Changes for page 2.2.4 Land use change and green space configuration

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1		-XWiki.hyunjicho
	1	+XWiki.emilywest

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11	11	* Track trends in private garden use to monitor a substantial green infrastructure asset over which local authorities have little influence;
12	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.
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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 fin-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, hi-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.
	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.
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16	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.
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25	25	Earth observation/Remote sensing methods:
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27		-* methods suitable for a range of geographical scales.
	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).
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30	30	====== **Data source [ link to data framework]** ======
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53	53	__**Applied methods:**__
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55	55	* 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.
56	56	* Goddard, MA, Dougill, AJ and Benton, TG (2010) Scaling up from gardens: biodiversity conservation in urban environments. TREE 1175 1–9.
57	57	* 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.
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69	69	* Taubenböck, H.; Esch, T.; Felbier, A. Monitoring urbanization in mega cities from space. Remote Sens. Environ. 2012, 117, 162–176
70	70	* 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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73	73	Revisions
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