• Energy Research

Urban green space is a type of open space furnished with grass, trees, flowers, water as well as some necessary infrastructures. It is an essential element of cities to the quality of life for urban residents. In current years, more and more urban planners pay great concern with the relationship of climate change and urban green space system. They are looking for smart ways of urban green space planning to meet the needs of climate adaptation and climate change mitigation. This paper explores green space in the city of Adelaide in South Australia and compares green space policies and practices in Adelaide city with Fuzhou city in South China, and analyses and summarises the main conceptions and data of Fuzhou green space system planning (2015–2020) with the aim of promoting Adelaide city green space planning in future. An improved green space system in Adelaide will strengthen the resilience of the city to climate change as well as other challenges presented with the growing population and the growing urban areas.

More well-maintained green spaces leading toward sustainable, smart green cities mean that alternative water resources (e.g., wastewater) are needed to fulfill the water demand of urban greenery. These alternative resources may introduce some environmental hazards, such as salt leaching through wastewater irrigation. Despite the necessity of salinity monitoring and management in urban green spaces, most attention has been on agricultural fields. This study was defined to investigate the capability and feasibility of monitoring and predicting soil salinity using proximal sensing and remote sensing approaches. The innovation of the study lies in the fact that it is one of the first research studies to investigate soil salinity in heterogeneous urban vegetation with two approaches: proximal sensing salinity mapping using Electromagnetic-induction Meter (EM38) surveys and remote sensing using the high-resolution multispectral image of WorldView3. The possible spectral band combinations that form spectral indices were calculated using remote sensing techniques. The results from the EM38 survey were validated by testing soil samples in the laboratory. These findings were compared to remote sensing-based soil salinity indicators to examine their competence on mapping and predicting spatial variation of soil salinity in urban greenery. Several regression models were fitted; the mixed effect modeling was selected as the most appropriate to analyze data, as it takes into account the systematic observation-specific unobserved heterogeneity. Our results showed that Soil Adjusted Vegetation Index (SAVI) was the only salinity index that could be considered for predicting soil salinity in urban greenery using high-resolution images, yet further investigation is recommended.