Spatial deaggregation of agricultural statistics using constrained cross entropy minimisation
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Examensarbete för masterexamen
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Every year, millions of hectares of forests disappears in the tropics, something that has
impacts on both local and global scale. Locally, the forest loss impacts ecosystem services
such as water, energy and food security. Globally, the tropical forest loss releases large
amounts of carbon dioxide into the atmosphere, making it the second largest driving
factor of climate change, after combustion of fossil fuels.
In research aiming to catalogue the main driving factors behind deforestation linked
to land usage, there is a need to spatially downscale agricultural statistics from large
statistical reporting units into smaller, in order to increase the accuracy of the driver
identification. This thesis will investigate how one feasibly can do this deaggregation.
Specifically, we have used constrained cross entropy minimisation, a method which aims to
minimise the difference between a target distribution and a prior assessment of the spatial
distribution of land usage, while also taking certain limiting constraints into account.
For our investigations, we have chosen to focus on the Brazilian state Mato Grosso, a
region that has experienced deforestation due to the spread of agricultural land uses. The
prior will be created from land cover maps, generated from satellite imagery. Here we
evaluated different prior preprocessing methods, finding that a rescaling of the land cover
classifications using the land cover map’s confusion matrix was the method with the most
promising result, capturing the general shape of the true distribution while also having a
reasonable area distribution. It was however also noted that this method has a difficulty,
common when dealing with land cover classification, in separating certain similar land
uses from one another. It is suspected that this is due to the quality of the prior, and it
would be interesting to investigate further how adjustments to the prior creation would
improve the results found in this thesis.
Beskrivning
Ämne/nyckelord
Cross entropy minimisation, agricultural statistics, deaggregation, deforestation, land use, prior, certainty parameter, confusion matrix