Course Description

Digital Remote Sensing is designed to provide students with a fundamental and working knowledge of biophysical – quantitative remote sensing. Both the theoretical basis and practical aspects of this approach to remote sensing are addressed, including sections on energy-matter interactions, radiation measurements and sensors, and digital image processing and analysis. Lectures and reading assignments will be supplemented by problems and exercises providing hands-on experience in working with remote sensing data and digital image processing, as well as in-class discussion.

Credits:                      3

Grading:                     Midterm Exams (2)                 30%                           

                                    Final Exam                              20

                                    Problems and Projects           50                                              

                                                                                  100%

 

Likely grades:  A = 90 - 99%, B = 80 - 89%, C = 70 - 79%

            – the plus/minus system of grading will be used

                                                           

Text:                           Introductory Digital Image Processing, Jensen,

                                        Prentice-Hall, 3^rd ed., 2004

 

Supplemental Texts:

Remote Sensing Digital Image Analysis, 

    Richards, Springer-Verlag, 1993

Remote Sensing--The Quantitative Approach, 

    Swain and Davis (eds.), McGraw-Hill, 1978

 

Reference Materials:

                                    Supplemental texts and other reference materials are on reserve in the Forestry Library, B-50, Skok Hall

 

Prerequisite:       FR 3262 or 5262, Remote Sensing of Natural Resources and Environment, or consent of instructor

 

Location/Time:          203 Green Hall, 8:30 - 9:20, Monday, Wednesday, Friday

                                   

Laboratory:             There is not a scheduled laboratory. Computers and software for problems and projects are available in 210A Green Hall.

 

Instructor:                  

Marvin Bauer

220B Green Hall           

Phone: 624-3703           

Email:  mbauer@umn.edu

Office Hours:             9:30 - 10:30 Wednesday or by appointment. Or, come by and see if I am available; often I will be. I will welcome questions and interacting with you.

 

 

 

Course Format

 

The format of the class will be a combination of lecture, discussion and work on problems and projects. I encourage you to ask questions in class; questions are an important part of learning. Some I will answer immediately; others, I may pass on to the class for discussion.

 

Approximately two thirds of class time will be lectures, with the remainder devoted to demonstrations, presentations by students, looking at information from remote sensing web sites, and discussion of problems. 

 

There will be several problems and projects to that will amplify and illustrate what we cover in lecture. These include:

 

      1.   An exercise with a vegetation canopy reflectance model (known as the SAIL model), equal to 5% of the course grade.

 

      2.   Image processing and classification project. This will be a team approach with teams of two and is 35% of the grade.

 

      3.   Preparation and presentation (mini-lecture) of a report on a selected aspect of remote sensing that we have not explicitly covered in class. This is 10%.

Reference Materials – Available in the Forestry Library (B50 – Skok Hall)

 

Texts and Other Books (on Reserve)

Introductory Digital Image Processing (Jensen)        

Remote Sensing and Image Interpretation (Lillesand, Kieffer and Chipman)    

Introduction to Remote Sensing (Campbell)  

Remote Sensing: The Quantitative Approach (Swain and Davis)

Theory and Applications of Optical Remote Sensing (Asrar)

Remote Sensing Digital Image Analysis (Richards)

Remote Sensing: The Image Chain Approach (Schott)

Remote Sensing and GIS in Ecosystem Management (Sample, ed.)

Environmental Remote Sensing from Regional to Global Scales (Foody, ed.)

Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (Congalton and Green)

Scale in Remote Sensing and GIS (Quattrochi and Goodchild, eds.)

Remote Sensing for Sustainable Forest Management (Franklin)

Looking at Earth (Strain and Engle)

Satellite Atlas of the World (National Geographic Society)

Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies (NASA Pub.1139)

Plant Leaf Optical Properties in Visible and Near-Infrared Light (Gausman)

 

Remote Sensing Journals

Remote Sensing of Environment

Photogrammetric Engineering and Remote Sensing

International Journal of Remote Sensing

Canadian Journal of Remote Sensing

 

Folders of Reprints (on Reserve)

Approximately 20 selected papers -- listed under assigned readings -- will be on reserve. They are identified by a folder number and the authors’ name in italics in the course outline.

 

 

It is recommended that you read the assigned material prior to the lecture. Key concepts will be emphasized in lecture and discussion, and reading assignments will supplement lectures.

 

 

Credits and Workload Expectations

One credit is defined as equivalent to an average of three hours of learning effort per week (over a full semester) necessary for an average student to achieve an average grade in the course. A student taking a three-credit course that meets for three hours a week should expect to spend an additional six hours a week on course work outside the classroom. 

 

University Grading Standards

A       Achievement that is outstanding relative to the level necessary to meet course requirements.

B       Achievement that is significantly above the level necessary to meet course requirements.

C       Achievement that meets the course requirements in every respect.

D       Achievement that is worthy of credit even though it fails to meet fully the course requirements.

S       Achievement that is satisfactory, which is equivalent to a C- or better.

F       Represents failure (or no credit) and signifies that the work was either (1) completed but at a level of achievement that is not worthy of credit or (2) was not completed and there was no agreement between the instructor and the student that the student would receive an Incomplete.

I        Incomplete. Assigned at the discretion of the instructor when, due to extraordinary circumstances (e.g., hospitalization) a student is prevented from completing the work of the course on time.  Requires a written agreement between instructor and student.

Academic Dishonesty

Academic integrity is essential to a positive teaching and learning environment. All students enrolled in University courses are expected to complete coursework responsibilities with fairness and honesty. Failure to do so by seeking unfair advantage over others or misrepresenting someone else’s work as your own, can result in disciplinary action. The University Student Conduct Code defines scholastic dishonesty as follows:

Scholastic Dishonesty: Scholastic dishonesty means plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; altering forging, or misusing a University academic record; or fabricating or falsifying data, research procedures, or data analysis.

Within this course, a student responsible for scholastic dishonesty can be assigned a penalty up to and including an "F" or "N" for the course. If you have any questions regarding the expectations for a specific assignment or exam, ask.

The College of Natural Resources followed an Honor System for exams in its classes since 1915. Under the Honor System, students accept responsibility for student conduct during exams. It operates under the assumption that students are honest and enjoy working in an environment where their honesty and the honesty of others are not in question. It operates to respect honesty and to prevent cheating, as well as to punish those who cheat. This course will continue the Honor System.