Lesson: Explore the use of a mosaic dataset to provide extensive image management capabilities.In this lesson, you'll focus on the management and storage of large volumes of imagery and remote sensing data in ArcGIS Pro. As a remote sensing and GIS analyst for the Upper Austria government, you have received a collection of orthophotos that you must manage and share effectively with stakeholders. You will explore the challenges of working with multiple images individually and create a mosaic dataset that will allow you to work with the collection of seamless images, making them accessible and turning them into useful information products for both visualization and analysis. Next, you will enhance the mosaic dataset by applying and incorporating analysis functionality and, finally, add and use a catalog of imagery from an ArcGIS Living Atlas mosaic dataset.This lesson was last tested on May 26, 2021, using ArcGIS Pro 2.8. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.RequirementsArcGIS Pro (get a free trial)Lesson Plan1. Work with multiple raster datasetsExplore the challenges of working with multiple images individually.15 minutes2. Create a mosaic datasetCreate a mosaic dataset that will allow you to work with a collection of seamless images.15 minutes3. Use a mosaic dataset as a dynamic imageEnhance the mosaic dataset by applying and incorporating analysis functionality.30 minutes4. Use a mosaic dataset as a catalog of imageryAdd and use a catalog of imagery from an ArcGIS Living Atlas mosaic dataset.30 minutes

Lesson: Use stereo mapping in ArcGIS Pro to collect 3D features from vertical overhead imagery.The Los Angeles city property map for the Hollywood Hills area consists of 2D building footprints that do not have any z-values associated to rooftop vertexes. To visualize new and rebuilt properties, it is important to have accurate z-values, as the area is in hilly terrain and city bylaws regulate maximum heights allowed for new and redeveloped buildings.Upon inspection and reviewing the area with newer imagery, it is noticeable that several building footprints have changed and that the pace of redevelopment and rebuilding outpaces the rate at which officials can update their database. It is also obvious that many of the newly built and redeveloped homes consist of multiple levels and are terraced to take advantage of elevation that maximizes the view and displays unique architectural details. It is unclear whether these buildings now meet bylaws in accordance with maximum height restrictions, and a 3D map of building rooftop heights would greatly assist in policing and regulating development.Using a collection of stereo imagery loaded into a mosaic dataset, you will use stereo viewing and mapping in ArcGIS Pro to collect new 3D rooftop point features. This will help establish a pattern that city officials can use to visually analyze imagery and compile three-dimensional (3D) features that can be used to update existing data and determine whether bylaws are being broken.This lesson was last tested on August 12, 2021, using ArcGIS Pro 2.8. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.RequirementsArcGIS Pro (get a free trial)You must have either active shutter eyewear or anaglyph 3D glasses.Lesson Plan1. Set up stereo map environmentBuild the environment for viewing a stereo map.30 minutes2. Create 3D rooftop featuresUse stereo mapping in ArcGIS Pro to collect 3D features from vertical overhead imagery.45 minutes

Lesson: Use a multidimensional stack of Landsat imagery to visualize how a Chilean copper mine has changed over time.The Chuquicamata mine in northern Chile is the largest open pit copper mine by excavated volume on the planet. It opened in 1882, is still operational today, and has expanded significantly over the last decades. In this lesson, you are interested in monitoring the expansion of the Chuquicamata mining area so you can analyze the impact on surrounding ecosystems.Multidimensional raster data, or image cubes, consists of rasters or imagery that have been collected over multiple times, depths, or heights and are stacked into a single dataset. You can use this data to monitor changes and trends in environmental phenomena, urban development, natural resources, and more. In ArcGIS Pro, you'll learn how to generate a multidimensional mosaic dataset that contains Landsat multispectral imagery, showing the copper mine at different points in time. You'll convert the dataset to Esri's native Cloud Raster Format (CRF) and run a quick analysis for visualizing how this copper mine has changed over time. This will give you a general understanding of how to get started with multidimensional multispectral raster data.This lesson was last tested on December 14, 2021, using ArcGIS Pro 2.9. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.RequirementsArcGIS Pro (get a free trial)Lesson Plan1. Create a multidimensional raster from Landsat dataCreate a mosaic dataset from imagery collected from Landsat Thematic Mapper, and build multidimensional information.15 minutes2. Work with a multidimensional CRFUse a geoprocessing tool to convert the mosaic dataset to Esri's native multidimensional raster type and visualize change.10 minutes3. Enhance spectral informationGenerate a multidimensional band ratio layer to see how the Chuquicamata copper mine can be analyzed.15 minutes

Notice: this is 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 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.