Green roof ecosystem services in various urban development types: A case study in Graz, Austria

Hoeben, Annechien Dirkje; Posch, Alfred

doi:10.1016/j.ufug.2021.127167

Cited by 18 publications

(4 citation statements)

References 62 publications

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“…These studies have generally focused on the use of GIS to analyze the suitability of different roof surfaces for greening, as well as to plan and design green roof systems. One study by [13] used GIS to analyze the suitability of different roof surfaces for greening in the city of Xiamen, China. The study found that the majority of the city's flat and gently sloping roofs were suitable for greening, and that these roofs could provide a significant amount of green space if properly utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

Establishment and Application of Spatial Distribution Model of Roof Greening Based on GIS Technology

2023

IJIIS Int. J. Informatics Inf. Syst.

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The stage from the beginning of the 21st century to now is the stage of the scale development of roof greening(RG) in China. With the continuous progress of urban construction, on the one hand, the ecological environment is deteriorating, on the other hand, the available green space in the urban center area becomes more and more scarce. The development of this contradiction promotes the rapid development of RG. RG plays an important role in enriching urban landscape, increasing urban green quantity, reducing urban heat island effect, conserving rainwater and reducing temperature, and creating a new green space for the city, including leisure, entertainment and ecology, and improving urban ecological landscape. This paper mainly studies the establishment and application of spatial distribution(SD) model of RG Based on GIS technology. Through understanding the SD design of RG, the paper investigates and analyzes the status quo of green space of roof, macro layout and the integration of multiple functional spaces. This paper studies and establishes a multiobjective optimization model of SD of RG, and analyzes the purification effect of RG on rainwater pollutants and the overall performance of ecosystem service equity. The results show that 26.31% of the people think that the degree of RG is insufficient, and 5.14% think that RG is scarce. In the cognition of the status of RG space, 12.41% of people don't care about RG, 8.69% think it is face engineering, and even 9.31% think it is a waste of resources, which indicates that the promotion of RG needs to be improved.

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Section: Literature Reviewmentioning

confidence: 99%

Establishment and Application of Spatial Distribution Model of Roof Greening Based on GIS Technology

2023

IJIIS Int. J. Informatics Inf. Syst.

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“…To identify trade-offs and synergies, Sylla et al [66] used Getis-Ord for spatial hot-spot analysis and principal component analysis. Grunwald et al [67] proposed a data-based approach and defined specific metrics to assess measures, which other studies have adopted (e.g., [68]). However, this approach was limited to roof areas and did not quantify expected benefits.…”

Section: State Of the Artmentioning

confidence: 99%

Namares—A Surface Inventory and Intervention Assessment Model for Urban Resource Management

et al. 2022

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Densely built-up areas are challenged by reduced biodiversity, high volumes of runoff water, reduced evaporation, and heat accumulation. Such phenomena are associated with imperviousness and low, unsustainable utilisation of land and exterior building surfaces. Local authorities have multiple objectives when (re-)developing future-proof districts. Hence, exploiting local potentials to mitigate adverse anthropogenic effects and managing the resource of urban land/surfaces have become key priorities. Accordingly, a five-level hierarchy for a land-sensitive urban development strategy was derived. To support the operationalisation of the hierarchy, we present the model Namares, a highly resolved GIS-based approach to enable spatially explicit identification and techno-economic and environmental assessment of intervention measures for advantageous utilisation of available surfaces per land parcel. It uses existing data and covers the management of economic, natural, and technical resources. Nine intervention measures are implemented to identify potentials, estimate investments and annual costs, and assess the appeal of existing subsidies. The approach was applied to a case study redevelopment area in a large city in Germany. The results provide spatially explicit information on greening potentials, estimated investments, subsidy demand, and other quantified benefits. The case study results show the limited potential for additional unsealing of impervious surfaces by transforming ca. 10% of sealed ground surface area into new urban gardens. At the same time, up to 47% of roof and 30% of facade surfaces could be utilised for greening and energy harvesting. The approach enables a comprehensive localisation and quantitative assessment of intervention potentials to enhance decision support in land-sensitive urban development strategies.

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“…Peng and Jim, 2013;Virk et al, 2015;Cuthbert et al, 2022). Green roofs have the potential to provide a range of benefits to humans and to the wider ecological system in cities by providing habitats for wildlife (Filazzola et al, 2019;Hoeben and Posch, 2021), and they can act as a carbon sink (Getter et al, 2009). Furthermore, green roofs may be able to contribute to the removal of air pollutants (Baik et al, 2012) and storm water retention (Mentens et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

An open-source automatic survey of green roofs in London using segmentation of aerial imagery

Simpson

Brousse

Mohajeri

et al. 2023

Earth Syst. Sci. Data

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Abstract. Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate-resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine learning algorithm based on U-Net (Ronneberger et al., 2015) to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset (https://doi.org/10.5281/zenodo.7603123, Simpson et al., 2023). We estimate that there was 0.23 km2 of green roof in the Central Activities Zone (CAZ) of London, 1.07 km2 in Inner London, and 1.89 km2 in Greater London in the year 2021. This corresponds to 2.0 % of the total building footprint area in the CAZ and 1.3 % in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9 % of the total building footprint area. Test set accuracy was 0.99, with an F score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.

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Green roof ecosystem services in various urban development types: A case study in Graz, Austria

Cited by 18 publications

References 62 publications

Establishment and Application of Spatial Distribution Model of Roof Greening Based on GIS Technology

Establishment and Application of Spatial Distribution Model of Roof Greening Based on GIS Technology

Namares—A Surface Inventory and Intervention Assessment Model for Urban Resource Management

An open-source automatic survey of green roofs in London using segmentation of aerial imagery

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