Land cover/land use change analysis using multi-spatial resolution data and object-based image analysis

Abstract

The purpose of this paper is to develop and test land use and land cover change techniques when only Landsat imagery is available for the first date and high spatial resolution imagery is available for the second. The study site is the city of Accra, the capital and largest city in Ghana, located in Western Africa and characterized by rapid urbanization and land change in recent years. The paper probes two important questions regarding remote sensing derived land cover change analyses in urban environments in the developing world. First, given the classes of interest (residential, non-residential, and undeveloped) in the context of analyzing LCLUC in major urban areas of Ghana, how accurately and reliably can LCLU be identified based on Landsat ETM+ moderate spatial resolution satellite imagery? Secondly, what is the utility of a post-classification change identification approach that is based on an initial object-based classification of a high spatial resolution time 2 image, which is used to constrain the segmentation and subsequent classification of a time 1 moderate spatial resolution image? Land use change (LCLUC) analysis with remote sensing requires image datasets of the different periods to be as similar as possible, which includes near anniversary date of capture, similar spatial, spectral, and radiometric resolutions and coverage. However, this is not always feasible due to several factors, including unavailability of high spatial resolution imagery for historical dates and frequent cloud cover. While it is preferable to use high spatial resolution imagery to perform detailed urban land use and land cover change, such imagery for much of the tropics is not readily available for the early 2000s. Results suggest the suitability of using Landsat imagery combined with more contemporary high resolution imagery.