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Historic gardens are green spaces characterised by tree stands with several veteran specimens of high artistic and cultural value. Such valuable plant components have to cope with biotic and abiotic stress factors as well as ongoing senescence processes. Maintaining tree health is therefore crucial to preserve their ecosystem services, but also to protect the monument and visitor health. In this context, finding smart, fast and cost-effective management solutions to monitor health and detect critical conditions for both stands and individual veteran trees can promote garden conservation. For this reason, we developed a novel framework based on Sentinel2 imagery, LiDAR sources and automatic cameras to identify risk spots regarding trees in historic gardens. The pilot study area consists of two closed Italian gardens from the 16th century, which were analysed as a unique Historic Garden System (HGS). The tree health status at stand level was assessed using a criterion based on the Normalized Difference Vegetation Index weighed on tree volume (NDVI t ) and validated by a visual crown defoliation assessment. At the tree level, the health status of four veteran trees defined by the NDVI t was also evaluated using green chromatic coordinates (GCC) obtained from digital images acquired by cameras at daily intervals during one growing season. The 33% of the tree population was classified as being in poor health, i.e. "at risk". Veteran trees classified as "at risk" showed an anticipation of phenological phases and a lower GCC compared to reference trees. Despite variability determined by Sentinel medium resolution, the proposed framework showed good accuracy (0.74) for monitoring historical gardens. The semi-automatic risk point mapping system tested here proved to be effective in facilitating the management of historic gardens, which in turn could be applied in the wider context of urban greening., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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.