Course Description
Remote sensing, with emphasis on aerospace applications in the natural sciences. Fundamental properties of the electromagnetic spectrum and remote sensing devices, such as multispectral and hyperspectral sensors, thermal infrared scanners, and LiDAR.
Additional Requirements for Graduate Students:
Additional readings, assignments, and questions on tests.
Athena Title
Remote Sensing of Environment
Prerequisite
GEOG 4330/6330-4330L/6330L or permission of department
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
Successful completion of this course will provide the following learning outcomes: A basic understanding of the physics of electromagnetic energy that underlie the acquisition, analysis and application of remote sensing data, including aerial photographs, airborne and satellite optical digital images and thermal/microwave digital imagery. An appreciation of remote sensing information content and ethical issues of privacy and proper use of remote sensing data. An awareness of the concepts and application of scale, coordinate systems, geometric and thematic accuracy, uncertainty and thematic content. An understanding of the historical development of remote sensing technology, national and international remote sensing programs and its integration in geographic information systems. A greater cognizance of the importance of remote sensing in our daily lives and the crucial role it can play in monitoring environmental change, assessing human impacts and influencing policy decision-making. This course meets the following General Education Abilities by accomplishing the specific learning objectives listed below: Communicate effectively through writing. This is met by a series of writing assignments associated with supplemental reading and data analysis. Communicate effectively through speech. This is met by oral presentations, discussion leading, and classroom participation. Computer Literacy is addressed through course administration, student-faculty electronic interaction, data analysis activities and assignments, and exposure to GIS technologies. Critical Thinking is central to the learning objectives of this class, and are developed through homework assignments, lecture, classroom discussion, and inquiry- based learning efforts. Moral Reasoning (Ethics) is an important element of this course, as it considers ethical guidelines for use of remote sensing technologies and considers the role of mapping sciences in economic development and human welfare. Moral reasoning is developed through lectures, writing assignments, classroom discussion, and inquiry- based learning activities.
Topical Outline
Fundamentals of the electromagnetic spectrum; radiance Raster data structure and representation Lab 1: Intro to Digital Image Processing Photographic systems Aerial photograph geometry; scanning; resolving power; scale Lab 2: Scale measurements and scanning Digital Photogrammetry: geometric correction, image rectification, polynomials, orthoimage generation Visual interpretation of image data Lab 3: Analysis of imaging spectrometer data: spectral response of features in remotely sensed images Applications of visual interpretation of image data Multispectral, thermal, hyperspectral remote sensing overview Lab 4: Accessing remotely sensed data via the worldwide web Remote sensor scanners geometry; orbits; stereo Thermal and hyperspectral imagery Lab 5: Multispectral/hyperspectral satellite images Remote sensing systems: history Landsat Lab 6: Landsat TM and ETM+; Rectification SPOT and high resolution commercial satellite systems NASA’s Earth Observing System Lab 7: On-screen compilation of land use/land cover from Landsat TM data search and retrieval Digital image processing: georeferencing; polynomial transformations; data formats Image enhancement, contrast manipulation, pan-sharpening Lab 8: Image enhancement; pan-sharpening Spatial feature manipulation: filtering Multi-image manipulation: ratios, NDVI Lab 9: Image filtering, ratios Supervised image classification: theory, methods, classifiers Supervised classification: training and implementation Lab 10: Supervised classification of Landsat TM Unsupervised classification Hybrid classification; mixed pixels; output; post-classification smoothing; hyperspectral classification Lab 11: Unsupervised classification; post-classification processing Classification accuracy assessment Change detection; data merging and integration Lab 12: Change detection and accuracy assessment Contextual classification Stereo image data and DEM generation Lab 13: DEM generation from ASTER stereo image data; terrain visualization Radar, SRTM, microwave systems Lidar and applications
Syllabus