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39 results for “green roof”
Air quality, soil moisture, green roof moisture and weather data from Meetjestad
<p>Soil moisture sensors were developed by citizen science collective Meet je Stad (Measure your City). Measure your City was started in 2015 by inhabitants of the City of Amersfoort, with the goal of measuring climate related indicators. To be able to do so, collaboration was sought with the City of Amersfoort (COA), the local Water Authority and the University of Applied Sciences of Amsterdam. For the first three years the initiative focused on measuring temperature and humidity. Importantly, citizens develop their own research questions, analyze the data together with professionals and discuss potential implications. By doing so, the collective uses citizen science to spread knowledge on both technology and climate change in the most grass-roots manner possible. Within the SCOREwater project, Measure your City was asked to expand measurements with soil moisture measurements and additional temperature and humidity sensors.</p> <p>An important note here is that Measure your City develops their own sensors, has developed their own data platform and uses its own gateways purchased from the Things Network. As a result, much effort is put into constructing sensors that are reliable, low-maintenance and accurate. The latter is important for the City of Amersfoort as well, which intends to not only work on shared knowledge and understanding, but also use the data for policy making. To do so the data has to be reliable. By deploying both these sensors and purchasing company-built sensors, we can compare the data to assess how reliable the Measure your City sensors are.</p> <p>The Measure your City can also be deployed on green roofs to measure soil moisture. Whereas the soil moisture sensor measures soil moisture on two depths (10 centimeter and 40 centimeter), the sensor on a roof only measures soil moisture on one depth. In addition to soil moisture, Measure your City also measures air temperature and relative humidity. Some sensors also measure air quality (particle matter).</p>
Tropical green roofs vegetation dynamics data
The data archive is here: https://doi.org/10.2737/RDS-2021-0024 please use this DOI when citing this dataset. This publication contains data collected in 2017 from three green roofs at the International Institute of Tropical Forestry in San Juan, Puerto Rico and one green roof at the Social Sciences Faculty of the University of Puerto Rico in Río Piedras. Data from these extensive green roofs include substrate depth as well as species counts within a sampled quadrant, as well as species identification information. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen
<p>This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail: </p> <p><strong>Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. </strong><a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a></p> <p>The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d<sup>-1</sup>)</p> <p>The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.</p> <p> </p>
A FIELD STUDY ON THERMAL INSULATION PERFORMANCE OF GREEN ROOF FOR BUILDINGS IN HOT DRY CLIMATE OF ZARIA, NIGERIA
<p>Although the practice of employing the use of green roof for thermal insulation is gaining a wideranging acceptance across the globe, its use in Nigeria and the sub Saharan Africa has remained unpopular. Study has shown that, although a vast literature on different approaches in the application of green roof system exists, there is substantial misrepresentation of inferences regarding its applications in Nigeria. The purpose of the study therefore, is to assess the thermal performance of green roof in facilitating thermal insulation for building interiors of hot dry climate in Nigeria. The study is carried out through an empirical field observation in the premises of Ahmadu Bello University Zaria, Nigeria. Two miniature live models were built and covered with galvanised iron roofing sheets on timber trusses; one of which was covered with green roof, while the other was left bare. This is with the view to determine the rate of thermal insulation a green roof system can offer over the bare roof in the building interiors of the study area. Using the experimental approach in green roof investigation, a data logging system was installed in the two thermal zones and readings of the temperature profile was taken. The results showed that; a reduction of 2.08°C in indoor air temperature was obtained on the diurnal ranges, while 19.78% of temperature fluctuation was achieved. Generally, the result showed that higher temperature ranges were recorded in the bare roofed case than the green roof. The maximum, mean and minimum record for the bare roof was 45.20°C, 32.03°C and 21.10°C respectively; while the recorded values for the green roof were 41.90°C, 29.95°C, and 21.00°C respectively. This implies that, the presence of vegetation on the green roofed case has offered a degree of thermal insulation required to achieve better passive cooling in order to attain thermal comfort in the interiors of the study area.</p>
An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery: Dataset
<p>This archive contains code and data to go with the paper <em>*An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*</em>.</p> <p> </p> <p>This archive contains geospatial data, as well as the code used to generate the geospatial data.</p> <p>The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery.</p> <p>The data is described in detail in the manuscript <em>*An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*</em>. See abstract below.</p> <p> </p> <p>GeoJSON format:</p> <p>GeoJSON is a format for encoding geospatial data, see https://geojson.org/.</p> <p>GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR.</p> <p> </p> <p>Contents:</p> <p>`geospatial_data/buffered_polygons_2021.zip` a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2021 and is the main result, which can be opened in any GIS program after being unzipped.</p> <p>`geospatial_data/buffered_polygons_2019.zip` a zip archive containing a geojson file. It is the estimated locations of green roofs in London in 2019 and is a secondary result, which can be opened in any GIS program after being unzipped. The predictions were made with the same model as the 2021 results.</p> <p>`geospatial_data/labelled_area.zip` a zip archive containing a geojson file. Identifies the area which was hand-labelled.</p> <p>`geospatial_data/manual_2021.zip` a zip archive containing a geojson file. Manually labelled green roof from 2021 imagery.</p> <p>`geospatial_data/manual_2019.zip` a zip archive containing a geojson file. Manually labelled green roof from 2019 imagery.</p> <p>`segmentation_code` contains the code used to produce the segmentation from the aerial imagery.</p> <p>`analysis_code` contains the code used to produce the plots and tables for the paper.</p> <p> </p> <p>Imagery availability:</p> <p>Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at [Digimap](https://digimap.edina.ac.uk) provided your institution has the license.</p> <p> </p> <p>Abstract:</p> <p>Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset \cite[]{simpson_charles_2022_6861929}. We estimate that there was 0.23 km^2 of green roof in the Central Activities Zone (CAZ) of London, (1.07 km^2) in Inner London, and (1.89 km^2) in Greater London in the year 2021. This corresponds to 2.0% of the total building footprint area in the CAZ, and 1.3% in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9% of the total building footprint area. Test set accuracy was 0.99, with an f-score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data, and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods, and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km^2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling.</p>
High-fidelity simulation of the effects of street trees, green roofs and green walls on the distribution of thermal exposure in Prague-Dejvice
<p>Archive with PALM simulation results. All data were used in paper <a href="https://doi.org/10.1016/j.buildenv.2022.109484">https://doi.org/10.1016/j.buildenv.2022.109484</a></p>
Green Roofs Footprints for New York City, Assembled from Available Data and Remote Sensing
<p><strong><em>Summary:</em></strong></p> <p>The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md</a>). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.</p> <p>These data are associated with an existing GitHub Repository, <a href="https://github.com/tnc-ny-science/NYC_GreenRoofMapping">https://github.com/tnc-ny-science/NYC_GreenRoofMapping</a>, and as needed and appropriate pending future work, versioned updates will be released here.</p> <p><strong><em>Terms of Use:</em></strong></p> <p>The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.</p> <p><strong><em>Associated Files:</em></strong></p> <p>As of this release, the specific files included here are:</p> <ul> <li><em>GreenRoofData2016_20180917.geojson</em> is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).</li> <li><em>GreenRoofData2016_20180917.gpkg</em> is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917_Shapefile.zip</em> is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.</li> <li><em>GreenRoofData2016_20180917.csv</em> is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.</li> </ul> <p><strong><em>Column Information for the datasets:</em></strong></p> <p>Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.</p> <ul> <li><em>fid</em> - Unique identifier</li> <li><em>bin</em> - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: <a href="https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh">https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh</a>. Associated metadata for fields from that dataset are available at <a href="https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md">https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md</a>.</li> <li><em>bbl</em> - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (<a href="http://gis.nyc.gov/taxmap/map.htm">http://gis.nyc.gov/taxmap/map.htm</a>) and PLUTO/MapPLUTO (<a href="https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page">https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page</a>). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.</li> <li><em>gr_area</em> - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>bldg_area</em> - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).</li> <li><em>prop_gr</em> - Proportion of the building covered by green roof according to this layer (<em>gr_area</em>/<em>bldg_area</em>).</li> <li><em>cnstrct_yr</em> - Year the building was constructed, pulled from the Building Footprint data.</li> <li><em>doitt_id</em> - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.</li> <li><em>heightroof</em> - Height of the roof of the associated building, pulled from the Building Footprint Data.</li> <li><em>feat_code</em> - Code describing the type of building, pulled from the Building Footprint Data.</li> <li><em>groundelev</em> - Lowest elevation at the building level, pulled from the Building Footprint Data.</li> <li><em>qa</em> - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'</li> <li><em>notes</em> - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.</li> <li><em>classified</em> - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)</li> <li><em>digitized</em> - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)</li> <li><em>newlyadded</em> - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)</li> <li><em>original_source</em> - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.</li> <li><em>address</em> - Address based on MapPLUTO, joined to the dataset based on <em>bbl</em>.</li> <li><em>borough</em> - Borough abbreviation pulled from MapPLUTO.</li> <li><em>ownertype</em> - Owner type field pulled from MapPLUTO.</li> <li><em>zonedist1</em> - Zoning District 1 type pulled from MapPLUTO.</li> <li><em>spdist1</em> - Special District 1 pulled from MapPLUTO.</li> <li><em>bbl_fixed</em> - Flag to indicate whether <em>bbl</em> was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)</li> </ul> <p>For <em>GreenRoofData2016_20180917.csv</em> there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):</p> <ul> <li><em>xcoord</em> - Longitude in decimal degrees.</li> <li><em>ycoord</em> - Latitude in decimal degrees.</li> </ul> <p><strong><em>Acknowledgements: </em></strong></p> <p>This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.</p>
Data from: Norway and Sweden Green Roof (GF) plant data
<p>Standard succulent vegetation mixes developed mostly in temperate climates are being increasingly used on green roofs in different climate zones with uncertain outcome regarding vegetation survival and cover. We investigated vegetation on green roofs at nine temperate, cold and/or wet locations in Norway and Sweden covering wide ranges of latitude, mean annual temperature, annual precipitation, frequencies of freeze-thaw cycles and longest annual dry period. The vegetation on the roofs were surveyed in two consecutive years, and weather data were compiled from meteorological databases. At all sites we detected a significant decline in species compared to originally intended (planted/sown) species. Both the survival rate and cover of the intended vegetation were positively related to the mean annual temperature. Contrary to a hypothesis, we found that intended vegetation cover was negatively rather than positively related to mean annual precipitation. Conversely, the unintended (spontaneous) vegetation was favoured by high mean annual precipitation, and low mean annual temperature, possibly by enabling it to colonise bare patches and outcompete the intended vegetation. When there is high mortality and variation in cover of the intended vegetation, predicting the strength of ecosystem services the vegetation provides on green roofs is difficult. The results highlight the needs for further investigation on species traits and the local factors driving extinction and colonisations in order to improve survivability and ensure a dense vegetation throughout the successional stages of a green roof.</p>
Data from: Shading enhances plant species richness and diversity on an extensive green roof
<p>Green roofs can promote biodiversity in urban areas. The extent to which green roofs stimulate plant diversity can depend on roof characteristics such as roof age, substrate depth and shading. We exploratively studied the vegetation on a Dutch green roof in 50 permanent plots (1 m<sup>2</sup>) over eight years (2012–2019) following roof construction. Plots were situated either on low substrate depth (6 cm light-weight extensive substrate) or high substrate depth (6 cm light-weight extensive substrate topped with 14 cm native soil) and differed in the amount of shading received from a higher building floor. Increased substrate depth and shading additively increased plant species richness and plant diversity, with high shaded plots supporting on average 6.4 more plant species than low unshaded plots. Shading likely acts via reducing drought stress, whereas increasing substrate depth with native soil may also enhance plant diversity via addition of nutrients and native seeds. The vegetation composition on the roof was dynamic and changed over the years. Sedum acre was initially dominant but disappeared within the first years, whereas Sedum kamtschaticum increased and became dominant in the last years. Trifolium arvense was the most abundant forb species and was especially dominant three years after roof construction. We conclude that increased substrate depth and shading can promote plant species richness and diversity and recommend that both aspects are considered when green roofs are designed. Shading can be achieved by a stepped building architecture and by placing structures on the roof itself, such as solar panels on standards.</p>
Vegetation cover and plant diversity on cold climate green roofs
<p>Both vegetation abundances and community compositions play important roles for the functions of green roofs (e.g. stormwater retention, habitat provision, aesthetic appearance). However, green roof vegetation can change significantly over time, which may consequently affect the functions related to them. This study investigated vascular plant covers and species compositions on 41 roof sections located in Sweden's subarctic and continental climate zones. For the roof sections with a known originally intended vascular plant composition (n=32), on average 24±9% of the intended species were present in surveys while unintended species made up 69±3% of the the species found. The Intended species dominated plant cover (93±3%) and <i>Sedum acre </i>(58±36% cover) was the most commonly found species. As revealed in previous studies, substrate depth had a positive relationship with plant cover and species richness. The vascular plant cover of the roofs in this study was not related to species richness as hypothesized but instead had a significant negative correlation with moss cover. The results in this study emphasize the importance of substrate depth for both plant abundance and species diversity, and that even in a cold climate, colonising unintended species can have a great contribution to the species richness of green roofs. However, since most colonising species formed sparse cover on the roofs, their potential benefit to green roof functions that benefit from a dense vegetation cover (e.g. stormwater management and thermal performance) could be limited while the intended vegetation performs these functions more effectively.</p>
Excel sheet to predict Multi-Hydro green roof module's behaviour
<p>To prevent spending long computational times running a lot of simulations using Multi-Hydro it is possible to use this excel sheet to predict the its green roof module's behaviour. It is usefull when some parameters need to be calibrated or estimated before the final simulation.</p>
Data from: Norway and Sweden Green Roof (GF) plant data
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Data from: Shading enhances plant species richness and diversity on an extensive green roof
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Vegetation cover and plant diversity on cold climate green roofs
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Data from: Soil and ground-dwelling arthropod diversity on green roofs: Functional groups are strongly influenced by substrate depth and plant community
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Data from: Pollinator-mediated gene flow connects green roof populations across the urban matrix: a paternity analysis of the self-compatible forb Penstemon hirsutus
Gene flow between populations can help maintain genetic diversity and prevent inbreeding, which is especially important for small, fragmented habitats. Many plant species rely on pollinators to move pollen between populations. In urban areas, insufficient pollinator services may result in limited gene flow, which can have negative consequences such as genetic drift and inbreeding depression. Furthermore, restored populations that are established with few founders of low genetic diversity may have limited long-term population persistence. Here, we tested the hypotheses that populations of a self-compatible forb established on urban green roofs fromnursery stock are genetically depauperate and that limited gene (pollen) flow between populations will result in increased inbreeding. We compared the neutral genetic diversity of Penstemon hirsutus, using nine microsatellite loci, between three green roof populations established from nursery stock and three natural populations. We also established ten experimental populations on green roofs and measured rates of outcrossing and inbreeding and identified the movement of pollen within and between roofs using a paternity analysis. We found that neutral genetic diversity of populations established from nursery stock was lower than that of natural populations, although the level of inbreeding was also lower on the green roofs. In our experimental populations, we found that the rates of outcrossing and inbreeding varied between the roof populations. Our results suggest that inbreeding may be correlated with cover of co-flowering species but not with any of the other measured site properties. The location of likely pollen donors suggested that on average, 75% of pollen was derived from plants within the population (including self) and 25% came from plants on different roofs. Our results document realized pollen movement within and between green roofs, demonstrating that these habitats provide important connectivity in a fragmented environment.
Data from: Designed habitat heterogeneity on green roofs increases seedling survival but not plant species diversity
Urban areas benefit from the ecosystem services provided by low input green roofs. However, limited substrate depth on these green roofs creates challenging conditions for plant establishment and survival, leading to industry reliance on non-native succulents. Through a green roof and glasshouse study, we assessed the impact of simple design modifications to the green roof surface, including redistribution of substrate and addition of logs and pebble piles, on both substrate temperature and moisture content. We added seeds of 26 native species and quantified seedling density, species richness and composition over a single growing season. Overall effects of microsite heterogeneity on species diversity were assessed using species accumulation curves. The modifications altered substrate temperature and moisture. Deep substrate (10-12 cm) and the presence of surface features reduced temperature by 14.6°C and, while surface features had mixed effects on substrate moisture on the green roof, pebble piles slowed moisture loss during a six-week drought in the glasshouse. Following drought conditions, seedling density and species richness was greatest, relative to seeded controls, where substrate was deep on the green roof and where pebbles were present in glasshouse modules, despite high mortality overall. Design modifications did not result in differentiation of seedling communities among different microsite types. Species accumulation curves showed no difference in species richness between aggregates of modified vs. unaltered microsites. Synthesis and applications. Redistribution of green roof substrate and the addition of logs and pebble piles altered microsite conditions and created habitat heterogeneity on a green roof. These design modifications represent a minimalist strategy to ameliorate growing conditions, improve seedling survival and decrease species loss on shallow substrate green roofs.
Fig. 2 in Remarks on Hymenoptera on urban green roofs in Belgium
Fig. 2. Green roof RPBER (Recycling park Berchem), Berchem, Belgium, with Sedum album. © Jeffrey Jacobs.
Gładysz, K., Wrochna M., & Popek, R. (2024) Tracking Particulate Matter Accumulation on Green Roofs: A Comparative Study at Warsaw University Library - DATA
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Data from: Pollinator-mediated gene flow connects green roof populations across the urban matrix: a paternity analysis of the self-compatible forb Penstemon hirsutus
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