Mapping Urban Green Spaces Based on an Object-Oriented Approach

The advent of technology and its implications on especially remote sensing image processing using High Resolution Satellite Images (HRSI) to map land cover provide researchers to monitor land changes, make landscape analyses, and manage land transformation. One of land dynamics that should be mapped for the sustainability of urban area is green spaces. Urban green spaces, such as parks, playgrounds, and residential greenery may promote both mental and physical health. Besides, they contribute to ecosystem services such as reducing heat island effect and carbon storage, aiding water regulation etc. Therefore, mapping urban green infrastructure from a high-resolution satellite image provides an important tool to conduct studies, researches, and projects for sustainable development of urban areas. As the material of this research, one of the orthophotos of Aydin urban area exemplifies the park, the green cover in the agricultural area, the playground, and the residential garden, was used. For classifying land cover from the orthophoto with Object-Based Image Analysis (OBIA), eCognition Developer 9.0 software was utilized. To combine spectral and shape features, multiresolution segmentation was implemented. Additionally, features as brightness and ratio green were used for the extraction of urban green areas. In this research, urban green areas were successfully extracted from the orthophoto and accuracy assessment was performed on the classified image. OBIA of high resolution imagery enables to extract detailed information of various targets on urban areas. The result of accuracy assessment of the classification achieved 84.68% overall accuracy. To increase the accuracy via manual interventions, manual classification tool of eCognition Developer 9.0 may be used if needed.