References: ♢ Introductory Digital Image Processing: A Remote Sensing
Perspective (4th Edition). By ... Remote Sensing and Image Interpretation (7th
Edition).
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.