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Pixel Based and Object Oriented Multi-spectral Remotely Sensed Data Analysis for Flood Risk Assessment and Vulnerability Mapping.
Linköping University, Department of Computer and Information Science.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Geographical information system with remotely sensed data can be instrumental in many ways for disaster management and post disaster rehabilitation. During last few decades the usage of remotely sensed data has extensively increased, although image interpretation tools are not highly accurate but still considered as fast, reliable and useful way to get information from imagery. Disaster assessment, management and rehabilitation are always creates challenge for experts. Population growth, expansion in settlements either in the rural or in the urban areas bring more problems not only for the humans but it also affect the global environment Such global changes on the massive scale disturbs the ecological processes. GIS along with Remote sensing data can change the whole scenario in very short period of time. All the departments concerning to strategic disaster planning process can share their information by using the single platform, so for this purpose spatial database can be helpful by providing the spatial data in digital format to the department concerned. Spatial phenomena can be observed by using different image analysis techniques and the resultant thematic map display the spatial variations and changes that describe the particular phenomenon whether it was any disaster or change in soil type or vegetation type. Remotely sensed data like aerial, satellite and radar images are very useful for disaster management strategy formulation process. Integration of GIS and remote sensing proved itself the best especially for land-use, land-cover mapping. For this purpose pixel based, sub-pixel based, pre-field and object oriented classification approach are being in use around the world. But thematic maps created from image analyzed by using object oriented classifiers contain more accuracy than any other techniques.

Place, publisher, year, edition, pages
2010. , 162 p.
National Category
Other Computer and Information Science
URN: urn:nbn:se:liu:diva-64011ISRN: LIU-IDA/FFK-UP-A--11/001--SEOAI: diva2:385022
2010-12-22, Herbert Simon, Building E, Campus Valla, Linköping, Linköping, 15:00 (English)
Life Earth Science
Available from: 2011-01-11 Created: 2011-01-11 Last updated: 2011-01-11Bibliographically approved

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