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

Nature-based solution Ontology Semantics Trait-based modelling Typology Urban forest

Journal

Urban forestry & urban greening
ISSN: 1618-8667
Titre abrégé: Urban For Urban Green
Pays: Germany
ID NLM: 101229715

Informations de publication

Date de publication:
Dec 2022
Historique:
received: 28 04 2022
revised: 18 10 2022
accepted: 25 10 2022
entrez: 19 12 2022
pubmed: 20 12 2022
medline: 20 12 2022
Statut: ppublish

Résumé

Urban forests as nature-based solutions (UF-NBS) are important tools for climate change adaptation and sustainable development. However, achieving both effective and sustainable UF-NBS solutions requires diverse knowledge. This includes knowledge on UF-NBS implementation, on the assessment of their environmental impacts in diverse spatial contexts, and on their management for the long-term safeguarding of delivered benefits. A successful integration of such bodies of knowledge demands a systematic understanding of UF-NBS. To achieve such an understanding, this paper presents a conceptual UF-NBS model obtained through a semantic, trait-based modelling approach. This conceptual model is subsequently implemented as an extendible, re-usable and interoperable ontology. In so doing, a formal, trait-based vocabulary on UF-NBS is created, that allows expressing spatial, morphological, physical, functional, and institutional UF-NBS properties for their typification and a subsequent integration of further knowledge and data. Thereby, ways forward are opened for a more systematic UF-NBS impact assessment, management, and decision-making.

Identifiants

pubmed: 36532892
doi: 10.1016/j.ufug.2022.127780
pii: S1618-8667(22)00323-5
pmc: PMC9746330
doi:

Types de publication

Journal Article

Langues

eng

Pagination

None

Informations de copyright

© 2022 The Authors.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Références

Environ Res. 2018 Jan;160:306-313
pubmed: 29040950
Nat Commun. 2018 Mar 21;9(1):1160
pubmed: 29563541
Urban For Urban Green. 2022 Apr;70:127543
pubmed: 35291447
MethodsX. 2021;8:101558
pubmed: 34722168
Int J Behav Nutr Phys Act. 2015 Feb 21;12:26
pubmed: 25886212
J Environ Manage. 2015 Nov 1;163:134-45
pubmed: 26311086

Auteurs

Sebastian Scheuer (S)

Geography Department, Landscape Ecology Lab, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.

Jessica Jache (J)

Geography Department, Landscape Ecology Lab, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.

Martina Kičić (M)

Croatian Forest Research Institute, Division for International Scientific Cooperation in Southeast Europe, Cvjetno naselje 41, 10450 Jastrebarsko, Croatia.

Thilo Wellmann (T)

Geography Department, Landscape Ecology Lab, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.
UFZ - Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany.

Manuel Wolff (M)

Geography Department, Landscape Ecology Lab, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.
UFZ - Helmholtz Centre for Environmental Research, Department of Urban and Environmental Sociology, Permoserstr. 15, 04318 Leipzig, Germany.

Dagmar Haase (D)

Geography Department, Landscape Ecology Lab, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.
UFZ - Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany.

Classifications MeSH