Model to estimate climate regulation of vegetation in cities (python-based script)

This python script allows calculating the cooling capacity of cities for the atlantic zone. The case study used in the original work (Meier et al. 2022) are German cities with more than 50,000 inhabitants within the so-called functional urban areas (according to Copernicus Urban Atlas). The model so far includes parameters for atlantic climate conditions but can be adapted to mediterranean and continental climate using the proposed values from Zardo et al. (2017).

Short description of the model

Based on the land cover type, the percentage of tree canopy cover, and the area size, a cooling capacity value (CCA value) is determined for each area in cities. By intersecting population data with the cooling capacity, areas can be identified where the population receives appropriate cooling from the vegetation and areas where the amount of vegetation is relatively low in order to provide a good cooling service. The cooling capacity is a dimensionless variable ranging from 0 to 100. The model was developed with cities in Germany (with more than 50,000 inhabitants) that lie within urban functional areas, according to the Urban Atlas (Copernicus). The model is based on the Climate Cooling Assessment (CCA) from Zardo et al. (2017).

Kurzbeschreibung des Modells (German)

Über den Bodenbedeckungstyp, den Anteil der Baumkronenbedeckung und der Flächengröße wird ein Kühlungskapazitätswert (CCA-Wert) für jede Fläche in Städten ermittelt.
Durch die Verschneidung von Einwohnerdaten mit der Information zur Kühlungskapazität kann ermittelt werden, wo Einwohner in welchem Maß mit Kühlungsleistung durch urbanes Grün versorgt werden. Die Kühlkapazität ist eine dimensionslose Größe von 1-100.
Das Modell wurde zur Anwendung auf Städte mit mehr als 50.000 Einwohnern in Deutschland entwickelt, die sich innerhalb der sogenannten 'Urban Functional Areas" (Urban Atlas, Copernicus) befinden.
Das Modell basiert auf dem Climate Cooling Assessment (CCA) von Zardo et al. (2017).

 
Input data for the model

• 'lbm_DE': Land cover model Germany - a dataset with land use and land cover types (according to CORINE land cover) from the Federal Agency of Cartography and Geodesy (BKG). Minimum mapping unit: 1 ha. The time period used in the model: 2018 (LBM-DE 2018, source: BKG 2020).

• 'Einwohnergrid': Grid with the number of habitats of the buildings in the cities. The original dataset can be obtained from the German Statistical Office and is a raster file (Zensus-Raster, source: Destatis 2015). Time period of population zensus used in the model: 2011.

• The Zensus-raster was converted to vector format (grid) by two steps in ArcGIS: 1) Raster to Point, 2) Create Fishnet with and use Points-Features created in 1) as labels.

• 'Stadtgruenraster': Raster dataset including amongst others high vegetation (trees). Three land cover types were chosen from this raster dataset to include in the climate regulation model: broad-leaved trees, coniferous trees, and built-up areas mixed with vegetation. The time period used in the model: 2018 (source: Krüger et al. 2022).

• 'Street_Tree': Vector dataset from Copernicus (2021) (Urban Atlas - Street Tree layer). Time period used in the model: 2018, Release 2021 (not validated).

• 'vg25_GEM_Selektion_Stadt': Cities with more than 50,000 inhabitants. Taken from VG25, Federal Agency of Cartography and Geodesy (source: BKG 2017).

• 'Urban_Functional_Areas': Indicates those cities where Street Tree data from Urban Atlas are available (source: Copernicus 2021). Only cities (inhabitants > 50,000) were used in the model that fell in the urban functional areas. As a consequence, some cities, especially in the states of Baden-Württemberg and North Rhine Westfalia could not be considered in the modelling approach.

Modelling process

Land use and land cover types get assigned specific climate cooling capacities regarding the tree cover, soil property, and area size.
Land cover and land use information are taken from the land cover model Germany (LBM-DE), information on tree cover was derived from 'Stadtgruenraster' and the 'Street-Tree-Layer" dataset from Copernicus. For areas with a high cooling capacity (above 80) and an area larger than 2 ha, a cooling distance of 100 m was estimated in the case study of Germany. For further details regarding the analysis, please refer to Meier et al. (2022).

The users are advised to check carefully, if they need to adapt the cooling capacity values, cooling distances and area size thresholds according to the climate and city structure for their specific study area.

Outputs dataset

The output datasets (vector polygons) are:
* a dataset with the climate cooling capacity for each land use/land cover area
* a dataset with the mean climate cooling capacity of each city
* a dataset with the percentage of inhabitants living in areas that show a good - very good cooling capacity (cooling capacity more than 60)

Article associated with this work

Meier, S., Syrbe, R.-U., Moyzes, M., Grunewald, K. (2022): Klimaregulation in Städten als Ökosystemleistung. Vorschlag eines nationalen Indikators zur Bewertung der Ökosystemleistung Klimaregulation in Städten. Naturschutz und Landschaftsplanung 54: 20-29. [German]

Link to data resource and literature

• BKG - Bundesamt für Kartographie und Geodäsie (2017): Verwaltungsgebiete 1:25.000 (VG25) zum Gebietsstand von Deutschland vom 31.12.2016, Stand der Dokumentation: 05.04.2017. Frankfurt (Main). URL: https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/verwaltungsgebiete/verwaltungsgebiete-1-25-000-ebenen-stand-31-12-vg25-ebenen.html (Accessed: 09 Nov 2022).

• BKG - Bundesamt für Kartographie und Geodäsie(2020): Dokumentation. Digitales Landbedeckungsmodell für Deutschland LBM-DE 2018. Stand 20.11.2020. Frankfurt (Main), S.63. URL: https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/digitale-landschaftsmodelle/digitales-landbedeckungsmodell-fur-deutschland-stand-2018-lbm-de2018.html (Accessed: 09 Nov 2022).

• Copernicus (2021): Urban Atlas. Street Tree Layer (STL) 2018. URL: https://land.copernicus.eu/local/urban-atlas/street-tree-layer-stl-2018 (Accessed: 09 Nov 2022).

• Destatis (2015): Zensusatlas 2011. Updated in 2015. URL: https://atlas.zensus2011.de/ (Accessed: 09 Nov 2022).

• Krüger, T., Eichler, L., Meinel, G., Tenikl, J., Taubenböck, H., Wurm, M. (2022). Urban Green Raster Germany 2018 (1 (2021)) [Data set].  https://doi.org/10.26084/ioerfdz-r10-urbgrn2018.

• Zardo, L., Geneletti, D., Pérez-Soba, M. und van Eupen, M. (2017): Estimating the cooling capacity of green infrastructures to support urban planning. Ecosystem Services: 26: 225-235. https://doi.org/10.1016/j.ecoser.2017.06.016.