Module Information

Module Identifier
EAM3620
Module Title
Skills in Remote Sensing
Academic Year
2014/2015
Co-ordinator
Dr George Petropoulos
Semester
Semester 2
Pre-Requisite
EAM4020
Other Staff
 

Course Delivery

Delivery Type Delivery length / details
Lecture 5 X 2 hrs
Practical 10 x 2 hrs (laboratory) 8 hrs (field work)
 

Assessment

Assessment Type Assessment length / details Proportion
Semester Assessment Coursework. fieldwork report based on analysis of remote sensing data (3000 words)   50%
Semester Exam 3 hours practical-based examination  50%
Supplementary Assessment Resubmission of failed assignment  50%
Supplementary Exam 3 Hours   Supplementary Exam  To be scheduled in F4, due to nature of computers used for exam.  50%

Learning Outcomes

On successful completion of this module students should be able to:

  1. Identify, for particular applications, the most appropriate remote sensing datasets.
  2. Independently using remote sensing software for the analysis of multispectral, hyperspectral, lidar and radar data.
  3. Implement approaches to the derivation of products from remote sensing data (e.g., vegetation indices and digital elevation models).
  4. Undertake field studies to support the interpretation and analysis of remote sensing data.
  5. Be able to apply an object oriented classification of remote sensing imagery

Brief description

The module is intended to provide students with a background in advanced processing and analysis of a range of remote sensing data for applications in physical geography, biology, computer science and physics. The module also includes a field visit within the local area, during which insight into the interpretation of remote sensing data acquired by a range of sensors will be obtained.

Content

  1. Object-orientated image analysis and classification
  2. Techniques for change detection between datasets
  3. Hyperspectral data analysis
  4. Laser scanning
  5. Radar data anaylsis
  6. Thermal remote sensing data analysis

Module Skills

Skills Type Skills details
Application of Number Problem solving assignment
Communication Skills report writing and submission of a discussion paper. Discussion groups within the Blackboard teaching and learning environment
Improving own Learning and Performance Library and web-based referencing; literature review and discussions with scientists
Information Technology Use of commercial and open sources software for practical applications. Specific skills in programming and statistical analysis.
Personal Development and Career planning Awareness of scientific literature, functionality of software and programming. Provision of advice and information for careers.
Problem solving Image classification through programming and development of skills in remote sensing data interpretation; awareness of scientific literature and future directions for research.
Research skills Basic strategies relating to remote sensing data sources and acquisition strategies, data integration and processing within the framework of a GIS and effective analysis and interpretation of remote sensing data, field data collection to support interpretation of remote sensing data. Reviewing literature.
Subject Specific Skills As above
Team work Fieldwork will be undertaken in groups who will be involved in decisions relating to data collection and image analysis and interpretation

Reading List

Essential Reading
Borengasser, Marcus, Hungate,William, S., and Watkins, Russell (2008) Hyperspectral Remote Sensing: Principles and Applications CRC Press, Taylor & Francis Group Primo search Thenkabail, Prasad S. (Editor), Lyon, John G. (Editor), Huete, Alfredo (Editor) (2012) Hyperspectral Remote Sensing of Vegetation CRC Press, Taylor & Francis Group Primo search Drake, J.B., Knox, R.G., Clark, D.B., Condit, R., Blair, J.B. and Hofton, M. Global Ecology & Biogeography Above -ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Vol 12: pp. 147-159 Primo search Dubayah, R. and Drake, J.B. Journal of Forestry Lidar remote sensing for forestry applications Vol 98: pp. 44-46 Primo search Imhoff, M. IEEE Transactions on Geoscience and Remote Sensing Radar backscatter and biomass saturation: ramifications for global biomass inventory 33, No 2, pp. 511-518 Primo search Justice, C.O., Townshend, J.R.G., Vermote, E.F., Masouoka, E., Wolfe, R.E., Saleous, N., Roy, D.P. and Morissete, J.T. Remote Sensing of the Environment An overview of MODIS land data processing and product status Vol 83: pp. 3-15. Primo search Kasischke, E.S., Melack, J.M. and Dobson, M.C. Remote Sensing of the Environment The Use of Imaging Radars for Ecological Applications - A Review 59, pp. 141-156 Primo search Martonchik, J., Diner, D., Pinty, B., Verstraete, M., Myneni, R., Knyazikhin, Y., and Gordon, H. IEEE Transactions on Geoscience and Remote Sensing Determination of land and ocean refrective, radiative, and biophysical properties using multiangle imaging 36, No. 4, pp. 1266-1281 Primo search Means, J.E., Acker, S.A., Harding, D.A., Blair, B.J., Lefsky, M.A., Cohen, W.B., Harmon, M. and McKee, W.A. Remote Sensing of Environment Use of large footprint scanning airborne lidar to estimate forest stand characteristics in the western Cascades of Oregon 67, pp. 298-308 Primo search

Notes

This module is at CQFW Level 7