An advanced classification system for processing multitemporal landsat imagery and producing Kyoto Protocol products

dc.contributor.authorChen, Hao.en_US
dc.contributor.supervisorGoodenough, D.en_US
dc.date.accessioned2008-04-10T05:57:43Z
dc.date.available2008-04-10T05:57:43Z
dc.date.copyright2004en_US
dc.date.issued2008-04-10T05:57:43Z
dc.degree.departmentDept. of Computer Scienceen_US
dc.description.abstractCanada has 418 million hectares of forests, representing 10% of the forested land in the world [I]. In 1997, Canada signed the Kyoto Protocol and agreed to cut greenhouse gas emissions by six percent below the 1990 level between 2008 and 2012 [2]. This agreement was ratified in December 2002. It requires Canada to report Canada's sustainable forest resources, including information about forest carbon, afforestation, reforestation, and deforestation (ARD). To fulfill this commitment, effective and accurate measuring tools are needed. One of these tools is satellite remote sensing, a cost-effective way to examine large forested areas in Canada for timely forest information. Historically, the study of forest aboveground carbon was carried out with detailed forest inventory and field sampling from temporary and permanent sample plots, which severely limited the forest area that could be studied. For regional and global scales, it is necessary to use remote sensing for aboveground carbon and ARD mapping due to time and financial constraints. Therefore, the purpose of this research is to develop, implement, and evaluate a computing system that uses multitemporal Landsat satellite images [3] to estimate the Kyoto-Protocol-related forest parameters and create geo-referenced maps, showing the spatial distribution of these parameters in a Geographic Information System (GIs). The new computing system consists of a segment-based and supervised classification engine with feature selection functionality and a Kyoto-Protocol-products estimation unit. The inclusion of the feature selection reduces the large dimensionality of the feature space of multitemporal remote Landsat data sets. Thus, more images could be added into the data sets for analysis. The implementation of the segment-based classifiers provides more accurate forest cover classifications for estimating the Kyoto Protocol products than pixel-based classifiers. It is expected that this approach will be a new addition to the current existing methodologies for supporting Canada's reporting commitments on the sustainability of the forest resources in Canada. This approach can also be used by other countries to monitor Canada's compliance with international agreements.en_US
dc.identifier.urihttp://hdl.handle.net/1828/453
dc.subject.lcshForests and forestry -- Remote sensingen_US
dc.subject.lcshForests and forestry -- Canadaen_US
dc.titleAn advanced classification system for processing multitemporal landsat imagery and producing Kyoto Protocol productsen_US

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