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Remote Sensing Education

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Title: Remote Sensing Education


1
Remote Sensing Education Training
  • Pam Lawhead
  • Dan Civco
  • James Campbell

Preparing Students for Careers in Remote Sensing
Thursday, August 15, 2002
2
Remote Sensing Education Training
  • Some History
  • The Remote Sensing Model Curriculum
  • Discussion
  • Summary

Preparing Students for Careers in Remote Sensing
3
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
4
Remote Sensing Education Timeline
Remote Sensing Education Training
5
PERS 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
6
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. 239-50.

Remote Sensing Education Training
7
Remote Sensing Education Timeline
Remote Sensing Education Training
8
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
9
Remote Sensing Education Timeline
Remote Sensing Education Training
10
RSCC
Remote Sensing Education Training
11
Remote Sensing Education Timeline
Remote Sensing Education Training
12
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
  • Baseline the Remote Sensing Industry (RSI)
  • Develop a 10-Year RSI market forecast
  • Provide improved information for decision
    makers
  • Develop attendant processes

Some slides from the 25 April 2002
ASPRS Presentation follow
Remote Sensing Education Training
13
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
14
Level of Education by Sector
  • 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
15
Degrees by Discipline by Sector
Geography GIS Dominate
  • 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

16
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
17
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

Based on Phase II 733 Survey Responses
Manager/Supervisor 188, Manager/User 402, User 143
18
Employer Sponsored Training by Sector
Employer Sponsored Training is infrequent
Based on Phase II 734 Survey Responses
Academic 142, Commercial 247, Government 345
19
Remote Sensing Education Timeline
Remote Sensing Education Training
20
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
21
Remote Sensing Education Training
  • Some History
  • The Remote Sensing Model Curriculum
  • Discussion
  • Summary

Preparing Students for Careers in Remote Sensing
22
Remote Sensing Education Timeline
Remote Sensing Education Training
23
Dr. Pamela Lawhead
Dr. Jay Johnson
(662) 915-3500 geospat_at_olemiss.edu http//geoworkf
orce.olemiss.edu The University of Mississippi
24
The Project
25
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
self-sustaining.
To have 50 online courses in RS in five years
26
Our History
  • Stennis, St. Petersburg, Washington
  • ASPRS
  • Request for Proposals
  • Course Fellows Selection Symposium
  • Course Fellows Award Workshop
  • (Pecora)

27
National Advisory Panel
Ahmed 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
28
National Advisory Panel
29
ASPRS
  • Meeting in St. Petersburg
  • Model Curriculum Workshop
  • FIG 2002/ASPRS in D.C.
  • Educational Partnership, Announced in August

30
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

31
(No Transcript)
32
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

33
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

34
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

35
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.
  • gt 84 animations created thus far
  • Game Engine Plug-in due Aug. 31.
  • 2 External Contracts in place

36
Current Status
  • Staff of four at work, two positions await
    space
  • Teams in place
  • Animations
  • Information Technology
  • Course Delivery
  • Public Relations

37
Remote Sensing Education Timeline
Remote Sensing Education Training
38
February 6, 2002 Course Creation Meeting
  • 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

Remote Sensing Education Training
39
Phase I 2002
  • Introduction to Geospatial Information
    Technology
  • Sensors and Platforms
  • Photogrammetry
  • Remote Sensing of the Environment
  • Digital Image Processing - Course under
    development
  • Advanced Digital Image Processing
  • Aerial Photographic Interpretation
  • Information Extraction using LIDAR Imagery
  • Information Extraction using Microwave Data
  • Information Extraction using Multispectral,
    Hyperspectral and Ultraspectral Data
  • Orbital Mechanics - Course under development
  • Geospatial Data Synthesis and Modeling

40
Model Curriculum Outlines
41
  • Introduction to Geospatial Information Technology
  • Level Lower Division Undergraduate
  • Credits Classroom 3 creditsLaboratory 1
    credit (required)
  • Prerequisites Pre-calculusPhysicsGeographyCom
    puter Science
  • DescriptionThis 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/interpre
    tation, 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)

42
  • Sensors and Platforms
  • LevelUpper 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

43
  • Photogrammetry
  • Level Upper Division Undergraduate and Graduate
    Credits Classroom 3 credits Prerequisites
    Introduction to Geospatial Information Technology
    Description TBD. Photogrammetric Basics
  • Perspective projectionRelief displacementParalla
    x 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

44
  • 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

45
  • 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

46
  • 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

47
  • Advanced Digital Image Processing contd
  • Orthorectification (terrain)
  • AerialFilmDigital
  • SatelliteMedium resolutionHigh resolution
  • Hyperspectral Data Processing
  • DisplayInformation Extraction
  • Advanced Methods and Models for Atmospheric
    CorrectionChange 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)

48
  • 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)

49
  • 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

50
  • Information Extraction using LIDAR DataLevel
    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 approachesHistoryMi
    ssion planningSensor selectionLIDAR vs.
    PhotogrammetrySignificance of data
    voidsIntensity informationLIDAR image
    geometryGPS/INS integration3D feature
    extraction3D urban modeling

51
  • Information Extraction using Microwave
    DataLevel 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.
  • ContentUnique 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

52
  • 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.

53
  • Geospatial Data Synthesis and Modeling
  • Level Upper Division UndergraduateGraduate
  • Credits Classroom 3 creditsLaboratory 1
    credit (required)
  • PrerequisitesIntroduction to Geospatial
    Information TechnologySensors and
    PlatformsDigital Image ProcessingGISStatistics
    Bioscience
  • Description TBD
  • Content Ground control
  • GPSSpectrophotometer
  • Remote sensing vs. GIS data models Fields vs.
    objects

54
  • 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

55
Remote Sensing Education Timeline
Remote Sensing Education Training
56
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
57
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/Ultraspec
    tral Data
  • Geospatial Data Synthesis and Modeling

Remote Sensing Education Training
58
Remote Sensing Education Timeline
Remote Sensing Education Training
59
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/Ultraspec
    tral 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
60
Remote Sensing Education Timeline
Remote Sensing Education Training
61
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
62
http//geoworkforce.olemiss.edu
63
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

64
On-Line Remote Sensing Instruction at Virginia
Tech
  • Jim Campbell,
  • Geography
  • Randy Wynne, Forestry
  • Lewis Erskine, BSI
  • Supported by Virginia Techs Center for
    Innovation in Learning

65
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

66
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

67
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

68
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.

69
Development Process
  • Understand instructional context
  • Develop learning goals
  • Select instructional strategies
  • Develop prototypes
  • Formative evaluation
  • Assess each learning goal
  • Summative evaluation

70
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

71
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.

72
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.

73
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

74
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.

75
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.

76
Its 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

77
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

78
Provide ability to navigate within tutorial
within course
79
Remote Sensing Education Training
  • Some History
  • The Remote Sensing Model Curriculum
  • Discussion
  • Summary

Preparing Students for Careers in Remote Sensing
80
Remote Sensing Education Timeline
Remote Sensing Education Training
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Remote Sensing Education Training
  • Some History
  • The Remote Sensing Model Curriculum
  • Discussion
  • Summary

Preparing Students for Careers in Remote Sensing
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Remote Sensing Education Training
  • Pam Lawhead
  • Dan Civco
  • James Campbell

Thank You !
Preparing Students for Careers in Remote Sensing
Thursday, August 15, 2002
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Job Skills Needed versus Degrees Granted
  • Disconnect?
  • Will Certification Help Solve?
  • Local Business partnering
  • with Colleges/Universities?

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Course Fellow Awards
  • Introduction to Geospatial Information Technology
  • Fellow Arthur Lembo, Cornell University
  • Sensors and Platforms
  • Fellow Rus Congalton, University of New
    Hampshire
  • Photogrammetry
  • Fellow Gouguing Zhou, Old Dominion University
  • Remote Sensing of the Environment
  • Fellows Karen Seto and Erica Fleishman,
    Stanford University
  • Advanced Digital Image Processing
  • Fellow Lori Bruce, Mississippi State University
  • Aerial Photographic Interpretation
  • Fellow James Campbell, Virginia Tech
  • Information Extraction using Microwave Data
  • Fellow Richard Forster, University of Utah
  • Information Extraction using Multi/Hyper/Ultraspec
    tral Data Hyperspectral and Ultraspectral Data,
  • Fellows Conrad Bielski, JPL and Khaled Hasan
    and Greg Easson, UM
  • Geospatial Data Synthesis and Modeling
  • Fellow Lynn Usery, University of Georgia
  • Digital Image Processing

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 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)

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An Example
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