|| EAM1720 |
|| ADVANCED TECHNIQUES IN REMOTE SENSING |
|| 2006/2007 |
|| Dr Richard M Lucas |
|| Semester 2 |
|| Dr Aled P Rowlands |
|| EAM1320 |
| Course delivery
|| Lecture || 10 Hours. |
|| Practical || 10 Hours. |
|| Other || 30 Hours. Fieldwork |
|Assessment Type||Assessment Length/Details||Proportion|
|Semester Assessment|| Research report focusing on the use specific remote sensing techniques for practical applications.||50%|
|Semester Assessment|| Fieldwork report based on planning, acquisition, analysis, interpretation and presentation of remote sensing data obtained for the Cambrian Mountains.||50%|
|Supplementary Assessment|| Resubmit failed elements of coursework. No resit available if student failed to attend the fieldtrip without documented and approved special circumstances.|| |
Learning outcomesOn successful completion of this module students should be able to:
1. Identify, for particular applications, the most appropriate remote sensing datasets.
2. Independently use remote sensing software (including IDL and eCognition) for the analysis of multispectral, hyperspectral, LIDAR and radar and appropriately select tools for specific applications.
3. Derive digital elevation models from a range of remote sensing data.
4. Undertake field studies to support the interpretation and analysis of remote sensing data.
The course, which is applications focused, will provide students with a sound knowledge for processing and interpreting a range of remote sensing data for various applications. The course will also contain a field visit to the Cambrian Mountains.
Classification of land surfaces using a multi-spectral and hyperspectral data including Compact Airborne Spectrographic Imagery (CASI), HYMAP, Hyperion and Landsat sensor data. The students will be introduced to specialized techniques for extracting information from these sensors, which vary in terms of the spatial, spectral and radiometric resolutions. Processing of hyperspectral data will be in undertaken using IDL ENVI.
The theory of radar remote sensing, including the observation and modeling of microwave interaction with different land surfaces and structures. Applications of AIRSAR, JERS-1, ERS-1/2 SAR data for wetland monitoring, archaeology, biomass and land use/cover change. Techniques for processing and analysing airborne and spaceborne SAR data will be implemented using IDL ENVI.
Generation of digital elevation models (DEMs) using a combination of lidar, stereo aerial photography, ASTER, IKONOS and spaceborne SAR (including interferometry). Students will be introduced to new image processing software for DEM generation and analysis including SOCET SET, Erdas Imagine Orthobase and Gamma SAR processing software. The analysis of the derived DEMs will be undertaken in EAM1620: Advanced Applications in GIS.
Image segmentation and classification using eCognition software will be demonstrated with emphasis placed on the analysis of fine spatial resolution data for vegetation characterization and time-series datasets for monitoring tropical and cover, the dynamics of agricultural land and semi natural vegetation and glacial environments.
Field visit (in conjunction with GIS module) to the Berwyn Mountains, mid Wales, to conduct ground truthing to support the analysis and interpretation of spaceborne SAR and optical data and also airborne hyperspectral data.
|| Computer programming and development of skills in remote sensing (e.g., generation of digital elevation models). |
|| 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. |
|| 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. |
|| Fieldwork will be undertaken in groups who collectively will be involved in the experimental design. |
|| Use of commercial remote sensing software (IDL ENVI, eCognition, SOCET SET, Gamma and Erdas imagine) software for practical applications. Specific skills in programming and statistical analyses. |
|Personal Development and Career planning
|| Provision of advice and information on careers. |
|Subject Specific Skills
|| Use of imaging processing software.
** Recommended Text
Drake, J.B., Knox, R.G., Clark, D.B., Condit, R., Blair, J.B. and Hofton, M. (2003) (2003) Above -ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Global Ecology & Biogeography, Vol 12: 147-159
Dubayah, R. and Drake, J.B. (2000) Lidar remote sensing for forestry applications, Journal of Forestry, Vol 98: 44-46.
Imhoff, M. (1995) Radar backscatter and biomass saturation: ramifications for global biomass inventory, IEEE Transactions on Geoscience and Remote Sensing, 33, No 2, 511-518.
Justice, C.O., Townshend, J.R.G., Vermote, E.F., Masouoka, E., Wolfe, R.E., Saleous, N., Roy, D.P. and Morissete, J.T. (2002) An overview of MODIS land data processing and product status, Remote Sensing of the Environment, Vol 83: 3-15.
Kasischke, E.S., Melack, J.M. and Dobson, M.C. (1997) The Use of Imaging Radars for Ecological Applications - A Review. Remote Sensing of Environment, 59, 141-156.
Martonchik, J., Diner, D., Pinty, B., Verstraete, M., Myneni, R., Knyazikhin, Y., and Gordon, H. (1998) Determination of land and ocean refrective, radiative, and biophysical properties using multiangle imaging, IEEE Transactions on Geoscience and Remote Sensing, 36, No. 4, 1266-1281.
Means, J.E., Acker, S.A., Harding, D.A., Blair, B.J., Lefsky, M.A., Cohen, W.B., Harmon, M. and McKee, W.A. (1999) Use of large footprint scanning airborne lidar to estimate forest stand characteristics in the western Cascades of Oregon, Remote Sensing of Environment, 67, 298-308.
This module is at CQFW Level 7