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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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Presentation on theme: "Remote Sensing Education & Training Pam Lawhead Dan Civco James Campbell Preparing Students for Careers in Remote Sensing Thursday, August 15, 2002."— Presentation transcript:

1 Remote Sensing Education & Training Pam Lawhead Dan Civco James Campbell Preparing Students for Careers in Remote Sensing Thursday, August 15, 2002

2 Some History The Remote Sensing Model Curriculum Discussion Summary Remote Sensing Education & Training Preparing Students for Careers in Remote Sensing

3 Remote Sensing Education & Training Jay Morgan Towson State University 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 An observation addressing education versus training

4 Remote Sensing Education Timeline Remote Sensing Education & Training S Surveys By Dahlberg And Kiefer

5 Civco, D.L., R.W. Kiefer, and A. Maclean Perspectives on earth resources mapping education in the United States. Photogrammetric Engineering and Remote Sensing 63(8) PE&RS 1992 Remote Sensing Education & Training

6 Civco, D.L., R.W. Kiefer, and A. Maclean 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: Serie Geografica 1993 Remote Sensing Education & Training

7 Remote Sensing Education Timeline Remote Sensing Education & Training

8 Estes, J.E.and T. Foresman Development of a Remote Sensing Core Curriculum. Geoscience and Remote Sensing Symposium, IGARSS '96. 'Remote Sensing for a Sustainable Future.', Volume: 1, 1996, Pages 820 –822. IGARSS 96 Remote Sensing Education & Training Actually preceded by ASPRS-EOSAT workshop

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 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 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 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% AcademicCommercialGovernment High SchoolSome College Associates Degree (2 year or equivalent)Bachelor's Degree or equivalent Master's Degree or equivalentDoctoral 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

15 0% 10% 20% 30% 40% 50% 60% Agriculture Computer Science Physics Environmental Science Geology Civil Engineering Other Engineering Photogrammetry Forestry Geography and GIS Business Related Social Sciences General Sciences Discipline % of Respondents AcademicCommercialGovernment 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

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 NoneOne CourseSeveral CoursesCertificate Program Other (s) Training Responses Based on Phase II 733 Survey Responses: Manager/Supervisor 188, Manager/User 402, User 143

18 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% monthlyquarterlysemi-annuallyannuallyless often than annually never Training Frequency % of Respondents AcademicCommercialGovernment 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 Disciplines –Photogrammetry –Remote Sensing –Geographic Information Systems Education Requirements/Suggestions –High School –Community Colleges and Technical Institutions –Colleges and Universities –Internships –Continuing Education Careers in the Geospatial Sciences ASPRS Careers Brochure Remote Sensing Education & Training

21 Some History The Remote Sensing Model Curriculum Discussion Summary Remote Sensing Education & Training Preparing Students for Careers in Remote Sensing

22 Remote Sensing Education Timeline Remote Sensing Education & Training

23 Dr. Pamela LawheadDr. Jay Johnson (662) The University of Mississippi

24 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

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 NoorOld Dominion Stan MorainU 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 Meeting in St. Petersburg Model Curriculum Workshop FIG 2002/ASPRS in D.C. Educational Partnership, Announced in August ASPRS

30 Request for Proposals Sent out in ASPRS newletter Appeared on our Web Site Sent as to all ASPRS members 60 intents to present 30 proposals submitted 29 actual presenters

31

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. > 84 animations created thus far Game Engine Plug-in due Aug 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 Allan Falconer Stan Morain Lynn Usery Roger Hoffer Tom Lillesand Dan Civco John Jensen George Hepner Carolyn Merry Vincent Tao Paul Hopkins Randy Wynne Chris Friel Ahmed Noor February 6, 2002 Course Creation Meeting Remote Sensing Education & Training

39 Phase I : 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

40 Model Curriculum Outlines

41 Introduction to Geospatial Information Technology Level: Lower Division Undergraduate Credits: Classroom: 3 credits Laboratory: 1 credit (required) Prerequisites: Pre-calculus Physics Geography Computer Science Description: This course in designed as an introduction to the integration of the foundational components of geo-spatial information science and technology into a geographic information system (GIS). The components are the fundamentals of geodesy, GPS, cartographic design and presentation, image interpretation, and spatial statistics/analysis. The course must address the manner in which the components are merged in a geo-spatial information systems approach. While basics must be presented, the course should directly address the leading edge science and technology for the future. Content Geodesy- geoid, spheroids, datums, projections coordinate systems, simple surveying, accuracy GPS – design, processing modes, international systems Cartography – types of mapping (thematic, topographic, planinmetric), field mapping,cartographic representation of geographic objects, visual variables, map perception/interpretation, visualization advancements. Image Interpretation – image geometry, elements ( location, context, tone, texture, etc.) Spatial Statistics/Analysis – introductory statistics for spatial data, issues of scale, accuracy and modifiable areal units spatial autocorrelation Image Analysis – biophysical models, need and levels of atmospheric and radiometric calibration, fieldwork for calibration GIS- data models, data types and sources, scaling, data accuracy, types of analyses (overlay, network)

42 Sensors and Platforms Level: Upper Division Undergraduate Graduate Credits: Classroom: 3 credits Prerequisites: Introduction to Geospatial Information Technology, Physics Description : Material introduces student to basic design attributes of imaging sensor systems and the platforms on which they operate. Course provides an introduction to cameras, scanners, and radiometers operating in the ultraviolet, visible, infrared and microwave regions of the spectrum. The approach is historical showing the evolutionary trends in sensor technology from 1960 to the present – revealing the heritage of modern sensors. Aerial platforms including fixed wing aircraft, helicopters, UAV and balloons in addition to satellite platforms are also covered. Content : Sensor Systems Overview Resolution Spatial Spectral Radiometric Temporal Spectral Bands, NEAP, NEAT Image swath Principles of detection and data capture Specific Sensors Metric cameras Digital cameras Multispectral scanners Hyperspectral scanners Platforms Aerial Satellite Orbital characteristics and mechanics Swathing Gimbaling Return visit Equatorial crossing

43 Photogrammetry Level: Upper Division Undergraduate and Graduate Credits: Classroom: 3 credits Prerequisites: Introduction to Geospatial Information Technology Description: TBD. Photogrammetric Basics Perspective projection Relief displacement Parallax and stereo Epipolar lines and planes Imaging geometry Coordinate reference frames Interior orientation Exterior orientation Absolute orientation Photogrammetric data reduction Resection Intersection Relative / absolution orientation Block triangulation Error analysis Softcopy Photogrammetry Digital imagery Image resampling Image rectification Image mosaic Image matching Feature extraction Photogrammetric mapping DEM generation Orthoimage generation 3D feature extraction Interface to GIS Non-topographic photogrammetry

44 Remote Sensing of the Environment Level: Upper Division Undergraduate Graduate Credits: Classroom: 3 credits Laboratory: 1credit (required) Prerequisites: Introduction to Geospatial Information Technology Sensors and Platforms Digital Image Processing Description: The course will review environmental mapping, monitoring and management techniques and relate these to remote sensing platforms, practices, sensors and techniques. The principles and practice of environmental mapping, environmental surveys and the preparation of environmental impact statements are reviewed and the role of geospatial technology is examined. Remote sensing and geographic information systems (GIS) used together to analyze data are demonstrated as powerful tools in environmental research. Mapping, monitoring and modeling environmental systems using remote sensing and GIS technologies to provide the essential geographic component of these activities forms the major focus of the laboratory activity. Content Environmental studies Components: Topography Geology Climate Hydrology Geomorphology Soils Vegetation Land Cover Land Use Economic Infrastructure

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 ecoregions Dynamics of land cover types Data preparation and processing Map accuracy & metadata Atmospheric correction effects on classification Registration and impact on feature definition Temporal registration Seasonal and cyclical events Data sampling and resampling Data fusion Data management systems for environmental analysis Environmental Units Definition Classification accuracy assessment Ancillary data use Mapping Accuracy Modeling environmental regions Complex interactions and the contributions of remote sensing Environmental Studies Classification and mapping of Environments Analytical classification and definition of sensitive areas or core areas Predictive modeling Data presentation and product design EIA and EIS products using geospatial technologies

46 Advanced Digital Image Processing Level: Upper Division Undergraduate Graduate Credits: Classroom: 3 credits Laboratory: 1 credit (required) Prerequisites: Introduction to Geospatial Information Technology Sensors and Platforms Digital Image Processing Description Course will address leading edge science and technology developments in aerial and satellite image processing and pattern recognition. Principals and applications will address real-world situations and problems. Data to be examined will be principally from the optical wavelengths of the electromagnetic spectrum. High spatial and hyperspectral resolution data will be addressed as will more traditional medium resolution multispectral data. Content Advanced Classification Neural networks Expert systems Fuzzy logic Decision trees Hybrid classifiers Canonical discriminant analysis Sub-pixel classification Fuzzy accuracy assessment Object-oriented image analysis Segmentation Hierarchical Classification Spectral Spatial ContextuaL

47 Advanced Digital Image Processing contd… Orthorectification (terrain) Aerial Film Digital Satellite Medium resolution High resolution Hyperspectral Data Processing Display Information Extraction Advanced Methods and Models for Atmospheric Correction Change Detection Advanced methods Accuracy assessment Advanced Spatial Filtering Spatial domain Frequency domain (e.g., Fourier, wavelets) Wavelet Applications Image data fusion Image data compression Empirical Modeling of Biophysical Parameters (e.g., spatial and non-spatial regression)

48 Aerial Photographic Interpretation Level: Lower Division Undergraduate Credits: Classroom: 3 credits Prerequisites: Introduction to Geospatial Information Technology Description Introduction to the principles and techniques utilized to interpret aerial photography. Emphasis is on interpreting analog photographs visually in a range of application areas; also includes an introduction to acquiring and analyzing aerial photographic data digitally. Content Elements of Photographic Systems Films Filters Analog Cameras Digital Cameras Video Recording Digitizing Analog Photographs Fundamentals of Visual Image Interpretation Basic Image Characteristics (Shape, Size, Pattern, Tone, Texture, Shadows, Site, Association) Other Factors in the Image Interpretation Process (Scale, Resolution, Timing, Image Quality) Photointerpretation Equipment Stereo Viewing Interpretation Keys Role of Reference Data Approaching the Photointerpretation Process (Classification Systems, Minimum Mapping Unit, Effective Areas)

49 Aerial Photographic Interpretation contd... Sample Applications of Aerial Photographic Interpretation Land Use/Land Cover Mapping Geologic and Soil Mapping Agricultural Applications Forestry Applications Water Resource Applications Urban and Regional Planning Applications Wildlife Ecology Applications Archaeological Applications Landform Identification and Evaluation Hazards and Emergency Response Digital Photointerpretation Data Sources Image Enhancement Image Classification Integrating Digital Data into a GIS

50 Information Extraction using LIDAR Data Level: Upper Division Undergraduate Graduate Credits: Classroom: 3 credits Laboratory: 1 credit (required) Prerequisites: Introduction to Geospatial Information Technology, Sensors and Platforms Digital Image Processing Advanced Digital Image Processing Description: TBD Content Full waveform vs. small footprint LIDAR vs. small footprint with intensity Vegetation removal LIDAR instrumentation Basic LIDAR concepts Bare Earth DEM Applications Wireless communications Topographic mapping Forestry Fusion with multispectral and hyperspectral data Using multiple returns Multiband LIDAR Neighborhood / machine approaches History Mission planning Sensor selection LIDAR vs. Photogrammetry Significance of data voids Intensity information LIDAR image geometry GPS/INS integration 3D feature extraction 3D urban modeling

51 Information Extraction using Microwave Data Level Upper Division Undergraduate Graduate Credits Classroom: 3 credits Laboratory: 1 credit (required) Prerequisites Introduction to Geospatial Information Technology Sensors and Platforms Digital Image Processing Advanced Digital Image Processing Treatment of the principles of acquiring and processing imagery recorded in the microwave portion of the electro-magnetic spectrum.Course to include an introduction to primary applications for use of microwave data. Content Unique aspects of microwave radiation Passive microwave Fundamental principles of microwave (active) Synthetic Aperture Radar Backscatter principles and models Interferometry Phase relationships Processing radar data Environmental influences on radar returns Applications

52 Information Extraction using Multispectral, Hyperspectral, and Ultraspectral Data Level: Upper Division Undergraduate Graduate Prerequisites: Calculus Introductory physics Introduction to Geospatial Information Technology Sensors and Platforms Digital Image Processing Description Characteristics of airborne and satellite multispectral, hyperspectral, and ultraspectral sensor systems are described. Primary methodologies, such as supervised classification, unsupervised classification (clustering), imaging spectroscopy and inversion theory must be discussed. Field techniques necessary for proper radiometric calibration of sensor data are documented. Atmospheric correction techniques essential for image interpretation and analysis are described. Geometric correction of sensor data is also included. Multispectral analysis techniques to include principal components, minimum distance classifier, parallelpiped classification, Euclidean distance classification, maximum likelihood techniques, Bayesian classifier, textural transformations, contextual classifiers, multitemporal techniques, and band ratioing (to include NDVI indices) are described. Advanced classification techniques to include spectroscopic characterization, continuum removal, subpixel unmixing (end member analysis, linear and nonlinear spectral mixing), tuned match filtering, image cube analysis, spectrum matching and spectral data library development are described. Neural networks and expert systems are other advanced classification techniques that can be used for feature extraction. While basics must be presented, the course should directly address the leading edge science and technology for the future.

53 Geospatial Data Synthesis and Modeling Level: Upper Division Undergraduate Graduate Credits : Classroom: 3 credits Laboratory: 1 credit (required) Prerequisites: Introduction to Geospatial Information Technology Sensors and Platforms Digital Image Processing GIS Statistics Bioscience Description: TBD Content Ground control GPS Spectrophotometer Remote sensing vs. GIS data models Fields vs. objects

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 Introduction to Geospatial Information Technology Sensors and Platforms Photogrammetry Remote Sensing and the Environment Advanced Digital Image Processing June 3-5, 2002 Course Creation Fellows Selection Workshop Remote Sensing Education & Training

57 Aerial Photographic Interpretation Information Extraction using LIDAR Imagery Information Extraction using Microwave Data Information Extraction using Hyper/Multi/Ultraspectral Data Geospatial Data Synthesis and Modeling June 3-5, 2002 Course Creation Fellows Selection Workshop Remote Sensing Education & Training

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/Ultraspectral Data Hyperspectral and Ultraspectral Data, Conrad Bielski, JPL and Khaled Hasan and Greg Easson, UM Geospatial Data Synthesis and Modeling Lynn Usery, University of Georgia Digital Image Processing John Jensen, University of South Carolina Orbital Mechanics John Graham, University of Mississippi Information Extraction using LIDAR Imagery No fellow selected at this time Remote Sensing Education & Training

60 Remote Sensing Education Timeline Remote Sensing Education & Training

61 Phase II Advanced Sensor Systems and Data Collection Advanced Photogrammetry Information Extraction using Thermal Infrared Data Land Use and Land Cover Applications Smart Growth and Urban Regional Planning Applications Ecosystems Modeling Applications (GAP, biodiversity, fish/wildlife) Water Resources Applications Forestry Applications Mapping ( Topographic ) Business Geographics ( industrial site location, banking, real estate, simulation and video games and individual) 15th William T. Pecora Memorial Remote Sensing Symposium, November 8 to 15, 2002, Denver Remote Sensing Education & Training

62

63 On-Line Course Development in Remote Sensing at Virginia Tech Preparing Students for Careers in Remote Sensing 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 Some History The Remote Sensing Model Curriculum Discussion Summary Remote Sensing Education & Training Preparing Students for Careers in Remote Sensing

80 Remote Sensing Education Timeline Remote Sensing Education & Training

81 Some History The Remote Sensing Model Curriculum Discussion Summary Remote Sensing Education & Training Preparing Students for Careers in Remote Sensing

82 Remote Sensing Education & Training Pam Lawhead Dan Civco James Campbell Preparing Students for Careers in Remote Sensing Thursday, August 15, 2002

83 Job Skills Needed versus Degrees Granted Disconnect? Will Certification Help Solve? Local Business partnering with Colleges/Universities?

84 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/Ultraspectral 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 John Jensen, University of South Carolina Orbital Mechanics John Graham, University of Mississippi Information Extraction using LIDAR Imagery No fellow selected at this time

85 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) Phase II : 2003

86 An Example


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