Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

Notice: this is not the latest Heat Island Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, with patching from summer of 2021 where necessary.Federal 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 or cooler than the average temperature for that same city as a whole. 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 The 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.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 The 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). The 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.

The Grid Garage Toolbox is designed to help you undertake the Geographic Information System (GIS) tasks required to process GIS data (geodata) into a standard, spatially aligned format. This format is required by most, grid or raster, spatial modelling tools such as the Multi-criteria Analysis Shell for Spatial Decision Support (MCAS-S). Grid Garage contains 36 tools designed to save you time by batch processing repetitive GIS tasks as well diagnosing problems with data and capturing a record of processing step and any errors encountered.

Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to:

• List, describe and manage very large volumes of geodata.
• Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models.
• Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed.
• Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs.
• Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets.

The Grid Garage is intended for use by anyone with an understanding of GIS principles and an intermediate to advanced level of GIS skills. Using the Grid Garage tools in ArcGIS ModelBuilder requires skills in the use of the ArcGIS ModelBuilder tool.

Download Instructions: Create a new folder on your computer or network and then download and unzip the zip file from the GitHub Release page for each of the following items in the 'Data and Resources' section below. There is a folder in each zip file that contains all the files. See the Grid Garage User Guide for instructions on how to install and use the Grid Garage Toolbox with the sample data provided.

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. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal 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 The 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 The 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). The 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.

Abstract The dataset was derived by the Bioregional Assessment Programme from the GEODATA TOPO 250K Series 3 dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. A clip to the boundary of the NIC bioregion of the original Geodata Topo 250k Series 3 data. Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-ecbd-7506-e044-00144fdd4fa6/GEODATA+TOPO+250K+Series+3+%28Packaged+-+Shape+file+format%29 ## Dataset History These data have been derived from the TOPO 250K Series 3 data by clipping the data to the external boundary of the Northern Inland Catchments bioregion using the ESRI ArcGIS Analysis Clip tool. Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-ecbd-7506-e044-00144fdd4fa6/GEODATA+TOPO+250K+Series+3+%28Packaged+-+Shape+file+format%29 ## Dataset Citation Bioregional Assessment Programme (XXXX) Northern Inland Catchments bioregion GEODATA TOPO 250K Series 3. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/3bd6389b-be36-4e91-bc20-131e18f209c2. ## Dataset Ancestors * Derived From Natural Resource Management (NRM) Regions 2010 * Derived From Bioregional Assessment areas v03 * Derived From Bioregional Assessment areas v01 * Derived From Bioregional Assessment areas v02 * Derived From GEODATA TOPO 250K Series 3 * Derived From NSW Catchment Management Authority Boundaries 20130917 * Derived From Geological Provinces - Full Extent * Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Grid Garage provides tools that function using a list based approach to batch processing where both inputs and outputs are specified in tables to enable selective batch processing and detailed result reporting. In many cases the tools simply extend the functionality of standard ArcGIS tools, providing some or all of the inputs required by these tools via the input table to enable batch processing on a 'per item' basis. This approach differs slightly from normal batch processing in ArcGIS, instead of manually selecting single items or a folder on which to apply a tool or model you provide a table listing target datasets. In summary the Grid Garage allows you to: List, describe and manage very large volumes of geodata. Batch process repetitive GIS tasks such as managing (renaming, describing etc.) or processing (clipping, resampling, reprojecting etc.) many geodata inputs such as time-series geodata derived from satellite imagery or climate models. Record any errors when batch processing and diagnose errors by interrogating the input geodata that failed. Develop your own models in ArcGIS ModelBuilder that allow you to automate any GIS workflow utilising one or more of the Grid Garage tools that can process an unlimited number of inputs. Automate the process of generating MCAS-S TIP metadata files for any number of input raster datasets.

ArcGIS Pro/QGIS to modify layers R for scripts

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS).

This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations.

Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015.

Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below) This is one of sixteen associated .zip files relating to thermal resource interpolation results within the Thermal Quality Analysis task of the Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. This file contains an ArcGIS Toolbox with 6 ArcGIS Models: WellClipsToWormsSections, BufferedRasterToClippedRaster, ExtractThermalPropertiesToCrossSection, AddExtraInfoToCrossSection, and CrossSectionExtraction.

The sixteen files contain the results of the thermal resource interpolation as binary grid (raster) files, images (.png) of the rasters, and toolbox of ArcGIS Models used. Note that raster files ending in “pred” are the predicted mean for that resource, and files ending in “err” are the standard error of the predicted mean for that resource. Leave one out cross validation results are provided for each thermal resource.

Several models were built in order to process the well database with outliers removed. ArcGIS toolbox ThermalRiskFactorModels contains the ArcGIS processing tools used. First, the WellClipsToWormSections model was used to clip the wells to the worm sections (interpolation regions). Then, the 1 square km gridded regions (see series of 14 Worm Based Interpolation Boundaries .zip files) along with the wells in those regions were loaded into R using the rgdal package. Then, a stratified kriging algorithm implemented in the R gstat package was used to create rasters of the predicted mean and the standard error of the predicted mean. The code used to make these rasters is called StratifiedKrigingInterpolation.R Details about the interpolation, and exploratory data analysis on the well data is provided in 9_GPFA-AB_InterpolationThermalFieldEstimation.pdf (Smith, 2015), contained within the final report.

The output rasters from R are brought into ArcGIS for further spatial processing. First, the BufferedRasterToClippedRaster tool is used to clip the interpolations back to the Worm Sections. Then, the Mosaic tool in ArcGIS is used to merge all predicted mean rasters into a single raster, and all error rasters into a single raster for each thermal resource.

A leave one out cross validation was performed on each of the thermal resources. The code used to implement the cross validation is provided in the R script LeaveOneOutCrossValidation.R. The results of the cross validation are given for each thermal resource.

Other tools provided in this toolbox are useful for creating cross sections of the thermal resource. ExtractThermalPropertiesToCrossSection model extracts the predicted mean and the standard error of predicted mean to the attribute table of a line of cross section. The AddExtraInfoToCrossSection model is then used to add any other desired information, such as state and county boundaries, to the cross section attribute table. These two functions can be combined as a single function, as provided by the CrossSectionExtraction model.

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 web application highlights some of the capabilities for accessing Landsat imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Landsat images on a daily basis.

Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Landsat Explorer app provides the power of Landsat satellites, which gather data beyond what the eye can see. Use this app to draw on Landsat's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the entire Landsat archive to visualize how the Earth's surface has changed over the last forty years.

Quick access to the following band combinations and indices is provided:

• Agriculture : Highlights agriculture is bright green; Bands 6, 5, 2
• Natural Color : Sharpened with 15m panchromatic band; Bands 4, 3, 2 +8
• Color Infrared : Healthy vegetation is bright red; Bands 5, 4 ,3
• SWIR (Short Wave Infrared) : Highlights rock formations; Bands 7, 6, 4
• Geology : Highlights geologic features; Bands 7, 6, 2
• Bathymetric : Highlights underwater features; Bands 4, 3, 1
• Panchromatic : Panchromatic images at 15m; Band 8
• Vegetation Index : Normalized Difference Snow Index(NDVI); (Band 5 - Band 4)/(Band 5 + Band 4)
• Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 5 - Band 6)/(Band 5 + Band 6)
• SAVI : Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 5 - Band 4)/(Band 5 + Band 4 + 0.5))
• Water Index : Offset + Scale*(Band 3 - Band 6)/(Band 3 + Band 6)
• Burn Index : Offset + Scale*(Band 5 - Band 7)/(Band 5 + Band 7)
• Urban Index : Offset + Scale*(Band 5 - Band 6)/(Band 5 + Band 6)

The application is written using Web AppBuilder for ArcGIS accessing imagery layers using the ArcGIS API for JavaScript.

Welcome to the LandsatLook Viewer!The LandsatLook Viewer is a prototype tool that was developed to allow rapid online viewing and access to the USGS Landsat image archives. This viewer allows you to:Interactively explore the Landsat archive at up to full resolution directly from a common web browserSearch for specific Landsat images based on area of interest, acquisition date, or cloud coverCompare image features and view changes through timeDisplay configurable map information layers in combination with the Landsat imageryCreate a customized image display and export as a simple graphic fileView metadata and download the full-band source imagerySearch by address or place, or zoom to a point, bounding box, or Sentinel-2 Tile or Landsat WRS-1 or WRS-2 Path/RowGenerate and download a video animation of the oldest to newest images displayed in the viewerWe welcome feedback and input for future versions of this Viewer! Please provide your comments or suggestions .About the ImageryThis viewer provides visual and download access to the USGS LandsatLook "Natural Color" imageproduct archive.BackgroundThe Landsat satellites have been collecting multispectral images of Earth from space since 1972. Each image contains multiple bands of spectral information which may require significant user time, system resources, and technical expertise to obtain a visual result. As a result, the use and access to Landsat data has been historically limited to the scientific and technical user communities.The LandsatLook “Natural Color” image product option was created to provide Landsat imagery in a simple user-friendly and viewer-ready format, based on specific bands that have been selected and arranged to simulate natural color. This type of product allows easy visualization of the archived Landsat image without any need for specialized software or technical expertise.LandsatLook ViewerThe LandsatLook Viewer displays the LandsatLook Natural Color image product for all Landsat 1-8 images in the USGS archive and was designed primarily for visualization purposes.The imagery within this Viewer will be of value to anyone who wants to quickly see the full Landsat record for an area, along with major image features or obvious changes to Earth’s surface through time. An area of interest may be extracted and downloaded as a simple graphic file directly through the viewer, and the original full image tile is also available if needed. Any downloaded LandsatLook image product is a georeferenced file and will be compatible within most GIS and Web mapping applications.If the user needs to perform detailed technical analysis, the full bands of Landsat source data may also be accessed through direct links provided on the LandsatLook Viewer.Image ServicesThe imagery that is visible on this LandsatLook Viewer is based on Web-based ArcGIS image services. The underlying REST service endpoints for the LandsatLook imagery are available at https://landsatlook.usgs.gov/arcgis/rest/services/LandsatLook/ImageServer .Useful linksLandsat- Landsat Mission (USGS)- Landsat Science (NASA)LandsatLook- Product Description- USGS Fact Sheet- LandsatLook image services (REST)Landsat Products- Landsat 8 OLI/TIRS- Landsat 7 ETM+- Landsat 4-5 TM- Landsat 1-5 MSS- Landsat Band DesignationsLandsatLook images are full-resolution files derived from Landsat Level-1 data products. The images are compressed and stretched to create an image optimized for image selection and visual interpretation. It is recommended that these images not be used in image analysis.LandsatLook image files are included as options when downloading Landsat scenes from EarthExplorer, GloVis, or the LandsatLook Viewer (See Figure 1).Figure 1. LandsatLook and Level-1 product download optionsLandsatLook Natural Color ImageThe LandsatLook Natural Color image is a .jpg composite of three bands to show a “natural” looking (false color) image. Reflectance values were calculated from the calibrated scaled digital number (DN) image data. The reflectance values were scaled to a 1-255 range using a gamma stretch with a gamma=2.0. This stretch was designed to emphasize vegetation without clipping the extreme values.Landsat 8 OLI = Bands 6,5,4Landsat 7 ETM+ and Landsat 4-5 TM = Bands 5,4,3Landsat 4-5 MSS = Bands 2,4,1Landsat 1-3 MSS = Bands 7,5,4LandsatLook Thermal ImageThe LandsatLook Thermal image is a one-band gray scale .jpg image that displays thermal properties of a Landsat scene. Image brightness temperature values were calculated from the calibrated scaled digital number (DN) image data. An image specific 2 percent clip and a linear stretch to 1-255 were applied to the brightness temperature values.Landsat 8 TIRS = Band 10Landsat 7 ETM+ = Band 61-high gainLandsat 4-5 TM = Band 6Landsat 1-5 MSS = not availableLandsatLook Quality ImageLandsatLook Quality images are 8-bit files generated from the Landsat Level-1 Quality band to provide a quick view of the quality of the pixels within the scene to determine if a particular scene would work best for the user's application. This file includes values representing bit-packed combinations of surface, atmosphere, and sensor conditions that can affect the overall usefulness of a given pixel. Color mapping assignments can be seen in the tables below. For each Landsat scene, LandsatLook Quality images can be downloaded individually in .jpg format, or as a GeoTIFF format file (_QB.TIF) within the LandsatLook Images with Geographic Reference file.Landsat Collection 1 LandsatLook 8-bit Quality Images DesignationsLandsat 8 OLI/TIRSLandsat 7 ETM+, Landsat 4-5 TMLandsat 1-5 MSSColorBitDescriptionBitDescriptionBitDescription 0Designated Fill0Designated Fill0Designated Fill 1Terrain Occlusion1Dropped Pixel1Dropped Pixel 2Radiometric Saturation 2Radiometric Saturation ​2Radiometric Saturation 3Cloud3Cloud3Cloud 4Cloud Shadow4Cloud Shadow 4Unused 5Snow/Ice 5Snow/Ice 5Unused 6Cirrus 6Unused6Unused 7Unused7Unused7UnusedUnusedTable 1. Landsat Collection 1 LandsatLook 8-bit Quality Images Designations LandsatLook Images with Geographic ReferenceThe LandsatLook Image with Geographic Reference is a .zip file bundle that contains the Natural Color, Thermal, and the 8-bit Quality images in georeferenced GeoTiff (.TIF) file format.Figure 2. LandsatLook Natural Color Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013Figure 3. LandsatLook Thermal Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013Figure 4. LandsatLook Quality Image: Landsat 8 Path 45 Row 30 Acquired April 23, 2013 with background color set to dark grey. Additional Information About LandsatLook ImagesMany geographic information systems and image processing software packages easily support .jpg images. To create these files, Landsat data is mapped to a 1-255 range, with the fill area set to zero (if a no-data value is set to zero, the compression algorithm may introduce zero-value artifacts into the data area causing very dark data values to be displayed as no-data).

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains an ArcGIS shapefile of the river regions in the Gippsland bioregion. ## Dataset History This dataset was created within ArcMap 10.2 using the 'Clip' tool to clip the River Regions layer from the Geofabric Hydrology Reporting Regions - V2.1 dataset (GUID: 3d482415-233f-4ffd-9dd1-20d37b85229f), to the Gippsland Project Boundary (GUID: 27413de5-d13a-4231-ac79-fc77f4cbb5f7). ## Dataset Citation Bioregional Assessment Programme (2015) River Regions in Gippsland Basin bioregion. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/9f5f89a9-b1e2-4105-8183-e5d2ce0f65c6. ## Dataset Ancestors * Derived From Victoria - Seamless Geology 2014 * Derived From Gippsland Project boundary * Derived From GEODATA TOPO 250K Series 3 * Derived From Geofabric Hydrology Reporting Regions - V2.1

More Metadata

The Ridge Feature Protection layer was developed using best available elevation data and the Geomorphon Landforms geoprocessing tool available thru Esri ArcGIS Pro software. The Geomorphon Landforms geoprocessing tool is based on a powerful algorithm that combines elevation differences and visibility concepts to classify terrain into landform types. A geomorphon is a representation of landscape based on elevation differences within the surrounding area of a target cell.

The following parameters and data refinements were used for the Geomorphon Landforms geoprocessing tool in order to provide a general representation of primary and other prominent ridge features in Loudoun County.

Parameters:

[Flat Angle Threshold = 3 degrees, Search Distance = 2,640 feet (1/2 mile), and Skip Distance = 660 feet (1/8 mile), Landform Values = Peak, Ridge OR Shoulder]

Data Refinements:

• All features outside of the MOD were removed.
• All features within the MOD but below the critical elevation defined within the MOD were removed.
• All features within MOD less than 20 acres in size and not contiguous with the County’s boundary were removed.
• All features within MOD not contiguous with a primary ridge feature were removed.
• All features within MOD contiguous with a primary ridge feature but 400 feet or more in elevation below the primary ridge feature were removed.

The Ridge Feature Protection layer is intended to be used only as a reference. Better site-specific elevation data may improve the precision and accuracy of the Ridge Features layer. To assure that Loudoun County is using the best available elevation data, please provide the Office of Mapping and Geographic Information with information concerning any errors, omissions, or other discrepancies discovered in this data.

Source Data

Digital Elevation Model/DEM (~ 10 m cell size)

Census 2020 blocks in King County with selected P.L. 94-171 redistricting data.

🇺🇸 미국 English The Watershed Boundary Dataset (WBD) is a seamless, national hydrologic unit dataset. Hydrologic units represent the area of the landscape that drains to a portion of the stream network. (https://www.usgs.gov/national-hydrography/watershed-boundary-dataset) It is maintained by the U.S. Geological Survey (USGS) in partnership with the states. The Department of Water Resources is the steward for the California portion of this dataset.The hydrologic units (HU) in the WBD form a standardized system for organizing, collecting, managing, and reporting hydrologic information for the nation. The HUs in the WBD are arranged in a nested, hierarchical system with each HU in the system identified using a unique code. Hydrologic unit codes (HUC) are developed using a progressive two-digit system where each successively smaller areal unit is identified by adding two digits to the identifying code the smaller unit is nested within. WBD contains eight levels of progressive hydrologic units identified by unique 2- to 16-digit codes. The dataset is complete for the United States to the 12-digit hydrologic unit. The 8-digit level unit is often referred to as HUC8 and is a commonly used reference framework for planning and environmental assessment. This particular version of the dataset was created by downloading the CA State extract of the National Hydrography Dataset from the USGS website https://www.usgs.gov/national-hydrography/access-national-hydrography-products and then performing a geoprocessing operation in ArcGIS Pro software to clip the HUC8s at the state of California political boundary. (https://data.cnra.ca.gov/dataset/california-county-boundaries2). A web map service was created with this dataset, but at it's original digitized resolution it can take a long time to render in a web map application. This dataset is a simplified version, created by use of the ArcGIS Simplify Polygon tool with the Douglas-Peucker Line simplification algorithm, reducing the vertex count from 1,095,449 to 9108. This dataset was reprojected from the original NAD 83 Geographic Coordinate System to WGS 1984 Web Mercator auxiliary sphere for use in web map applications. Any questions about this dataset may be sent to jane.schafer-kramer@water.ca.gov

This is the DEM data for the watershed boundary for French Broad River near Newport. This resource includes one TIFF image presenting the projected DEM data of watershed boundary for French Broad River Near Newport and one txt file containing the Python code used to download and process the original DEM data. The input data is http://www.hydroshare.org/resource/860155baa06d46f1a0c3e0bf48e9b348 , provided by Dr. Venkatesh Merwade. The code has some minor problems with the last line of Cell 7. Currently it can download, merge and project the DEM data and save them as TIFF file. We're still working on clipping the TIFF image so it can be complete. To run the code, read the instructions in readme.txt.

EarthExplorer

Ce MNT a été créé à partir du jeu de données de terrain principal (MTD) de Hakai à l'aide de l'outil « MNT to raster » dans ArcGIS for Desktop d'ESRI à l'aide d'une méthode d'échantillonnage Natural Neighbour. Le DEM a été créé nativement à une résolution de 20 m. Ce MNE a été coupé sur le littoral de l'île. Une combinaison de différentes altitudes autour de l'île a été utilisée pour créer le rivage.

La grille de ce MNT est alignée ou « accrochée » à la grille raster des mesures de la canopée de végétation et des rasters associés.

Le MNT qui en résulte est un modèle d'élévation hydro-aplati à terre nue et donc considéré comme « topographiquement complet ». Chaque pixel représente l'altitude en mètres au-dessus du niveau moyen de la mer de la terre nue à cet endroit. Le système de référence vertical est « Canadian Geodetic Vertical Datum 1928 » (CGVD28).

Hakai a produit des DEM à différentes résolutions nativement à partir du MTD de données LiDAR. Veuillez utiliser le produit de résolution approprié de ceux produits par Hakai à des fins de recherche. Afin de maintenir l'homogénéité, il n'est pas recommandé d'échantillonnager/augmenter à partir de produits à haute résolution, car il peut introduire et propager des erreurs de grandeur variable dans les analyses en cours ; veuillez utiliser des produits déjà disponibles, et si vous avez besoin d'une résolution non disponible, contactez data@hakai.org afin d'obtenir un MNT produit directement à partir du MTD.

Les DEM topographiquement complets suivants ont été produits nativement à partir du DTM par Hakai :

MNT topographiquement complet de 3 m. Ce produit a été utilisé pour produire les jeux de données hydrologiques de Hakai (Streams and Watersheds) MNE topographiquement complet de 20 m. Compatible avec les mesures de la canopée de végétation de Hakai et les rasters associés. MNT topographiquement complet de 25 m Compatible avec les produits de données BCGov TRIM. MNE topographiquement complet de 30 m. Compatible avec les produits STRM.

Création d'un jeu de données de MNT principal :

Clouds de points LiDAR provenant de missions effectuées en 2012 et 2014 au-dessus de Calvert Island où ils sont chargés (XYZ uniquement) dans une classe d'entités ponctuelles dans une géodatabase ESRI.

Seul ground (classe 2) renvoie lorsqu'il est chargé sur la géodatabase.

Le « jeu de données de MNT » ESRI a été créé dans la même géodatabase à l'aide des points LiDAR comme points de masse intégrés.

Les lacs et étangs TEM Plus ayant des valeurs d'altitude moyennes au-dessus des miroirs des plans d'eau ont été utilisés comme lignes de fracture de remplacement dur pour obtenir un aplatissement hydro-aplati.

La géométrie limite minimale de tous les blocs de fichiers LAS contigus a été utilisée comme masque de clip souple lors de la création du jeu de données de MNT en tant que limite de projet.

Le système de coordonnées horizontales et la référence utilisés pour le jeu de données de MNT sont : UTM Zone 9 NAD1983 ; le système de référence vertical a été défini sur CGVD28. Les deux systèmes de référence correspondent au système de référence natif des nuages de points LiDAR.

L'espacement minimal des points défini lors de la création du jeu de données de MNT a été défini sur 1.

The region is the top tier of local government in New Zealand. There are 16 regions of New Zealand (Part 1 of Schedule 2 of the Local Government Act 2002). Eleven are governed by an elected regional council, while five are governed by territorial authorities (the second tier of local government) who also perform the functions of a regional council and thus are known as unitary authorities. These unitary authorities are Auckland Council, Nelson City Council, Gisborne, Tasman, and Marlborough District Councils. The Chatham Islands Council also perform some of the functions of a regional council, but is not strictly a unitary authority. Unitary authorities act as regional councils for the purposes of a wide range of Acts and regulations. Regional council areas are based on water catchment areas. Regional councils are responsible for the administration of many environmental and public transport matters.Regional Councils were established in 1989 after the abolition of the 22 local government regions. The local government act 2002, requires the boundaries of regions to confirm as far as possible to one or more water catchments. When determining regional boundaries, the local Government commission gave consideration to regional communities of interest when selecting water catchments to included in a region. It also considered factors such as natural resource management, land use planning and environmental matters. Some regional boundaries are conterminous with territorial authority boundaries but there are many exceptions. An example is Taupo District, which is split between four regions, although most of its area falls within the Waikato Region. Where territorial local authorities straddle regional council boundaries, the affected area have been statistically defined in complete area units. Generally regional councils contain complete territorial authorities. The unitary authority of the Auckland Council was formed in 2010, under the Local Government (Tamaki Makarau Reorganisation) Act 2009, replacing the Auckland Regional Council and seven territorial authorities.The seaward boundary of any costal regional council is the twelve mile New Zealand territorial limit. Regional councils are defined at meshblock and area unit level.Regional Councils included in the 2013 digital pattern are:Regional Council CodeRegional Council Name01Northland Region02Auckland Region03Waikato Region04Bay of Plenty Region05Gisborne Region06Hawke's Bay Region07Taranaki Region08Manawatu-Wanganui Region09Wellington Region12West Coast Region13Canterbury Region14Otago Region15Southland Region16Tasman Region17Nelson Region18Marlborough Region99Area Outside RegionAs at 1stJuly 2007, Digital Boundary data became freely available.Deriving of Output FilesThe original vertices delineating the meshblock boundary pattern were digitised in 1991 from 1:5,000 scale urban maps and 1:50,000 scale rural maps. The magnitude of error of the original digital points would have been in the range of +/- 10 metres in urban areas and +/- 25 metres in rural areas. Where meshblock boundaries coincide with cadastral boundaries the magnitude of error will be within the range of 1–5 metres in urban areas and 5 - 20 metres in rural areas. This being the estimated magnitude of error of Landonline.The creation of high definition and generalised meshblock boundaries for the 2013 digital pattern and the dissolving of these meshblocks into other geographies/boundaries were completed within Statistics New Zealand using ESRI's ArcGIS desktop suite and the Data Interoperability extension with the following process: 1. Import data and all attribute fields into an ESRI File Geodatabase from LINZ as a shapefile2. Run geometry checks and repairs.3. Run Topology Checks on all data (Must Not Have Gaps, Must Not Overlap), detailed below.4. Generalise the meshblock layers to a 1m tolerance to create generalised dataset. 5. Clip the high definition and generalised meshblock layers to the coastline using land water codes.6. Dissolve all four meshblock datasets (clipped and unclipped, for both generalised and high definition versions) to higher geographies to create the following output data layers: Area Unit, Territorial Authorities, Regional Council, Urban Areas, Community Boards, Territorial Authority Subdivisions, Wards Constituencies and Maori Constituencies for the four datasets. 7. Complete a frequency analysis to determine that each code only has a single record.8. Re-run topology checks for overlaps and gaps.9. Export all created datasets into MapInfo and Shapefile format using the Data Interoperability extension to create 3 output formats for each file. 10. Quality Assurance and rechecking of delivery files.The High Definition version is similar to how the layer exists in Landonline with a couple of changes to fix topology errors identified in topology checking. The following quality checks and steps were applied to the meshblock pattern:Translation of ESRI Shapefiles to ESRI geodatabase datasetThe meshblock dataset was imported into the ESRI File Geodatabase format, required to run the ESRI topology checks. Topology rules were set for each of the layers. Topology ChecksA tolerance of 0.1 cm was applied to the data, which meant that the topology engine validating the data saw any vertex closer than this distance as the same location. A default topology rule of “Must Be Larger than Cluster Tolerance” is applied to all data – this would highlight where any features with a width less than 0.1cm exist. No errors were found for this rule.Three additional topology rules were applied specifically within each of the layers in the ESRI geodatabase – namely “Must Not Overlap”, “Must Not Have Gaps” and “"Area Boundary Must Be Covered By Boundary Of (Meshblock)”. These check that a layer forms a continuous coverage over a surface, that any given point on that surface is only assigned to a single category, and that the dissolved boundaries are identical to the parent meshblock boundaries.Topology Checks Results: There were no errors in either the gap or overlap checks.GeneralisingTo create the generalised Meshblock layer the “Simplify Polygon” geoprocessing tool was used in ArcGIS, with the following parameters:Simplification Algorithm: POINT_REMOVEMaximum Allowable Offset: 1 metreMinimum Area: 1 square metreHandling Topological Errors: RESOLVE_ERRORSClipping of Layers to CoastlineThe processed feature class was then clipped to the coastline. The coastline was defined as features within the supplied Land2013 with codes and descriptions as follows:11- Island – Included12- Mainland – Included21- Inland Water – Included22- Inlet – Excluded23- Oceanic –Excluded33- Other – Included.Features were clipped using the Data Interoperability extension, attribute filter tool. The attribute filter was used on both the generalised and high definition meshblock datasets creating four meshblock layers. Each meshblock dataset also contained all higher geographies and land-water data as attributes. Note: Meshblock 0017001 which is classified as island, was excluded from the clipped meshblock layers, as most of this meshblock is oceanic. Dissolve meshblocks to higher geographiesStatistics New Zealand then dissolved the ESRI meshblock feature classes to the higher geographies, for both the full and clipped dataset, generalised and high definition datasets. To dissolve the higher geographies, a model was built using the dissolver, aggregator and sorter tools, with each output set to include geography code and names within the Data Interoperability extension. Export to MapInfo Format and ShapfilesThe data was exported to MapInfo and Shapefile format using ESRI's Data Interoperability extension Translation tool. Quality Assurance and rechecking of delivery filesThe feature counts of all files were checked to ensure all layers had the correct number of features. This included checking that all multipart features had translated correctly in the new file.