Remote Sensing Education

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Remote Sensing Education Training

• Pam Lawhead
• Dan Civco
• James Campbell

Preparing Students for Careers in Remote Sensing
Thursday, August 15, 2002
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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
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Remote Sensing Education Timeline
Remote Sensing Education Training
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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
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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
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Remote Sensing Education Timeline
Remote Sensing Education Training
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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
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Remote Sensing Education Timeline
Remote Sensing Education Training
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RSCC
Remote Sensing Education Training
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Remote Sensing Education Timeline
Remote Sensing Education Training
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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
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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
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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
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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

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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
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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
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Employer Sponsored Training by Sector
Employer Sponsored Training is infrequent
Based on Phase II 734 Survey Responses
Academic 142, Commercial 247, Government 345
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Remote Sensing Education Timeline
Remote Sensing Education Training
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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
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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
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Dr. Pamela Lawhead
Dr. Jay Johnson
[phone] geospat_at_olemiss.edu http//geoworkf
orce.olemiss.edu The University of Mississippi
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The Project
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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
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Our History

• Stennis, St. Petersburg, Washington
• ASPRS
• Request for Proposals
• Course Fellows Selection Symposium
• Course Fellows Award Workshop
• (Pecora)

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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
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National Advisory Panel
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ASPRS

• Meeting in St. Petersburg
• Model Curriculum Workshop
• FIG 2002/ASPRS in D.C.
• Educational Partnership, Announced in August

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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

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(No Transcript)
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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

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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

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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

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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

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Current Status

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

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Remote Sensing Education Timeline
Remote Sensing Education Training
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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

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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

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Model Curriculum Outlines
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• 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)

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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)

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• 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)

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• 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

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• 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

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• 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

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• 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.

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• 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

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• 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

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Remote Sensing Education Timeline
Remote Sensing Education Training
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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
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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
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Remote Sensing Education Timeline
Remote Sensing Education Training
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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

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Remote Sensing Education Timeline
Remote Sensing Education Training
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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)

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http//geoworkforce.olemiss.edu
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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

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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

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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

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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

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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

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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.

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Development Process

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

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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

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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.

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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.

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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

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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.

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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.

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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

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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

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Provide ability to navigate within tutorial
within course
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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
81
Remote Sensing Education Training

• Some History
• The Remote Sensing Model Curriculum
• Discussion
• Summary

Preparing Students for Careers in Remote Sensing
82
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