Abstract
Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities’ green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016–2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.
Highlights
Green infrastructure (GI) has become a priority in most cities worldwide and has been recognized as an essential element in urban planning and development
This paper proposes a novel GI monitoring approach based on prototype integrated visible and infrared thermal cameras to automatically obtain different vegetation indices (VIs) based on growth and tree water status parameters on a tree-by-tree scale
Data acquisition was performed in Melbourne, Australia, mainly based on an integrated visible and infrared thermal camera developed and processed using customized computer vision algorithms
Summary
Green infrastructure (GI) has become a priority in most cities worldwide and has been recognized as an essential element in urban planning and development. The urban green infrastructure, which includes natural vegetation, parks, street trees, green roofs, and small gardens, provides various benefits to the environment, community, and the economy. The GI of a city contributes valuable benefits such as regulation and reduction of temperature during heatwaves [1] through plant transpiration [2], while green roofing decreases albedo [3]. GI improves air quality [2] and reduces flood risk [4,5] and stormwater pollution [6], among other environmental benefits. GI has been linked to improving people’s physical and mental health [1]. Within the major challenges in urban cities are extreme heat and the urban heat island effect (UHI)
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