SylvCiT - An AI-based support to urban forest resilience
Maxime Nicol, Annick St-Denis, Raouf Moncef Belbahar, Fanny Maure, Arcady Gascon-Afriat, Christian Messier, Marie-Jean Meurs
TL;DR
SylvCiT is an AI system that helps cities plant diverse and resilient urban forests by recommending suitable tree species based on location and ecological traits.
Contribution
SylvCiT introduces a novel AI-based approach to optimize urban tree diversity and resilience through functional traits and spatial analysis.
Findings
SylvCiT increased species and functional group diversity in simulated Montreal parks.
The system analyzed tree diversity and carbon storage in a Montreal neighborhood.
User experience and transparency were emphasized in the system's design.
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…
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Taxonomy
TopicsUrban Green Space and Health · Remote Sensing and LiDAR Applications · Species Distribution and Climate Change