Abstract
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.
Highlights
Urban areas are considered as economic, social, and cultural centers, which are the most challenging areas for remote sensing analysis due to high spatial and spectral diversity of surface materials (Herold et al, 2003; Poursanidis et al, 2015; Hu et al, 2016)
Mapping urban land cover provides the primary data for various studies such as change detection (Stefanov et al, 2011; Rogan and Chen, 2004; Shalaby and Tateishi, 2007; Rawat and Kumar, 2015), assessment of urban public health (Wolch et al, 2014; Akpinar, 2016), evaluation of urban ecosystem services (Derkzen et al, 2015; Baró et al, 2016; Grafius et al, 2016), and monitoring urban climate (Bechtel et al, 2015; Blok and Tschötschel, 2016)
One of land dynamics that should be mapped for the sustainability of urban area is green spaces
Summary
Urban areas are considered as economic, social, and cultural centers, which are the most challenging areas for remote sensing analysis due to high spatial and spectral diversity of surface materials (Herold et al, 2003; Poursanidis et al, 2015; Hu et al, 2016). Mapping Urban Green Spaces Based on an Object-Oriented Approach. Based on remotely sensed data, image classification is an important process for mapping Land Use/Land Cover (LULC) (Foody, 2002; Bartholome and Belward, 2005). This research focuses on an object-based decision tree classification using an orthophoto of Aydin urban area and aims to present how can be urban green spaces mapped based on an object-oriented approach. In this context, it is comprehensively used the spectral features to extract the green space information from the test area that exemplifies the park, the green cover in the agricultural area, the playground, and the residential garden. Features as brightness and Green-Red Vegetation Index (GRVI) were used for the extraction of urban green areas
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