Remote Sensing for Geographical Analysis

Transcription

1 Remote Sensing for Geographical Analysis Geography 651, Fall 2008 Department of Geography Texas A&M University (3 credit hours) Instructor: Dr. Hongxing Liu Office hours: Tue & Thur 10:00AM-12:00AM, O&M 707E Tel: ; Lectures: Tue & Thur 12:45am-2:00pm, TEAG002C Labs: To be scheduled Class web page: Class discussion list: Course Overview This course is a comprehensive introduction to the principles, techniques and applications of remote sensing technology in geosciences. It combines lectures with a substantial practical component. The lectures cover electromagnetic (EM) radiation principles, energy-matter interaction, remote sensing data acquisition methods, earth resources satellite systems (LANDSAT, SPOT, RADARSAT, ERS, MODIS, AVHRR, IKONOS, etc), spectral signature and visual image interpretation, thermal infrared remote sensing, passive microwave remote sensing, radar remote sensing (SAR), LiDAR, hyperspectral remote sensing systems, remote sensing of polar regions, remote sensing of urban land use/land cover changes, remote sensing of benthic habitats, and remote sensing of coastal geomorphology. The application examples focus on the analysis and interpretation of remotely sensed image data to measure metric and thematic properties of the Earth s surface for studying the key earth processes, impacts of human activities on environmental systems, and land cover/land use dynamics. The practical component involves a series of step-by-step lab exercises. This will give students extensive hands-on practice in using remote sensing software to process, analyze, and interpret both airborne and spaceborne remote sensing data. Students, given the completion of the course requirements, will be able to: (1) understand the basic principles and concepts of remote sensing and get familiar with the properties and potential application areas of major satellite sensors; (2) gain hands-on experience and skills in using remote sensing data and software in support of geoscientific analysis and modeling. Textbook (required) Jensen, J.R Remote Sensing of the Environment: An Earth Resource Perspective, Second Edition. Upper Saddle River, NJ, Prentice Hall. 592 pp. 1

2 Lab Reference Books ENVI 4.0: User s Guide, 2003 Edition, Research System Inc (RSI). 1083p. ENVI 4.0: Tutorials, 2003 Edition, Research System Inc (RSI). 658p. Reference Books Lillesand, T.M. and Kieffer, R.W Remote Sensing and Image Interpretation. New York, John Wiley & Sons. 724 pp. Remote Sensing Core Curriculum- Remote Sensing Journals Photogrammetric Engineering and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing International Journal of Remote Sensing Remote Sensing of Environment Course Requirements and Policies Class Participation Lecture and lab attendance is mandatory. Students are expected to attend all the lectures and labs, and take lecture notes. Students attendance and participation for this course will be recorded and account for 5% of final grades. Missing a significant part of lectures without making up may result in incomplete for your final grade. Reading the assigned text in advance is highly recommended. Exams There will be two close-book exams. Midterm exam covers about half of the course. The final will be comprehensive, although the emphasis is on the second half of the course. No make-up exams would be given unless students missed an exam for a valid and verified reason. An unexcused absence from an exam will result in a zero point for that exam. Labs This course has ten lab assignments. The practical lab exercises provide a way to acquire skills in processing, analyzing and interpreting various digital remote sensing data. ENVI will be used as the major software for lab exercises. All lab assignments are due at 5 pm on Fridays. A penalty of 10% per day will be deducted for late labs. Each lab assignment normally requires both written responses to questions and creation of images, figures, and maps. Write-ups for lab assignments must be typed, and hand-written work will not be accepted for grading. Evaluation and grading Grades will be assigned on the scale of A (>=90%), B (80-89%), C (70-79%), D (60-69%), and F (<60%) and weighted as follows: Mid-term: 20% Final exam: 25% Lab assignments: 50% Class participation 5% Total: 100% Extra credit: The instructor reserves up to 3 extra credit points to reward students who make special efforts and show creativity. 2

3 Academic Integrity Policy The honor code is fundamental to the value of the A&M experience. Know the Aggie Code of Honor: An Aggie does not lie, cheat, or steal, or tolerate those who do. ( Texas A&M have a Scholastic Dishonesty policy to which both students and faculty must comply. As commonly defined, plagiarism consists of passing off as one s ideas, words, writings, etc. those which belong to another. In accordance with this definition, you are committing plagiarism if you copy the work of another person and turn it in as your own, even if you should have the permission of that person. Plagiarism is one the worst academic sins, for the plagiarist destroys the trust among colleagues without which research cannot be safely communicated. Please read the section "Scholastic Dishonesty" of the Texas A&M University Student Rules ( The Americans with Disabilities Act (ADA) The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Department of Student Life, Services for Students with Disabilities in Room 126 of the Koldus Building. The phone number is For additional information see University Rules Regarding Attendance The student is responsible for providing satisfactory evidence to the instructor to substantiate the reason for absence. Among the reasons absences are considered excused by the university are defined in the section 7 of the Texas A&M University Student Rules ( The rules and regulations regarding religious observances can be found at The Copyright Policy All materials used in this class are copyrighted. These materials include but are not limited to syllabi, quizzes, exams, lab problems, in-class materials, review sheets, and additional problem sets. Because these materials are copyrighted, you do not have the right to copy the handouts, unless permission is expressly granted. 3

4 Geography 651 Remote Sensing for Geographical Analysis Tentative Lecture Schedule Lecture topic 1: Introduction to remote sensing Lecture topic 2: Electromagnetic EM radiation Principles Lecture topic 3: Energy-matter interaction Lecture topic 4: Image georeferencing and rectification Lecture topic 5: Satellite remote sensing systems: platforms and sensors Lecture topic 6: Spectral Signature and Multispectral Image Interpretation Lecture topic 7: Thermal Infrared Remote Sensing Lecture topic 8: Radar Remote Sensing Lecture topic 9: LiDAR Remote Sensing Lecture topic 10: Hyperspectral remote sensing Lecture topic 11: Digital image processing and analysis Lecture topic 12: Remote sensing of Urban Land Use/Cover Chnages Lecture topic 13: Remote Sensing of Benthic Habitats Lecture topic 14: Remote sensing of Coastal Geomorphology Lecture topic 15: Remote Sensing of Polar Regions Note: November (Thursday-Friday): Thanksgiving holiday, no class. December 2 (Tuesday): last day of fall semester classes. Exam Schedule Academic Calendar Mid-term: October 14 (Tuesday) Final exam: December 10 (Wednesday) 8:00-10:00 am 4

5 Geography 651 Remote Sensing for Geographical Analysis Tentative Lab Schedule Lab 1: Introduction to Computer and Windows XP Lab 2: ENVI Basics and the Nature of Digital Remote Sensing Images Lab 3: Georeferencing and co-registering Satellite Images Lab 4: Analyzing and visualizing Multispectral Remote Sensing Data Lab 5: Extracting information from Thermal remote sensing data Lab 6: Extracting Information from Radar Remote Sensing Data Lab 7: Unsupervised classification of land use and land cover using SPOT XS data Lab 8: Supervised classification of Land Use and Land Cover using Landsat ETM+ data Lab 9: Remote Sensing of Urban Environment Lab 10: Remote Sensing of Coastal and Marine Environment 5