remote sensing education & training pam lawhead dan civco james campbell preparing students for...

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


Preparing Students for Careers in Remote Sensing


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


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


• 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



1992 1994 1996 1998 2000 2002 2004


• 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


Actually preceded by ASPRS-EOSAT workshop


1992 1994 1996 1998 2000 2002 2004


RSCC



1992 1994 1996 1998 2000 2002 2004


• 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


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?


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


• 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


Employer Sponsored Training by Sector

Employer Sponsored Training is infrequent

Based on Phase II 734 Survey Responses: Academic 142, Commercial 247, Government 345


1992 1994 1996 1998 2000 2002 2004


• 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



1992 1994 1996 1998 2000 2002 2004


Dr. Pamela LawheadDr. Jay Johnson

(662) 915-3500

[email protected]

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

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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

# #

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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


1992 1994 1996 1998 2000 2002 2004


• 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


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



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



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


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


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


1992 1994 1996 1998 2000 2002 2004


• 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


• 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



1992 1994 1996 1998 2000 2002 2004


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



1992 1994 1996 1998 2000 2002 2004


• 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


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


1992 1994 1996 1998 2000 2002 2004



Pam Lawhead

Dan Civco

James Campbell

Preparing Students for Careers in Remote Sensing Thursday, August 15, 2002

remote sensing education & training pam lawhead dan civco james campbell preparing students for...

Documents

remote sensing slide

remote sensing symposium

remote sensing industry

remote sensing core

level of education

kiefer slide

asprseosat workshop

year college degree