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Plagiarism Policy

Exam #1 Study Guide

Exam #2 Study Guide

Final Exam Study Guide

Example Final Exam

Example Exam #2

 





Forest Resources 472: Remote Sensing of Environment

Course details   

Instructor:     Dr. Paul Gessler

                        Office Hours: MWF 9:30-11:30 am or by appointment
                        Office location: CNR 17C
                        Phone: 885-2595
                        Email: paulg@uidaho.edu
                        Course web pages:
                        http://www.cnrhome.uidaho.edu/remotesensing

Lecture Teaching Assistant:    

                         
                       
                        
                        Email:

Lectures
:          MWF Teaching/Learning Center 44                    8:30-9:20 a.m.

Lab:              RSGIS laboratory CNR 26, labs will be handed out
                    for completion outside of lecture periods.
                    CNR 26 will be exclusively available for class use: MW: 10:30-12:20 

LINK TO LAB DATA ON FTP SITE

Course Book
:    

Remote Sensing of the Environment: An Earth Resource Perspective
                                               
John R. Jensen, 2000, (2007), Prentice Hall. 

Other books that lectures will draw from include (on reserve at library):
    Introduction to remote sensing, 2nd Edition, J.B. Campbell 
    Remote sensing and image interpretation, 3rd & 4th Editions, Lillesand & Kiefer.

**Supplemental readings may be assigned.

Course objectives:

  • develop an understanding of the basic principles of remote sensing;
  • develop a basic understanding of digital image acquisition, processing, display and analysis for environmental monitoring;
  • survey the types and variety of remote sensing systems available;
  • develop an understanding of potential applications of remotely sensed data, tools and techniques for natural resource management.
  • have fun!!

Student responsibilities:

  • attend class;
  • read book chapters and other assigned readings PRIOR to lecture;
  • participate in class and discuss concepts;
  • read your email email will be used as a tool to communicate course info;
  • check course web page for old exams, etc.
  • complete labs, exams and quizes;
  • show enthusiasm and thirst for knowledge!!

Grade calculation:              

Main 3-Credits:
3 exams  30%
4 labs 15%
quizes, class participation 35%
application report (written) 20%


Policy on late work/Make-up exams
:

Labs and homework will be assigned for completion outside of class. Due dates for assignments will be specified when handed out. The grade on all late work will be reduced 10% for each day the assignment is late. That is, the maximum grade for work one day late is 90%. 

At the discretion of the instructor, make-up exams will be written, oral or a combination of both. If you must miss an exam or quiz (quizzes may not always be announced ahead of time!!) please organize to take it prior to the scheduled time. This is the responsibility of the student.

Plagiarism:

You plagiarize when you use someone elses words or ideas without giving them credit. All sources of ideas or information must be cited (given credit). Anything that is not cited is either so widely known that a citation is unnecessary, or it is your own original thought.

The University of Idaho and Department of Forest Resources will not tolerate plagiarism. When you plagiarize, you are stealing someone elses words or ideas. As a student, it is your responsibility to understand what plagiarism is and to how to avoid it.

If you are unsure what plagiarism means, please visit the Department of Forest Resources web page (http://www.class.uidaho.edu/engl101prentiss/Front%20Pages/plagerism.htm) and navigate to the "Academic Programs" sub web where you will find our Plagiarism Policy.


Course outline:

Fall, 2006

 

Lectures:



Introductory Material


Week 1:  [Aug. 21, 23, 25] Introduction and the Remote Sensing Process

Lecture 1: Course logistics (pdf) 
Lecture 2: Introduction (pdf)
Lecture 3: The Remote Sensing Process (pdf) 

 

Reading:  Jensen Chapter 1

 

Week 2: [Aug. 28, 30 Sept. 1 ] Intro to Electromagnetic radiation principles

Lecture 4: Introduction to Electromagnetic Radiation I (pdf) 
Lecture 5:

 

Reading:  Jensen Chapter 2

 



Visual Analysis
                                  

Week 3: [Sept. 6, 8 (Monday off)] Visual Image Interpretation

September 4th:    Labor Day - No Class
Lecture 6: Introduction to Electromagnetic Radiation II (pdf)
Lecture 7: Visual Image Interpretation (pdf)

 

Reading:  Jensen Chapter 5



Digital Image Analysis


Week 4: [Sept. 11, 13, 15 ]
Lecture 8: Quiz #1 key  Visual Image Interpretation
Lecture 9:  Interactive discussion: People and the Environment (Writing assign.)
Lecture 10: Demonstration of Field Spectroradiometer

 

 

 

Week 5: [Sept. 18, 20, 22] Digital image Interpretation & Feature Extraction

Lecture 11: Digital Image Processing: Image Enhancement
Lecture 12: Digital Image Processing: Geometric Correction
Lecture 13: Digital Image Processing: Radiometric Correction

 

Reading: Lillesand & Kiefer Chpt. 7

 

Week 6: [Sept 25, 27, 29 ] Image classification

Lecture 14: Feature Extraction & Separability
Lecture 15: Classification: Supervised and Unsupervised
Lecture 16: Classification: Textural Analysis and Accuracy

 

Reading: Lillesand & Kiefer Chpt. 7



Remote Sensing Systems: Passive

Week 7: [Oct. 2, 4, 6 ] Multispectral RS systems 

Lecture 17: Multispectral Systems I (pdf)
Lecture 18: Multispectral Systems II (pdf) 
Exam 1 (All Material up to and Including Lecture 16)    Old Exam  Exam #1 key       

 

Jensen Chapter 7

 

Week 8: [Oct. 9, 11, 13] Thermal Remote Sensing

Lecture 19: Thermal Remote Sensing I (pdf)
Lecture 20: Thermal Remote Sensing II (pdf)
Lecture 21: Thermal Remote Sensing II (pdf) 

 

Reading:  Jensen Chapter 8




Remote Sensing Systems: Active

Week 9: [Oct. 16, 18, 20] Lidar

Lecture 22: Introduction to Lidar (pdf)
Lecture 23: Lidar Applications I (pdf)
Lecture 24: Lidar Applications II (same pdf as above)

  

Reading:  Jensen Chapter 10


Week 10
: [Oct. 23, 25, 27] Radar, SAR, and Microwave

Lecture 25: Radar, SAR, and Microwave
Lecture 26: Radar, SAR, and Microwave I (pdf)
Lecture 27: Quiz 2 key & Radar, SAR, and Microwave II (same pdf as above)

 

Reading:  Jensen Chapter 9



 Remote Sensing Applications in Natural Resources

Week 11: [Oct 30. Nov 1, 3] Remote Sensing of Vegetation

Lecture 28:
Lecture 29:
Lecture 30:

 

Reading:  Jensen Chapters 11

 

Week 12: [Nov. 6, 8, 10]

Exam 2 (Material from Lecture 15 - Lecture 29)  Exam #2 Key
Nov. 8th: Guest Lecture: Andy Hudak - Lidar
Nov. 10th: Guest Lecture: Jan Eitel - Precision Agriculture
    

    

 

 


Week 13: [Nov. 13, 15, 17] Remote Sensing of Vegetation

Nov. 13th: Vegetation Lecture Notes
Nov. 15th: Vegetation
Nov. 17th: Guest Lecture: Eva Strand - Juniper Monitoring

 

Reading: Jensen Chapter  11

 

 

 

Week 14: [Nov. 20-24 - No Classes!!]   Fall/Thanksgiving Break
 


 Week 15: [Nov. 27, 29 Dec. 1] Remote Sensing of Water

Nov. 27: Water and Snow Lecture Notes
Nov. 29: Guest Lecture: Alistair Smith - Fire RS Applications  Quiz #3,  Lab #4 due
Dec. 1: Guest Lecture: Lee Vierling - Global Change Monitoring w/ RS

 

 

Reading: Jensen Chapter 12  

 

 


 Week 16:  Dec. 4, 6, 8  Remote Sensing of Range and Semi-Arid Lands
  
December 4th: Arid Lands Lecture Notes
December 6th: Soil-landscape Modeling: Gessler
Dec. 8th:  Final Exam Preparation, Application Reports Due
       

  

Reading: 

 


Lab Sessions:

Labs are to be conducted in your own time (but if you need help - please ask!). The documents for each lab are available as pdf documents via the following links:

NEW: CNR 26 Login and password on whiteboard

Lab 1 - Introduction to RS software  Lab #1 Key
Lab 2 - Manipulation of RS imagery   Lab #2 Key
Lab 3 - Unsupervised Classifications
Lab 4 - Supervised Classifications


Quizzes:


Each of 3 Quizzes will only cover the material covered since the last Quiz or Exam:


Exams:


Dates to Remember:


Final - Exam 3 – Friday, Dec. 15th, 7:30-9:30 AM

 

Spring semester begins Wed. Jan. 10

 


 
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