• Contracts to Fellows went out yesterday
• 2 Short Courses under construction
• Consultant on Pedagogy on board
• 34 students at work on animations and course
delivery process
Current Status
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National Advisory Board to Meet in Pecora
2 papers accepted at SPIE
Knowledge Engine set for Oct. 10 (Alpha Release)
Virtual Campus release Oct. 1
Course Fellow Concept Map Due Sept. 23.
> 84 animations created thus far
Game Engine Plug-in due Aug. 31.
2 External Contracts in place
Current Status
• Staff of four at work, two positions await space
• Teams in place:
• Animations
• Information Technology
• Course Delivery
• Public Relations
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
February 6, 2002 Course Creation Meeting
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Remote Sensing Education & Training
Allan Falconer
Stan Morain
Lynn Usery
Roger Hoffer
Tom Lillesand
Dan Civco
John Jensen
George Hepner
Carolyn Merry
Vincent Tao
Paul Hopkins
Randy Wynne
Chris Friel
Ahmed Noor
Phase I : 2002
1.
Introduction to Geospatial Information Technology
2.
Sensors and Platforms
3.
Photogrammetry
4.
Remote Sensing of the Environment
5.
Digital Image Processing - Course under development
6.
Advanced Digital Image Processing
7.
Aerial Photographic Interpretation
8.
Information Extraction using LIDAR Imagery
9.
Information Extraction using Microwave Data
10. Information Extraction using Multispectral,
Hyperspectral and Ultraspectral Data
11. Orbital Mechanics - Course under development
12. Geospatial Data Synthesis and Modeling
Model Curriculum
Outlines
Introduction to Geospatial Information Technology
Level:
Lower Division Undergraduate
Credits:
Classroom: 3 credits
Laboratory: 1 credit (required)
Prerequisites:
Pre-calculus
Physics
Geography
Computer Science
Description:
This course in designed as an introduction to the integration of the foundational components of geo-spatial
information science and technology into a geographic information system (GIS). The components are the
fundamentals of geodesy, GPS, cartographic design and presentation, image interpretation, and spatial
statistics/analysis. The course must address the manner in which the components are merged in a geo-spatial
information systems approach. While basics must be presented, the course should directly address the leading
edge science and technology for the future.
Content
Geodesy- geoid, spheroids, datums, projections coordinate systems, simple surveying, accuracy
GPS – design, processing modes, international systems
Cartography – types of mapping (thematic, topographic, planinmetric), field mapping,cartographic
representation of geographic objects, visual variables, map perception/interpretation, visualization
advancements.
Image Interpretation – image geometry, elements ( location, context, tone, texture, etc.)
Spatial Statistics/Analysis – introductory statistics for spatial data, issues of scale, accuracy and modifiable
areal units spatial autocorrelation
Image Analysis – biophysical models, need and levels of atmospheric and radiometric calibration, fieldwork for
calibration
GIS- data models, data types and sources, scaling, data accuracy, types of analyses (overlay, network)
Sensors and Platforms
Level:
Upper Division Undergraduate
Graduate
Credits: Classroom: 3 credits
Prerequisites: Introduction to Geospatial Information Technology, Physics
Description
:
Material introduces student to basic design attributes of imaging sensor systems and the platforms on which they operate.
Course provides an introduction to cameras, scanners, and radiometers operating in the ultraviolet, visible, infrared and
microwave regions of the spectrum. The approach is historical showing the evolutionary trends in sensor technology from
1960 to the present – revealing the heritage of modern sensors. Aerial platforms including fixed wing aircraft, helicopters,
UAV and balloons in addition to satellite platforms are also covered.
Content :
Sensor Systems Overview
Resolution
Spatial
Spectral
Radiometric
Temporal
Spectral Bands, NEAP, NEAT
Image swath
Principles of detection and data capture
Specific Sensors
Metric cameras
Digital cameras
Multispectral scanners
Hyperspectral scanners
Platforms
Aerial
Satellite
Orbital characteristics and mechanics
Swathing
Gimbaling
Return visit
Equatorial crossing
Photogrammetry
Level: Upper Division Undergraduate and Graduate
Credits: Classroom: 3 credits
Prerequisites: Introduction to Geospatial Information Technology
Description: TBD. Photogrammetric Basics
Perspective projection
Relief displacement
Parallax and stereo
Epipolar lines and planes
Imaging geometry
Coordinate reference frames
Interior orientation
Exterior orientation
Absolute orientation
Photogrammetric data reduction
Resection
Intersection
Relative / absolution orientation
Block triangulation
Error analysis
Softcopy Photogrammetry
Digital imagery
Image resampling
Image rectification
Image mosaic
Image matching
Feature extraction
Photogrammetric mapping
DEM generation
Orthoimage generation
3D feature extraction
Interface to GIS
Non-topographic photogrammetry
Remote Sensing of the Environment
Level:
Upper Division Undergraduate
Graduate
Credits:
Classroom: 3 credits
Laboratory: 1credit (required)
Prerequisites:
Introduction to Geospatial Information Technology
Sensors and Platforms
Digital Image Processing
Description: The course will review environmental mapping, monitoring and management techniques and
relate these to remote sensing platforms, practices, sensors and techniques. The principles and practice of
environmental mapping, environmental surveys and the preparation of environmental impact statements are
reviewed and the role of geospatial technology is examined. Remote sensing and geographic information
systems (GIS) used together to analyze data are demonstrated as powerful tools in environmental research.
Mapping, monitoring and modeling environmental systems using remote sensing and GIS technologies to
provide the essential geographic component of these activities forms the major focus of the laboratory activity.
Content
Environmental studies
Components:
Topography
Geology
Climate
Hydrology
Geomorphology
Soils
Vegetation
Land Cover
Land Use
Economic Infrastructure
Remote Sensing of the Environment contd….
Systems to map and characterize environments
Ecoregions
Classification
Characterization
Use
Scale
Sub units
Sensors and systems to provide information for environmental studies
Resolution
Spatial
Spectral
Temporal
Feature definition
Phenology
Diagnostics of species
Dynamics of ecoregions
Dynamics of land cover types
Data preparation and processing
Map accuracy & metadata
Atmospheric correction effects on classification
Registration and impact on feature definition
Temporal registration
Seasonal and cyclical events
Data sampling and resampling
Data fusion
Data management systems for environmental analysis
Environmental Units
Definition
Classification accuracy assessment
Ancillary data use
Mapping Accuracy
Modeling environmental regions
Complex interactions and the contributions of remote sensing
Environmental Studies
Classification and mapping of Environments
Analytical classification and definition of sensitive areas or core areas
Predictive modeling
Data presentation and product design
EIA and EIS products using geospatial technologies
Advanced Digital Image Processing
Level:
Upper Division Undergraduate
Graduate
Credits:
Classroom: 3 credits
Laboratory: 1 credit (required)
Prerequisites:
Introduction to Geospatial Information Technology
Sensors and Platforms
Digital Image Processing
Description
Course will address leading edge science and technology developments in aerial and satellite image
processing and pattern recognition. Principals and applications will address real-world situations and problems.
Data to be examined will be principally from the optical wavelengths of the electromagnetic spectrum. High
spatial and hyperspectral resolution data will be addressed as will more traditional medium resolution
multispectral data.
Content
Advanced Classification
Neural networks
Expert systems
Fuzzy logic
Decision trees
Hybrid classifiers
Canonical discriminant analysis
Sub-pixel classification
Fuzzy accuracy assessment
Object-oriented image analysis
Segmentation
Hierarchical
Classification
Spectral
Spatial
ContextuaL
Advanced Digital Image Processing contd…
Orthorectification (terrain)
Aerial
Film
Digital
Satellite
Medium resolution
High resolution
Hyperspectral Data Processing
Display
Information Extraction
Advanced Methods and Models for Atmospheric Correction
Change Detection
Advanced methods
Accuracy assessment
Advanced Spatial Filtering
Spatial domain
Frequency domain (e.g., Fourier, wavelets)
Wavelet Applications
Image data fusion
Image data compression
Empirical Modeling of Biophysical Parameters
(e.g., spatial and non-spatial regression)
Aerial Photographic Interpretation
Level:
Lower Division Undergraduate
Credits:
Classroom: 3 credits
Prerequisites:
Introduction to Geospatial Information Technology
Description
Introduction to the principles and techniques utilized to interpret aerial photography. Emphasis is on interpreting
analog photographs visually in a range of application areas; also includes an introduction to acquiring and
analyzing aerial photographic data digitally.
Content
Elements of Photographic Systems
Films
Filters
Analog Cameras
Digital Cameras
Video Recording
Digitizing Analog Photographs
Fundamentals of Visual Image Interpretation
Basic Image Characteristics (Shape, Size, Pattern, Tone, Texture, Shadows, Site, Association)
Other Factors in the Image Interpretation Process (Scale, Resolution, Timing, Image Quality)
Photointerpretation Equipment
Stereo Viewing
Interpretation Keys
Role of Reference Data
Approaching the Photointerpretation Process (Classification Systems, Minimum Mapping Unit,
Effective Areas)
Aerial Photographic Interpretation contd...
Sample Applications of Aerial Photographic Interpretation
Land Use/Land Cover Mapping
Geologic and Soil Mapping
Agricultural Applications
Forestry Applications
Water Resource Applications
Urban and Regional Planning Applications
Wildlife Ecology Applications
Archaeological Applications
Landform Identification and Evaluation
Hazards and Emergency Response
Digital Photointerpretation
Data Sources
Image Enhancement
Image Classification
Integrating Digital Data into a GIS
Information Extraction using LIDAR Data
Level:
Upper Division Undergraduate
Graduate
Credits:
Classroom: 3 credits
Laboratory: 1 credit (required)
Prerequisites:
Introduction to Geospatial Information
Technology, Sensors and Platforms
Digital Image Processing
Advanced Digital Image Processing
Description: TBD
Content
Full waveform vs. small footprint LIDAR vs. small footprint with intensity
Vegetation removal
LIDAR instrumentation
Basic LIDAR concepts
Bare Earth DEM
Applications
Wireless communications
Topographic mapping
Forestry
Fusion with multispectral and hyperspectral data
Using multiple returns
Multiband LIDAR
Neighborhood / machine approaches
History
Mission planning
Sensor selection
LIDAR vs. Photogrammetry
Significance of data voids
Intensity information
LIDAR image geometry
GPS/INS integration
3D feature extraction
3D urban modeling
Information Extraction using Microwave Data
Level
Upper Division Undergraduate
Graduate
Credits
Classroom: 3 credits
Laboratory: 1 credit (required)
Prerequisites
Introduction to Geospatial Information Technology
Sensors and Platforms Digital Image Processing
Advanced Digital Image Processing
Treatment of the principles of acquiring and processing imagery recorded in the microwave portion of the
electro-magnetic spectrum.Course to include an introduction to primary applications for use of microwave data.
Content
“Unique” aspects of microwave radiation
Passive microwave
Fundamental principles of microwave (active)
Synthetic Aperture Radar
Backscatter principles and models
Interferometry
Phase relationships
Processing radar data
Environmental influences on radar returns
Applications
Information Extraction using Multispectral, Hyperspectral, and Ultraspectral Data
Level:
Upper Division Undergraduate
Graduate
Prerequisites:
Calculus
Introductory physics
Introduction to Geospatial Information Technology
Sensors and Platforms
Digital Image Processing
Description
Characteristics of airborne and satellite multispectral, hyperspectral, and ultraspectral sensor systems are described.
Primary methodologies, such as supervised classification, unsupervised classification (clustering), imaging spectroscopy and
inversion theory must be discussed. Field techniques necessary for proper radiometric calibration of sensor data are
documented. Atmospheric correction techniques essential for image interpretation and analysis are described. Geometric
correction of sensor data is also included. Multispectral analysis techniques to include principal components, minimum
distance classifier, parallelpiped classification, Euclidean distance classification, maximum likelihood techniques, Bayesian
classifier, textural transformations, contextual classifiers, multitemporal techniques, and band ratioing (to include NDVI
indices) are described. Advanced classification techniques to include spectroscopic characterization, continuum removal,
subpixel unmixing (end member analysis, linear and nonlinear spectral mixing), tuned match filtering, image cube analysis,
spectrum matching and spectral data library development are described. Neural networks and expert systems are other
advanced classification techniques that can be used for feature extraction. While basics must be presented, the course
should directly address the leading edge science and technology for the future.
Geospatial Data Synthesis and Modeling
Level:
Upper Division Undergraduate
Graduate
Credits :
Classroom: 3 credits
Laboratory: 1 credit (required)
Prerequisites:
Introduction to Geospatial Information Technology
Sensors and Platforms
Digital Image Processing
GIS
Statistics
Bioscience
Description: TBD
Content
Ground control
GPS
Spectrophotometer
Remote sensing vs. GIS data models
Fields vs. objects
Geospatial Data Synthesis and Modeling contd….
Integration issues
Data types and sealing
Spatial anticorrelation
Modifiable units of resolution
Processing differences
Artifacts from processing
Multiple layers, temporal, metadata
Modeling tools
Integrated raster / vector environment
Geostatistics / spatial statistics
Simulation, visualization and animation
Monte Carlo
Other locations
Applications
Land cover change models
Watershed models, AGNPS
Weather forecasting
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
June 3-5, 2002 Course Creation
Fellows Selection Workshop
• Introduction to Geospatial
Information Technology
• Sensors and Platforms
• Photogrammetry
• Remote Sensing and the
Environment
• Advanced Digital Image
Processing
Remote Sensing Education & Training
June 3-5, 2002 Course Creation
Fellows Selection Workshop
• Aerial Photographic
Interpretation
• Information Extraction using
LIDAR Imagery
• Information Extraction using
Microwave Data
• Information Extraction using
Hyper/Multi/Ultraspectral Data
• Geospatial Data Synthesis and
Modeling
Remote Sensing Education & Training
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
August 2002 Course Content
Fellows Conference
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Introduction to Geospatial
Information Technology
• Arthur Lembo, Cornell University
Sensors and Platforms
• Russ Congalton, University of New
Hampshire
Photogrammetry
• Gouguing Zhou, Old Dominion
University
Remote Sensing of the Environment
• Karen Seto and Erica Fleishman,
Stanford University
Advanced Digital Image Processing
• Lori Bruce, Mississippi State
University
Aerial Photographic Interpretation
• James Campbell, Virginia Tech
Information Extraction using
Microwave Data
• Richard Forster, University of
Utah
Remote Sensing Education & Training
•
•
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•
•
Information Extraction using
Multi/Hyper/Ultraspectral Data
Hyperspectral and Ultraspectral
Data,
• Conrad Bielski, JPL and Khaled
Hasan and Greg Easson, UM
Geospatial Data Synthesis and
Modeling
• Lynn Usery, University of Georgia
Digital Image Processing
• John Jensen, University of South
Carolina
Orbital Mechanics
• John Graham, University of
Mississippi
Information Extraction using LIDAR
Imagery
• No fellow selected at this time
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
15th William T. Pecora Memorial Remote
Sensing Symposium, November 8 to 15, 2002, Denver
• Phase II - 2003
• Advanced Sensor Systems and Data Collection
• Advanced Photogrammetry
• Information Extraction using Thermal Infrared
Data
• Land Use and Land Cover Applications
• Smart Growth and Urban Regional Planning
Applications
• Ecosystems Modeling Applications (GAP,
biodiversity, fish/wildlife)
• Water Resources Applications
• Forestry Applications
• Mapping (Topographic)
• Business Geographics (industrial site location,
banking, real estate, simulation and video games and
individual)
Remote Sensing Education & Training
http://geoworkforce.olemiss.edu
On-Line Course Development
in
Remote Sensing at Virginia Tech
Preparing Students for Careers in Remote
Sensing
15-17 August 2002
J.B. Campbell,
R.H. Wynne, & L. Erskine
On-Line Remote Sensing
Instruction at Virginia Tech
• Jim Campbell,
Geography
• Randy Wynne, Forestry
• Lewis Erskine, BSI
• Supported by Virginia
Tech’s Center for
Innovation in Learning
On-Line Remote Sensing
Instruction at Virginia Tech
• Joint Geography &
Forestry
• Focus on learning
activities
• On-line delivery
• Dual use: both
contact and distance
learning
Joint Geography & Forestry
• Geography 4354: Introduction to Remote
Sensing: An upper level undergraduate
and lower-level graduate students.
Students with interests in remote
sensing, and in application areas.
• Forestry 5000: Advanced Image Analysis:
A graduate level class for students
specializing in remote sensing
Joint Geography & Forestry
• Develop consistency and continuity in the
way that some topics are presented;
• Consistent tools, approach, vocabulary;
• Allow students to advance in understanding
within a common learning environment;
Incentives for On-line Format
• Broadens population of students,
geographically both demographically
• Permits accommodation of varied student
learning styles;
• Efficient use of instructional staff and
computer laboratories;
• Compliments other teaching approaches.
Development Process
•
•
•
•
•
•
•
Understand instructional context
Develop learning goals
Select instructional strategies
Develop prototypes
Formative evaluation
Assess each learning goal
Summative evaluation
Stakeholder Needs
• Course learning objectives should be
matched to needs of stakeholders;
• Difficult for instructors and institutions to
develop this information;
• Should be developed by professional
societies, umbrella organizations,
• Results should be stratified geographically,
by size, etc, to enhance use
Overall Learning Model
• Present basic concepts, knowledge &
principals;
• Guide student through an initial case study,
structured to focus student learning on a few
key facets of the process;
• Present additional case studies, reducing
structure offered to students;
• Students then are prepared to conduct further
Without strong guidance.
Focus on Learning Activities
• Students learn basic principles and
techniques in classroom lectures, text,
or other on-line modules.
• Develop on-line activities that apply
classroom knowledge– lab,
homework, case studies, or projects.
Dual Use
• Contact use: In traditional classroom, or
short courses-- reduce demands on
computer classroom space, and
instructional staff
• Distance learning: serve students at
remote locations
Course Architecture
• Course designed to be used with a
commercially available image processing
system running on student computers;
• Course software runs parallel to image
processing system; designed to be as generic as
possible;
• Although the course guides students in
execution of specific steps, it does not attempt
to teach use of that system.
Evaluation & Feedback
• Provide feedback to students, so they can
focus on problem;
• Provide feedback to instructors, so they can
tailor instruction to problem topics;
• For image classification case studies, our
module includes reference data, so students
see error matrices for their classifications.
It’s the Students, Stupid!
• Define learning goals to match student and
stakeholder needs;
• Match contents and techniques to learning
goals;
• Avoid use of technology that does not
clearly advance a learning goal;
• Use technology to address weaknesses in
conventional instruction
Instructional Design Staff
• Brings knowledge of past experience;
avoids mistakes that others have made;
• Brings objective perspective; if its not clear
to the instructional designer, its not clear for
students;
• Brings knowledge of other projects with
similar issues;
Provide ability to navigate within tutorial
& within course
Remote Sensing Education & Training
•
•
•
•
Some History
The Remote Sensing Model Curriculum
Discussion
Summary
Preparing Students for Careers
in Remote Sensing
Remote Sensing Education Timeline
1992
1994
1996
Remote Sensing Education & Training
1998
2000
2002
2004
Remote Sensing Education & Training
•
•
•
•
Some History
The Remote Sensing Model Curriculum
Discussion
Summary
Preparing Students for Careers
in Remote Sensing
Remote Sensing Education & Training
Pam Lawhead
Dan Civco
James Campbell
Preparing Students for Careers
in Remote Sensing
Thursday, August 15, 2002