[UW-GIS-L] ESRM430 Hyperspatial Remote Sensing (Spring 2009)
L. M. Moskal
lmmoskal at u.washington.edu
Sun Mar 1 14:11:25 PST 2009
ESRM430 - Hyperspatial Remote Sensing
Aerial Photos & LiDAR Remote Sensing in Natural Resources
Lectures: TTh 12:30 - 1:20 ROOM: WINK201
Labs: T 1:30 - 4:20 MGH 030
Instructor: Dr. L.M. Moskal
Course website - http://courses.washington.edu/esrm430/
Course objectives: To develop an understanding of hyperspatial remote sensing fundamentals & the ability to interpret & manipulate remotely sensed images & data sets. Students will be presented with the traditional & 'state of the art' image processing techniques, & a firm theoretical & practical background in hyperspatial remote sensing applications. By the end of the course students will be expected to evaluate available remote sensing data sources & design simple projects related to environmental applications
(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.
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