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Description

In this course we will examine how the earth and its atmosphere can be viewed from aircraft and satellites using different parts of the electromagnetic spectrum, and how the images obtained can be analyzed to provide information on land use and surface features, and on the composition of the atmosphere. There are hundreds of applications for such data including the monitoring of local and global environmental changes, weather forecasting, storm tracking, seismic analysis, geomorphic mapping, land use studies, urban planning, the detection and monitoring of natural hazards, oil and mineral exploration, vegetation studies, crop classification, and oceanography.

In this course students will learn the principles of remote sensing, and gain hands-on experience in image analysis using ERDAS Imagine, a sophisticated image processing software package. Some exercises will also incorporate ArcGIS software. Through structured computer-based exercises, students will be guided through processing and interpretation of the images and learn how to apply them in a variety of geographic studies.

Attendance

The class sessions will be divided into two parts 鈥� lecture and lab. Class attendance is required. Because we will be doing hands-on exercises in class and it will be useful to receive guidance with these, students will find it particularly difficult to do well without attending class. The ERDAS Imagine software is now available to students outside the lab.. Interested students should contact the instructor for downloading and licensing information. Students attending class are expected to arrive on time and remain in class until dismissed. Cell phones and beepers must be turned off. Students are asked to refrain from using the computers during the lecture portion of the class.

Text

The text for the course is 鈥淩emote Sensing of the Environment鈥� (2nd edition) by John R. Jensen (Prentice Hall, ISBN 0-13-188950-8). An earlier (or international) edition of the text is acceptable.

Exams and grading

There will be three exams worth 100 points each. The final will include material from the entire course. Tentative dates for these are:

Wednesday, Sept 25
Monday, Nov 4
Final Exam (2 hrs): Wed, Dec 11 at 10:15 鈥� 12:15

Lab. assignments will be worth an additional 100 points. These assignments are intended to be learning exercises primarily not assessment tools. The total number of points for the course is 400. For graduate students the final exam will be replaced with an alternative assignment (see below).
The plus and minus system will be used in awarding grades. Students will receive a single grade for the combined 407 and 407L sections.
Make-up exams will only be given in exceptional circumstances. A doctor鈥檚 note is required to make-up an exam missed for illness. No extra-credit is available.

Schedule of classes

The following table shows the tentative schedule of classes (and is subject to change). Please use the last column to guide your reading as it shows where the topics are covered in your text. Some topics may change, and we may progress faster or slower than this schedule. If you miss a class please make arrangements with a classmate to obtain class notes.

		Topics covered	Assigned Reading
week 1	Aug 26, 28	Introduction, basic principles, electromagnetic radiation Only available through the course Moodle page Exercise 1: Measurement and Analysis of Reflectance	Chpt 1, Chpt 2
week 2	Sept 4	electromagnetic radiation, spectral reflectance Only available through the course Moodle page Exercise 2: Reflectance Spectra	Chpt 2
week 3	Sept 9, 11	ERDAS Imagine, remote sensing systems, spectral bands Exercise 3: Introduction to Imagine	Chpt 2, Chpt , Imagine Tour Guide
week 4	Sept 16, 18	orbits and satellites satellite overviews Only available through the course Moodle page Exercise 4: Download, stack and subset a Landsat image	Chpt 7
week 5	Sept 23 Sept 25	satellite overviews Exam 1	Web sites, technical documentation
week 6	Sept 30, Oct 2	image classification 鈥� signatures and feature space Only available through the course Moodle page Exercise 5A: Classification signatures	Imagine Field Guide
week 7	Oct 7, 9	image classification 鈥� decision rules, unsupervised Only available through the course Moodle page Exercise 5B: Decision Rules in Classification Exercise 5C: Unsupervised Classification	Imagine Field Guide
week 8	Oct 14, 16	Atmospheric constituents 鈥� AIRS
week 9	Oct 21, 23	digital elevation models	Chpt 6
week 10	Oct 28, 30	vegetation (NDVI) and other indices	Chpt 11
week 11	Nov 4	Exam 2	Chpt 11
	Nov 6	vegetation (NDVI) index
week 12	Nov 11 Nov 13	Holiday 鈥� Veteran’s Day fire mapping
week 13	Nov 18, 20	fire mapping	Chpt 11
week 14	Nov 25, 27	microwave remote sensing	Chpt 9
week 15	Dec 2, 4	microwave remote sensing	Chpt 9
week 16	Dec 11	Final Exam 10:15 am 鈥� 12:15 pm.

Learning Outcomes and Assessment

The following shows the course student learning outcomes and assessment together with the Geography Department SLOs that are met by this course (shown in parentheses).

Goal G1: Knowledge (Geog. SLO 1.1)
听听听听听听听听听听听 Students will understand the basic principles of remote sensing.听听听听听
听听听听听听听听听听听 Students will learn the electromagnetic spectrum and its properties.

• Assessment/Evaluation tool: Weekly exercises, mid-term exam, final exam

Students will learn the concepts of light transmission, reflection and absorption.听听听
Students will understand how these concepts are applied in remote sensing experiments.
Students will learn the spectral bands of the common satellite instruments.

• Assessment/Evaluation tool: Weekly exercises, mid-term exam, final exam

Students will understand the mechanics of satellite orbits and the reasons for their selection.

• Assessment/Evaluation tool: Weekly exercises, mid-term exam, final exam

Students will understand the concept of image classification.
Students will understand applications of remote sensing to vegetation, land use and hydrology studies.

• Assessment/Evaluation tool: Weekly exercises, mid-term exam, final exam

Students will learn how to operate a sophisticated image processing software package.

• Assessment/Evaluation tool: Hands-on exercises

Goal G2: Acquiring Knowledge (Geog SLO 2.1, 2.3, 2.5)

Students will develop skills for acquiring new knowledge.
Students will take comprehensive lecture notes during class.
Students will read supplementary material referenced in class.
Students will learn to find ancillary material from the Internet.
Students will learn to download and acquire data from internet resources.

• Assessment/Evaluation tool: Hands-on exercises, mid-term exam, final exam

           
听听听听听 Goal G3: Problem Solving Skills (Geog SLO 3.4, 3.5)
Students will assimilate knowledge from different parts of the course to understand how remote sensing can be used to environmental issues.

• Assessment/Evaluation tool: mid-term exam, final exam

Students will demonstrate their ability to apply remote sensing to the study of environmental problems by the use image processing software.

• Assessment/Evaluation tool: hands-on lab. exercises

听听听听听 Goal G4: Communicating Knowledge (Geog SLO 4.1, 4.3, 4.4)
Students will communicate the knowledge they have gained to explaining the principles by which remote sensing instruments work.

• Assessment/Evaluation tool: class participation, mid-term exam, final exam

Students will communicate the knowledge they have gained by selecting appropriate remote sensing satellites to address specific environmental problems.

• Assessment/Evaluation tool: class participation, mid-term exam, final exam

Goal G5: Citizenry (Geog SLO 5.2)
Students will understand the effect of humans on the atmosphere and the earth.

• Assessment/Evaluation tool: class participation, lab exercises

Geography Department Student Learning Outcomes

• G1: Students are knowledgeable- Demonstrate a foundation of knowledge characteristic of the learned individual
• SLO 1.1: Students recognize, recall and identify facts and ideas constituent of the core content knowledge of physical geography.
• SLO 1.2: Students recognize, recall and identify facts and ideas constituent of the core content knowledge of human geography.
• G2: Students are skilled learners 鈥� Demonstrate skills necessary to effectively acquire and synthesize new facts in a fashion characteristic of life-long learners.
• SLO 2.1: Student constructs a useful list of research data sources.
• SLO 2.2: Student demonstrates ability to construct a literature review.
• SLO 2.3: Student demonstrates ability to collect useful data from a database.
• SLO 2.4: Student demonstrates ability to collect data or information from field observation.
• SLO 2.5: Student employs an effective strategy for collecting data or information.
• G3: Students are effective problem solvers- demonstrate abilities as effective evaluators and critical analyzers of information and ideas; creatively and/or productively apply analytical skill to problem or argument.
• SLO 3.1: Student uses logic to effectively argue a point or make a case.
• SLO 3.2: Student uses prior research to construct an argument or to evaluate a hypothesis.
• SLO 3.3: Student uses a statistical instrument to evaluate a hypothesis.
• SLO 3.4: Student uses spatial analytical device to evaluate a hypothesis.
• SLO 3.5: Student discusses the strength and weaknesses of a research strategy or logical argument.
• G4: Students are effective communicators 鈥� demonstrate ability to communicate effectively using textual, oral, graphic or numeric media.
• SLO 4.1: Student writes an effective essay.
• SLO 4.2: Student writes an effective research paper.
• SLO 4.3: Student communicates effectively using maps, tables, charts or other graphics.
• SLO 4.4: Student communicates effectively using numbers, statistics.
• G5: Students are good citizens 鈥� demonstrate productive civic and global citizenship; awareness of and respect for human diversity.
• SLO 5.1: Students demonstrate awareness of, and respect for, the value of human diversity.
• SLO 5.2: Students will demonstrate awareness of their individual role as global citizens.

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