Social challenge/ impact linked to:

Co-creation phase linked to:

Level of importance

1 - High Importance / must have

Description

Records change in land use (e.g., from brownfield to green areas by adding vegetated brownfield to UGI resource) and accounting for configuration (e.g., individual gardens, groups of gardens and socio-economic factors impact on the utility of private gardens for native biodiversity conservation).

Identifying urban land-use patterns is important for decision-makers to ensure sustainable development. Typical metrics for this indicator comprise the use of land use and land cover maps. These are typically obtained by classifying and modelling Remotely Sensed (RS) data, for example, Landsat in a GIS environment. The use of remote sensing involves the application of multi-temporal datasets to quantitatively analyse the temporal effects of the land-use changes as well as green space configuration. Due to the high degree of complexity of urban issues, GIS and remote sensing (RS) technologies have long been used to facilitate scientists to assess the overall state of the urban environment, to manage the urban infrastructures and improve the efficiency and rationality of its spatial management. A necessary prerequisite for the improvement of the urban environment is the rationality of its spatial management – the optimal division of urban spaces by their functional predestination. One of the approaches suited to this is functional zonation of the city – spatial management of basic types of activities – labour, household, recreational. Data on land-use change and greenspace configuration collected in these ways can be used to:

• Track land-use change on sites in relation to ecosystem service provision;
• Track trends in private garden use to monitor a substantial green infrastructure asset over which local authorities have little influence;
• Set targets for land-use change, for example recognising the highest quality brownfield sites for biodiversity and ecosystem service delivery and prioritising the beneficial reuse of brownfield sites with little environmental value.

Scale as linked to D2.2 framework

• City

Data source and input type

• Data collected manually by the project personnel.
• Satellite-derived land use maps such as CORINE.

Connection with SDGs

• Goal 11: Sustainable cities and communities
• Goal 13: Climate action
• Goal 15: Life on land

Required Data

• New green space created by the DivAirCity interventions
• Pre-existing area of greenspace enhanced by the DivAirCity interventions
• Number of households within 300m, 1200m and 450m (of the DivAirCity interventions)
• Number of businesses within 300m and 1200m (of the DivAirCity interventions)
• Number of residents within 300m and 1200m (of the DivAirCity interventions)
• Other beneficiaries ?
• Tree cover after the prototypes
• Total area of green roofs after the DivAirCity interventions including:  
  • area of intensive green roofs
  • area of semi-intensive green roofs
  • area of extensive green roofs
• Percentage evergreen (new planting)

Expected Outputs

• The CO2 absorption capacity of the DivAirCity interventions in city district
• Changes in terms of conversion of paved area to green areas in city district (after the interventions)
• Changes to the proportion of the population in the designated area not living within ten minutes walking from public green space (after the interventions)

Calculation methods

• Please use GI Val calculators (https://www.merseyforest.org.uk/services/gi-val/)

Level of expertise required

Links and references

• https://www.merseyforest.org.uk/services/gi-val/