The mission was the first of a series of NASA Applications Explorer Missions and is also known as AEM-A. Day/night coverage over a given area occurred at intervals ranging from 12 to 36 hours with a 16 day repeat cycle. The satellite was operational from April 1978 to September 1980. The initial orbit of 620 km was lowered to 540 km in February of 1980. Coverage includes parts of the United States, Canada, Europe, Africa, and Australia. The source data was transmitted to seven ground stations and stored on binary magnetic tape. The source data on tape is no longer readable and the only remaining set of HCMM data is on black and white film. Since the data could be of historical value for global change research, the images have been scanned at 1000 dpi (25 micron) making the data accessible to the scientific community. The collection includes approximately 47,000 scenes with a Hotine Oblique Mercator projection. The Heat Capacity Mapping Mission Radiometer operated with two channels. The first detected visible to near infrared (0.5 – 1.1 micrometers) radiation and the second detected thermal infrared (10.5 – 12.5 micrometers) radiation. HCMM nomenclature refers to the visible to near infrared channel as Vis and the thermal infrared channel as IR. The scenes are designated as Day-Vis, Day-IR or Night-IR. A HCMM scene has a width of 715 km with a resolution of 500 meters for the visible channel and 600 meters for the thermal channel.

Cities in the U.S. are getting hotter, and that is causing significant health risks, especially to minorities, the elderly, and impoverished. There is significant spatial variation in temperature across a city due to changes in the landscape (elevation, tree cover, development, etc). NOAA has been engaged in a nationwide effort with CAPA Strategies to use a combination of Sentinel-2 satellite data along with temperature readings recorded from car- and bike-mounted sensors to generate detailed maps of the urban areas most impacted by heat. These measurements have been combined into single raster layers for morning, afternoon, and evening temperatures. As of 2020, 27 cities (26 in the U.S) have been mapped; a total of 50 cities will be mapped by the end of 2021. This layer shows the census tract (neighborhood) averages for those temperatures, along with additional information calculated for each neighborhood including:Temperature anomaly (neighborhood temperature compared to the citywide average based on the CAPA data)Impervious surfaceTree coverDemographicsTotal populationPopulation <5Population >65MinorityMedian incomePovertyCombining these different types of information can help planners identify areas at risk and help to develop mitigation and resilience plans to improve urban living conditions. More information about the campaign can be found in this Story Map by NOAA.

The HCMM Digital Source dataset includes approximately 2400 scenes of recovered digital data with a resolution of 100 dpi. The original scenes are 715 km wide and vary in length from 715 to 3,000 km. The file size is 3-13 MB depending on the length of the scene and is stored in a TIFF format. The source data was transmitted to seven ground stations and stored on binary magnetic tape. The source data on tape is no longer readable and the only remaining set of HCMM data is on black and white film.

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as feature services.

This metadata record describes the topographic mapping of Hot Springs, AR during 2007. Products generated include lidar point clouds in LAS 1.0 format, random-spacing ASCII bare earth DEM, gridded DEM in ASCII format, 2 foot contours using lidar collected with a Leica ALS-50 Aerial Lidar Sensor.

Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of background conductive heat flow. Heat flow maps were constructed using a custom-developed iterative process using weighted regression, where convectively influenced outliers were de-emphasized by assigning lower weights to measurements that are very different from the estimated local trend (e.g., local convective influence). The weighted regression algorithm is 2D LOESS (locally estimated scatterplot smoothing; Cleveland et al., 1992), which was used for local linear regression, and smoothness was controlled by varying the number of nearby points used for each local interpolation. Three maps are i ...

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The standard minimum mapping unit for NPS vegetation mapping projects is defined as 0.5 hectare, although several mapped polygons were smaller for HOSP. The vegetation map reveals that the distribution of communities generally conforms to the lay of the land (Figure 12). Communities often follow long ridges and side slopes and are thus distributed in a linear fashion. The Shortleaf Pine – Oak Dry-Mesic Woodland type was most abundant at HOSP, covering 35.7% of the area, exclusive of cultural areas and water. The Ouachita-Ozark Small Stream Hardwood Forest was second in area, followed closely by the Ozark/Ouachita Shortleaf Pine – Oak Dry Woodland, at about 20% of the area each. The Ozark/Ouachita Dry-Mesic White Oak – Black Oak – Hickory Forest accounted for 11.4% of the area. The Ouachita Novaculite Glade only covered 3.3 hectares, but this unique type was among the only areas of the park without a continuous woody canopy. Minimum Mapping Unit = 0.5 hectare Minimum Patch Size = 0.01 hectares Total Size = 1402 Polygons Average Polygon Size = 3.92 acres (1.58 hectares) Overall Thematic Accuracy = 79.1% Project Completion Date: 08/2015

Snake River Plain Play Fairway Analysis - Phase 1 CRS Raster Files. This dataset contains raster files created in ArcGIS. These raster images depict Common Risk Segment (CRS) maps for HEAT, PERMEABILITY, AND SEAL, as well as selected maps of Evidence Layers. These evidence layers consist of either Bayesian krige functions or kernel density functions, and include: (1) HEAT: Heat flow (Bayesian krige map), Heat flow standard error on the krige function (data confidence), volcanic vent distribution as function of age and size, groundwater temperature (equivalue interval and natural breaks bins), and groundwater T standard error. (2) PERMEABILTY: Fault and lineament maps, both as mapped and as kernel density functions, processed for both dilational tendency (TD) and slip tendency (ST), along with data confidence maps for each data type. Data types include mapped surface faults from USGS and Idaho Geological Survey data bases, as well as unpublished mapping; lineations derived from maximum gradients in magnetic, deep gravity, and intermediate depth gravity anomalies. (3) SEAL: Seal maps based on presence and thickness of lacustrine sediments and base of SRP aquifer. Raster size is 2 km. All files generated in ArcGIS.

About the App This app hosts data from Heat Resilience Solutions for Boston (the Heat Plan). It features maps that include daytime and nighttime air temperature, urban heat island index, and extreme heat duration. About the DataA citywide urban canopy model was developed to produce modeled air temperature maps for the City of Boston Heat Resilience Study in 2021. Sasaki Associates served as the lead consultant working with the City of Boston. The technical methodology for the urban canopy model was produced by Klimaat Consulting & Innovation Inc. A weeklong analysis period during July 18th-24th, 2019 was selected to produce heat characteristics maps for the study (one of the hottest weeks in Boston that year). The data array represents the modelled, average hourly urban meteorological condition at 100 meter spatial resolution. This dataset was processed into urban heat indices and delivered as georeferenced image layers. The data layers have been resampled to 10 meter resolution for visualization purposes. For the detailed methodology of the urban canopy model, visit the Heat Resilience Study project website.

This reference contains the imagery data used in the completion of the baseline vegetation inventory project for the NPS park unit. Orthophotos, raw imagery, and scanned aerial photos are common files held here. The mapping component was produced by identifying land cover on air photos and hand digitizing on-screen. Heads-up digitizing was accomplished at a display scale of not more than 1:1,000 against a back-drop of air photos. This included 2010 leafon true color and 2006 leaf-on color infrared images (Figure 5). Additionally, georeferenced aerial photography from the 1940’s, 1960’s, 1980’s, and 1990’s were used to identify areas where previous disturbance had occurred, including demolished buildings, old roads, clearings, and historical timber harvest sites. Hot Springs National Park = 5,465 acres (2,211.8 hectares) Base Imagery used for mapping (acquired by MoRAP): 2010, Arkansas County, AR, leaf-on, true color, 1m 2006, Arkansas County, AR, leaf-off, CIR, 1m Additional Imagery acquired and viewed by MoRAP: SPOT, leaf on SPOT, leaf off

The "Transmission Generation Heat Map" data table provides an indication of the potential opportunities (or constraints) to connect to SP Energy Networks' transmission network by detailing all connected and contracted projects. This allows potential customers to have an interactive representation of the network and view the type of projects connected to specific substations within the SP Transmission area.The table gives the following information:Location of projectConnection site of projectMW connectedMW increase/decreaseCumulative total capacityProject status and date effective fromFor additional information on column definitions, please click the Dataset schema link below.DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).This heatmap will be updated on a monthly basis using the published data from the ESO's TEC register, the latest ECR and the contracted demand data to ensure we have an accurate representation of projects the ESO has considered as connected and/or contracted. It is important to note, our refresh of this data won't always be aligned to the latest available version of the ESO TEC register. Therefore, there may be small discrepancies between the two datasets. For the most up-to-date version of this data, please visit the ESO TEC register. Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Transmission Generation Heat Map dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

With the help of data from satellites, MSB has produced a heat mapping of Sweden. The mapping shows the maximum soil temperatures measured during the summer months of 2017-2022. The map service can be used to support municipalities’ climate adaptation work.

The data presented on this page concern the 2020-2022 mapping of temperature differences, the classification maps of these temperature differences (i.e. urban heat and freshness islands) and the map of the urban heat island intensity index. These different maps are detailed below: - The mapping of Temperature differences in °C represents the temperature difference in the city compared to a nearby forest. It was produced at the scale of the ecumene of Quebec (2021 census, 185,453 km2). This mapping, provided on a grid with a spatial resolution of 15 m, was carried out with a predictive machine learning model built on Landsat-8 satellite data provided by the *United States Geological Survey (USGS) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) * as well as from other geospatial variables such as hydrography and topography. - Mapping of classes of surface temperature differences, i.e. _Islands of urban heat and freshness (ICFU) _ was conducted for * population centers from the 2021 census * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants per km2 to which is added a 2 km buffer zone. It thus covers all major urban centers, i.e. 14,072 km2. The method for categorizing ICFUs is the ranking of predicted temperature differences for each population center into 9 levels. Classes 8 and 9 are considered Urban Heat Islands and classes 1, 2, and 3 as Urban Freshness Islands. The interval values for each class and population center are shown in the production metadata file. Since the surface temperatures were analyzed at the scale of the Quebec ecumene, but the classification intervals were calculated for each population center individually, the differences in temperature grouped into the different classes vary from one region to another. Thus, there are differences observed in the predicted temperature differences between North and South Quebec and according to urban realities. For example, a temperature difference of 2°C may be present in class 1 (cooler) in a population center located in southern Quebec, but may be present in class 9 (very hot) in a population center in northern Quebec. It is therefore important to interpret the identification of heat islands in relation to the relative temperature difference data produced at the Quebec ecumene scale. In addition to this map, the map of * Temperature variations for the urbanization perimeters of the smallest municipalities 2020-2022 * covers all the urbanization perimeters that are not (or only partially) covered by the ICFU map. Thus, the two maps put side by side allow a complete coverage of all population centers and urbanization perimeters in Quebec. - The _Urban Heat Island Intensity Index (SUHII) _ map _ represents the Surface Urban Heat Island Intensity (SUHII) index _ represents the Surface Urban Heat Island Intensity (SUHII) index. This index is calculated for each * dissemination island * (ID) of Statistics Canada included in the * 2021 census population centers * (CTRPOP) * () * (CTRPOP). It highlights areas with higher heat island intensity, by calculating a weighted average from the classes of temperature differences, giving more weight to the hottest classes. This weight is proportional to the class number (for example, a class 9 surface is 9 times more important in the index than the same area with a class 1). These maps as well as those of * 2013-2014 * are used for the * Analysis of change between the mapping of heat/freshness islands 2013-2014 and 2020-2022 *. For more details on the creation of the various maps as well as their advantages, limitations and potential uses, consult the * Technote * (simplified version) and/or the * methodological report * (version complete). The production of this data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Center for Forestry Education and Research (CERFO), funded under the * 2013-2020 Climate Change Action Plan * of the Quebec government entitled Le Québec en action vert 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Urban heat mapping, heat islands and social vulnerability datasets based on thermal data captured over metropolitan Adelaide in 2022.

This geodatabase was built to cover several geothermal targets developed by Flint Geothermal in 2012 during a search for high-temperature systems that could be exploited for electric power development. Several of the thermal springs and wells in the Routt Hot Spring and Steamboat Springs areahave geochemistry and geothermometry values indicative of high-temperature systems. Datasets include: Results of reconnaissance shallow (2 meter) temperature surveys Air photo lineaments Groundwater geochemistry Georeferenced geologic map of Routt County Various 1:24,000 scale topographic maps

Area-wide modeled near-surface temperature for 6-7 am on July 27, 2020, based on temperature and humidity data collected for a one-day heat mapping project conducted by King County, Seattle Public Utilities, and the City of Seattle. Data collected on July 27, 2020 in partnership with project volunteers and CAPA Strategies. Data analysis and maps produced by CAPA strategies. This predictive temperature model was created from multi-band land cover rasters from Sentinel-2 satellite and raw heat data from sensor SD cards using the 70:30 holdout method.Heat maps also available for 6-7 am and 7-8 pm. Results can be viewed using this ArcGIS web app viewer. More information on the project available in Heat Watch Report for Seattle & King County. Contact CAPA Strategies for questions on the data, maps, and data analysis methods.

'NASA\'s Heat Capacity Mapping Mission (HCMM) project collected Earth data in the visible and thermal bands between April 1978 and September 1980. This was an experimental satellite program which observed day and night thermal conditions of Earth\'s surface. The black and white scenes cover large areas (approximately 500,000 square km at a resolution of 500 meters for the visible channel and 600 meters for the thermal channel. The scale of the imagery is 1:400,000. Areas covered include parts of the United States, western Canada, western Europe, northern Africa, and eastern Australia. The HCMM Digital Source dataset includes approximately 2400 scenes of recovered digital data with a resolution of 100 dpi. The original scenes are 715 km wide and vary in length from 715 to 3,000 km. The file size is 3-13 MB depending on the length of the scene and is stored in a TIFF format. The HCMR transmitted analog data in real time to selected receiving stations. The radiometer was similar to the surface composition mapping radiometer (SCMR) of Nimbus 5 (72-097A). The HCMR had a small instantaneous geometric field of view of 0.83 mrad, high radiometric accuracy, and a wide 716-km swath coverage on the ground so that selected areas were covered within the 12-h period corresponding to the maximum and minimum of temperature observed. The spacecraft was spin stabilized at a rate of 14 rpm. The HCMM circular sun-synchronous orbit allowed the spacecraft to sense surface temperatures near the maximum and minimum of the diurnal cycle. '

The "Distributed Generation SP Distribution Heat Maps - SPD Grid Substations" data table provides an indication of SP Energy Networks’ network capabilities and potential opportunities to connect Distributed Generation (DG) to the 11kV and 33kV network for the SP Distribution (SPD) licence area (covering Central & Southern Scotland).Each substation and circuit are assigned to one of the following categories:Green: All operational factors are within tolerable limits and so opportunities may exist to connect additional Distributed Generation without reinforcing the network (subject to detailed studies).Amber: At least one factor is nearing its operational limit and hence, depending on the nature of the application, network reinforcement may be required. However, this can only be confirmed by detailed network analysis.Red: At least one factor is close to its operational limit and so installation of most levels of Distributed Generation and a local connection is highly unlikely. It may also require extensive reinforcement works or, given the lack of a local connection, require an extensive amount of sole user assets to facilitate such a connection.The table gives the following information:Generation connected by substationGeneration contracted by substationCapacity per substationMaximum load per substationFor additional information on column definitions, please click on the Dataset schema link below.DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this).Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Distributed Generation Heat Maps dataset. To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information.Download dataset metadata (JSON)

The National Integrated Heat Health Information System (NIHHIS) and CAPA Strategies are now accepting applications from organizations interested in participating in the 2022 cohort of the Urban Heat Island (UHI) mapping campaigns. Over the past four years, more than three dozen cities across the United States. have participated in the UHI campaign program to map the hottest parts of their communities. Cities in 11 states participated in the 2021 campaign, which finished data collection in September. Mapping reports will be released on a rolling basis through December. NOAA will provide funding to CAPA Strategies to support campaigns in approximately 8-10 communities in 2022. Additionally, there are three new features planned for the 2022 mapping campaigns:

This layer is sourced from gis.ngdc.noaa.gov.

The Thermal Springs data available online from NOAA's National Centers for Environmental Information (NCEI) contains 1661 hot springs for the United States. The content was originally published in 1980, and has not been updated since.

Compiled by George W. Berry, Paul J. Grimm, and Joy A. Ikelman.
NOAA KGRD No. 12 (3 MB PDF)

© NOAA National Centers for Environmental Information