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

Remote Sensing

Stephan Pauleit¹  Jingxia Wang²  Ellen Banzhaf² 

[1] Chair for Strategic Landscape Planning and Management, TUM School of Life Sciences, Technical University of Munich (TUM), Emil-Ramann-Str. 6, 85354 Freising, Germany;Department Urban and Environmental Sociology, UFZ—Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany;

关键词: Ecosystem Services (ESS);    multifunctionality;    GI assessment;    urban planning;    sustainable development;    remote sensing application;   

DOI  :  10.3390/rs11161869

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown