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Tree monitoring is a challenging task due to the labour-intensive and time-consuming data collection methods required. We present TreeEyed, a QGIS plugin designed to facilitate the monitoring of trees using remote sensing RGB imagery and artificial intelligence models. The plugin offers several tools including tree inference process for tree segmentation and detection. This tool was implemented to facilitate the manipulation and processing of Geographical Information System (GIS) data from different sources, allowing multi resolution, variable extent, and generating results in a standard GIS format (georeferenced raster and vector). Additional options like postprocessing, dataset generation, and data validation are also incorporated.

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. • Senescent tree maintenance is a main issue of historic garden management. • Precision forestry techniques may simplify historic garden management. • We tested smart monitoring techniques on the tree component of two historic gardens. • A criterion to select risk spots based on Sentinel2 and LIDAR data was validated. • Images from automatic cameras were analysed for veteran tree monitoring. [ABSTRACT FROM AUTHOR]

ABSTRACT: Leaf hyperspectral reflectance has been used to estimate nutrient concentrations in plants in narrow bands of the electromagnetic spectrum. The aim of this study was to estimate leaf nutrient concentrations using leaf hyperspectral reflectance and verify the variable selection methods using the partial least squares regression (PLSR). Two studies were carried out using stands with Eucalyptus clones. Study I was established in Eucalyptus stands with three clones, classifying leaves into five colour patterns using the Munsell chart for plant tissues. Immediately after leaf collection, leaf reflectance was read and the chemical analysis was performed. Study II was carried out in commercial clonal stands of Eucalyptus performing the same leaf sampling and chemical analysis as used in Study I. All leaf reflectance spectra were smoothed and three more pre-processing procedures were applied. In addition, three methods of PLSR were tested. The first derivative was more accurate for predicting nitrogen ( R c v 2 = 0.95), phosphorous ( R c v 2 = 0.93), and sulphur concentration ( R c v 2 = 0.85). The estimates for concentrations of calcium ( R c v 2 = 0.81), magnesium ( R c v 2 = 0.22), and potassium ( R c v 2 = 0.76) were more accurate using the logarithm transformation. Only the estimates for iron concentrations were performed with higher accuracy ( R c v 2 = 0.35) using the smoothed reflectance. The copper concentrations were more accurate ( R c v 2 = 0.78) using the logarithm transformation. Concentrations of boron ( R c v 2 = 0.68) and manganese ( R c v 2 = 0.79) were more accurate using the first derivative, while zinc ( R c v 2 = 0.31) concentration was most accurate using the second derivative.

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. [ABSTRACT FROM AUTHOR]