Healthy urban greenery is a fundamental asset to mitigate climate change phenomenons such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. The current visual or instrumented inspection techniques often require a high amount of human labor making frequent assessments infeasible at a city-wide scale. In this work, we present the GreenScan Project, a ground-based sensing system designed to provide health assessment of urban trees at high space-time resolutions, with low costs. The system utilises thermal and multi-spectral imaging sensors, fused using computer vision models to estimate two tree health indexes, namely NDVI and CTD. Preliminary evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates the potential of autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low-costs.