REMOTE SENSING
GIS 5038c (sec. 2926) Environmental Remote Sensing (Grads)
and
GIS 4037 (sec. 6234) Digital Image Processing (Environmental Remote Sensing) (Undergrads)




AVHRR 10-Day NDVI Mosaic

SYLLABUS as of 29 July 2012: This syllabus is being revised. Exams and other due dates may change up to the first day of class. Topics of lectures and labs will probably change.

Instructor: Dr. Michael W. Binford

Office Hours: 4:00 p.m.- 5:00 p.m. Monday and Wednesday or by Appointment

Office: 3141A Turlington Hall
Phone: (352) 392-0494 x 203; I'll answer if I'm in, but seldom return voice mail
E-mail: mbinford@ufl.edu

Teaching Assistant: Jessica Steele
Office Hours: X:XX - X:XX PM, XXXday; TUR 3006 (the lab room)
E-mail:

Required Textbook: Jensen, J.R. 2004. Introductory Digital Image Processing: A Remote Sensing Approach. Prentice-Hall, Saddle River, NJ. 544 pages.

Recommended Textbook: Jensen, J.R. 2006. Remote Sensing of the Environment: An Earth Resources Perspective. Prentice-Hall. Upper Saddle River, NJ.

Required Equipment and Materials: You must have a USB data storage device (Flash Drive, Thumb Drive, Data Stick, Travel Drive, Portable Hard Drive, or any other) with at least 4 Gigabytes of capacity to store class data files and to use as working data storage. Two drives would be better. The lab uses the Virtual Desktop Interface (VDI), which erases everything you do when you log out.


Class Meetings - All Students: Lectures Mondays Period 7-8, 1:55-3:50; Laboratory: Wednesdays Period 7-8 for Undergrads, 1:55-3:50; Wednesdays Period 5-6 for Grad Students, 11:45 - 1:40.

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).



Prerequisites: Senior Standing (4037), Graduate Standing (GIS5038c), or Permission of Instructor. Facility with operations in MS Windows, College-level Statistics or Quantitative Analysis in Geography and College-level Algebra are required, basic courses in Ecology or other Environmental Sciences, Physics recommended. A prior course in GIS would be helpful, but is not required. Likewise, this course would give students an advantage in a GIS course.

OBJECTIVES OF THE COURSE:

1. Introduce students to the basic concepts, data, analytical methods, and software of satellite remote sensing as applied to environmental systems,
e.g. geomorphologic studies, classification of land cover and habitat, landscape analysis, land-cover/land-use change analysis, ecosystem pattern and process analysis, landscape monitoring, etc.

2. At the end of the class, students will be able to conduct basic analysis of environmental systems using satellite remote sensing data and the software ERDAS Imagine 2011.

3. The course will provide a learning environment in which students will learn to teach themselves new software functions, read and implement methods presented in the peer-reviewed and technical literature, and generally be independent scientists and technicians with beginning expertise in remote sensing.

Basis of Grade:  90-80-70-60; A-B-C-D (with 88-90, 78-80, etc. earning +, 90-92, 80-82, etc. earning minus grades except C- which is 68-70)

Activity                                    %

Laboratory Exercises            65%

Midterm Examination             12%

Final Exam                           18%

Future Directions in RS           5%


(NOTE THAT THIS SCHEDULE IS ALWAYS TENTATIVE, EXCEPT FOR EXAM DATES AND OTHER DEADLINES, AND WILL BE REVISED CONSTANTLY. CHECK BACK OFTEN.)

Lectures

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
WeekLecture TopicLab TopicReading
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 1Ch 5, 8 through p 272
3 - 3 SeptemberNo Lecture - Labor Day

LAB WILL MEET!

Lab 2: Image Display & Cursor Operations .doc file
 Lab pdf file;lab_2 Introduction

Key to Lab 2

Ch 2 (read carefully), 3 (read quickly), 4 (through page 141)
4 - 10 SeptemberLecture 2: More Physics of Remote Sensing; Multispectral Instruments and Platforms - Link to 6-slide/page lecture imagesLab 3: Data Formats, Constrast Stretching and Slicing; Key to Lab 3Ch 7.
5 - 17 SeptemberLecture 3: Color Models and Composite Imagery; Multispectral Instruments and Platforms; 6-slide lecture handoutLab 4: Image Annotation and Map Composition pdf fileCh. 6,  pages 194-222.
Chander et al. 2009
6 - 24 SeptemberLecture 4: Orbital Characteristics, Finding Data, Importing, Preprocessing (Spectral Correction), Lecture Slides; 6-slide/page notesLab 5: Geometric Correction; Key to Lab 5Ch. 8, pages 274 -  301
7 - 1 OctoberLecture 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 PCACh. 9
9 - 15 October

MIDTERM EXAM - IN CLASS - Download Study Guide.

Exam Key

Lab 8: Image ClassificationCh. 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.
Ch. 12
11 - 29 October

Lecture 8: Classification 2: Supervised Classification & Accuracy; Change Detection; 6-slide/page notes

Foody 2002 paper on classifcation accuracy assessment.

Lu and Weng 2007 paper on improving classifications

Lab 10: Supervised Classification and Accuracy Assessment

FGDL Metadata for Accuracy Assessment Data
Ch. 12



12 - 5 November


Lecture 9: Change Detection and Analysis; 6-slide/page pdf file

Lu et al. 2004 Int. J. Remote Sensing Change Detection Paper


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.
Ch 13

Ch. 6
14 - 19 November

Lecture 10: Advanced Change Analysis (Trajectories); Radiometric Correction Calculations for Image Calibration, Practical Aspects; 6-slide/page notes.

Bibliography of atmospheric corrections

Paper by Barsi et al. on atmospheric correction;

Atmospheric Correction Web Site (NASA)

Example output from Atmospheric Correction Web Site.

Lab 11, part 2 due today along with Lab 12. Otherwise only a work day.

HAPPY THANKSGIVING!
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:

Monzon ppt
Cahill ppt; Cahill report

16 December - finals week. Final exam is scheduled for Tuesday, December 15 5:30 - 7:30 PMClass 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: ASSIGNMENTS

WEEKLY 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.

EXAMS - Midterm (5 October - 12%) and Final (15 December - 18%) Two examinations will be given. Both exams will use short-answer, problem-solving, image interpretation, and essay questions as format. Graduate students will have additional questions to answer. Make-up exams are not given unless written proof/documentation of the emergency which caused you to miss the exam is given.

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 mbinford@ufl.edu, 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.

ACADEMIC HONESTY

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.