An Image Service of select Alaska Geologic raster map images. These geologic maps are scanned hardcopy maps or direct raster exports from GIS. The raster images are stored as TIFFs typically at 300 dpi which results in an average raster cell size of ~30 meters. If required TIFFs are georeferenced within ArcPro with their native coordinate system. Each raster is added to a mosaic dataset which converts all rasters into Albers Equal Area Projection, and then referenced in this Image Service. This Image Service clips all map rasters to the main map portion of each map. To view an Image Service of geologic maps with full collars, see the Alaska Geologic Maps Images with collars Image Service instead.Key Field include:ZOrder: field that is used as the default drawing over. Larger values draw first.Map_type: general map classification; Geologic, Bedrock, Surficial, Permafrast, Engineering Geologic.url: link the DGGS citation page for the map.citation_id: reference id for the map’s citation that can be used to relate the map index record for this map.Numerous other fields that are copies of the DGGS map Index Record are included as well. To view the map index feature service, see: the Map Index feature service.For question contact the Alaska DGGS GIS group.

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Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

This raster dataset is a high-resolution mosaic of 4 Corona-KH4 satellite photos over the northern Seward Peninsula in Northwest Alaska dated to 28 July 1962. The mosaic covers a total area of about 26,000 km2 with about 2/3 of this being land area, including portions of the Bering Land Bridge National Preserve, the Cape Espenberg Lowland, the Baldwin Peninsula, and the settlements of Kotzebue and Shishmaref. The images of the Corona KH-4 camera system were ordered through the Earth Explorer web interface. The Corona images are part of a batch of US military satellite imagery declassified for civilian use in 1995 under an Executive Order by the US President. Each of the 4 images used in this dataset, originally film negatives, was scanned in 4 segments in high resolution (7 micron, 3600 dpi) by the USGS EROS data center. The processing, all conducted in ArcGIS 9.3, included georeferencing each individual image segment to a common base (three L1T terrain-corrected scenes of Landsat-5 TM and Landsat-7 ETM+). Between 22 and 53 Ground Control Points were identified between a segment and base image, achieving Root Mean Square Errors (RMSE) from 6.9m to 16.4m. The individual segments were then rectified with a 3rd polynomial order warping using bilinear pixel resampling. Histogram matching in overlapping image portions was used to smooth the transition between greyscale values between image strips. All segments were then mosaicked into one raster. The image mosaic was gridded to a 6m ground resolution and exported as a GeoTIFF (3.9 GB) raster image with a projection in UTM 3N, WGS-84.

This raster dataset is a high-resolution mosaic of 4 Corona-KH4 satellite photos over the northern Seward Peninsula in Northwest Alaska dated to 28 July 1962. The mosaic covers a total area of about 26,000 km2 with about 2/3 of this being land area, including portions of the Bering Land Bridge National Preserve, the Cape Espenberg Lowland, the Baldwin Peninsula, and the settlements of Kotzebue and Shishmaref. The images of the Corona KH-4 camera system were ordered through the Earth Explorer web interface. The Corona images are part of a batch of US military satellite imagery declassified for civilian use in 1995 under an Executive Order by the US President. Each of the 4 images used in this dataset, originally film negatives, was scanned in 4 segments in high resolution (7 micron, 3600 dpi) by the USGS EROS data center. The processing, all conducted in ArcGIS 9.3, included georeferencing each individual image segment to a common base (three L1T terrain-corrected scenes of Landsat-5 TM and Landsat-7 ETM+). Between 22 and 53 Ground Control Points were identified between a segment and base image, achieving Root Mean Square Errors (RMSE) from 6.9m to 16.4m. The individual segments were then rectified with a 3rd polynomial order warping using bilinear pixel resampling. Histogram matching in overlapping image portions was used to smooth the transition between greyscale values between image strips. All segments were then mosaicked into one raster. The image mosaic was gridded to a 6m ground resolution and exported as a GeoTIFF (3.9 GB) raster image with a projection in UTM 3N, WGS-84.

Indonesia Export: Value: Laser Photoplotters or Image Setters with A Raster Image Processor; for Roll Film of A Width More than 35 Mm data was reported at 0.001 USD mn in Mar 2024. This records a decrease from the previous number of 0.002 USD mn for Jun 2021. Indonesia Export: Value: Laser Photoplotters or Image Setters with A Raster Image Processor; for Roll Film of A Width More than 35 Mm data is updated monthly, averaging 0.001 USD mn from Dec 2019 (Median) to Mar 2024, with 4 observations. The data reached an all-time high of 0.002 USD mn in Jun 2021 and a record low of 0.000 USD mn in Feb 2020. Indonesia Export: Value: Laser Photoplotters or Image Setters with A Raster Image Processor; for Roll Film of A Width More than 35 Mm data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Foreign Trade – Table ID.JAH089: Foreign Trade: by HS 8 Digits: Export: HS90: Optical, Photographic, Cinematographic, Measuring, Checking, Medical or Surgical Instruments and Apparatus, Parts, and Accessories.

Abstract This dataset contains a raster surface representing depth to top of the Hutton Sandstone. It was exported from the Cooper Basin Trinity model, then imported into ArcGIS for reporting in the Cooper Stage 2 Hydrogeology Technical Appendix. 100 metre contour lines derived from this raster are included. ## Attribution Geological and Bioregional Assessment Program ## History This dataset contains a raster surface representing depth to top of the Hutton Sandstone. It was exported from Cooper Basin Trinity-Genesis-KinEx petroleum systems model, then imported into ArcGIS. 100 metre contours derived from this raster are also included. The data has been clipped to the Cooper GBA region outline.