The University of Connecticut	Daniel L. Civco[1]
College of Agriculture & Natural Resources	WB Young 310

Natural Resources Management & Engineering 238V

Remote Sensing Image Processing

Course Outline[2]

Spring 2008

Objectives

This course will focus on quantitative approaches to the analysis of remote sensing data. Computer-assisted analysis of earth observation satellite data will provide students with both an understanding of the principles of numerically-oriented remote sensing and experience with tools that will prove useful in his or her career in resource management. While previous work with computers would be helpful, it is not essential since the fundamentals will be introduced in lab sessions. Some of the goals of this course are to:

· reinforce an understanding of the reflectance properties of various earth surface materials

· examine how digital multispectral data are collected and processed

· become familiar with the basic, elementary mathematical and statistical concepts used in computer-assisted digital remote sensing data analysis

· examine and apply geometric and radiometric transformations to aerial and satellite remote sensing data

· investigate and apply various strategies for classification of these data in extracting earth resources information such as land use and land cover

· compare and contrast aircraft and satellite remote sensing data of different spectral and spatial properties (e.g., ADAR, Landsat ETM+, TM and MSS, SPOT, IKONOS, QuickBird)

· evaluate the utility of multispectral data from one season over those from another

· perform accuracy assessments of the information derived from computer-assisted land use and land cover classification of Landsat TM data

· become aware of emerging innovative approaches to the analysis of satellite remote sensing and ancillary earth resources data

· gain experience in the use of a popular and powerful software system for digital image processing

· develop an organized, logical approach to computer-assisted processing of earth resources data for effective land management

Texts and References

· Jensen, John R., 2004, Introductory Digital Image Processing, 3rd Ed., Upper Saddle River, NJ: Prentice Hall, 544 pages.

· ERDAS Field Guide. 2005. Leica Geosystems GIS & Mapping, Atlanta, GA. 744 p.

· Available as Adobe Portable Document Format (PDF) file

· ERDAS Tour Guides. 2006. Leica Geosystems GIS & Mapping, Atlanta, GA. 730 p.

· Available as Adobe Portable Document Format (PDF) file

· Short, N.M. The Remote Sensing Tutorial. (formerly with the Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD.)

· Canada Center for Remote Sensing (CCRS) Fundamentals of Remote Sensing Tutorial.

· Centre for Remote Imaging, Sensing and Processing (CRISP) Principles of Remote Sensing

· John Jensen’s Lectures:

· Remote Sensing of the Environment

· Introductory Digital Image Processing

Other

· ERDAS Imagine on-line reference manuals

· Recordable CD-R or USB drive for backup and archival of large datasets

Library Reserve Room

Texts

· Congalton, R.G. and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Boca Raton, FL. 137 p.

· Lillesand, Thomas M., Ralph W. Kiefer, and Jonathan W. Chipman. 2003. Remote Sensing and Image Interpretation, 5th Edition. John Wiley & Sons Publishers, 784 p.

· Swain, P.H. and S.M. Davis. 1978. Remote Sensing: the Quantitative Approach. McGraw-Hill Intl. Book Co., New York. 396 p.

Other

· Civco, D.L. 1989a. Knowledge-based land use and land cover mapping. in Proc. ASPRS/ACSM Annual Convention, Baltimore, MD. (3):276-291.

· Civco, D.L. 1989b. Topographic normalization of Landsat Thematic Mapper digital imagery. Special Issue on Image Processing: Photogrammetric Engineering and Remote Sensing 55(9):1303-1309.

· Civco, D.L., Y. Wang, and J. Silander. 1995. Characterizing forest ecosystems in Connecticut by integrating Landsat TM and SPOT Panchromatic data. in Proc. 1995 Annual ASPRS/ACSM Convention, Charlotte, NC. 2:216-224.

· Wang, Y. and D.L. Civco. 1996. Three artificial neural network paradigms in high dimensional multisource spatial data classification. The Association of Chinese Professionals in GIS Geographic Information Science 1(2):73-87.

On-line

· Civco, D.L. 1993. Artificial neural networks for land cover classification and mapping. International Journal of Geographic Information Systems 7(2):173-186.

· Civco, D.L. and J.D. Hurd. 1997. Impervious surface mapping for the state of Connecticut. Proc. 1997 ASPRS/ACSM Annual Convention, Seattle, WA. 3:124-135.

· Civco, D.L. and J.D. Hurd. 1999. A hierarchical approach to land use and land cover mapping using multiple image types. Proc. 1999 ASPRS Annual Convention, Portland, OR. pp. 687-698.

· Civco, D.L., J.D. Hurd, E.H. Wilson, M. Song, and Z. Zhang. 2002a. A comparison of land use and land cover change detection methods. Proc. 2002 ASPRS Annual Convention, Washington, D.C. 12 p.

· Civco, D.L., J.D. Hurd, E.H. Wilson, C.L. Arnold, and S. Prisloe 2002b. Quantifying and Describing Urbanizing Landscapes in the Northeast United States. Photogrammetric Engineering and Remote Sensing 68(10): 1083-1090.

· Flanagan, M.C. and D.L. Civco. 2001.Subpixel impervious surface mapping. Proc. 2001 ASPRS Annual Convention, St. Louis, MO. 13 p.

· Gopal, S. and C. Woodcock. 1994. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing 60(2):181-188.

· Hurd, J.D., E.H. Wilson, S. Lammey, and D.L. Civco. 2001. Characterization of forest fragmentation and urban sprawl using time sequential Landsat imagery. Proc. 2001 ASPRS Annual Convention, St. Louis, MO. 12 p.

· Hurd, J.D., E.H. Wilson, and D.L. Civco. 2002. Development of a forest fragmentation index to quantify the rate of forest change. Proc. 2002 ASPRS Annual Convention, Washington, D.C. 10p.

· Lusch, D.P. 1989. Fundamental considerations for teaching the spectral reflectance characteristics of vegetation, soil, and water. in Proc. of Current Trends in Remote Sensing Education, Geocarto International, Hong Kong. pp. 5-27.

· Wilson, E.H., J.D. Hurd, and D.L. Civco. 2002. Development of a model to quantify and map urban growth. Proc. 2002 ASPRS Annual Convention, Washington, D.C. 11p.

· Zhou, J. and D.L. Civco. 1998. A wavelet transform method to merge Landsat TM and SPOT Panchromatic Data. International Journal of Remote Sensing 19(4):743-757.

Proceedings, Journals and Magazines[3]

· Earth Imaging Journal

· Imaging Notes

· Earth Observation Magazine

· GeoSpatial Solutions

· GeoWorld

· Proceedings of the Annual Conventions of ASPRS

· Recent proceedings in PDF are available from DL Civco

· Photogrammetric Engineering and Remote Sensing

· Many years archive available from DL Civco

· PDFs of recentl years available on-line to ASPRS Members

· Remote Sensing of Environment

· International Journal of Remote Sensing

World Wide Web Homepages and Other URLs[4]

Education

CCRS[5] RS Tutorial: http://ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php

CCRS Intro to Digital Image Analysis: http://ccrs.nrcan.gc.ca/resource/tutor/digitech/index_e.php

Jan Clever’s Remote Sensing Baics:

http://www.geo-informatie.nl/courses/grs20306/lectures/introduction.htm

· Centre for Remote Imaging, Sensing and Processing (CRISP) Principles of Remote Sensing

Remote Sensing Core Curriculum: http://www.r-s-c-c.org/

NASA RS Tutorial: http://www.fas.org/irp/imint/docs/rst/

UConn CLEAR[6]: http://clear.uconn.edu

Yale Center for Earth Observation: http://www.yale.edu/ceo/index.html

Commercial

ERDAS, Inc.: http://gis.leica-geosystems.com/

ESRI: http://www.esri.com/

Earth Resources Mapping: http://www.ermapper.com

PCI Geomatics: http://www.pcigeomatics.com/

ITT Visual Information Solutions ENVI: http://www.ittvis.com/envi/

Definiens eCognition: http://www.definiens.com/image-analysis-for-earth-sciences_45_7_9.html

DigitalGlobe.: http://www.digitalglobe.com/

GeoEye: http://www.geoeye.com/

SPOT Image: http://www.spot.com/

Government

NASA Earth Observatory: http://www.earthobservatory.nasa.gov/

NASA Earth Science: http://www.earth.nasa.gov/

NASA GSFC Landsat Gateway: http://landsat.gsfc.nasa.gov/

USGS Landsat 7: http://landsat.usgs.gov/index.php

Landsat Data Continuity Mission: http://ldcm.nasa.gov/

Terra ASTER: http://asterweb.jpl.nasa.gov/

USGS Earth Explorer: http://edcsns17.cr.usgs.gov/EarthExplorer/

USGS Global Visualization Viewer: http://glovis.usgs.gov/

EOS Data Gateway: http://edcimswww.cr.usgs.gov/pub/imswelcome/

EROS Data Center: http://edcwww.cr.usgs.gov

The Federal Geographic Data Committee: http://www.fgdc.gov/

Organizations

ASPRS Homepage: http://www.asprs.org/

ISPRS Homepage: http://www.p.igp.ethz.ch/isprs/

Open Source Remote Sensing Effort: http://www.remotesensing.org/

Employment-related

GIS Jobs Clearinghouse: http://www.gjc.org/

Geo Job Source: http://www.geojobsource.com/

GeoSearch: http://www.geosearch.com/

Data

Map and Geographic Information Center (MAGIC): http://magic.lib.uconn.edu/

Connecticut DEP GIS: http://www.ct.gov/dep/cwp/view.asp?a=2698&q=322898

Global Land Cover Facility: http://glcf.umiacs.umd.edu/index.shtml

Landsat.Org: http://www.landsat.org/index.html

ESRI Geography Network: http://www.geographynetwork.com/

Geospatial Data Viewers

ERDAS ViewFinder 2.1: ftp://ftp.gi.leica-geosystems.com/software/imagine/VF_Setup_21.EXE

ERMapper Viewer 7.1: http://www.ermapper.com/download_files/ERViewer_71.exe

PCI Geomatics FreeView: http://www.pcigeomatics.com/products/freeview.html

ESRI ArcExplorer: http://www.esri.com/software/arcexplorer/

Cool Sites

Google Earth: http://earth.google.com/

NASA World Wind: http://worldwind.arc.nasa.gov/

NASA Scientific Visualization Studio: http://svs.gsfc.nasa.gov/

Course Grading

Midterm Exam (take home) 25 %

Laboratory Exercises 40 %

Portfolio 10 %

Final Examination 25 %

(Image Analysis Presentation)

Notes of Special Concern

Students are expected to attend class, both lecture and laboratory, regularly and to be present at scheduled examinations. Absence without an acceptable excuse or prior consent will result in an exam grade of zero. The policies on cheating and plagiarism as outlined in the University of Connecticut Student Conduct Code will be adhered to. If there is any student in this class who has special needs because of learning disabilities or other kinds of disabilities, please feel free to discuss the problem with me.

Comments about the Laboratory Exercises

This course is structured to provide both the theory and principles of digital remote sensing data analysis, and to do so in a hands on environment, so that you will be equipped with both the knowledge and skills to apply these tools as a resource management professional. The value of the practical experience afforded by the hands-on aspect of this course cannot be over-emphasized. The laboratory component of this course will make extensive use of Imagine from ERDAS, Inc.[7], an integrated suite of computer software designed for the processing, analysis, and display of digital image data, principally from earth observational satellites such as Landsat or SPOT. ERDAS Imagine can be used with a variety of digital earth resources data such as digital orthophotographs, digital elevation models (DEM), vector data such as roads and hydrography, and many others. We will use principally Landsat MSS, TM, and ETM+ data of Connecticut, as well as SPOT satellite remote sensing data, Digital Elevation Model (DEM) data, and airborne digital multispectral camera data of Connecticut. While a knowledge of microcomputer usage will be helpful, it is not necessary. We will cover together the essential operations and procedures to enable you to use the facilities in the Laboratory for Earth Resources Information Systems[8]. As you can see, the laboratory component of this course is weighted heavily, constituting 50 percent of your total grade. It will be to your advantage to spend as much time as necessary in LERIS working with Imagine (and other geoprocessing and graphical tools). If group-oriented lab exercises are scheduled, students will be expected to participate equally. In order to maintain a timely and logical progression to the laboratory material, exercise assignments should be pursued as soon as possible after they have been discussed or demonstrated.