Assessing developmental footprint within an agricultural system using multi-temporal remotely sensed data.

M. Sc. University of KwaZulu-Natal, Pietermaritzburg 2014.The advent of the new political dispensation in South Africa has seen an exponential growth in the rate of land transformation and encroachment by other land uses into agricultural land in the uMngeni Local Municipality. Accurate evaluation of the rate of transformation is necessary for effective monitoring and management of the natural agricultural resources. In this regard, the use of multi-temporal remote sensing data provides efficient and cost-effective method. The current research assesses the extent to which the development footprint in uMngeni Local Municipality has affected agricultural land categories or zones, using multi-temporal remote sensing data. The study endeavoured to map and quantify the magnitude of change in built-up land cover and other infrastructure by focusing on two time intervals: the periods from 1993 – 2003 and 2003 – 2013. Medium spatial resolution Landsat image data acquired for these periods were analysed to classify and extract the built-up features to appraise the level of change. Results revealed positive change in built-up infrastructure: ~13% increase between 1993 and 2003, ~38% increase from 2003 – 2013, with overall ~32% for the 20 years (1993 – 2013) period under consideration. Next, factors possibly contributing to the encroachment of other land uses into the agricultural landscape and the potential threats to the sustainability of the agricultural system are highlighted

Doctor of Philosophy

dissertationWith the tremendous growth of data produced in the recent years, it is impossible to identify patterns or test hypotheses without reducing data size. Data mining is an area of science that extracts useful information from the data by discovering patterns and structures present in the data. In this dissertation, we will largely focus on clustering which is often the first step in any exploratory data mining task, where items that are similar to each other are grouped together, making downstream data analysis robust. Different clustering techniques have different strengths, and the resulting groupings provide different perspectives on the data. Due to the unsupervised nature i.e., the lack of domain experts who can label the data, validation of results is very difficult. While there are measures that compute "goodness" scores for clustering solutions as a whole, there are few methods that validate the assignment of individual data items to their clusters. To address these challenges we focus on developing a framework that can generate, compare, combine, and evaluate different solutions to make more robust and significant statements about the data. In the first part of this dissertation, we present fast and efficient techniques to generate and combine different clustering solutions. We build on some recent ideas on efficient representations of clusters of partitions to develop a well founded metric that is spatially aware to compare clusterings. With the ability to compare clusterings, we describe a heuristic to combine different solutions to produce a single high quality clustering. We also introduce a Markov chain Monte Carlo approach to sample different clusterings from the entire landscape to provide the users with a variety of choices. In the second part of this dissertation, we build certificates for individual data items and study their influence on effective data reduction. We present a geometric approach by defining regions of influence for data items and clusters and use this to develop adaptive sampling techniques to speedup machine learning algorithms. This dissertation is therefore a systematic approach to study the landscape of clusterings in an attempt to provide a better understanding of the data