SylvCiT - An AI-based support to urban forest resilience

Abstract

Urban trees are attracting increasing interest due to their contribution to mitigating some negative urbanization effects. Indeed, trees provide numerous ecosystem services such as carbon sequestration, heat island mitigation, habitats for myriad living creatures, and aesthetic values. However, a lack of tree diversity at the street and neighborhood levels threatens their resilience and service delivery. This article presents SylvCiT, a machine learning and optimization-based system that recommends a diversity of suitable tree species based on functional traits, planting location, and neighboring trees, and therefore maximizes functional diversity at different spatial scales. Special emphasis is placed on human-machine interfaces, including factors that affect user experience, recommendation acceptance and transparency. We show two use cases within SylvCiT. First, we analyze the urban forest of a Montreal neighborhood (Quebec, Canada) in terms of tree diversity, structure, and carbon storage. Second, we assessed species and functional group richness and diversity in 10 parks of Montreal and simulated the effects of planting the recommended species, which resulted in higher species and functional group diversity.