Mapping Urban Green Spaces Based on an Object-Oriented Approach

Year 2018, , 71 - 81, 31.12.2018

Derya Gülçin , Abdullah Akpınar

https://doi.org/10.30516/bilgesci.486893

Cited By: 7

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.  

Keywords

Urban green space, Green infrastructure, Object-based classification

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