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129 results for “urban greening”
Data from: What determines how we see nature? Perceptions of naturalness in designed urban green spaces
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Keep it real: Selecting realistic sets of urban green space indicators - Supplementary data
<p>This excel sheet contains, for each of the four studied cities, the conceptual framework that is described in the paper "Keep it real: Selecting realistic sets of urban green space indicators".</p> <p>Each of the cities first listed all possible indicators that they could think of. Next, they indicated how each of those indicators relates to each of the KPI; i.e. whether the indicator can not at all (0), somewhat (1) or perfectly measure (2) the KPI. Lastly, the city authorities indicated whether the indicators are implemented of not, and scored some measures of indicator quality (relevance, feasibility, clarity, and credibility)</p>
Urban Green Raster Germany 2018
<p><strong>Abstract</strong></p> <p>The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.<br> The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).<br> The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.</p> <p><strong>Data acquisition</strong></p> <p>The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.</p> <p><strong>Data collection</strong></p> <p>Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).</p> <p>The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).</p> <p>The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.</p> <p>GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.</p> <p><strong>Value of the data</strong></p> <p>The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].</p> <p>Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].</p> <p>Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).</p> <p><strong>Data description</strong></p> <p>A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).</p> <p>A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.</p> <p><strong>Experimental design, materials and methods</strong></p> <p>The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].</p> <p>Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.</p> <p>To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.</p> <p>Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.</p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).</p> <p><strong>References</strong></p> <p>[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: <a href="https://ec.europa.eu/eurostat/web/lucas/data/database">https://ec.europa.eu/eurostat/web/lucas/data/database</a></p> <p>[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32</p> <p>[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39</p> <p>[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: <a href="https://doi.org/10.1016/j.jag.2020.102065">https://doi.org/10.1016/j.jag.2020.102065</a></p> <p>[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030</p> <p>[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687</p>
Datasets used in: Modelling eye-level visibility of urban green space: Optimising city-wide point-based viewshed computations through prototyping
<p>Research data supporting our publication. Full workflows using the R programming language have been provided on <a href="https://github.com/STBrinkmann/protoVS">GitHub</a>. Here we provide external data that has been used for our research, as well as the resulting Viewshed Greenness Visibility Index (VGVI) raster.</p> <p><strong>Datasets</strong></p> <p>Digital Terrain Model (DTM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DTM_1m.tif<br> </li> </ul> <p>Digital Surface Model (DSM):</p> <ul> <li>Spatial Resolution: 1 m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DSM_1m.tif<br> </li> </ul> <p>Landuse</p> <ul> <li>Spatial Resolution: 2 m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_LULC_2m.tif<br> </li> </ul> <p>VGVI map</p> <ul> <li>Spatial Resolution: 5 m</li> <li>Source: Resulting dataset from our analysis</li> <li>Licence: MIT License</li> <li>File name: vgvi_van.tif</li> </ul>
Walking Green: The Effects of Walking in Forested and Urban Areas
ClinicalTrials.gov study NCT03950661. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Urban green roofs provide habitat for migrating and breeding birds and their arthropod prey
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Raw data of urban green development efficiency in China, 2002-2018
<p>Raw data of urban green development efficiency in China, 2002-2018</p>
Wind tunnel measurements of particulate matter concentration and wind speed to determine the deposition effect of urban green
<p>This dataset contains measurements of particulate matter (PM) concentration and wind speed at the in- and outlet of a test section of an open-circuit wind tunnel to study PM deposition on urban green. The vegetation under study was the climber species <em>Hedera Helix</em>, which was grown against a screen in a planter. PM was introduced in the wind tunnel at the inlet bend (number 2 on the figure WT-design) and consisted of a mixture of Arizona fine test dust (d<sub>p</sub> > 0.3 µm) and soot (d<sub>p</sub> < 0.3 µm). The concentration measurements were performed with an optical particle sizer and a scanning mobility particle analyser. Wind speed was measured with a hot wire anemometer. Details of the files can be found in README.txt. This research was performed by the Sustainable Energy, Air and Water Technology (DuEL) group at the University of Antwerp (Belgium).</p>
Urban greenness and hedonic dataset in Busan, South Korea
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