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16 results for “greenspace”
Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'
<p>Datasets supporting the paper 'Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador'</p> <p>Contact: C. Scott Watson. c.s.watson@leeds.ac.uk</p> <p>DRR_greenspace<br> DRR_greenspace_polygon.shp - classified potential DRR greenspace (minimum 100 m2)<br> DRR_greenspace_zone_points.shp - classified potential DRR greenspace aggregated to zones<br> DRR_greenspace_zone_polygons_top10.shp - Top 10 maximum capacitated analysis of classified potential DRR greenspace aggregated to zones.<br> <br> Land_cover<br> rf_1986_mode_clipped.tif - 1986 land cover classification<br> rf_2020_mode_clipped.tif - 2020 land cover classification<br> landcover_classes.PNG - land cover classes<br> accuracy_assessment_points_1986 - 1986 land cover accuracy assessment points<br> accuracy_assessment_points_2020 - 2020 land cover accuracy assessment points<br> modified_urban_growth_scenario.shp - hazard-modified urban growth scenario</p> <p> </p>
Urban landscapes with more natural greenspace support higher pollinator diversity
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High Resolution Greenspace Land Cover in Philadelphia, Pennsylvania
<p>This dataset provides a high resolution (1-m) land cover map for Philadelphia, Pennsylvania in the United States of America during the summer of 2017. This dataset was created to differentiate two types of green space in Philadelphia: tree and grass cover. The dataset includes four numerically coded land cover classes.</p> <p><strong>Input data:</strong></p> <p>This classification is derived from National Agriculture Imagery Program (NAIP) 1-m aerial imagery captured in the State of Pennsylvania during June of 2017. To improve classification accuracy, NAIP data was stacked with Sentinel-2 level 1C 10-m and 20-m data using the .addBands() function in Google Earth Engine. For the Sentinel-2 data, a median composite was calculated from cloud-masked images collected between April and October of 2017. Sentinel-2 input bands included blue, green, red, red edge 1, red edge 2, red edge 3, near infrared, and shortwave infrared 1. An additional normalized difference vegetation index (NDVI) was calculated from the NAIP and Sentinel-2 bands using the formula:</p> <p>NDVI = (Near infrared - Red) / (Near infrared + Red)</p> <p><strong>Classification methods:</strong></p> <p>We classified the input data using a Random Forest classifier with 200 trees. Data was classified into four coded land cover classes:</p> <p>1 - Tree</p> <p>2 - Grass</p> <p>3 - Human-built structures</p> <p>4 - Open water</p> <p>8,961 land cover reference points were collected with 70% used to train and 30% to test the classifier. Results were smoothed using a 3x3 square kernel based on the mode of a pixel’s neighbors.</p> <p><strong>Accuracy:</strong></p> <p>Measures of accuracy including overall accuracy and per class user’s (UA) and producer’s accuracy (PA) of the random forest classifier were calculated.</p> <p>Overall accuracy: 93%</p> <p>Tree: UA = 89.73% PA = 93.90%</p> <p>Grass: UA = 93.41% PA = 88.21%</p> <p>Human-built structures: UA = 98.28% PA = 97.47%</p> <p>Open water: UA = 93.56% PA = 98.95%</p> <p><strong>Code link:</strong></p> <p>The Google Earth Engine code used in this analysis is publicly available.</p> <p><a href="https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f">https://code.earthengine.google.com/32d3a77e70955a6279ec22233778bd8f</a></p> <p><strong>Data for download:</strong></p> <p>Two files are available for download.</p> <ol> <li>Philadelphia_classification_points.zip</li> </ol> <p>Contains a shapefile of the 8,961 reference points used to train and test the classifier.</p> <p> 2. Philadelphia_Landcover_2017.zip</p> <p>Contains a GEOTIFF of the classified image over Philadelphia, Pennsylvania for the summer of 2017.</p> <p> </p>
Greenspace and bluespace quality indicators
<p>Data extracted from literature on green and blue space quality indicators. Protocol for data extraction found here:</p> <pre>https://doi.org/10.5281/zenodo.6594870</pre> <p>Funded by Rural and Environment Science and Analytical Services Division (JHI-C6-1)</p>
A dataset of nectar sugar production for flowering plants found in urban greenspaces
<ol> <li>Nectar and pollen are floral resources that provide food for insect pollinators, so quantifying their supplies can help us to understand and mitigate pollinator declines. However, most existing datasets of floral resource measurements focus on native plants found in rural landscapes, so cannot be used effectively for estimating supplies in urban green spaces, where non-native ornamental plants often predominate.</li> <li>We sampled floral nectar sugar in 225 plant taxa found in UK residential gardens and other urban green spaces, focussing on the most common species. The vast majority (94%) of our sampled taxa are non-native, filling an important research gap and ensuring these data are also relevant outside of the UK.</li> <li>Our dataset includes values of daily nectar sugar production for all 225 taxa and nectar sugar concentration for around half (102) of those sampled. Nectar extraction was conducted according to published methods, ensuring our values can be combined with other datasets.</li> <li>We anticipate that the two main uses of these data are (1) to estimate the nectar production of habitats and landscapes, and (2) to identify high-nectar plants of conservation importance. To increase the utility of our data we provide guidance for scaling nectar values up from single flowers to floral units, as is commonly done in field studies.</li> </ol>
A dataset of nectar sugar production for flowering plants found in urban greenspaces
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Data from: Infection and host-feeding patterns of West Nile virus vectors varies by urban greenspace composition
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Biodiversity dataset of vascular plants and birds in Chinese urban greenspace
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Data from: Sunrise in the city: disentangling drivers of the avian dawn chorus onset in urban greenspaces
Urban systems are known to have a number of effects on avian richness, density, and morphological and behavioral traits. However, no study to date has simultaneously examined the wide range of urban variables in relation to the avian dawn chorus, a complex behavioral phenomenon. Previous studies investigating adjustments of the dawn chorus onset in urban settings have mainly been confined to relationships with noise and light levels. In addition to noise and light levels, in this study we included other potentially related environmental characteristics describing vegetation structure, urban infrastructure, and human activity, all of which have been shown to be drivers of bird diversity in urban areas. We conducted dawn chorus surveys at 38 Los Angeles urban greenspaces and used a classification and regression tree analysis to identify specific urban scenarios that best explained timing differences in the dawn chorus onset. Our results show that light level was the most important variable related to the dawn chorus onset time, in which, counter-intuitively, bird communities in greenspaces with higher light levels had later onsets. In addition, noise was an important factor for the chorus onset in greenspaces with higher light levels. Although our results differ from those of previous studies, these findings highlight the importance of noise and light levels in explaining dawn chorus onset variation, indicating the need for further research in untangling this complex and ecologically important phenomenon.
Functional traits and size interact to influence growth and carbon sequestration among trees in urban greenspaces
<ol> <li>There is persistent uncertainty about how integrated plant functions, like growth, are mechanistically constrained and practically predicted by functional traits. For trees, these knowledge gaps persist for two reasons: first, studies of 'natural' forests are observational, with highly variable and confounding resource limitation and competition; second, most studies investigate only a few popular traits and ignore context-dependencies in trait-effects on growth (e.g., trait-environment or trait-ontogeny interactions).</li> <li>We assessed 17 traits as predictors of radial growth and aboveground carbon sequestration for 182 trees, including individuals of 42 species common to temperate cities. By focusing exclusively on planted trees growing in isolation, our unique study is a pseudo-experiment that spans a large range of taxonomic and trait variability and minimizes confounding effects of environmental heterogeneity (e.g., shifts in light availability with tree size). Focal traits included not only commonly measured traits related to leaf economics and plant size, but also wood traits and whole-plant phenology.</li> <li>Models with indices of tree ontogeny (size) and traits explained 80% and 72% of variability in relative growth and carbon sequestration, respectively, and traits accounted for ~20% of the variation. Traits related to said tree functions included leaf dry matter content (LDMC), leaf N content, mature height, wood anatomy, and phenology. LDMC was positively correlated with wood growth and C sequestration across all size classes, while the positive effects of leaf N, mature height, and wood density were only apparent for smaller trees. Ring-porous species had higher rates of growth and C sequestration than diffuse-porous species.</li> <li>Consistent with recent theory, growth rates of isolated, urban trees vary as a function of simple and interactive effects of traits and size. Our findings are useful for optimizing reforestation efforts in temperate cities, where planners and land managers can select species for rapid growth and C sequestration using freely available data for the 'effect' traits we identified, including wood anatomy and density, leaf N, and LDMC. Lastly, the trait-growth relationships we describe here may reflect those of 'naturally' isolated trees growing in savannas and/or woodlands and provide an insightful frame of reference for trees in closed-canopy forests.</li> </ol>
Differences in the genomic potential of soil bacterial and phage communities between urban greenspaces and natural arid soils.
<p>This repository holds the final data products from metagenomics processing of bacteria and viruses from the article : "Differences in the genomic potential of soil bacterial and phage communities between urban greenspaces and natural arid soils"</p> <p>Contents: </p> <ul> <li>LU_metadata.csv: information on the samples</li> <li>soil_chemistry.txt: physicochemical information on samples</li> <li>*_len.csv: tables containing the length information for annotated genes, divided by database. These are used to calculate RPKM abundances from count tables. </li> <li>BACTERIA</li> <li>ko_table, ko_unknown, ko2level, ko_description: count table of KEGG annotations, total counts for unnanotated genes, match of ko number to level and description</li> <li>all_bracken.csv: count table of taxonomic bacterial annotations using kraken2 and bracken</li> <li>mags_tax.csv: taxonomy assignments to MAGs (metagenome assembled genomes)</li> <li>mags_count_table.csv: abundante table of MAGs in counts</li> <li>ags_result, gc_mean, gc_variance: functional traits results, average genome size, and gc content</li> <li>lu_c_count, lu_n_count, card_d0, metals_count_table: abundance tables of genes annotated with Cazy (carbon), Ncydb (nitrogen), CARD (antibiotic resistance genes), and Bacmet (heavy metal resistance genes)</li> <li>VIRUS</li> <li>amg_summary.csv: results from AMG annotation with DRAM-V, filtered to keep genes of interest</li> <li>genomad_virus_summary.tsv: viral taxonomy annotations with geNomad</li> <li>virus_len.txt: length of inferred viruses (used for calculation of RPKM from count tables)</li> <li>all_host_prediction_to_genus.csv: virus host annotation with IPhop</li> <li>final_checkv.tsv: table of final viral inferences with quality estimates</li> <li>viral_species_count_table.txt: abundance table of infered viral contigs in counts</li> </ul>
Local and landscape-scale environmental filters drive the functional diversity and taxonomic composition of spiders across urban greenspaces
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Data from: Sunrise in the city: disentangling drivers of the avian dawn chorus onset in urban greenspaces
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Functional traits and size interact to influence growth and carbon sequestration among trees in urban greenspaces
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The Effects of Virtual Reality Greenspace on Stress Among Adults With Mobility Impairments
ClinicalTrials.gov study NCT06682143. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Walking in Greenspace and the Built Environment in Adults With Prediabetes: A Randomized Crossover Trial
ClinicalTrials.gov study NCT06365723. IPD Sharing: NO. Countries: 1. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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