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: |
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| 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.
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Course outline:
Fall, 2006
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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) |
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Reading: Jensen Chapter 1 |
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Week 2: [Aug. 28, 30 Sept. 1 ] Intro to Electromagnetic radiation principles
Lecture 4: Introduction to Electromagnetic Radiation I (pdf) Lecture 5:
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Reading: Jensen Chapter 2 |
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Visual Analysis |
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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) |
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Reading: Jensen Chapter 5 |
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Digital Image Analysis
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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 |
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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 |
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Reading: Lillesand & Kiefer Chpt. 7 |
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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 |
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Reading: Lillesand & Kiefer Chpt. 7 |
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Remote Sensing Systems: Passive
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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 |
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Jensen Chapter 7 |
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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) |
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Reading: Jensen Chapter 8 |
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Remote Sensing Systems: Active
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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) |
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Reading: Jensen Chapter 10 |
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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)
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Reading: Jensen Chapter 9 |
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Remote Sensing Applications in Natural Resources
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Week 11: [Oct 30. Nov 1, 3] Remote Sensing of Vegetation
Lecture 28: Lecture 29: Lecture 30:
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Reading: Jensen Chapters 11 |
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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 |
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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 |
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Reading: Jensen Chapter 11 |
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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
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Reading: Jensen Chapter 12 |
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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 |
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Reading: |
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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:
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Dates to Remember:
Final - Exam 3 – Friday, Dec. 15th, 7:30-9:30 AM
Spring semester begins Wed. Jan. 10
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