This course will provide a thorough introduction to remote sensing theory, technology, and application. The emphasis in this course is on understanding the underlying principles of acquiring and interpreting data from imaging systems covering the electromagnetic spectrum from the ultraviolet, visible, infrared, thermal, to microwave and applying them. Remote sensing is now the technique of choice for mapping land, ocean, and atmosphere of Earth and for exploring other planets and their satellites (such as Mars and Moon). 

No prerequisites, though basic math, physics, and computer skills required, GIS background a plus.

Instructor:  

Dr. Hongjie Xie , Email: hongjie.xie@utsa.edu, Tel: 210-458-5445
Department of Earth and Environmental Sciences at UTSA, http://www.utsa.edu/LRSG

Tuesday 3:30-5:30 pm or by appointment at room of SB 2.02.16

Lecture and Lab:

Lecture and Lab: 5:30 - 8:45 pm, Tuesday at room SB 2.01.02

You are required to attend all lectures and labs except you have a good excuse (you should let me know prior to the class).

Textbook:

Required:

Remote Sensing of the Environment: An Earth Resource Perspective (2nd edition), John R. Jensen, 2007, Prentice Hall press (ISBN number is 0131889508). You can get this book from UTSA Book Store or from online bookstores such as http://amazon.com and http://www.addall.com/

 

Supplemental:  

Introduction to the physics and techniques of remote sensing (2nd edition), Elachi and van ZYL, 2006. Wiley Press (ISBN number is 0471475696).

Remote Sensing for GIS Managers, Stan Aronoff, 2005. ESRI Press (ISBN number is 1589480813).

 

Grade Policy:

The final grade will be determined as below:

For graduate students:

Lab exercises    40%
Midterm exam   15%

Final exam         25%
Term project     20% 

For undergraduate students:

Lab exercises    50%
Midterm exam   20%

Final exam         30%
 

• Active class participation and exceptional performance in term project will be rewarded with extra credits. However, if you miss a class without permission (prior to the class), you will loss 1 point (based on 100 points) per missing class.
• Final grade will be 90-100 (A), 80-89 (B), 70-79 (C), 60-69 (D), and less than 60 (F).
• October 23 is the last day to drop an individual course or withdraw from all classes and receive an automatic grade of "W".

Lab exercise:

Lab exercise will be assigned on Tuesday and due right before the Tuesday class in the following week. Late exercise is unacceptable, unless you do have an good excuse. No make-up lab exercise. Email submission is unacceptable unless you have to miss the class (you should let me know in advance). All lab exercises should use MS Word or others, please no handwriting (it is difficult to read). Lab exercise is very important for you to actually understand the remote sensing concepts,  to use image processing software package, and to prepare you to solve real world problems

Term project:  

For graduate students, a fair portion (20%) of this class is a term project. You (or up to 2 persons) will submit a complete proposal (5%) (no less than 2 pages, double space, 12 font) on October 23, including a title, student name(s), introduction or question statement, data and methods to be used, and expecting results. Each student (or group) will have a 15 minutes class presentation (7%) and will submit a final project paper (8%) (no less than 5 pages, single space, 12 font). Instructor will also give some topics for your reference. More details will be given in the Term Project Assignment on Oct 9.  You are always very welcome to discuss with me about your project. You are encouraged to review the class projects carried out by your fellow students who took this class before (PowerPoint presentations and final papers are all available through my teaching website).

For undergraduate students, a term project is not required. However, if you would like to do a project (you can team up with a graduate student or undergraduates or by yourself), an extra 10 points will be given toward your final grade. You may also select to review a research article published in Remote Sensing of Environment and earn a 10 extra points.

Academic dishonesty policy:

All works must be original. Plagiarizing or cheating in any form will be reported and a failing mark will be assigned.

Tentative Schedule: