Object-oriented change detection for the city of Harare, Zimbabwe

Object building and the extraction of homogeneous landscape units on which spatial statistics can be applied is useful in assessing land use and land cover change. Object-oriented processing techniques are becoming more popular compared to traditional pixel-based image analysis. A hierarchical image segmentation approach was adopted to extract the objects from multi-temporal Landsat images over Zimbabwe. The spatial arrangement of t0 and t1 objects was independent as the segmentation process was independently applied, although object change of t1 was based on t0 boundaries. We applied a Standardized, Object Oriented, Automatic Classification (SOOAC) method based on fuzzy logic. The error matrix for the TM image had an overall accuracy of 95.6% and a KIA value of 94.7%, the ETM showed slightly lower overall accuracy. Various LULC changes were identified over the 13 year period per object and also per class, mainly vegetation decrease. Object-oriented change information is necessary in decision support systems and uncertainty management strategies. This approach addresses some of the major issues in object-based GIS change analysis as it is based on stable object geometry.