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18 results for “street tree”
Environmental Data for Soil, Leaf, and Root samples Boston Street Trees and Massachusetts Rural and Urban Forests in Summer 2021
This dataset provides detailed environmental and tree-level data and metadata for over 850 samples collected from 91 trees across an urban-to-rural gradient in Massachusetts. The dataset captures key variables characterizing urban environmental gradients, including soil moisture, pH, temperature, and nitrogen availability. Tree-level attributes include species identification, diameter at breast height (DBH), and growth rate based on previous tree census data. Geographic coordinates and site-specific context (urban forest, rural forest, street tree, forest edge, forest interior) are included to enable spatial analyses. The microbial sequence data associated with this environmental metadata can be found in the NCBI SRA under BioProject accession number PRJNA1297772.
Analysis of a complex role of trees in street canyon using LES model (experiment: Terronska)
<h1>README</h1> <p>This is a companion dataset to the paper <em>Analysis of a complex role of trees in street canyon using LES</em> model by <em>Řezníček et al.</em>, to be submitted to <em><span>Quarterly</span> <span>Journal</span> of the Royal Meteorological Society</em>. All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:</p> <p>1. <em>01_palm_source_code.zip</em> contains the source code for the current version of the PALM model used for this experiment</p> <p>2. <em>02_inputs-configs.zip</em> which contains:</p> <ul> <li>static driver files (for cases 01 = full-trees, 02 = half-trees, 03 = no-strees)</li> <li>dynamic driver files (for different winds directions W = west, SW = southwest, S = south and stratifications C = convective, N = neutral + stable)</li> <li>configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr) for each of the performed simulations</li> <li>the files with N02 are apllied for child domain</li> </ul> <p>3. 03_maps-GIS contains maps in gis or png format with one hour averages outputs: </p> <ul> <li>the cases are terC/N_W/SW/S_01/02/03 for the stratifications, wind direcrions and trees-scenario (see the legend above)</li> <li>abs for absolute values, diff for differences from no-tree scenario, 01h = 1 hour average</li> <li>variables are bio_UTCI - universal thermal climate index [deg C], kc_PM10 = PM10 concentration in 2m or 10m height [<span>μ</span>/m^3], theta_2m = temperature in 2m [deg C], wspeed_10m = wind-speed in 10m, tsurf = surface temperature [deg C], rad_sw_in = incoming shortwave radiation flux [W/m^2] and rad_lw_out = outgoing longwave radiation flux [W/m^2]</li> </ul> <p>4. 04_cuts contains svg and png plots with vertical and horizontal (xy) cuts </p> <ul> <li>the cases are terC/N_W_01/02/03 for the stratifications and trees-scenario (see the legend above) and west winds</li> <li>jugp-ciirc = the vertical cut for (JugP) street (near the ciirc-CTU building), terr-street = the vertical cut for (Terr) street</li> </ul> <h1>PALM MODEL INSTALLATION AND USAGE GUIDE</h1> <h2>A. Installation</h2> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <h2>B. Usage</h2> <p>After a successful installation, the executables for all packages have been linked into the directory <code>/bin</code> and a default PALM configuration file can be found at <code>/.palm.config.default</code>. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p> <h1>ACKNOWLEDGEMENT</h1> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic<br>georisks.</p> <p>The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Nairobi_Street_Trees_Distribution_Diversity
<p>Input data and code to accompany the paper:</p> <p>Alice Gerow, Vivian Kathambi, Dexter Locke, Mark Ashton, Craig Brodersen. Street tree communities reflect socioeconomic inequalities and legacy effects of colonial planning in Nairobi, Kenya. Urban Forestry & Urban Greening. <a href="https://doi.org/10.1016/j.ufug.2024.128530">https://doi.org/10.1016/j.ufug.2024.128530</a></p> <p>The input data consists in street tree observations collected during a field survey conducted between June and August 2023 in Nairobi, Kenya. The code includes descriptive tables and plots, statistical tests, and alpha and beta diversity metrics and visualizations used to compare ecological communities across social groups.</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>
Data used in manuscript Carbon sequestration potential of street tree plantings in Helsinki
<p>Data and model runs used in manuscript "Carbon sequestration potential of street tree plantings in Helsinki". This data set includes model runs for the Surface Urban Energy and Water balance Scheme (SUEWS) and soil carbon model Yasso.</p> <p><br> The data files are:</p> <p><strong>Met_Gapfilling</strong></p> <ul> <li>ConvertMeteorologyInput.m (MATLAB) is the main file and functions gapfilling.m (with other measurements) and gapfillingfill.m (with interpolations) are used in the gap filling</li> <li>Includes files for meteorological measurement data <ul> <li>Airport: Data from Helsinki-Vantaa airport; airportdata.m, where data is cleaned</li> <li>Precipitation: Data from multiple locations; Pres_Gap.m for gap filling precipitation and function PrecipitationGap.m</li> <li>Roof: Data from rooftop</li> <li>SMEARIII: Monthly meteorological data from Kumpula (2003-2016)</li> </ul> </li> <li>SUEWS_met file for the final gap filled meteorological files for SUEWS </li> </ul> <p><strong>Fits</strong></p> <ul> <li>Includes FitCO2_parameter.m for fitting CO2 parameters for SUEWS</li> <li>Includes functions Pho6.m and Resp0.m that have the function forms</li> <li>Includes data files for measurement data <ul> <li>CO2Data: Canopy photosynthesis and canopy respiration estimated with SPP model (KumpulaX.out for Tilia site and Kumpula2X.out for Alnus site)</li> <li>Met_2016: Meteorology from Kumpula for June to August in 2016</li> <li>SWCdata: Soil water content from two streets and three soil types</li> </ul> </li> </ul> <p><strong>ModelRuns</strong></p> <ul> <li>SUEWS model runs separately for Alnus and Tilia sites <ul> <li>Includes input and output files and model codes</li> <li>Alnus site includes both the Baserun and Finalrun</li> </ul> </li> <li>Yasso model runs <ul> <li>Model run in file yasso.f90</li> <li>Output files: DecRate...txt includes three soil types and values for each month from 2002 to 2016</li> <li>Yasso_meteorology_month.m creates meteorological input files for Yasso (Clim_month_xx.txt) using meteorology from SUEWS</li> <li>Lifetimerun: 30 year simulations that includes estimations for leaves and pruned branches</li> </ul> </li> </ul> <p><strong>FigCodes</strong></p> <ul> <li>Includes MATLAB codes for figures and statistics</li> <li>Includes measurement data for CO2, sap flow and SWC</li> </ul> <p> </p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
Point clouds from terrestrial laser scanning of 30 trees along Malet Street, London
<p>Point clouds of 30 street trees scanned along <a href="https://goo.gl/maps/7x3dutn6vHcxVPdC6">Malet Street, London, UK</a>. </p> <p>Tree species is predominantly London Plane (<em>Platanus × hispanica</em>).</p> <p>Data was captured on 8/2/2017 (leaf-off) with a RIEGL VZ-400 terrestrial laser scanner. 24 scans were conducted from 12 positions along the street. The weather was good, with little to no noticeable wind.</p> <p>Data is a binary PLY format with <em>xyz</em> fields in an arbitrary coordinate system. Trees have been extracted from the global point cloud and have been "cleaned" to remove the ground and neighbouring trees (however there may be some errors). Data has been downsampled to a voxel size of 0.04 m.</p> <p>Raw data can be accessed from here.</p> <p>Please acknowledge the data set authors if using this data.</p>
Floristic monitoring of the 1,324 alignment tree bases of 15 streets in the district of Bercy, Paris, France, from 2009 to 2018
<p>Floristic monitoring of the 1,324 alignment tree bases of 15 streets in the district of Bercy, Paris, France, from 2009 to 2018.</p> <p>Nathalie Machon (CESCO, MNHN-CNRS-Sorbonne-Université)</p> <p>Data collectors : Noëlie Maurel, Marion Noualhaguet, Marion Dubois, Sébastien Julliard, Ambre Zéléla Bouvard, Paul Haenel, Florence Devers, Hélène Beaugeard, Gwendoline Chastel, laure Schneider-Maunoury and Mona Omar</p> <p>Centre d’Ecologie et des Sciences de la Conservation, Muséum national d’Histoire naturelle, 61 rue Buffon, 75005 Paris, nathalie.machon@mnhn.fr</p> <p>In cities, trees planted along streets host at their base a high number of spontaneous plants. Thus, they may provide shelters and corridors across the urban matrix.</p> <p>From 2009, we monitor urban tree bases in streets of Paris, France. Our objective is to follow the dynamics of these plant communities (Omar et al. 2018, 2019).</p> <p> </p> <p><strong>Study area and floristic inventories</strong></p> <p>The monitoring was performed in the 12th administrative district of Paris (Postal code: 75012; France; 48°50′26.91″N, 2°23′17.46″E),</p> <p>We monitored the 1,324 tree bases distributed along the 15 streets or avenues which contained at least 30 alignment trees in the district.</p> <p>Tree bases (TB) were for some of them covered by metal grills (grill/soil) to prevent soil compaction to preserve tree roots.</p> <p>The present file gives the list of all wild vascular plant taxa observed in each tree base, in May or June, each year from 2009 to 2018 except in 2013 because of a lack of observers. The taxonomic reference is the French Flora Reference TAXREF v8.0 (Gargominy et al., 2016).</p>
Data from: Beyond the metropolis: street tree communities and resident perceptions on ecosystem services in small urban centers in India
<p>This dataset includes road transect characteristics, tree data and interview data (linked through transect number) from two cities in India - Kochi and Panjim, collected in 2019-2020 as part of the study:</p> <p>Beyond the metropolis: street tree communities and resident perceptions on ecosystem services in small urban centers in India</p> <p> </p> <p> </p>
Data from: The abundance and distributional (in)equalities of forageable street tree resources in Lagos Metropolis, Nigeria
<p>Foraging for wild resources links urban citizens to nature and biodiversity while providing resources important for local livelihoods and culture. However, the abundance and distributional (in)equity of forageable urban tree resources have rarely been examined. Consequently, this study assessed the abundance of forageable street trees and their distribution in Lagos metropolis, Nigeria. During a survey of 32 randomly selected wards across 16 local government areas (LGAs) in the metropolis, 4,017 street trees from 46 species were enumerated. The LGA with the highest number of street trees was Ikeja, with 818 trees, while Lagos Island had the lowest count, with two trees. This disparity in tree numbers could be attributed to variations in human population density within each LGA. Ninety-four percent of the street trees surveyed had at least one documented use and 76 % had two, and thus were potentially forageable. However, the most common species had relatively low forageability scores. Only 5.6 % of the total street tree population was rated as highly forageable, with a usability score of at least 11 out of 15. The most forageable street trees were fruit trees and non-native species. The forageable street trees in the LGAs showed a significant disparity in their distribution, as evidenced by a Gini coefficient of 0.81. Overall, richer neighbourhoods had a higher street tree abundance, richness, and forageability potential. To meet greening and foraging goals and address the current inequitable distribution, we suggest allocating more funds for greening, particularly in low-income neighbourhoods. Further research should evaluate forageable species from other sites to acquire a detailed understanding of the distribution and abundance of forageable resources in Lagos metropolis.</p>
Vehicle pollution is associated with elevated insect damage to street trees
<p>1. Vehicle pollution is a pervasive aspect of anthropogenic change across rural and urban habitats. The most common emissions are carbon- or nitrogen-based pollutants that may impact diverse interactions between plants and insect herbivores. However, the effects of vehicle pollution on plant-insect interactions are poorly understood.</p> <p>2. Here, we combine a city-wide experiment across the Sacramento Metropolitan Area and a laboratory experiment to determine how vehicle emissions affect insect herbivory and leaf nutritional quality.</p> <p>3. We demonstrate that leaf damage to a native oak species (Quercus lobata) commonly planted across the western US is substantially elevated on trees exposed to vehicle emissions. In the laboratory, caterpillars preferred leaves from highway-adjacent trees and performed better on leaves from those same trees.</p> <p>4. Synthesis and applications. Together, our studies demonstrate that the heterogeneity in vehicle emissions across cities may explain highly variable patterns of insect herbivory on street trees. Our results also indicate that trees next to highways are particularly vulnerable to multiple stressors, including insect damage. To combat these effects, urban foresters may consider planting trees that are less susceptible to insect herbivory along heavily traveled roadways.</p>
Data from: The abundance and distributional (in)equalities of forageable street tree resources in Lagos Metropolis, Nigeria
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Data from: The importance of street trees to urban avifauna
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Vehicle pollution is associated with elevated insect damage to street trees
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Data for: Sun, Ogushi, Tseng -Lepidoptera species richness and community composition in urban street trees
<p>The triple threats of climate change, habitat loss, and environmental pollution have stimulated discussion on how urban areas can be modified to both mitigate heat increases and provide habitat for wildlife such as insects. The strategy of using trees to reduce temperatures has been adopted by numerous cities. However, the majority of street trees planted around the world are non-native. Studies conducted in non-urban areas have demonstrated in comparison to native plants, non-native plants are less likely to support native insect diversity. Here we use a database approach to quantify the number of native Lepidoptera species associated with 76 of the most common street tree species planted in Vancouver, Canada. We tested the prediction that compared to non-native trees, native street trees will support a higher diversity and unique community of native Lepidoptera. As predicted, native street trees were associated with five times as many native Lepidoptera species, and the Lepidoptera communities supported by native vs. non-native street trees were distinct. There was no difference in native Lepidoptera associations between broadleaf vs. coniferous street trees. These results are consistent with studies that have used active sampling techniques to investigate insect richness on a smaller subset of native and non-native tree species. Collectively, these data provide good evidence that the planting native instead of non-native trees will help stem the loss of insect diversity in urban areas.</p>
Input data for article "Large eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations"
<p>Input dataset used when performing LES simulations for journal article "Large eddy simulation of the optimal street-tree layout for pedestrian-level aerosol particle concentrations" (Karttunen et al., in preparation). The dataset was used with the PALM model system revision 3698 and most likely it won't work on older or newer versions.</p> <p>Instructions for use:<br> A precursor run must be run first. Output data (BINOUT) of it should be linked into a BININ directory of the actual scenario runs. You'll most likely have to tweak the CPU grid settings in ENVPAR and PARIN files in order to fit them to your computational resources. For more information on usage please refer to the PALM model documentation available online in <a href="https://palm.muk.uni-hannover.de/trac/wiki/doc">https://palm.muk.uni-hannover.de/trac/wiki/doc</a>.</p>
Data for: Sun, Ogushi, Tseng -Lepidoptera species richness and community composition in urban street trees
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Fig. 1 in The comparative growth rates of indigenous street and garden trees in Grahamstown, South Africa
Fig. 1. Mean annual diameter increment (cm/yr) of indigenous street (n = 45) and garden (n = 56) trees relative to their age (Species abbreviations are genus and species (4 and 3 letters, respectively; the species 'other' refers to nine species for which the sample was less than three stems per species).
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