Details

Autor(en) / Beteiligte

• Wang, Jingxia
• Pauleit, Stephan
• Banzhaf, Ellen

Titel

An Integrated Indicator Framework for the Assessment of Multifunctional Green Infrastructure—Exemplified in a European City

Ist Teil von

• Remote sensing (Basel, Switzerland), 2019-08, Vol.11 (16), p.1869

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2019

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• The aim of this study is to provide an integrated indicator framework for the Assessment of Multifunctional Green Infrastructure (AMGI) to advance the evolution of the Green Infrastructure (GI) concept, and simultaneously deliver an approach do conduct a GI assessment using remote sensing datasets at multiple spatial and spectral scales. Based on this framework, we propose an explicit methodology for AMGI, while addressing the multi-dimensional pillars (ecology, socio-economy, socio-culture, and human health) for urban sustainability and the multifunctionality of GI. For the purpose of validation, we present the extensive process of employing our framework and methodology, and give an illustrative case exemplified in a European city, i.e., Leipzig, Germany. In this exemplification, we deployed three stages regarding how a single assessment can be conducted: from conceptual framework for priority setting, contextual assessment, to retrospective assessment. In this illustrative case study, we enclosed 18 indicators, as well as identified hot and cold spots of selected GI functions and their multifunctionality. A clear framework and methodology is crucial for the sustainable management of spatially oriented GI plans over time and for different stakeholder groups. Therefore, GI planners and policy makers may now refer to our integrative indicator framework and provided application methodology as common grounds for a better mutual understanding amongst scientists and stakeholders. This study contributes to discourses regarding the enhancement of the GI concept and is expected to provoke more discussion on the improvements of high-quality Remote Sensing (RS) data as well as the development of remote sensing-based methods at multiple spatial, temporal, and spectral scales to support GI plans.