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Course objectives: To develop an understanding of hyperspatial remote sensing fundamentals and the ability to interpret and manipulate high-resolution remotely sensed images and datasets. Students will be presented with the traditional and ‘state of the art’ image processing techniques, and a firm theoretical and practical background in hyperspatial remote sensing applications. By the end of the course students will be expected to evaluate available remote sensing data sources and design simple projects related to environmental applications. |
Examples of data & analysis you will learn about in ESRM430
LiDAR data colored by height
High resolution image segmentation
Other terrestrial LiDAR RSGAL research examples
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Course Summary SYLLABUS SPRING 2009 (5 credits = 2 lecture credits + 3 lab credits) Students will be exposed to the principles of photogrammetry, image and point cloud interpretation and hyperspatial (high spatial resolution) remote sensing applications in natural resource management. In the first half of the course, manual and computer based laboratory exercises emphasize conventional analysis of aerial photographs and high resolution satellite imagery. Students will have the opportunity to apply these principles and obtain hands-on experience. The second half of the course focuses on the application of active remotely sensed data, specifically LiDAR (Light Detection and Ranging). The uses of hyperspatial remotely sensed information for wetlands, watersheds, forest resources, wildlife habitat, point and non-point pollution, environmental monitoring, land use planning, urban-suburban-forestry interfaces, and outdoor recreation will be discussed and illustrated using research examples throughout the course. Practitioners and users from public and private institutions may be involved as guest lecturers. Students will come out of this course with a mastery of a wide variety of interpretation, measurement, environmental monitoring and map making skills specific to hyperspatial remote sensing. Textbook & Readings
Recommended Readings
Other Resources
Required Course Supplies Grading Check your grades on GradeBook
The actual number of labs and quizzes might be lower but not higher. Approximate letter grades will be 93% (A=4.0), 82 % (B= 3.0), 71 % (C= 2.0), and 60% (D= 1.0). You will fail the course if your cumulative % is below 59 % (F = 0.0). *Annotated Bibliographies (Graduate Students ONLY) Every week a typewritten annotated bibliographic reference based on a remote sensing- theme refereed journal article will be due at the beginning of each lab session; for a total of 8 annotated bibliographies (no bibliographies are due 1st and last week of the quarter). Thus, graduate student are expected to attend the labs, however, the annotated bibliographies will substitute for the lab and midterm grades. Annotated bibliographies can be emailed to Dr. Moskal (lmmoskal@u.washington.edu), however, the subject line MUST include: ESRM430 Annotated Bibliography. Each bibliographic reference will be graded as follows: 10 pts = Excellent, 7 pts = Good, 5 pts = Fair, 3 pts = Poor, 0 pts = Late or did not hand in. Lab Submissions: A link to submit your lab will be provided in the class schedule section of the course website below, links will be activated the day of the lab session and you will have till the start of the next lab session to submit your lab in the course drop box. Course Schedule Week 1
Week 2
Week 3
Week 4
Week 5
Midterm
Week 6
Week 7
Week 9
Week 10
Final Exam - (Due 5PM on June 9th) final exam study guide -- Final Exam Dropbox |
University of Washington |
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| College of the Environment, School of Forest Resources | Phone: 206.221.6391 |
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| Bloedel 334, Box 352100 | Fax: 206.685.3091 |
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| Seattle, WA 98195-2100 | email: lmmoskal at u.washington.edu |
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© 2008 Dr. L. M. Moskal - Last updated on
19.07.09 15:59
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