Syllabus

Geog 577 Advanced Remote Sensing and Image Processing

Spring 2008

Instructor: Dr. Conghe Song 

Course Description: This is an advanced course in remote sensing. The instructor assumes that you have taken Geog 477 or with equivalent knowledge. The goal of this course is to help students obtain independent research skills using space borne optical remote sensing imagery. The underlying theme of the course is to extract vegetation information from remotely sensed data in space. We will integrate satellite observations with ground observations using Global Positioning Systems, and develop models to extract vegetation information. Topics of the course include key preprocessing steps of digital images for data analysis, spectral mixture analysis, classification and change detection and extraction of leaf area index (LAI) using image spectral and spatial information, measurements of LAI in the field. Students will gain knowledge in these topics through reading critical papers in the discipline and hand-on research oriented labs. The format the course will include instructor lectures, guest lectures, paper reading and discussions, hands-on laboratory exercises and semester long mini-project. Each student will working on a project at his choice using remotely sensed images. The student need to discuss the project with the instructor first and submit a final project proposal before the spring break. Each student will present their research results on the last day of class. There will be no final exam for the class.

 

Lectures: TR 09:30-10:45AM

Location: Saunders 322

Office Hours: 11:00am-12:00pm TR @ 310 Saunders Hall

Grading Policy:

            Labs: 40%

            Paper Discussion: 20%

            Final Project: 20%

            Quiz: 15%

            Participation: 5%

          

Text Book (Recommended, NOT required):

Liang, S. 2004. Quantitative Remote Sensing of Land Surfaces. John Wiley & Sons, Hoboken, NJ. ISBN: 0-471-28166-2.

 

Other Reference Books:

Lillesand, T. M. and R. W. Keifer, 2000. Remote Sensing and Image Interpretation. Forth Edition. New York, NY, J.Wiley and Sons. ISBN: 0-47-125515-7.

 

Jensen, J. R. 2000. Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ: Prentice Hall. ISBN:0-13-489733-1

 

Schowengerdt, R. A. 1997.Remote Sensing: Models and Methods for Image Processing. Academic Press, San Diego, CA. ISBN: 0-12-628981-6.

 

Major Journals in Remote Sensing:

            Remote Sensing of Environment (RSE)             

            International Journal of Remote Sensing (IJRS)

            IEEE Transactions on Geoscience and Remote Sensing (TGRS)

            Photogrammetric Engineering and Remote Sensing (PERS)

 

 

Part I: Preprocessing

Week 1: Jan 7-13

Tuesday (Jan 8): no class

Thursday (Jan 10): Guest Lecture: Dr. Aaron Moody on Sources of Error in Remotely Sensed Images

Week 2: Jan 14-20

Tuesday (Jan 15): Guest Lecture: Dr. Stephen Walsh on Spectral Mixture Analysis

Thursday (Jan 17): Guest Lecture: Dr. Lihong Su on BRDF

Week 3: Jan 21-27

Tuesday (Jan 22):  Instructor Lecture: The essence of radiometric correction

Thursday (Jan 24):

Paper discussion: Chavez, 1996; Teillet et al., 2004

Week 4: Jan 28-Feb 3

Tuesday (Jan 29): Instructor Lecture: Landsat Sensor Degradations

Thursday (Jan 31): Paper Discussion: Hall et al., 1991; Song et al., 2001

            Lab 1: Correcting Atmospheric Effects on Remotely Sensed Images

Week 5: Feb 4-10

Tuesday (Feb 5): Instructor Lecture: Geometric Correction

Thursday (Feb 7):

Paper Discussion: Townshend et al., 1992; Dai et al., 1998.

            Lab 2: Geometric Correction

Part II: Spectral Information of Remotely Sensed Images

Week 6: Feb 11-17

Tuesday (Feb 12): Instructor Lecture: Image Transformation

                              Quiz I: Preprocessing

Thursday (Feb 14):

Paper Discussion: Collins et al., 1996; Byrne et al., 1980.

Lab 3: Image Transformation

 

Week 7: Feb 18-24

Tuesday (Feb 19): Instructor Lecture: Change Detection

Thursday (Feb 21):

Paper Discussion: Lu et al. 2004; Seto et al. 2002.

            Lab 4: Change Detection

 

Week 8: Feb 25-Mar 2

Tuesday (Feb 26):  Instructor Lecture: Classification

Thursday (Feb 28):  Paper Discussion: Bauer et al, 1994; Friedl et al., 2002

                        

Week 9: Mar 3-9

Tuesday (March 5): Instructor Lecture: Spectral Mixture Analysis

Thursday (March 7): 

Paper Discussion: Ridd, 1995; Small, 2001

Lab 5: Spectral Mixture Analysis

Final Project Proposal Due

 

Week 10: Mar 10-16

Spring Break!

Week 11: Mar 17-23

Tuesday (March 18): Instructor Lecture: Remote Sensing of LAI

Quiz II: Spectral Information (Study Guide)

 

Thursday (March 20):

Paper Discussion: Chen and Cihlar, 1995; Stenberg, 1994

Lab 6: Extracting LAI from Remotely Sensed Imagery

Week 12: Mar 24-30

Fieldwork: Measuring LAI in the field (Track Device Manual)

 

Part III: Spatial Information of Remotely Sensed Images

Week 13: Mar 31-Apr 6

Tuesday (April 1): Instructor Lecture: Spatial Analysis

Thursday (April 3):

            Paper Discussion: Woodcock et al., 1987; Woodcock et al., 1988

             Lab 6: Extracting Tree Crown Size

Week 14: Apr 7-13

Tuesday (April 8): Instructor Lecture: Use of Remote Sensing in Global Change Studies

Quiz III: Spatial Information(Study Guide)

 

Thursday (April 10): Paper Discussions: Porter et al., 1993; Mu et al., 2007

Week 15: Apr 14-20

AAG week. Finishing up final Project

Instructor away to AAG meeting.

Week 16: Apr 21-27

Tuesday (April 22): Introduction to IPW, an alternative free image processing tool.

 Thursday: Final Project Presentations