Advancing urban forest and ecosystem service assessment through the integration of remote sensing and i-Tree Eco: A systematic review

Abstract

Urban forests support the health and well-being of billions of people living in cities globally. To better manage urban forests, it is crucial to assess their ecosystem services. This systematic review analyzes two established urban forest assessment approaches—i-Tree Eco and remote sensing—which have developed independently but hold significant potential for integration. The review, comprising the years 2008 to 2022, evaluates the current status of both methods in assessing urban forest structure and ecosystem services, highlighting opportunities for synergy. The literature shows that while both approaches primarily focus on regulatory services, remote sensing offers more versatile tools for assessing a broader range of ecosystem services beyond i-Tree's standardized scope. Remote sensing holds potential to enhance i-Tree Eco by providing structural and location-specific data at scale, albeit with varying accuracies. Studies have shown that LiDAR data reliably derives tree height and crown width, and that, in combination with multispectral and hyperspectral imagery, it enhances species identification. Additionally, mobile, and terrestrial laser scanners accurately estimate diameter at breast height. However, gaps remain in using remote sensing to assess crown characteristics like crown missing and dieback, which, though not critical, are useful for enhancing ecosystem service estimates in i-Tree Eco. Despite the potential of remote sensing to automate urban tree inventories, limited research has shown its successful integration with i-Tree Eco. Future research should standardize remote sensing techniques for assessing tree crown health. Additionally, further work is needed on quantifying differences between remote sensing and groundbased measurements, with the aim of evaluating uncertainty levels and understanding how these uncertainties impact the reliability and usefulness of data for policymaking and planning.