Automated aboveground carbon estimation of forests with remote sensing

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dc.contributor.authorGordon, Piper
dc.contributor.supervisorGoodenough, D.
dc.contributor.supervisorMyrvold, W. J.
dc.date.accessioned2012-08-31T16:15:38Z
dc.date.available2012-08-31T16:15:38Z
dc.date.copyright2012en_US
dc.date.issued2012-08-31
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractCanada’s forests are believed to contain 86 gigatons of carbon, stored above and below ground. These forests are large in area, making them difficult to monitor using conventional means. Understanding the carbon cycle and the role of forests as carbon sinks is crucial in the investigation and mitigation of climate change to address national obligations. One economical solution for monitoring the carbon content of Canada’s forests is the development of an automated computer system which uses multisource remotely sensed data to estimate the aboveground carbon of trees. The process involves data fusion of remotely sensed hyperspectral data for tree species information and lidar (light detection and ranging) and radar (radio detection and ranging) for tree height. The size and dimensionality of the data necessitate the efficient use of computing resources for analysis. The outcome is a useful carbon measuring system. The three research questions are: (1) How do we map with remote sensing aboveground carbon in the forests? (2) How do we determine the accuracies of these aboveground carbon maps? (3) How can an automated system be designed for creating aboveground carbon maps?en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/4227
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectRemote Sensingen_US
dc.subjectClimate Changeen_US
dc.subjectForesten_US
dc.subjectAboveground Carbonen_US
dc.subjectBiomassen_US
dc.subjectAutomated Computer Systemen_US
dc.titleAutomated aboveground carbon estimation of forests with remote sensingen_US
dc.typeThesisen_US

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