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22 results for “city trees”
Residential housing segregation and urban tree canopy in 37 US Cities; data in support of Locke et al 2021 in npj Urban Sustainability
Our goal in this paper is to examine whether there are similar patterns in the distribution of tree canopy by Home Owners’ Loan Corporation (HOLC) graded neighborhoods across 37 cities. A pre-print of the paper can be found here: https://osf.io/preprints/socarxiv/97zcs This data packages contains: 1. City-specific file geodatabases with features classes of the HOLC polygons obtained from the Mapping Inequality Project https://dsl.richmond.edu/panorama/redlining/, and tables summarizing tree canopy, and in some cases other land cover classes. 2. An *.R script that replicates all of the analyses, graphs, and tables in the paper. Other double checks, exploratory, and miscellaneous outputs are created by the script too as a bonus. Everything in the paper can be done with the script; additional work outputs are also created. 3. A *.csv file containing city, the HOLC grade, and the percent tree canopy cover. This can be used to create the main findings of the paper and this flat file is provided as an alternative to running the R script to extract information from the geodatabases, combine, and analyze them. The intention is that this file is more widely accessible; the underlying information is the same. Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remains unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect
Data: The Role of Urban Trees in Reducing Land Surface Temperatures in European Cities
<p>Data on the LST differences between urban fabric, urban trees and urban green spaces for each city and the LST differences between urban fabric, rural forests and rural pastures (for hot days and JJA (June, July and August) average). In addition, estimates of the evapotranspiration of forests and pastures of each city and albedo estimates of urban fabric and forests are provided.</p> <p>The description of the column names is provided in the readme file.</p> <p> </p>
City's Low Hanging Fruit: Exploring Policy Alternatives for Exploiting Urban Fruit Trees
<p>City's Low Hanging fruit:<br><br>Map, Thematic analysis, Three quistionnaires, questionnaire answers<br><br>מפה, ניתוח תמטי, שלושת השאלונים, תשובות לשאלונים<br><br></p> <p>Abstract + methods - below<br>תקציר - למטה</p>
Urban Tree Canopy Cover, environmental and socioeconomic data to São Paulo city, Brazil
<p>Urban Tree Canopy Cover from 2017, elevation and roughness from 2011 and socioeconomic data from 2010, to São Paulo city, Brazil. These data are analyzed in: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128497" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ufug.2024.128497</a></p>
A dataset of 5 million city trees from 63 US cities: species, location, nativity status, health, and more.
<p>Sustainable cities depend on urban forests. City trees -- a pillar of urban forests -- improve our health, clean the air, store CO2, and cool local temperatures. Comparatively less is known about urban forests as ecosystems, particularly their spatial composition, nativity statuses, biodiversity, and tree health. Here, we assembled and standardized a new dataset of N=<span>5,660,237</span> trees from 63 of the largest US cities. The data comes from tree inventories conducted at the level of cities and/or neighborhoods. Each data sheet includes detailed information on tree location, species, nativity status (whether a tree species is naturally occurring or introduced), health, size, whether it is in a park or urban area, and more (comprising 28 standardized columns per datasheet). This dataset could be analyzed in combination with citizen-science datasets on bird, insect, or plant biodiversity; social and demographic data; or data on the physical environment. Urban forests offer a rare opportunity to intentionally design biodiverse, heterogenous, rich ecosystems.</p>
Oldest Tree In City Of London
This plane tree in the old churchyard of St Peter at the corner of Cheapside and Wood Street is said to be the oldest in the City of London. It is one of the Great Trees of London. Once described as the most valuable tree in the world, its protected status forbids nearby buildings from being built any taller. Date: 1850 possibly Canopy not shown. Read more: https://londonist.com/london/cheapside-plane 503 photos taken in June 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Ground Truth Data of Falcata (Paraserianthes falcataria) Trees and Tree Farms/Plantations in Butuan City, Philippines for the Year 2020
<p>This dataset comprises location and extent data of<strong> </strong>selected/visited Falcata (<em>Paraserianthes falcataria</em>) trees and tree farms/plantations in Butuan City, Philippines. The CSV and GIS Shapefiles were generated from data collected through a series of ground truth (field) surveys conducted from October 20-29, 2020, in various parts of the city. More details can be found in the accompanying technical report. The field surveys were conducted as part of the <a href="../communities/nicer-itp-project-1" target="_blank" rel="noopener">CSU NICER ITP Center - Project 1. Development of a Geodatabase of Industrial Tree Plantations In Caraga Region Using Remote Sensing and GIS.</a></p> <p>Please refer to the Project 1 terminal report (<a href="../records/13735736" target="_blank" rel="noopener">https://zenodo.org/records/13735736</a>) for additional details.</p>
Absorption Capacity of Trees in Valladolid City
<p>Calculated for the H2020 Project UrbanGreenUP for the City of Valladolid by CENTA. </p>
The City tree
The City tree relief concrete stone brutalist sculpture, public art statue object Artist: Kamil Sopko, 1972 @ Chomutov, CZ, public park • https://goo.gl/maps/wQMUCmG6Huktbdgb7 Apr 8 2022 Source: Objaverse 1.0 / Sketchfab
A dataset of 5 million city trees from 63 US cities: species, location, nativity status, health, and more.
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Data from: Biodiverse cities: the nursery industry, homeowners, and neighborhood differences drive urban tree composition
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Thermal acclimation of tree species in a Tropical Andean city: Exploring the role of species origin and thermal niche
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Individual Tree Data Survey: Twin Cities Household Ecosystem Project
We designed our methods to estimate carbon, nitrogen, and phosphorus fluxes through individual households, and to address two primary questions: 1. How are these fluxes distributed across households? 2. What biophysical and socioeconomic factors contribute to differences in these fluxes across households? Our hybrid approach combines: 1. A mailed survey 2. Energy provider records 3. On-the-ground landscape measurements 4. A computational tool (the Household Flux Calculator) 4. Parcel data (interpreted using GIS) The resulting dataset includes information on biophysical and socioeconomic variables that potentially influence household-level fluxes of elements. Using this method to study element fluxes at the household level allows us to explicitly link consumption choices and element fluxes.
Data from: Colonial history impacts urban tree species distribution in a tropical city
Urban forests associated with green infrastructure for sustainable outcomes are particularly critical in the Global South, where some of the world's fastest-growing cities are located. However, compared to temperate cities, the drivers of urban tree species distribution in tropical cities remain understudied. In this study, we quantify the spatial distribution and abundance of urban forests in the tropical city of Georgetown, Guyana. British colonialism has shaped this city, including forced movement of peoples under slavery from Africa and indentured servants from the Indian Subcontinent. We studied how this multicultural context has influenced tree species distributions in the capital city of the only Anglophone country in South America. We quantified the abundance of tree species using a stratified sampling design to distribute transects across fifteen neighborhoods that vary in distance to the colonial center of the city and ethnic composition. We recorded a total of 57 unique species, the majority of which (73%) were cultivated for their edible fruits. We identify tree species that likely represent Guyana's unique multicultural heritage by comparing our species list to flora in nine cities in neighboring countries (Venezuela and Brazil) with different colonial histories. This international comparison identified a set of tree species that occurred only in Guyana. Relationships between ethnic composition and colonial history and tree species distribution were weak at the neighborhood scale, where proportion of East Indian residents had little explanatory power and distance to colonial center was correlated with abundance of only some species groups. This apparent discrepancy between neighborhood and national scales may relate to the establishment of Guyanese food as a unifying national identifier across ethnicities. The prominence of edible fruit trees in our study suggests a set of species that could be incorporated into urban planning to strengthen biocultural linkages, foster cultural integration, and promote food security.
Data from: Modelling short-rotation coppice and tree planting for urban carbon management – a city-wide analysis
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Data from: Colonial history impacts urban tree species distribution in a tropical city
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GIS Shapefile - Identification of priority planting locations for trees within Baltimore City
Priority Planting locations by block group for Baltimore City using the index developed by Nowak et al. (unpublished) of the USDA Forest Service Northeastern Experiment Station. The criteria used to make the index were: - Population density: the greater the population density, the greater the priority for tree planting - Tree stocking levels: the lower the tree stocking level (the percent of available greenspace (tree, grass, and soil cover areas) that is occupied by tree canopies), the greater the priority for tree planting - Tree cover per capita: the lower the amount of tree canopy cover per capita (m2/capita), the greater the priority for tree planting Each criteria was standardized on a scale of 0 to 1 with 1 representing the census block with the highest value in relation to priority of tree planting (i.e., the census block with highest population density, lowest stocking density or lowest tree cover per capita were standardized to a rating of 1). Individual scores were combined based on the following formula to produce an overall priority index value between 0 and 100: I = (PD * 40) + (TS * 30) + (TPC * 30) Where I = index value, PD is standardized population density, TS is standardized tree stocking, and TPC is standardized tree cover per capita. Based on this index, Planting priority maps were produced. Standardized value for population density was calculated as PD = (PD_n - PD_m) / PD_r, where PD is the value (0-1), PD_n is the value for the census block (population / km2), PD_m is the minimum value for all census blocks, and PD_r is the range of values among all census blocks (maximum value - minimum value). Standardized value for tree stocking was calculated as TS = (100 - ((T/(T+G)) * 100)) / 100, where TS is the value (0-1), T is percent tree cover, and G is percent grass cover. Standardized value for tree cover per capita was calculated as TPC = 1 - [(TPC_n - TPC_m) / TPC_r], where TPC is the value (0-1), TPC_n is the value for the census block (m2/ca
GIS Shapefile, Tree Canopy Change 2007 - 2015 - Baltimore City
This layer is a high-resolution tree canopy change-detection layer for Baltimore City, MD. It contains three tree-canopy classes for the period 2007-2015: (1) No Change; (2) Gain; and (3) Loss. It was created by extracting tree canopy from existing high-resolution land-cover maps for 2007 and 2015 and then comparing the mapped trees directly. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed by development, storms, or disease were assigned to the Loss class. Trees planted during the interval were assigned to the Gain category, as were the edges of existing trees that expanded noticeably. Direct comparison was possible because both the 2007 and 2015 maps were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset will be subjected to manual review and correction. 2006 LiDAR and 2014 LiDAR data was also used to assist in tree canopy change.
Ecohydrology of urban trees under passive and active irrigation in a semiarid city
<p>The infiltration of stormwater runoff for use by urban trees is a major co-benefit of green infrastructure for desert cities with limited water resources. However, the effects of this passive irrigation versus regular, controlled moisture inputs, or active irrigation, is largely unquantified. We monitored the ecohydrology of urban mesquite trees (<i>Prosopis spp.</i>) under these contrasting irrigation regimes in semiarid Tucson, AZ. Measurements included soil moisture, sap velocity, canopy greenness, and leaf-area index. We expected both irrigation types to provide additional deep (>20 cm) soil moisture compared to natural conditions, and that trees would depend on this deep moisture for transpiration and phenological activity. Results show that active irrigation supported higher soil moisture throughout the study than passive irrigation. Passive irrigation only provided additional deep moisture when green infrastructure features received impervious runoff from a city street. Sap velocity and greenness were similar under both irrigation types, outside of isolated periods of time. These differences occurred during the extremely wet summer 2017 when passively irrigated trees exhibited a greenness peak, and the dry conditions of spring when actively irrigated trees had higher sap flow and relative greenness. Finally, it was not determined that deep soil moisture had a stronger relationship with mesquite productivity than shallow moisture, but both relationships were stronger in the spring, before summer rains. This study aims to contribute empirical observations of green infrastructure performance for urban watershed management.</p>
Ecohydrology of urban trees under passive and active irrigation in a semiarid city
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.