Investigating Temporal Patterns and Spatial Correlation in RSI

The world today is data-centric. The challenge that we have in such a world is extracting as much information from the data as the data contain. The challenge is made even worse when technology comes into the equation. Technology has led to the collection, storage, retrieval and analysis of data. With technology, it is possible to collect large volumes of data (such as remote sensing images, RSI, obtained both in space and time at a high frequency). Handling such kind of data becomes a challenge. This work partly presents one of the methods through which large volumes of data can be handled. The application of mathematical sciences in various disciplines is fascinating. Using mathematical sciences, methods for handling challenges in various sectors, such as environmental sector, are developed. Thus this work also presents one rare method that is used in analysing variation in terms of periodicity in multiple time series of, for example, climate data (this kind of data is mostly characterised by periodicity).

Autorentext

Alinune Musopole holds MSc in Envrionmental Statistics (2015, University of Glasgow, Scotland), PgDip in Mathematical Sciences (2012, African Institute for Mathematical Sciences, South Africa), and BSc in Mathematical Sciences Education (2009, University of Malawi). Dr. J.L. Mbotwa holds MSc, PhD in Biostatistics (2022), both from Leeds University.