Parcel Level Agricultural Land Cover Prediction

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dc.contributor.advisorGentle, James E.
dc.contributor.authorLisic, Jonathan James
dc.creatorLisic, Jonathan James
dc.date.accessioned2016-04-19T19:28:51Z
dc.date.available2016-04-19T19:28:51Z
dc.date.issued2015
dc.description.abstractThe purpose of this research is to develop and study the methodology and the underlying theory for prediction of agricultural land cover for a set of commercial crops at the parcel level. The observational unit will be called a land cover unit (LCU). Each LCU will have an associated land cover sequence. A land cover sequence is an ordered set of known categories indexed by a set of fixed consecutive years. An LCU is the maximally contiguous section of land with respect to a single land cover sequence, not transected by public transportation arteries or permanent hydrographic boundaries. LCUs are not observed, instead they are estimated through an image segmentation algorithm known as mean shift, applied to high resolution imagery products. The predictors of land cover are constructed under the assumption of temporal stationarity; this assumption limits the length of the land cover sequence that can be used to aid in the prediction. Land cover sequences for the LCUs are estimated from classified pixel level data. Prediction of future land cover is performed through a Bayesian hierarchical multinomial probit model accounting for spatially correlated crop rotation preferences. Application to major commercial crops in La Porte County, Indiana, is provided using high resolution imagery and thematic maps from the United States Department of Agriculture. The theory and methods are applicable to prediction of agricultural crops in other areas with a relatively stable pattern of agricultural land cover.
dc.format.extent150 pages
dc.identifier.urihttps://hdl.handle.net/1920/10182
dc.language.isoen
dc.rightsCopyright 2015 Jonathan James Lisic
dc.subjectStatistics
dc.subjectAgriculture
dc.subjectComputer science
dc.subjectCrop Rotation
dc.subjectImage Segmentation
dc.subjectLand Cover
dc.subjectMean Shift
dc.subjectMultinomial Probit
dc.subjectPrediction
dc.titleParcel Level Agricultural Land Cover Prediction
dc.typeDissertation
thesis.degree.disciplineComputational Sciences and Informatics
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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