A study of urban sprawl in the South Malta local plan using multiresolution remote sensing data

Abstract

Technological advancements and data availability, Geographic Information Systems (GIS) and remote sensing have become key to detect land cover changes over time and objects. GIS and its tools have proven to be indispensable to monitor land use and cover changes. Remote sensing is a process that consists of a number of parts, starting from the collection of data by means of sensors, to the processing and use of the data in remote sensing - related research. The phrase “urban sprawl” has been defined in many different ways, as it is a phenomenon that can be viewed from multiple angles. A number of key features used to define urban sprawl keep reappearing in multiple texts, including fragmentation, decentralization, loss of space and density features. The growth of populations, economic advancement and urban - to - rural migration all contribute to the urban sprawl process. Unsupervised classification is a process in which the image classification software defines and clusters pixels into unique classes without the need of training sites from the image analyst. It was the classification method chosen for this study. The area of study is the South Malta Local Plan (SMLP). 2 datasets, from 1996 and 2019, were used to detect urban sprawl. The datasets used were from SPOT - 2 and Landsat - 8 satellites. Variations in the unsupervised classification process were also analysed for accuracy improvement. Temporal change showed an increase in the Building land cover type, showing possible signs of urban sprawl.