Urban areas, which accommodate more than half of the global population, are the major contributors to climate change and are significantly affected by it. They are the growth engines responsible for generating a large share of global emissions. However, making these cities and human settlements inclusive, safe, resilient, and sustainable is a critical goal of the Sustainable Development Goals (SDGs) adopted by the United Nations in 2015. Moreover, rapid and unplanned urban expansion exacerbates various environmental challenges, such as heat stress, habitat loss, air pollution, water scarcity, and reduced green cover in urban areas. To address these issues, city planners and policymakers focus on sustainable urban development and the effective management of urban spaces. Vegetation is a crucial component of the urban ecosystem and plays a vital role in mitigating climate change impacts, reducing urban heat island effects, improving air and water quality, fostering urban biodiversity, and promoting human well-being. Green Infrastructure has proven to be one of the most successful techniques for carbon sequestration, and to ensure its continued success and optimal performance, regular monitoring is essential. Here, we have used multiple satellite-derived products for urban vegetation mapping, health monitoring, and trend analysis. These indicators were generated at 10/20 m spatial resolution from Sentinel-2 data using Google Earth Engine from 2017 to 2024 for Ealing, a borough of London in the UK. We created several band combinations and covariates for vegetation health and anomaly detection at the borough level. The results show that approximately the entire borough has experienced an ascending trend in tree cover over the selected time duration. Our study underscores that the indices derived from medium-to-high resolution satellite data can be utilised in urban green infrastructure monitoring with reasonable accuracy and calls for evidence-based strategies to achieve economic growth, social inclusion, and urban resilience.