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29 results for “urban heat island”
Data from: Evolution of thermal tolerance and its fitness consequences: parallel and non-parallel responses to urban heat islands across three cities
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Data from: Snail shell colour evolution in urban heat islands detected via citizen science
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Data from: The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity
Climate alteration is one of the most cited ecological consequences of urbanization. However, the magnitude of this impact is likely to vary with spatial scale. We investigated how this alteration affects the biological fitness of insects, which are especially sensitive to ambient conditions and well-suited organisms to study urbanization-related changes in phenotypic traits. We monitored temperature and relative air humidity in wooded sites characterized by different levels of urbanization in the surroundings. Using a split-brood design experiment, we investigated the effect of urbanization at the local (i.e., 200 × 200 m) and landscape (i.e., 3 × 3 km) scale on two key traits of biological fitness in two closely related butterfly species that differ in thermal sensitivity. In line with the Urban Heat Island concept, urbanization led to a 1°C increase in daytime temperature and an 8% decrease in daytime relative humidity at the local scale. The thermophilous species Lasiommata megera responded at the local scale: larval survival increased twofold in urban compared to rural sites. Urbanized sites tended to produce bigger adults, although this was the case for males only. In the woodland species Pararge aegeria, which has recently expanded its ecological niche, we did not observe such a response, neither at the local, nor at the landscape scale. These results demonstrate interspecific differences in urbanization-related phenotypic plasticity and larval survival. We discuss larval pre-adaptations in species of different ecological profiles to urban conditions. Our results also highlight the significance of considering fine-grained spatial scales in urban ecology.
Data from: The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity
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Heat Waves, Urban Heat Islands, and Wellbeing and Health: a Mobile Sensing Approach
ClinicalTrials.gov study NCT06850025. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018
The Yale Center for Earth Observation (YCEO) Surface Urban Heat Islands, Version 4, 2003-2018 includes annual, summertime, and wintertime Surface Urban Heat Island (SUHI) intensities for daytime and nighttime for over 10,000 global urban extents. This global SUHI data set was created using the Simplified Urban-Extent (SUE) algorithm and is available at the pixel and urban cluster-levels (i.e. at the level of larger urban agglomerations). Monthly composites are also available as urban cluster means. A summary of older versions, including changes from the data set created and analyzed in the originally published manuscript (Chakraborty and Lee, 2019) can be found on the YCEO Global Surface UHI Explorer website (https://yceo.yale.edu/research/global-surface-uhi-explorer).
Global Urban Heat Island (UHI) Data Set, 2013
The Urban Heat Island (UHI) effect represents the relatively higher temperatures found in urban areas compared to surrounding rural areas owing to higher proportions of impervious surfaces and the release of waste heat from vehicles and heating and cooling systems. Paved surfaces and built structures tend to absorb shortwave radiation from the sun and release long-wave radiation after a lag of a few hours. The Global Urban Heat Island (UHI) Data Set, 2013, estimates the land surface temperature within urban areas in degrees Celsius (average summer daytime maximum and average summer nighttime minimum) as well as the difference between those temperatures and the temperatures in surrounding rural areas, defined as a 10km buffer around the urban extent. Urban extents are from SEDAC�s Global Rural-Urban Mapping Project, Version 1 (GRUMPv1), and land surface temperatures are from SEDAC�s Global Summer Land Surface Temperature (LST) Grids, 2013, which are derived from the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime Land Surface Temperature (LST) 8-day composite data (MYD11A2). For most regions, the UHI data set provides the average daytime maximum (1:30 p.m. overpass) and average nighttime minimum (1:30 a.m. overpass) temperatures in urban and rural areas, and the urban-rural temperature differences, derived from LST data representing a 40-day time-span during July-August (Julian days 185-224) in the northern hemisphere and January-February (Julian days 001-040) in the southern hemisphere. LST grid cells with missing values resulting from high cloud cover in tropical regions were filled with daytime maximum and nighttime minimum LST values from April-May 2013 in the northern hemisphere and December 2013-January 2014 in the southern hemisphere, where available. Some data gaps remain in areas where data were insufficient (e.g., Central Africa).
Geographically Weighted Regression Modeling of a Nighttime Urban Heat Island in Dar es Salaam Metropolitan Areas
<p>Urban Heat Islands (UHI) is the urban microclimate with higher air temperature than surrounding areas. It is caused by both man-made and natural factors which vary geographically based on weather periods. To have a sustainable future in the environment, there is a need to regulate influence levels of various causative factors that generate UHI. Geographically Weighted Regression (GWR) model is among the spatial regression models that define the non-stationarity of variables. It generates a new equation on each sampled data unlikely global models like Ordinary Least Square (OLS). The study used GWR model to determine the influencing levels of three independent factors named Indexed-based Built-up Index (IBI), albedo and wind speed. Datasets were retrieved from MODIS satellite during the dry period of July from 2000 to 2019. IBI and Albedo were observed to have a strong negative influence with the maximum value of -0.045 and -0.053 respectively although, we expected to observe a positive influence on IBI since buildings emit absorbed energy during the night. Wind speed has a positive influence with the maximum value of 0.028 leading to the shift of heatwaves hence being termed as the secondary driving factor while IBI and Albedo as the primary driving factors. Wind speed is the highest driving factor that shifts emitted energies to other areas. We encourage an innovation in technology that produce higher albedo construction materials. We should improve environmental policies by introducing green cities through horizontal and vertical forests which might decrease the emitted energy into the atmosphere.</p>
Geographically Weighted Regression Modeling of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.
<p>Urban heat island is the phenomenon of having higher temperatures in urban areas compared to surrounding areas. It is caused by the replacement of natural vegetation with construction materials. Geographically Weighted Regression Model (GWR) determines non-stationarity among variables by generating a new equation for each sample size. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 are used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. The highest and lowest coverage of Urban Heat Island of 69% and 43% were observed in 2000 and 2005 respectively. IBI is the leading causative factor by having an influence of 0.023 followed by Albedo, wind speed and EVI with an average of 0.019, 0.016 and -0.015 respectively. We recommend innovation in producing higher albedo construction materials and introducing green city policies.</p>
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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.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.