Digital Remote Sensing and GIS

FNR 55800: Digital Remote Sensing and GIS 2016 Instructor: Guofan Shao, Office: PFRN 221B, Phone: 43630, E-mail: [email protected]

Class Goals:  

Practice advanced digital remote sensing and raster GIS technologies and their applications in land use pattern and change, vegetation responses to climate, urban heat island effects, and ecosystem/habitat conservation. Learn advanced skills of image data analysis with Erdas Imagine, and correctly and accurately use various geospatial data products.

Teaching Strategies:   

Learning by doing: critical theories are explained in lectures; step-by-step instructions are provided for each lab exercise; independent projects are designed for comprehensive practice of learned kills; grading without using closed-book exams/quizzes. Solving real-world problems: specific remote sensing data, from the local and around the world, are prepared and introduced to class, with which students learn how to solve real-world problems. Sharing experiences among students: homework answers and project reports are uploaded to the shared class website; entire class attendees participate in project evaluations.

Schedule:  Two lecture meetings per week: Tuesday and Thursday 11:30 – 12:20, PFEN 203  One lab per week: Wednesday 1:30 – 4:20 pm, PFEN 202 Under Campus Emergency: In the event of a major campus emergency, course requirements, deadlines and grading percentages are subject to changes that may be necessitated by a revised semester calendar or other circumstances beyond the instructor’s control. Contact instructor to get information about changes in this course.

Office Hours: Students may stop by instructor’s office any time, and the instructor will see students as long as he is neither with someone nor rushing to finish something. Grading:  Three projects and five homework assignments: 75 +75 + 100 + 100 = 350 points  Class participations and lab exercises are required. There will be a 10/20 point deduction for a class/lab absence.

Grading Scale: Total number of points for each student will be converted into a 100 scale. Grades will be given according to the following table: Grade

GPA Value

Range

A+,A AB+ B BC+ C CD+ D DF

4.0 3.7 3.3 3.0 2.7 2.3 2.0 1.7 1.3 1.0 0.7 0.0

93-100 90.0 - 92.9 87.0 - 89.9 83.0 - 86.9 80.0 - 82.9 77.0 - 79.9 73.0 – 76.9 70.0 – 72.9 67.0 – 69.9 63.0 – 66.9 60.0 – 62.9 < 60.0

Policies:  Class discussion is encouraged.  If you a student find it necessary to miss a class, it is his/her responsibility to arrange for obtaining the information covered;  Students are required to perform individual exercises and projects.

Outline (and major lecture references): Week 1 (08/22 – 08/26) An overview of remote sensing (Jesen 2015, Lillesand et al. 2015, Shao 2012a) Lab – Dealing with image data with Erdas Imagine Week 2 (08/29 – 09/02) Electromagnetic Radiation Principles (Jesen 2015, Lillesand et al. 2015, Shao 2016) Lab – Examining Various Remote Sensing Data, Computing Image Statistics Week 3 (09/05 – 09/09) (Labor Day: 09/05) Elements of Image Interpretation (Lillesand et al. 2015), Image Data Classification (Supervised) (Jesen 2015) Lab – Supervised classification, Starting Project 1 (75 points) Week 4 (09/12 – 09/16) Image Data Classification (Unsupervised) (Jesen 2015), Accuracy Assessment (Congalton and Green 1999) Lab – Unsupervised classification, accuracy assessment Week 5 (09/19 – 09/23) Hybrid Classification (Lang et al. 2008), Sample-Based Classification (Landgrebe 2003, Wu et al. 2002) Lab – Starting Project 1 Week 6 (09/26 – 09/30) Working on Project 1 (no class meetings) Lab – Working on Project 1 Week 7 (10/03 – 10/07)

Project 1 Presentations, Band Transformation (Jesen 2015, Shao and Duncan. 2007), Starting Project 2 (75 points) Lab – Learning to Use MultiSpec (Instructed by Larry Biehl) Week 8 (10/10 – 10/14) (October Break: 10/10 – 11) Working on Project 2 (no class meetings) Lab –Working on Project 2 Week 9 (10/17 – 10/21) Geometric Correction (Jesen 2015), Change Detection (Jesen 2015) Lab – Band Transformation, Project 2 Presentations Week 10 (10/24 – 10/28) Remote Sensing Data Collection (Shao 2012b), Thermal Remote Sensing (Lillesand et al. 2015) Lab – Change Detection Week 11 (10/31 – 11/04) LIDAR (Shao and Reynolds 2006), Drone Remote Sensing (Tang and Shao 2015) Lab – Object-oriented classification Week 12 (11/07 – 11/11) Raster GIS Methods Lab – GIS Applications Week 13 (11/14 – 11/18) Vegetation Remote Sensing (Shao 2011, Shao et al. 2003), Error Propagations (Shao et al. 2001, Shao and Wu 2008), Starting Final Project (100 points) Lab – Land Surface Temperature Detection (with modeler tools) Week 14 (11/21 – 11/25) (Thanksgiving vacation 11/24 – 27) Final Project Continues (no class meeting) Lab – Work on Final Project Week 15 (11/28 – 12/02) Object-Based Image Analysis (OBIA) (Li and Shao 2014) Lab – Work on Final Project Week 16 (12/05 – 12/09) Class Project Presentations Week 17 No Final Exam

References:  Congalton, R.G. & Green, K. (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Lewis Publishers, New York.  Sensen, J.R. 2015. Introductory Digital Image Processing: A Remote Sensing Perspective (4th Edition). Prentice Hall, Inc.

 Landgrebe, D.A. 2003. Signal Theory Methods in Multispectral Remote Sensing, John Wiley

and Sons, New Jersey.  Lang, R.L., G.F. Shao, B.C. Pijanowski, and R.L. Farnsworth. 2008. Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach. Computers & Geosciences 34: 1877-1885.  Li, X.X. and G.F. Shao. 2014. A county-scale object-based land-cover mapping in U.S. Midwest region with high resolution aerial photography. Remote Sensing 6(11): 11372-11390.  Lillesand, T., R.W. Kiefer, and J. Chipman. 2015. Remote Sensing and Image Interpretation (7th Edition). Wiley.  Shao, G.F. and K.M. Reynolds (ed.). 2006. Computer Applications in Sustainable Forest Management: including perspectives on collaboration and integration. Springer, Netherland, 276 pp.  Shao, G.F. 2011. Accurately Assessing Habitat with Remote Sensing: User's Perspective. In: Remote Sensing of Protected Land, Y.Q. Wang, ed, Taylor & Francis, pp. 145-155.  Shao G.F. 2012a. Remote sensing. In: Encyclopedia of Environmetrics Second Edition, A.-H. ElShaarawi and W. Piegorsch (eds). John Wiley & Sons Ltd, Chichester, UK, pp.2187-2193. DOI: 10.1002/9780470057339.var033.pub2.  Shao G.F. 2012b. Satellite data. In: Encyclopedia of Environmetrics Second Edition, A.-H. ElShaarawi and W. Piegorsch (eds). John Wiley & Sons Ltd, Chichester, UK, pp. 2390-2395. DOI: 10.1002/9780470057339.vnn057.  Shao, G.F. 2016. Optical remote sensing. In: The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. D. Richardson (ed.). Wiley & Sons, Inc.  Shao, G. and B.W. Duncan. 2007. Effects of band combinations and GIS masking on fire-scar mapping at local scales in East-Central Florida, USA. Canadian Journal of Remote Sensing 33 (4): 250-259.  Shao, G.F., D.G. Liu, and G. Zhao. 2001. Relationships of image classification accuracy and variation of landscape statistics. Canadian Journal of Remote Sensing 27(1): 33-43.  Shao, G.F. and J.G. Wu. 2008. On the accuracy of landscape pattern analysis using remote sensing data. Landscape Ecology 23: 505-511.  Shao, G.F., W.C. Wu, G. Wu, X.H. Zhou, and J.G. Wu. 2003. An explicit index for assessing the accuracy of cover class areas. Photogrammetric Engineering & Remote Sensing 69(8): 907-913.  Tang, L.N. and G.F. Shao. 2015. Drone remote sensing for forestry research and practices: a review. Journal of Forestry Research. 26(4):791–797. DOI 10.1007/s11676-015-0088-y  Wu, W.C. and G.F. Shao. 2002. Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation. Canadian. Journal of Remote Sensing 28 (4): 601609.