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dc.contributor.authorAbercrombie, Stewart Parkeren_US
dc.date.accessioned2016-02-05T16:01:47Z
dc.date.available2016-02-05T16:01:47Z
dc.date.issued2014
dc.identifier.urihttps://hdl.handle.net/2144/14299
dc.description.abstractLand cover information is a key input to many earth system models, and thus accurate and consistent land cover maps are critically important to global change science. However, existing global land cover products show unrealistically high levels of year-to-year change. This thesis explores methods to improve accuracies for global land cover classifications, with a focus on reducing spurious year-to-year variation in results derived from MODIS data. In the first part of this thesis I use clustering to identify spectrally distinct sub-groupings within defined land cover classes, and assess the spectral separability of the resulting sub-classes. Many of the sub-classes are difficult to separate due to a high degree of overlap in spectral space. In the second part of this thesis, I examine two methods to reduce year-to-year variation in classification labels. First, I evaluate a technique to construct training data for a per-pixel supervised classification algorithm by combining multiple years of spectral measurements. The resulting classifier achieves higher accuracy and lower levels of year-to-year change than a reference classifier trained using a single year of data. Second, I use a spatio-temporal Markov Random Field (MRF) model to post-process the predictions of a per-pixel classifier. The MRF framework reduces spurious label change to a level comparable to that achieved by a post-hoc heuristic stabilization technique. The timing of label change in the MRF processed maps better matched disturbance events in a reference data, whereas the heuristic stabilization results in label changes that lag several years behind disturbance events.en_US
dc.language.isoen_US
dc.subjectRemote sensingen_US
dc.subjectDecision treeen_US
dc.subjectLand cover classificationen_US
dc.subjectMarkov random fielden_US
dc.subjectTemporal consistencyen_US
dc.subjectTemporal stabilityen_US
dc.titleInter-annual stability of land cover classification: explorations and improvementsen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-01-22T18:55:35Z
etd.degree.nameM.A.en_US
etd.degree.levelmastersen_US
etd.degree.disciplineEarth & Environmenten_US
etd.degree.grantorBoston Universityen_US


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