remote sensing education & training pam lawhead dan civco james campbell preparing students for...
Post on 11-Dec-2015
216 Views
Preview:
TRANSCRIPT
Remote Sensing Education & Training
Pam Lawhead
Dan Civco
James Campbell
Preparing Students for Careers in Remote Sensing Thursday, August 15, 2002
• Some History• The Remote Sensing Model
Curriculum• Discussion• Summary
Remote Sensing Education & Training
Preparing Students for Careers in Remote Sensing
Remote Sensing Education & Training
Jay Morgan Towson State University
Knowing all the commands of ArcInfo will makeyou no more of a GIS Analyst …
… than will knowing all the commands of WordPerfect make you an author
An observation addressing education versus training
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
1970-80’SSurveys
By DahlbergAnd Kiefer
• 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.
PE&RS 1992
Remote Sensing Education & Training
• 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.
Serie Geografica 1993
Remote Sensing Education & Training
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
• 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.
IGARSS ‘96
Remote Sensing Education & Training
Actually preceded by ASPRS-EOSAT workshop
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
RSCC
Remote Sensing Education & Training
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
• 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:– Baseline the Remote
Sensing Industry (RSI)– Develop a 10-Year RSI
market forecast– Provide improved
information for decision makers
– Develop attendant processes
Remote Sensing Industry 10 Year Forecast
Some slides from the25 April 2002 ASPRSPresentation follow
Remote Sensing Education & Training
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
0%10%20%30%40%50%60%70%80%90%
100%
Academic Commercial Government
High School Some College
Associates Degree (2 year or equivalent)Bachelor's Degree or equivalent
Master's Degree or equivalent Doctoral Degree
• 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
0%
10%
20%
30%
40%
50%
60%
Agricul
ture
Compu
ter Sc
ienc
e
Phys
ics
Envi
ronm
enta
l Scien
ce
Geo
logy
Civil
Engi
neer
ing
Oth
er E
ngin
eering
Phot
ogra
mm
etry
Fore
stry
Geo
grap
hy a
nd G
IS
Busin
ess Rel
ated
Social
Scien
ces
Gen
eral
Scien
ces
Discipline
% o
f R
esp
on
den
ts
Academic Commercial Government
• 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
Degrees by Discipline by Sector Geography & GIS Dominate
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
• 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
0
50
100
150
200
250
300
350
None One Course Several Courses CertificateProgram
Other (s)
Training
Resp
on
ses
Based on Phase II 733 Survey Responses: Manager/Supervisor 188, Manager/User 402, User 143
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
monthly quarterlysemi-annuallyannuallyless often thanannually
never
Training Frequency
% o
f R
esp
on
den
ts
Academic Commercial Government
Employer Sponsored Training by Sector
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 1998 2000 2002 2004
Remote Sensing Education & Training
• 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
ASPRS Careers Brochure
Remote Sensing Education & Training
• Some History• The Remote Sensing Model
Curriculum• Discussion• Summary
Remote Sensing Education & Training
Preparing Students for Careers in Remote Sensing
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
Dr. Pamela LawheadDr. Jay Johnson
(662) 915-3500
geospat@olemiss.edu
http://geoworkforce.olemiss.edu
The University of Mississippi
The Project
• Goal: 50 courses in five years
• Located at the University of Mississippi
• Principal Investigators• Pamela B. Lawhead – Computer Science• Jay Johnson – Archaeology
• Courses created by content experts
• Multi-media intensive
Goals of the ProjectTo 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 self-sustaining.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 PanelAhmed Noor Old Dominion
Stan Morain U New Mexico
Lynn Usery U of Georgia, USGS
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
Paul Hopkins SUNY
Randy Wynne Virginia Tech
Chris Friel GIS Solutions, Inc.
Allan Falconer U of Miss/MSCI
#
#
#
#
#
##
#
## #
#
#
#
#
#
#
#
Non-Participating StatesParticipants of Workshop
# Asprs Participants : 18
1000 0 1000 Miles
N
EW
S
National Advisory BoardNational Advisory Panel
• Meeting in St. Petersburg• Model Curriculum Workshop• FIG 2002/ASPRS in D.C.• Educational Partnership, Announced in August
ASPRS
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
# #
# #
#
#
#
#
#
#
##
#
#
##
#
##
#
#
# #
Non-Participating StatesParticipating States : 14
# Participants : 30
N
EW
S
Geospatial Workforce DevelopmentNational Participation
400 0 400 800 Miles
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
• 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• 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 1998 2000 2002 2004
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
February 6, 2002 Course Creation Meeting
Remote Sensing Education & Training
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 creditsLaboratory: 1 credit (required)
Prerequisites: Pre-calculusPhysicsGeographyComputer 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.
ContentGeodesy- 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 UndergraduateGraduate
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
SpatialSpectralRadiometricTemporal
Spectral Bands, NEAP, NEATImage swathPrinciples of detection and data capture
Specific Sensors
Metric camerasDigital camerasMultispectral scannersHyperspectral scanners
Platforms
AerialSatelliteOrbital characteristics and mechanics
SwathingGimbalingReturn visitEquatorial crossing
Photogrammetry
Level: Upper Division Undergraduate and Graduate Credits: Classroom: 3 credits Prerequisites: Introduction to Geospatial Information Technology Description: TBD. Photogrammetric Basics
Perspective projectionRelief displacementParallax and stereoEpipolar lines and planes
Imaging geometry
Coordinate reference framesInterior orientationExterior orientationAbsolute orientation
Photogrammetric data reduction
ResectionIntersectionRelative / absolution orientationBlock triangulationError analysis
Softcopy Photogrammetry
Digital imageryImage resamplingImage rectificationImage mosaicImage matchingFeature extraction
Photogrammetric mapping
DEM generationOrthoimage generation3D feature extractionInterface to GISNon-topographic photogrammetry
Remote Sensing of the Environment
Level: Upper Division UndergraduateGraduate
Credits: Classroom: 3 credits Laboratory: 1credit (required)
Prerequisites: Introduction to Geospatial Information TechnologySensors 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 ecoregionsDynamics of land cover types
Data preparation and processingMap 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 areasPredictive modeling Data presentation and product design EIA and EIS products using geospatial technologies
Advanced Digital Image Processing
Level: Upper Division UndergraduateGraduate
Credits: Classroom: 3 creditsLaboratory: 1 credit (required)
Prerequisites: Introduction to Geospatial Information TechnologySensors and PlatformsDigital Image Processing
DescriptionCourse 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.
ContentAdvanced Classification
Neural networksExpert systemsFuzzy logicDecision treesHybrid classifiersCanonical discriminant analysisSub-pixel classificationFuzzy accuracy assessment
Object-oriented image analysis
SegmentationHierarchicalClassification
SpectralSpatialContextuaL
Advanced Digital Image Processing contd…
Orthorectification (terrain)
AerialFilmDigital
SatelliteMedium resolutionHigh resolution
Hyperspectral Data Processing
DisplayInformation Extraction
Advanced Methods and Models for Atmospheric Correction
Change Detection
Advanced methodsAccuracy assessment
Advanced Spatial Filtering
Spatial domainFrequency domain (e.g., Fourier, wavelets)
Wavelet Applications
Image data fusionImage 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
DescriptionIntroduction 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.
ContentElements of Photographic Systems
FilmsFiltersAnalog CamerasDigital CamerasVideo RecordingDigitizing 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 EquipmentStereo ViewingInterpretation KeysRole of Reference DataApproaching the Photointerpretation Process (Classification Systems, Minimum Mapping Unit, Effective Areas)
Aerial Photographic Interpretation contd...
Sample Applications of Aerial Photographic Interpretation
Land Use/Land Cover MappingGeologic and Soil MappingAgricultural ApplicationsForestry ApplicationsWater Resource ApplicationsUrban and Regional Planning ApplicationsWildlife Ecology ApplicationsArchaeological ApplicationsLandform Identification and EvaluationHazards and Emergency Response
Digital Photointerpretation
Data SourcesImage EnhancementImage ClassificationIntegrating Digital Data into a GIS
Information Extraction using LIDAR Data
Level: Upper Division UndergraduateGraduate
Credits: Classroom: 3 creditsLaboratory: 1 credit (required)
Prerequisites: Introduction to Geospatial InformationTechnology, Sensors and PlatformsDigital Image ProcessingAdvanced Digital Image Processing
Description: TBD
ContentFull waveform vs. small footprint LIDAR vs. small footprint with intensityVegetation removalLIDAR instrumentationBasic LIDAR conceptsBare Earth DEMApplications
Wireless communicationsTopographic mappingForestry
Fusion with multispectral and hyperspectral dataUsing multiple returnsMultiband LIDARNeighborhood / machine approachesHistoryMission planningSensor selectionLIDAR vs. PhotogrammetrySignificance of data voidsIntensity informationLIDAR image geometryGPS/INS integration3D feature extraction3D 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 ProcessingAdvanced Digital Image ProcessingTreatment 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 radiationPassive 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 UndergraduateGraduate
Prerequisites: CalculusIntroductory physicsIntroduction to Geospatial Information TechnologySensors and PlatformsDigital Image Processing
DescriptionCharacteristics 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 UndergraduateGraduate
Credits : Classroom: 3 creditsLaboratory: 1 credit (required)
Prerequisites:Introduction to Geospatial Information TechnologySensors and PlatformsDigital Image ProcessingGISStatisticsBioscience
Description: TBD
Content Ground control
GPSSpectrophotometer
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 1998 2000 2002 2004
Remote Sensing Education & Training
• Introduction to Geospatial Information Technology
• Sensors and Platforms• Photogrammetry• Remote Sensing and the
Environment• Advanced Digital Image
Processing
June 3-5, 2002 Course Creation Fellows Selection Workshop
Remote Sensing Education & Training
• 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
June 3-5, 2002 Course Creation Fellows Selection Workshop
Remote Sensing Education & Training
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
August 2002 Course Content Fellows Conference• 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
• 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 & Training
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
• 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)
15th William T. Pecora Memorial Remote Sensing Symposium, November 8 to 15, 2002, Denver
Remote Sensing Education & Training
http://geoworkforce.olemiss.edu
On-Line Course Development in
Remote Sensing at Virginia TechPreparing 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 furtherWithout 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
• Some History• The Remote Sensing Model
Curriculum• Discussion• Summary
Remote Sensing Education & Training
Preparing Students for Careers in Remote Sensing
Remote Sensing Education Timeline
1992 1994 1996 1998 2000 2002 2004
Remote Sensing Education & Training
• Some History• The Remote Sensing Model
Curriculum• Discussion• Summary
Remote Sensing Education & Training
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
top related