Content extraction of long-term Satellite Image Time Series: spatiotemporal analysis of Bucharest metropolitan area

Abstract

Current earth observation data repositories enable us to extract observations of the same geographical area over the course of time and to create satellite image time series (SITS). Such SITS are data sets of high complexity, embedding spatial, spectral and temporal information. The rich content of SITS has generated a great interest in the recent years, many SITS analysis methods being developed. Many of these methods are focused towards improving the precision of land use/land cover detection by exploiting the inherent redundancy of SITS data. Some other methods focus on the evolution of a specific land use/land cover type. Often these methods are applied on SITS that span over a short period of time, typical one or several years. In this paper we focus on a different use scenario of SITS analysis by extracting classes of dynamic evolutions from long-term satellite image time series. As such we create a SITS of over 100 Landsat datasets, spanning from 1984 to 2011, covering the metropolitan area of Bucharest, Romania – perhaps the longest SITS ever being used. The analysis of this SITS aims to evaluate the spatio-temporal evolutions, focusing on finding classes of temporal dynamics of the main land use/land cover classes of the studied area, such as urban areas, agricultural land, forestry and water bodies, identifying interesting evolutions in the studied area. Furthermore, the extracted dynamic classes are used to establish relationships with known ancillary data such as CORINE Land Cover vector data sets or statistical datasets for demographics of the studied area.