| Summary: | A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. Johannesburg, 30th May 2014. === The Multi-Angle Imaging SpectroRadiometer (MISR) is an Earth observation instrument operated by
NASA on its Terra satellite. The instrument is unique in imaging the Earth’s surface from nine cameras
at different angles. An extended system MISR-HR, has been developed by the Joint Research Centre
of the European Commission (JRC) and NASA, which derives many values describing the interaction
between solar energy, the atmosphere and different surface characteristics. It also generates estimates
of data at the native resolution of the instrument for 24 of the 36 camera bands for which on-board
averaging has taken place prior to downloading of the data. MISR-HR data potentially yields high
value information in agriculture, forestry, environmental studies, land management and other fields. The
MISR-HR system and the data for the African continent have also been provided by NASA and the
JRC to the South African National Space Agency (SANSA). Generally, satellite remote-sensing of the
Earth’s surface is characterised by irregularity in the time-series of data due to atmospheric, environmental
and other effects. Time-series methods, in particular for vegetation phenology applications, exist
for estimating missing data values, filling gaps and discerning periodic structure in the data. Recent
evaluations of the methods established a sound set of requirements that such methods should satisfy.
Existing methods mostly meet the requirements, but choice of method would largely depend on the
analysis goals and on the nature of the underlying processes. An alternative method for time-series exists
in Gaussian Processes, a long established statistical method, but not previously a common method
for satellite remote-sensing time-series. This dissertation asserts that Gaussian Process regression could
also meet the aforementioned set of time-series requirements, and further provide benefits of a consistent
framework rooted in Bayesian statistical methods. To assess this assertion, a data case study has
been conducted for data provided by SANSA for the Kruger National Park in South Africa. The requirements
have been posed as research questions and answered in the affirmative by analysing twelve
years of historical data for seven sites differing in vegetation types, in and bordering the Park. A further
contribution is made in that the data study was conducted using Gaussian Process software which was
developed specifically for this project in the modern open language Julia. This software will be released
in due course as open source.
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