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37 results for “Urban monitoring”
Long-term monitoring of stormwater runoff and water quality in urbanized watersheds of the greater Phoenix metropolitan area, ongoing since 2008
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Stormwater sampling is conducted at numerous locations. The longest running sampling location is near the outflow of the IBW ~0.6 km above its confluence with the Salt River. The sampling location coincides with a permanent USGS gauging sta
Sap-flux and associated environmental data from ash tree monitoring at four urban parks in St. Paul, Minnesota, USA, from May to November of 2023.
We measured the sap flux density of eighteen ash trees (Fraxinus spp.) of varying health and canopy conditions across four urban parks in the City of St. Paul, MN, USA in summer 2023 with a low-cost, compact data logger system we designed in-house. Although many ash trees in the city have either been killed or removed to control the spread of Emerald Ash Borer, chemical insecticide treatments are available for trees that are in early stages infestation. The trees selected for the research have all been receiving insecticide treatment for a few years, but their health and canopy conditions vary. We also have collocated temperature, soil moisture, and precipitation measurements at the same site for summer 2023.
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
USM Dataset - A Dataset for Polyphonic Sound Event Tagging in Urban Sound Monitoring Scenarios
<p>This dataset includes 24,000 5-seconds-long polyphonic stereo soundscapes composed of sounds taken from the FSD50k dataset:</p> <p>- Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, Xavier Serra. FSD50K: an Open Dataset of Human-Labeled Sound Events (<a href="https://arxiv.org/abs/2010.00475">https://arxiv.org/abs/2010.00475</a>)</p> <p>FSD50k samples used in the USM dataset were selected to allow for commercial usage.</p> <p>Find more details about the USM dataset at <a href="https://github.com/jakobabesser/USM">https://github.com/jakobabesser/USM</a></p>
A spatio-temporal dataset for ecophysiological monitoring of urban trees
<p>A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured <em>in situ</em> and in the laboratory; (ii) plant area density measured <em>in situ</em> under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.</p> <p>The repository comprised 3 files : </p> <ul> <li><strong>DATASET_PART1.csv</strong> : This file contains leaf trait measurements</li> <li><strong>DATASET_PART2.csv</strong> : This file contains plant area density measurements </li> <li><strong>DATASET_PART3.gpkg</strong> : This file contains two spatial vector layers: (1) <em>CROWN_EXTENT </em>that is<em> </em>a polygon layer describing tree crowns and (2) <em>TRUNK_LOCATION</em> that is a point layer describing tree location.</li> </ul> <p>More details on the study site, protocols and data can be found in the following reference:</p> <p>Théo Le Saint, Jean Nabucet, Cécile Sulmon, Julien Pellen, Karine Adeline, Laurence Hubert-Moy, A spatio-temporal dataset for ecophysiological monitoring of urban trees, Data in Brief, Volume 57, 2024, 111010, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.111010.</p>
Monitoring of urban areas on Sentinel-1
<p>Data from Sentinel-1 SLC product was used to determine the extent of the urbanised area. Such a solution is necessary in the case of rapidly developing cities, as in the case of the capital of India - New Dehli.</p> <p>Links to the presentation:</p> <p>http://fabspace.pl/wp-content/uploads/2017/11/Urban-area-on-S-1.pdf</p> <p> </p>
Figure 2 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 2. Daily average temperature in Kharkiv during 2013. Division of the year by phenological periods (blue – winter, green – spring, yellow – summer, orange – autumn), and periods of the bat life cycle; total number of bat records in a day (red columns). I-XII - months of the year.
Figure 12 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 12. The body mass dynamics by month for all E. serotinus during 2013 in Kharkiv. F – ♀♀, M – ♂♂ (red dot – mean value, line – median value, whiskers – min and max values, open dot – outlier).
Figure 8 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 8. Relationship between the number of recorded bats and the percentage of those that were dead or significantly injured, as shown using k-mean clustering.
Figure 11 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 11. The body mass dynamics by month of first-year individuals of N. noctula during 2013 in Kharkiv. F – ♀♀, M – ♂♂ (black dot – mean value, line – median value, whiskers – min and max values, open dot – outlier).
Figure 4 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 4. Spatial distributions of records of E. serotinus in Kharkiv. (a) The records in periods from 1 August to 2 December; (b) the records in winter, 1 January to 29 March and from 3 December to 31 December.
Figure 3 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 3. Spatial distributions of four bat species records throughout the year in Kharkiv. NNOC: N. noctula; PKUH: P. kuhlii; VMUR: V. murinus; PAUR: P.auritus.
Figure 10 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 10. The body mass dynamics by month of adult individuals of N. noctula during 2013 in Kharkiv. F – ♀♀, M – ♂♂ (black dot – mean value, line – median value, whiskers – min and max values, open dot – outlier).
Figure 9 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 9. Comparative ratios of the causes of death or significant injury during 2013 in Kharkiv: dir. people – directly killed or injured by people; cas. people – indirectly/casually killed or injured by people; attenuation – death after exhaustion; oil – death after fouling by oil products; cat – killed or injured by a cat; window trap – death as a result of becoming trapped in a window; un – uncertain.
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
Ecohydrological monitoring of urban ecological infrastructure at the Arizona State University Tempe Campus (Tempe, Arizona)
Urban Ecological Infrastructure (UEI) are urban ecological structures whose ecological functions are increasingly being used to deliver key urban services and address urban sustainability challenges. The growing use of UEI to address urban sustainability challenges can bring together teams of urban researchers and practitioners to co-produce UEI design, monitoring and maintenance. However, this co-production process received little attention in the literature, and has not been studied in the Phoenix Metro Area. This study examined several components of a co-produced design process and related project outcomes associated with a small-scale UEI project – bioretention basins installed at the Arizona State University (ASU) Orange Mall and Student Pavilion in Tempe, AZ. We explored the design process associated with the collaborative development of an ecohydrological monitoring protocol for assessment of post-construction performance of this UEI system. This co-production process involved both CAP LTER researchers and practitioners, designers, and managers at ASU and associated third-party consultants involved in site design and management. Together, these researchers and practitioners co-produced a suite of ecohydrological metrics to monitor the performance of the bioretention basins at Orange Mall. Specifically, this protocol evaluated the performance of the UEI system with regards to two of it’s key design goals: storm water capture and storm water quality improvement. The ecohydrological data produced by the implementation of this monitoring protocol are presented here. Monitoring equipment were installed throughout the site in June and July 2018. Site monitoring and data collection began in August 2018, and continued through Feb 2019.
Dataset and figures for "Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS"
<p>This dataset contains the deformation data for 191 data sets and figures (LOS velocities, amplitude and time offset of the annual deformation, decomposed vertical and EW velocities, rice paddy fields, NDVI, optical images, topography, and SB network) mentioned in the paper “Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS”</p> <p>Morishita, Y. Nationwide urban ground deformation monitoring in Japan using Sentinel-1 LiCSAR products and LiCSBAS. <em>Prog Earth Planet Sci</em> <strong>8, </strong>6 (2021). https://doi.org/10.1186/s40645-020-00402-7</p> <p>View on a web map:</p> <p>https://yumorishita.github.io/gsimaps_S1_Japan_LiCSBAS/#9/35.766572/140.038605/&base=std&base_grayscale=1&ls=std%2C0.5%7Chillshademap%2C0.5%7CallUD%7Clanduse_veg&blend=100&disp=1110&vs=c1j0h0k0l0u0t0z0r0s0m0f2&d=m</p>
SiEUGreen_Dataset_for_Monitoring_the_contribution_of_urban_agriculture_to_urban_sustainability:_an_indicator-based_framework
<p>The data was collected for scientific publication: Tapia, C., Randall, L., Wang, S.; Borges, L. A. (2021): Monitoring the contribution of urban agriculture to urban sustainability: an indicator-based framework. <em>Sustainable Cities and Society</em>. In press, <a href="https://doi.org/10.1016/j.scs.2021.103130">https://doi.org/10.1016/j.scs.2021.103130</a></p> <p>This dataset includes the data from the survey in Brabrand Fallaesgartneriet, which is the study case reported in the article.</p>
Data and code for "A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?"
<p><strong>Data and code for publication</strong></p> <p><em>Lena Mutzner, Viviane Furrer, Hélène Castebrunet, Ulrich Dittmer, Stephan Fuchs, Wolfgang Gernjak, Marie-Christine Gromaire, Andreas Matzinger, Peter Steen Mikkelsen, William R. Selbig, Luca Vezzaro,<br> A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?<br> Water Research, 2022.<br> https://doi.org/10.1016/j.watres.2022.118968</em></p> <p><strong>Data</strong></p> <p>Micropollutants concentrations in µg/l (incl. heavy metals) for 77 wet-weather discharge sites (36 combined sewer overflows, 41 stormwater outlets). The provided data set is a collection of raw data sets based on publications referenced below. The data description is provided in the folder Data\AA_DataDescription.txt</p> <p><strong>Abstract</strong></p> <p>Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.</p>
Figure 1 in Year-round monitoring of bat records in an urban area: Kharkiv (NE Ukraine), 2013, as a case study
Figure 1. Location of Kharkiv on the map of Europe, and a general view of the city area.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.