Optical Remote Sensing Method for Detecting Urban Green Space as Indicator Serving City Sustainable Development

Abstract

Urban green space, discovered by optical remote sensors, is the area covered by terrestrial vegetation in urban areas, and is considered an important factor in urban sustainability. Two sensors ALOS/AVNIR-2 and Landsat/OLI&TIR were used in this study to determine green space by Maximum Likelihood Classification method. The investigated area was Nha Trang city, located in the central Vietnam. This was found that the impervious surfaces were rapidly increased leading to significantly reduce urban green space within 10 years from 2007–2017. In urban areas, the green index was very low compared to the TCXDVN 9257: 2012. Based on the Markov chain, it is projected that over the next 10 years, the total vegetation cover of the city will continue to decline compared to that of today. This is likely to lead to increase catastrophe and environmental risks, especially floods and erosion in the coastal city of Nha Trang. The process could be very useful in mapping urban green space as indicator serving city sustainable development.

Keywords

• green index
• optical remote sensing sensor
• sustainable development
• urban green space