Description: This course, taught in different sections but with lectures at the same time for undergraduate and graduate students, provides an introduction to the use of remotely sensed data in environmental applications. Remote sensing is the science of acquiring data using the measurment of electromagnetic radiation by techniques that do not require actual contact with the object or area being observed. Most environmental applications of remote sensing use instruments carried on satellites. The different sensors used to collect this information, and the interpretation techniques vary quite widely, and are being developed at an astounding rate. In this course, we will focus on the interpretation and applications of data from spaceborne imaging systems (eg: Landsat MSS, Landsat TM, Landsat ETM+, Quickbird, IKONOS, MODIS, ASTER, SeaWIFS, HYPERION, SPOT, AVHRR).
Laboratory Exercises 65%
Midterm Examination 12%
Final Exam 18%
Future Directions in RS 5%
In the list below readings are indicated for Introductory Digital Image Processing: A Remote Sensing Perspective. Students are responsible for reading these materials on their own initiative, and the lack of mention of a reading in class does not mean that the chapter does not have to be read before class time.
|Lecture Schedule||Lab Schedule|
|Week||Lecture Topic||Lab Topic||Reading|
|1 - 20 August|
NO LECTURE TODAY: FIRST DAY OF CLASSES IS WEDNESDAY. LAB WILL MEET
|Lab 0: General Introduction "Fun" lab - getting acquainted with satellite remote sensing data and software. BRING YOUR USB FLASH DRIVE WITH YOU TODAY||Ch 1, 6 (p 175-194)|
|2 - 27 August|
Lecture 1: Introduction to Remote Sensing; Physics of Radiation and Remote Sensing pdf file; 6-slide/page handout pdf file.
|Lab 1: ERDAS Imagine 2011; Image Interpretation and Analysis of Satellite Data; Key to Lab 1||Ch 5, 8 through p 272|
|3 - 3 September||No Lecture - Labor Day|
LAB WILL MEET!
Lab 2: Image Display & Cursor Operations .doc file
Key to Lab 2
|Ch 2 (read carefully), 3 (read quickly), 4 (through page 141)|
|4 - 10 September||Lecture 2: More Physics of Remote Sensing; Multispectral Instruments and Platforms - Link to 6-slide/page lecture images||Lab 3: Data Formats, Constrast Stretching and Slicing; Key to Lab 3||Ch 7.|
|5 - 17 September||Lecture 3: Color Models and Composite Imagery; Multispectral Instruments and Platforms; 6-slide lecture handout||Lab 4: Image Annotation and Map Composition pdf file||Ch. 6, pages 194-222.|
Chander et al. 2009
|6 - 24 September||Lecture 4: Orbital Characteristics, Finding Data, Importing, Preprocessing (Spectral Correction), Lecture Slides; 6-slide/page notes||Lab 5: Geometric Correction; Key to Lab 5||Ch. 8, pages 274 - 301|
|7 - 1 October||Lecture 5 by Likai: More Preprocessing: Spectral Enhancements for Visual Analysis; Geometric Corrections; 6-slide/page notes|
Paper on Mapping Forest Change with SLC-Off
Paper on Geostatistical Estimation of SLC-off data.
|Lab 6: Spectral Enhancement: Band Ratios, Filters.||Ch. 8 (296-322)|
|8 - 8 October|
Lecture 6: Transformations and Special Indices pdf file; 6-slide/page handout pdf file.
|Lab 7: Spectral Enhancements: Transformations (Image Indices) and PCA||Ch. 9|
|9 - 15 October|
MIDTERM EXAM - IN CLASS - Download Study Guide.
|Lab 8: Image Classification||Ch. 9, 13 (pages 495 - 511)|
|10 - 22 October|
Lecture 7: Classification 1: Land Cover Classes and Classification; 6-slide/page notes
|Lab 9: Training Samples & More Classification|
Note that this file is a .docx format so you can modify the CIPEC Training Sample form.
Revised CIPEC Training Sample form for 2011 Class.
Crown density scale reproducible image for estimating crown density
Training Samples (MS Excel .xlsx format) Note: This is incomplete as of 9:30 PM on 1 November 2011. I will update it as the rest of the TS come in, but will accept maps made with this incomplete version.
|11 - 29 October|
Lecture 8: Classification 2: Supervised Classification & Accuracy; Change Detection; 6-slide/page notes
|Lab 10: Supervised Classification and Accuracy Assessment|
FGDL Metadata for Accuracy Assessment Data
|12 - 5 November|
Lab 11: Change Detection: Advanced Change Detection and Spatial Modeler
Lu et al. 2004 Int. J. Remote Sensing Change Detection Paper
|13 - 12 November|
No Lecture: Holiday - Veteran's Day
|Lab 12: Image Calibration and Thermal Calculations|
Image calibration model: Calibration.gmd
Temperature retrieval model: ThermalToTemp.gmd
NOTE: Bias and Gain values for thermal bands in the lab are given in the header/metadata file L71017039_03920020224_HTM.txt which is found in the S: data folder.
Chander et al. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113:893-903.
|14 - 19 November|
10: Advanced Change Analysis (Trajectories); Radiometric Correction
Calculations for Image Calibration, Practical Aspects; 6-slide/page
|Lab 11, part 2 due today along with Lab 12. Otherwise only a work day.|
|Chander et al. 2009|
|15 - 26 November||Lecture 11: Calculations for Radiometric Correction and Thermal Analysis; 6-slide/page notes.|
Landsat 7 Data Users Handbook - useful for all Landsat data
|Lab 13: MODIS data products and analysis|
|16 - 3 December|
Lecture 12: Other Satellites: the EO-1 System (MODIS, ASTER, HYPERION); 6-slide handout file.
NOTE TO BINFORD: STEELE DOES A LECTURE AND A LAB ON HYPERSPECTRAL DATA!Final Exam Posted 9 December, 4:52 PM.
|Digital Copies of Final Exam Questions and Answers are due today, 7 December;|
Lab 13: MODIS can be turned in at the end of the lab period today; no new lab to be done (last day of classes)
Extra Credit Presentations:
Cahill ppt; Cahill report
|16 December - finals week. Final exam is scheduled for Tuesday, December 15 5:30 - 7:30 PM||Class Evaluation|
Lecture 13: Future Directions in Remote Sensing, other topics in Remote Sensing pdf file, 6-slide/page lecture notes.
Saatchi et al. 2011. Future Directions Example Paper
Future Directions Presentations
Adewopo report; Adewopo paper
Arana report; Arana paper
Final Exam. NOTE ABOUT FINAL EXAM: Each student will submit 5 questions with answers. The exam will consist of 20 questions selected from all the questions submitted by students. Each student will answer 10 of the questions out of the 20. The written answers will be due when the university has scheduled the final exam for the class (Exam Schedule 15E: Thursday, 15 December, 5:30 - 7:30 PM).
FINAL EXAM KEY
|No labs during final period.|
Total = 65% of Class Grade
THE FINE PRINT: ASSIGNMENTSWEEKLY LABS = 65% of grade. Individual exercises in he lab manual will be posted online each week. You should create a file of the labs for yourself. Labs are frequently quite complex and much of each exercise will need to be completed outside of laboratory periods. Thirteen lab exercises are planned and these will take you from basic introductory tasks through intermediate and some more advanced remote sensing techniques. You are given 1 week to complete each lab and all labs must be handed with the answers printed and references given, at the beginning of the next lab period (completing a previous weeks lab during the lab session is not allowed). Late labs will not be accepted and a grade of 0 will be recorded. If you have a legitimate reason for missing a lab the absence MUST be documented, e.g., you are in a car wreck, then I need to see the accident report, a death in the family, I need to see the obituary and service times. Labs are critical to this class and it is easy to fall behind. It is to prevent this that I am so strict about not accepting late labs. You have been warned, if it is late you receive a ZERO! Labs are an integral part of the learning procedure in this course and are timed to coincide with the appropriate lectures and reading materials. As such they comprise a significant proportion of your grade and should be taken very seriously.
ASSIGNMENT – Future Directions of Remote Sensing (5%) Due in class on 3 December. For this assignment each student must find a research paper or other literature which they feel is an example or discussion of the Future Direction in the field of Remote Sensing. The piece must have been published in 2007-2011 (or else it won’t be very current). The student will hand in a paper copy of the paper, IN ADDITION to a Digital version (pdf or doc is fine) as well as a 1 page (typed, single-spaced, font size 12 Times New Roman, 1” margins) describing what the novelty of the piece you selected is, what are the new developments and also include why you picked the piece and what you think of their suggestions. These summary pieces should also be emailed to me email@example.com, and will be shared with all class members. Additional details will be given out in class but this is so you are aware of this assignment, can plan for it in terms of time, and can also keep an eye out for a suitable piece. As with the Labs, no late assignments will be accepted.
EXTRA CREDIT LAB. Many students have data or questions associated with their thesis, dissertation, or work-related activity. The last two lab periods will be devoted to work on, and presentations of, projects that are derived from outside the class. If you conduct and present a good project, your numeric grade will be increased by 12%. Thus, the extra credit project is equal in value to the mid-term exam. You may not, however, choose to do a project and neglect the exam. The exam is still required.
You are all bound by the student academic honor code.
“We, the members of the University of Florida community, pledge to hold ourselves and our peers to the
highest standards of honesty and integrity.”
"On my honor, I have neither given nor received unauthorized aid in doing this assignment."
The work you hand in for labs and for exams MUST be your own work. Any material obtained from other sources must be cited correctly. Do not plagiarize material. The first time a student is caught cheating they will get zero on the lab/test. On the second offense the student will be reported to the appropriate student body.
Cell phones – These MUST be turned off in both lectures and labs. Also note when you are in the labs outside class time Cell phones must be off or you will be asked to leave the lab.
Both class lecture and lab are mandatory if you wish to succeed in this course.