A trait-based typification of urban forests as nature-based solutions

Area of Science:

• Environmental Science
• Urban Planning
• Computer Science

Background:

• Urban forests (UF) are crucial nature-based solutions (NBS) for climate adaptation and sustainable development.
• Effective UF-NBS require integrated knowledge on implementation, impact assessment, and long-term management.
• A systematic understanding of UF-NBS is essential for successful integration of diverse knowledge domains.

Purpose of the Study:

• To develop a conceptual model for a systematic understanding of Urban Forests as Nature-based Solutions (UF-NBS).
• To implement this conceptual model as a formal, trait-based ontology for UF-NBS.
• To facilitate knowledge integration and improve UF-NBS assessment, management, and decision-making.

Main Methods:

• Semantic, trait-based modeling approach to develop a conceptual UF-NBS model.
• Implementation of the conceptual model into an extendible, re-usable, and interoperable ontology.
• Creation of a formal, trait-based vocabulary for expressing UF-NBS properties.

Main Results:

• A formal ontology for UF-NBS has been developed.
• The ontology provides a standardized vocabulary for UF-NBS properties (spatial, morphological, physical, functional, institutional).
• This enables typification and integration of diverse knowledge and data related to UF-NBS.

Conclusions:

• The developed UF-NBS ontology facilitates a systematic understanding of these nature-based solutions.
• It supports more robust impact assessments, informed management strategies, and improved decision-making for urban forests.
• This work paves the way for enhanced integration of knowledge and data in UF-NBS research and practice.