|Dr. L. Monika Moskal
|Hours: by appointment Bloedel 382
|Hours: in Bloedel 357 1:30-2:30 T
and Th or by appointment
TTh 12:30 – 1: 20 in More 220
|A - T 2:30 – 3:50 in Bloedel Hall 261
B - T 4:00 – 5:20 in Bloedel Hall 261
C - Th 2:30 – 3:50 in Bloedel Hall 261
|D - Th 4:00 – 5:20 in Bloedel Hall 261
5 credits = 2 lecture credits + 3 lab credits
There are no course requirements to enroll in the course although you will find that prior statistics, GIS and Remote Sensing classes are of benefit.
Individual arrangments need to be made to take course as W credit; see Dr, Moskal in person.
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. 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. Students will have the opportunity to apply these principles and obtain hands-on experience. 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. Practitioners and users from public and private institutions may be involved as guest lecturers.
Teaching philosophy: I consider the student/faculty mentor relationship to be the centerpiece of the academic culture, and perhaps the most rewarding professional experience. At her best, a professor acts as a mentor to her students - be it a group of noisy first-years or an independent star graduate. The precise strategy of achieving this relationship will change with circumstance, but the overall approach remains the same. While I can't claim to have developed an exhaustive philosophy on the subject, I have listed some of my guiding principles below.
- Respect: I bring a high standard of respect to the classroom, taking care to run on time, organize clear, high-quality lectures and labs, and encourage thoughtful dialog among class participants. I have always made an effort to treat students as peers, responding carefully to suggestions, and being honest when confronted by questions that I cannot answer. However, respect is a two-way street. I expect prepared (do the readings) attentive, punctual students who participate in the learning process.
- Excellence: I set clear, high standards at the beginning of a course and challenge students to reach for pre-defined goals. I instruct all of my students to focus on extracting value from their education in the form of identifiable skills and knowledge areas. I do not reward lazy or sloppy thinking. I encourage students to think critically, use their resources effectively, and hone their problem-solving abilities.
- Independence: Where possible, I try to incorporate problem- or inquiry-based learning approaches into each of my courses. I do not design assignments that follow a 'cook book' approach to a singular conclusion, instead preferring complex-structured problems with more than one workable solution. While initially frustrating to some students, my experience is that this sort of approach encourages competent, independent thinking and results in a far more satisfying conclusion to the learning experience.
Week 1 -- Lecture Slides
- What is Hyperspatial Remote Sensing?
- Principles of Image Interpretation
- Principles of Remote Sensing
- Readings: Campbell & Wynne Chapters 1, 5 and 10
- Optional Readings: Chambers et al. 2007, Tatem et al. 2008, Adams et al. 2004, Melesse at al. 2007 -- can be used for Graduate Student Annotated Bibliography
Week 2 -- Lecture Slides
- Aerial and high resolution imagery
- Research examples using aerial photography and high resolution imagery
- Readings: Campbell & Wynne Chapters 3 and 4; de Leeuw et al. 2011; Gordon, 2005
Week 3 -- Lecture Slides continued from week 2
- Natural Resource Management Remote Sensing Examples from Forestry, Landcover Change, Landscape Ecology, Geovisualization, Landuse Planning, and Wildlife Applications
- Stream Mapping Applications with Thermal Remote Sensing Guest Lecture by Dr. Christian Torgersen (USGS/UW)
- Readings for guest lecture:
- Handcock, R.N., Torgersen, C.E., Cherkauer, K.A., Gillespie, A.R., Tockner, K., Faux, R.N., Tan, J., 2012, Chapter 5- Thermal infrared remote sensing of water temperature in riverine landscapes In Carbonneau, P., Piegay, H., eds., Fluvial Remote Sensing for Science and Management, first edition: Chichester, UK, John Wiley & Sons, Ltd., p. 85-113.
- Torgersen, C.E., Faux, R.N., McIntosh, B.A., Poage, N., Norton, D.J., 2001, Airborne thermal remote sensing for water temperature assessment in rivers and streams: Remote Sensing of Environment, v. 76, p. 386-398.
- Readings: Campbell & Wynne Chapters 9, 13, 16 and 17; Franklin et al. 2000
Week 4 -- Lecture Slides
- Natural Resource Management Remote Sensing Examples from Forestry, Landcover Change, Landscape Ecology, Geovisualization, Landuse Planning, and Wildlife Applications Continued
- Readings: Campbell & Wynne Chapters 20 and 21; Moskal and Franklin 2004
Week 5 -- Midterm Prep Slides
Week 6 -- Lecture Slides continued from week 4
- Accuracy Assessments
- Field Data Collection Guest Lecture by Dr. Jeff Richardson and Meghan Halabisky (both with UW RSGAL)
- Readings for guest lecture:
- Halabisky, M.,L. M. Moskal and S. A. Hall, 2011. Object-Based Classification of Semi-Arid Wetlands, Journal of Applied Remote Sensing, 5(05351); p.13.
- Richardson, J. and L. M. Moskal, 2013. Uncertainty in Urban Forest Canopy Assessment: Lessons from Seattle, WA USA, Urban Forestry and Urban Greening, p. 12.
- Readings: Campbell & Wynne Chapter 14; Sullivan et al. 2009
Week 7 -- Lecture Slides
- Statistical Pattern Recognition and Image Segmentation
- Per-pixel and Object Based Image Classification
- Readings: Campbell & Wynne Chapters 11 and 12; Moskal et al. 2011; Myint et al. 2011
Week 8 -- Lecture Slides
- Active Remote Sensing
- Aerial and Terrestrail LiDAR
- Readings: Campbell & Wynne Chapter 7; Vaughn et al. 2011; Erdody and Moskal 2010; Zheng and Moskal 2009; Richardson et al. 2009; Moskal et al. 2009; Kato et al. 2009
Week 9 -- Lecture Slides (continued from week 8)
- LiDAR Applications
- Class Discussion on Future Trends in Remote Sensing
- Reading: Campbell & Wynne Chapter 8; Moskal and Zheng 2012
Week 10 -- Lecture Slides
Final Projects (Lab 10) Due
Week after last lab
Lab 1 Geo-wiki
Lab 2 Google Earth
Lab 3 UW Map Library
Lab 4 Introduction to Computer Aided Image Segmentation -- SPRING Software
Lab 5 Advanced Computer Aided Image Segmentation and GIS Integration
Lab 6 Historical Change Detection and Accuracy Assessment
Lab 7 Mobile GIS and
Remote Sensing (Ground Systems and Unmanned Aerial System) --
Structure from Motion Software (TBD)
Meet in the SEFS Courtyard, guest lab instructor: Dr. Matthew Dunbar from the UW CSDE
Lab 8 Introduction to LiDAR Data Analysis -- FUSION Software
Lab 9 Advanced LiDAR Data Analysis and GIS Integration
Lab 10 - Final Project
Final Projects (Lab 10) Due
Week after the last lab
The course spans traditional and very new sub-branches of remote sensing, thus, there is no one textbook that would best fit this class content. Most of the readings you are expected to do are peer-reviewed literature reviews and research articles, the course readings are found below. Also below, are suggested textbooks that relate to the course content.
Optional Textbooks --- copy of books on hold @ Odegard Library (2 hour loan)
Thomas Lillesand, Ralph
W. Kiefer and Jonathan
Chipman, 2015. Remote Sensing and Image
Interpretation, 7th ed. Wiley,p768.
- James Campbell and Randolph Wynne, 2011. Introduction to Remote Sensing, 5th ed. The Guilford Press, p.667.
- Thomas Blaschke, Stefan Lang and Geoffrey Hay, 2008. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications (Lecture Notes in Geoinformation and Cartography). 1st ed. Springer, p. 836
UW - ESRM430 Remote Sensing is a group in Environmental Sciences on Mendeley.
- Midterm 20%
- Labs (9 @ 5% each) 45 %*
- Final Project (Lab 10) 25%
- Random Quizzes (3-5) 10 %
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).
Individual arrangments need to be made to take course as W credit; see Dr. Moskal in person.
*Annotated Bibliographies (Graduate
Students ONLY): Graduate students do not submit labs. Every
week, starting week two, an 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 9 annotated bibliographies. Thus, graduate
student are expected to attend the labs, however, the
annotated bibliographies will substitute for the lab grade,
midterm grade and Final Project (Lab 10 grade) totaling 90%
of the graduate student grade; the remanding 10% of the
graduate student grade is based on quizzes. Instructions
on how to produce an annotated bibliography are available
Library Site. Each bibliographic reference will be
graded as follows: 10 pts = Excellent, 8 pts = Good, 6 pts =
Fair, 4 pts = Poor, 0 pts = Late or did not hand in.
Assignments, Lab, Exam Submissions:
ESRM 430 Digital Dropbox to submit your labs, midterm, final and annotated bibliography. Always use your name in the file name of your submission. Always assure that you are uploading files to the correct lab/assignment/exam folder. You will have till the start of the next lab session to submit your lab.
Course Related Resources
Free Software Used in the Course
Remotely Sensed Data