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Last modified by Emily West on 2022/03/08 17:12
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Hyunji Cho 1.1 1 = 2.2.4 Land use change and green space configuration =
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4 ====== **Description** ======
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6 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).
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8 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. 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 urban environment, to manage the urban infrastructures and improve the efficiency and rationality of its spatial management. A necessary prerequisite for the improvement of urban environment is rationality of its spatial management – the optimal division of urban spaces by their functional predestination. One of approaches suited to this is functional zonation of the city – a spatial management of basic types of activities – labour, household, recreational. Data on landuse change and greenspace configuration collected in these ways can be used to:
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10 * Track landuse change on sites in relation to ecosystem service provision;
11 * Track trends in private garden use to monitor a substantial green infrastructure asset over which local authorities have little influence;
12 * Set targets for landuse 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.
Hyunji Cho 2.1 13
Emily West 3.1 14 Even with advances in RS, the ability to distinguish various urban land use types accurately using classification algorithms remains difficult due to the fine-scale heterogeneity of land cover types in urban areas (Pauleit & Duhme, 2000; Jia et al., 2018). Methods that combine RS and more applied approaches have been developed to establish a greater level of precision in relation to this micro-variation. For instance, Pauleit & Duhme (2000) used a combination of existing habitat inventory, mapping land cover, and units and types from aerial photographs to reproduce the fine-grained patterns of land covers in the city of Munich. This enabled delineation of distinct units (e.g. configurations of built-up and open spaces such as detached houses, high-rise blocks, industrial areas, parks, agricultural lands etc.) which were grouped into 24 urban land cover types (e.g. houses, factory block, roads, railways, lakes and ponds, woodlands, parks and green spaces, cemeteries, sports fields, etc.). This enabled quantitative characterisation of physical features of units ~(% cover of sealed surfaces, vegetation, etc.) which was built into a GIS database. Environmental parameters (e.g. surface temperature, rainwater infiltration) were assigned to land covers to assess the likely environmental impacts of land cover changes, for instance rainwater infiltration.
Hyunji Cho 2.1 15
16 When considering land use change and greenspace configuration, it is important to consider how green (vegetated) urban brownfields can supplement urban green infrastructure by providing habitat, microclimate and recreational services (Mathey et al., 2015). Changes in their land use and physical structure as a result of urban planning decisions will impact ecosystem service provision. Development of brownfield sites can often have a negative impact on ecosystem service provision compared to their undeveloped state. As such, their consideration as a part of land use change indicators is not straightforward. Brownfield registers and Environmental Impact Assessments can provide source data regarding pre-development brownfield habitat structure/quality. Parameters for habitat services provided by green urban brownfields can be based on successional stage typologies: brownfield with pioneer vegetation; with persistent ruderal vegetation; with ruderal tall herbaceous vegetation; with spontaneous wood; and three biodiversity parameters: structural diversity, specific plant and animal groups, regenerative functions. This information can be supplemented by modelling of microclimate regulation based on vegetation parameters can be done at site level (ENVI-met) and city level (HIRVAC-2D). Data relating to perception, acceptance and use of/forms of use of brownfields by residents can be collected by questionnaires. Scenario analysis can show how changes in land use can impact ecosystem services (e.g modelling future development proposals). These aspects should be integrated into analytical and evaluation algorithms when devising city strategies for brownfields to secure ecosystem services. In terms of assessing the value of domestic gardens in relation to their ability to support biodiversity, several studies have developed methods for assessing/quantifying value. This includes methods for assessing value for urban birds (Daniels and Kirkpatrick 2006) and invertebrates (Smith et al. 2006). Goddard et al. (2010) present a comprehensive overview of a range of methods related to garden biodiversity.
Hyunji Cho 1.1 17 )))
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19 ====== **Scale as linked to D2.2 framework** ======
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21 Applied methods:
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23 * This indicator is generally applied at a city-scale, but neighbourhood and site level assessments can also be made.
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25 Earth observation/Remote sensing methods:
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Emily West 3.1 27 * methods suitable for a range of geographical scales depending on the resolution of the satellite imagery used (e.g. 250x250m for MODIS, 30x30m for Landsat or 10x10m for Sentinel-2).
Hyunji Cho 1.1 28 )))
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30 ====== **Data source [ link to data framework]** ======
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32 * Satellite-derived land use maps such as CORINE.
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35 ====== **Data needs** ======
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37 * Required data will depend on selected methods, for further details see applied and earth observation/remote sensing metrics reviews in: Connecting Nature Indicator Metrics Reviews Env42_Applied and Env42_RS
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40 ====== **Expected Outputs** ======
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42 * Percentage changes of land cover types
43 * Maps
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46 ====== **Calculation methods** ======
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48 * GIS
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51 ====== **Links and references** ======
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53 __**Applied methods:**__
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Emily West 3.1 55 (((
Hyunji Cho 1.1 56 * Daniels, G.D. and Kirkpatrick, J.B. (2006) Does variation in garden characteristics influence the conservation of birds in suburbia? Biological Conservation 133, 326–335.
57 * Goddard, MA, Dougill, AJ and Benton, TG (2010) Scaling up from gardens: biodiversity conservation in urban environments. TREE 1175 1–9.
58 * Jia, Y., Ge, Y., Ling, F., Guo, X., Wang, J., Wang, L., Chen, Y. and Li, X., 2018. Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data. Remote Sensing, 10(3): 446.
59 * Mathey, J., Rößler, S., Banse, J., Lehmann, I. and Bräuer, A. (2015) Brownfields as an element of green infrastructure for implementing ecosystem services into urban areas. Journal of Urban Planning and Development, 141(3), A4015001.
60 * Pauleit, S. and Duhme, F. (2000) Assessing the environmental performance of land cover types for urban planning. Landscape and urban planning, 52(1), 1-20.
61 * Smith, RM, Gaston, KJ, Warren, P and Thompson, K (2006) Urban domestic gardens (VIII): Environmental correlates of invertebrate abundance. Biodiversity and Conservation, 15, 2515–2545.
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63 __**Earth observation/Remote sensing methods:**__
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65 * Agarwal C., G. M. Green, J. M. Grove, T. P. Evans and C. M. Schweik (2002) A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice. Apollo the International Magazine of Art and Antiques, 1 (1).
66 * Fonji, S. F., & Taff, G. N. (2014). Using satellite data to monitor land-use land-cover change in North-eastern Latvia. SpringerPlus, 3, 61. doi:10.1186/2193-1801-3-61
67 * Hansen, M.C.; Loveland, T.R. A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ. 2012, 122, 66–74.
68 * Yang, X.; Lo, C.P. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Int. J. Remote Sens. 2002, 23, 1775– 1798.
69 * Baumann, M.; Ozdogan, M.; Kuemmerle, T.; Wendland, K.J.; Esipova, E.; Radeloff, V.C. Using the Landsat record to detect forest-cover changes during and after the collapse of the Soviet Union in the temperate zone of European Russia. Remote Sens. Environ. 2012, 124, 174–184.
70 * Taubenböck, H.; Esch, T.; Felbier, A. Monitoring urbanization in mega cities from space. Remote Sens. Environ. 2012, 117, 162–176
71 * Vanonckelen, S.; Lhermitte, S.; van Rompaey, A. The effect of atmospheric and topographic correction methods on land cover classification accuracy. Int. J. Appl. Earth Obs. Geoinf. 2013, 24, 9–21.
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Emily West 3.1 73 )))
Hyunji Cho 1.1 74
75 Revisions
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77 |**Version**|**Name**|**Date**|**Changes made**
78 |**0.1**|Emily (EcoWise)|17/01/2022|Factsheet created
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