Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

Vain tiivistelmä. Opinnäytteiden sidotut arkistokappaleet ovat luettavissa HY:n keskustakampuksen valtiotieteiden kirjastossa (Unioninkatu 35). Opinnäytteitä lainataan ainoastaan mikrokortteina kirjaston kaukopalvelun välityksellä.

Abstract only. The paper copy of the whole thesis is available for reading room use at the Library of Social Sciences (Unioninkatu 35) . Microfiche copies of these theses are available for interlibrary loans.

Endast avhandlingens sammandrag. Pappersexemplaret av hela avhandlingen finns för läsesalsbruk i Statsvetenskapliga biblioteket (Unionsgatan 35). Dessa avhandlingar fjärrutlånas endast som microfiche.