Combining high-resolution images and LiDAR data to model ecosystem services perception in compact urban systems. (January 2019)

• Record Type: Journal Article
• Authors: Lafortezza, Raffaele; Giannico, Vincenzo
• Is Part Of: Ecological indicators. Volume 96(2019)Part 2
• Page Start: 87
• Page End: 98
• Publication Date: 2019-01
• Subjects: Green infrastructure -- Urban green spaces -- Scaling up -- Remote sensing -- Urban planning; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2017.05.014
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Combining remote sensing technology allows modeling ecosystem services (ESS) perception in urban systems. Remote sensing and social data provide insights into understanding green space benefits. The NDGB indicator reveals the nexus between ESS perception and green space structure. A spatially explicit indicator predicts individuals' perceptions of green space benefits. The NDGB indicator can assist stakeholders to plan multifunctional urban green spaces. Abstract: In this study, we aim to understand how the provision of ecosystem services (ESS) is spatially distributed within a compact urban system considering the structure and spatial arrangement of green spaces in relation to built-up areas and other infrastructures. For this purpose, we devised an approach to assess the ESS provided by urban green spaces through the integration of social data (i.e., stakeholders' perception of the multiple benefits of green spaces) with remotely sensed data, such as high-resolution satellite images and Laser Imaging Detection and Ranging (LiDAR) point-cloud. We developed a spatially explicit indicator (or metric) called Normalized Difference Green-Building Volume ( NDGB ), derived from remote sensing, that can be used to predict the way people perceive the ESS conveyed by green spaces in cities. We designed the NDGB metric using the city of Bari, Southern Italy, as a case example by involving four groups of stakeholders (n = 202) to assess ten urban green spaces. Our results show a strong positive relationship between the NDGB and the way stakeholders perceive the ESS provided by these urban green spaces. Thus, our indicator accurately expresses the relationship between stakeholders' perceptions of ESS provided by green spaces and the physical data (i.e., green space structure) produced by remote sensing technology. The green space most highly evaluated by the NDGB indicator, the periurban park "Lama Balice", was also the one on which all stakeholder group responses converged, including the group of NGOs and associations, which assigned average low scores for perceived ESS across all the green spaces presented in the study. The study was developed using the city of Bari in Southern Italy as testbed. There is a need to further extend and replicate our approach to other urban systems across different regions (e.g., Northern Europe, North America, Asia), especially those which are in the process of pursuing more sustainable green infrastructure planning and development, as they could be inclined to adopt our approach in ongoing decision making processes. We believe our approach can inform planners and decision makers on ESS provision and supply them with evidence of the local co-benefits of green spaces as well as of the spatial distribution of ESS within compact urban systems.