Ludmila M. Moskal
Focuses on hyperspatial remote sensing fundamentals, interpretation and manipulation of aerial photography, satellite imagery, and Light Detection and Ranging (LiDAR). Uses traditional and 'state of the art' image processing techniques. Students learn to evaluate available hyperspatial remote sensing data sources and design simple projects related to environmental applications. Offered: W.
(5 credits = 2 lecture credits + 3 lab credits)
W credit available, please speak with the instructor on an individual basis.
Students will be exposed to the principles of photogrammetry, image and LiDAR 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. 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.
Student learning goals
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.
Course reading materials can be found at: http://www.mendeley.com/groups/912761/uw-esrm430-hyperspatial-remote-sensing/
Learn more about your course instructors:
Faculty: Dr. L. M. Moskal -- http://faculty.washington.edu/lmmoskal/ TA: Guang Zheng -- http://staff.washington.edu/guangz/
Learn more about applied remote sensing research at UW: http://depts.washington.edu/rsgal/
General method of instruction
Lecture and computer labs. Some class discussions and group assignments. Hands on in the field experience with MobileGIS is offered on campus during one of the lab sessions.
Previous introductory, statistics, GIS and remote sensing classes will provide background but are not necessary. Familiarity with every day computer data storage and management is required for successful completion of computer lab work.
Class assignments and grading
Midterm 20% Final Exam 20% Labs (10 @ 5% each) 50% Random Quizzes 10%
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).