Lesson Plan: Identify trees on a plantation and measure their health using imagery.Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require a lot of time and resources. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the deep learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.This lesson was last tested on December 6, 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)ArcGIS Image AnalystDeep Learning Libraries for ArcGIS ProRecommended: NVIDIA GPU with a minimum of 8 GB of dedicated memoryOptional: Publisher or Administrator role in an ArcGIS organizationLesson PlanConfigure your systemCheck your graphics card and install deep learning libraries.15 minutesCreate training samplesDigitize the location of sample palm trees to train a deep learning model.30 minutesDetect palm trees with a deep learning modelUse geoprocessing tools to detect the location of all palm trees in the imagery.30 minutesEstimate vegetation healthUse raster functions and the multiband imagery to calculate an index that is a proxy for vegetation health.1 hour

Lesson: Determine how much electricity could be generated from solar power in a city neighborhood.Your nonprofit organization recently launched a pilot program to encourage the residents of the Glover Park neighborhood in Washington, D.C., to install solar panels on their roofs. The goal is for the solar panels to produce a large part of the electric power consumed by each household.In this lesson, you will use ArcGIS Pro to determine how much solar radiation each rooftop in the neighborhood receives throughout the year. Then you will estimate how much electric power each rooftop (and the neighborhood as a whole) could generate if every suitable building was equipped with solar panels.This lesson was last tested on October 28, 2021, using ArcGIS Pro 2.9. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.RequirementsUser, Publisher, or Administrator role in an ArcGIS organization: ArcGIS Online (get a free trial) or ArcGIS Enterprise (learn about setting up Enterprise)ArcGIS Pro (get a free trial)ArcGIS Spatial Analyst extensionLesson PlanExplore the dataFamiliarize yourself with the geography and visualize the digital surface model.15 minutesMap solar energyGenerate a solar radiation raster, convert it to the correct unit of measurement, and symbolize it.15 minutesIdentify suitable rooftopsApply three criteria for solar panel suitability to buildings in the neighborhood.30 minutesCalculate power per buildingCompute the total amount of solar radiation per building based on suitable rooftops.30 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.

Lesson: Compare imagery to calculate area change in Lake Poyang, China.Lake Poyang, China's largest freshwater lake, is shrinking as upstream water is pulled from the Yangtze River at the Three Gorges Dam. Those whose livelihoods depend on the lake are alarmed, as the shrinking lake changes the land cover of the area and impacts the economy. To help them make a case to save the lake, you'll compare imagery between 1984 and 2014 to quantify the surface area of the lake and show changes over time.This lesson was last tested November 8, 2021 using ArcGIS Pro 2.9. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.RequirementsUser, Publisher or Administrator role in an ArcGIS organization (get a free trial) or ArcGIS Enterprise (see configuration details)ArcGIS Pro (get a free trial)ArcGIS Spatial Analyst extensionLesson Plan1. Display the lakeCompare the lake visually at three different dates.15 minutes2. Classify the land cover to identify the lakeClassify Landsat imagery using unsupervised classification.15 minutes3. Clean up the classificationUse generalization tools to improve the classification.15 minutes4. Calculate area over timeDetermine how the lake area has diminished between 1984 and 2014.15 minutes

Lesson: Compute the change in vegetation before and after a hailstorm in Alberta, Canada, with the SAVI index.The region of Taber and Barnwell, in Alberta, Canada, has a large production of corn and other crops. In early August 2019, the upcoming harvest was severely impacted when a major hailstorm ripped through the region. Hailstones driven by winds that reached more than 140 kilometers per hour (or 87 miles per hour) tore through the area. In this lesson, as an imagery analyst for a local farmer organization, you'll perform a first damage assessment, using ArcGIS Pro. You'll explore multispectral imagery captured before and after the hailstorm. You'll then apply a vegetation index on both images, compute the difference, and extract the average loss of healthy vegetation in each field.This lesson was last tested on June 16, 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)ArcGIS Spatial Analyst extensionLesson PlanViewing plan for: ArcGIS ProArcGIS OnlineArcGIS EnterpriseGet started and explore the imageryOpen the project, observe the before- and after-storm images in natural color, and explore the pixels' spectral profiles.15 minutesPerform change analysis with the SAVI indexApply the SAVI index to both images, compute the difference between the two resulting rasters, and extract the average loss of healthy vegetation in each field.15 minutes

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

Digitální vektorová databáze České republiky ArcČR® verze 4.3 obsahuje data administrativního členění České republiky propojená s vybranými statistickými údaji z Českého statistického úřadu a výsledky Sčítání lidí, domů a bytů 2021 (SLDB). Zdrojem dat jsou data z RÚIAN (Registru územní identifikace, adres a nemovitostí) exportovaná k 1. 1. 2024.Vrstva OBEC je doplněny o statistiky Územně analytických podkladů (ÚAP), např. o počet obyvatel v různých věkových kategoriích, zaměstnanost, podíl zemědělské půdy, vodních ploch, zástavby a lesů, počtu turistů nebo naděje dožití. Vrstvy OBEC, ORP, OKRES a KRAJ jsou doplněny o výsledky Sčítání lidí, domů a bytů 2021 (SLDB), např. o počet obyvatel, rodinný stav, stupně vzdělání, národnost, náboženská víra, vlastnictví domů a období jejich výstavby. Data si stáhnete ve formě projektu pro ArcGIS Pro, který obsahuje jak geodatabázi se všemi daty, tak i několik map - vizualizací dat administrativního členění, tematických map a také navrženého výkresu (layout):Administrativní členěníData administrativního členění ČR – stát, kraje, okresy, obce s rozšířenou působností, obce s pověřeným obecním úřadem, obce, městské obvody a městské části a základní územní jednotky v několika měřítkových úrovních.Ukázkový výkresVýkres je koncipován jako stránka - infografika s několika mapkami, textem, grafem a vloženým obrázkem. Představuje tak jeden ze způsobů, jak lze v ArcGIS Pro připravit tiskový výstup. Dlouhodobí uchazeči o zaměstnáníMapa používá data o nezaměstnanosti v obcích k zobrazení jednoho z negativních jevů – dlouhodobé nezaměstnanosti. Růžovou barvou jsou zobrazeny obce s více než 25 % (resp. 50 %) nezaměstnanými, kteří na zaměstnání čekají již více než 12 měsíců.Naděje dožitíNaděje dožití vyjadřuje modelově očekávanou zbývající délku života pro ty, kdo se v daném okresu narodili v roce 2022. K dispozici je vizualizace přesných hodnot prostřednictvím popisků dynamicky formátovaných pomocí Arcade a vizualizace formou bivariantního kartogramu.TurismusData krajů obohacená o počet a národnost zahraničních turistů. V mapě je vybráno šest nejvýznamnějších států, ze kterých turisté do ČR přijíždějí a jejich počty jsou znázorněny kartodiagramy.Podíly ploch obcíPodíly vinic, chmelnic, ovocných sadů a zastavěných ploch na výměře obcí.

Lesson: Sample multidimensional HYCOM ocean data in the Gulf of Mexico to determine where and when ocean currents are calm enough to conduct ROV well inspections.In this lesson, you are a mission planner operating a Remotely Operated Vehicle (ROV) in the Gulf of Mexico. Using the seven-day forecast obtained from the Hybrid Coordinate Ocean Model (HYCOM) multidimensional raster, you will determine where and when the ocean currents are predicted to be calm enough to operate the ROV. In planning the mission to inspect and cap a set of wells, you will sample the HYCOM raster in the time and depth dimensions, create a chart to show dive opportunities, and visualize and animate the ocean current dynamics in 3D.This lesson was last tested August 11, 2021, with ArcGIS Pro 2.8.View final resultRequirementsArcGIS Pro (get a free trial)ArcGIS 3D Analyst extensionLesson PlanAdd HYCOM raster to lesson projectOpen the ArcGIS Pro project package and extract the zipped HYCOM CRF raster folder, and add it to the map.10 minutesSample the HYCOM RasterSample the HYCOM CRF raster at 30 well locations in all depth and time dimensions in the Gulf of Mexico, and convert the u and v vectors to direction and velocity.10 minutesConfigure 3D ocean current symbolsAdd the sample points to the scene and symbolize them as time-aware custom 3D arrows showing ocean current direction and magnitude.10 minutesDiscover ROV dive windows with a matrix heat chartCreate a matrix heat chart using the ocean current data, showing time windows where an ROV can be operated at each well location.10 minutesAnimate ocean currents in 3DChoose a well location and create an annotated animation showing changes in ocean current for the week.20 minutes

Lesson: Answer questions about the earth's climate and map future climate models.Although climate change has become a major political topic, most people have never explored climate data or models of possible future climates. The data is often stored in scientific file formats that require specialized software and can seem unintelligible to those unfamiliar with climate terms and concepts. In this lesson, you'll map historical and projected climate data in ArcGIS Pro. You'll learn about climate at both local and global levels, as well as how climate might change in the future. Overall, you'll gain understanding of major climate concepts and familiarity with real climate data.This lesson was last tested on November 9, 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. Map baseline climate dataCreate layers and tables from NetCDF files, and symbolize and examine the data.45 minutes2. Compare projected climatesMap projected climate scenarios for the near future and distant future.30 minutes3. Locate a type of climateUse raster functions to locate the Mediterranean climate based on the Köppen classification.45 minutes

Lesson: Use distributed raster analysis to analyze burn severity, slope and landcover.Wildfires make the landscape more susceptible to landslides when rainstorms pass through an area after wildfires. Post-fire debris flows are particularly hazardous because they can occur with little warning, can exert great impulsive loads on objects in their paths, and can strip vegetation, block drainage ways, damage structures, and endanger human life. Often there is not enough time between a fire and a rainstorm to implement an effective emergency response plan. However, various post-fire debris-flow hazard assessment models have been developed to estimate the probability and volume of debris flows that may occur in response to a storm.For more detailed information on hazard assessment, review the following resources:Emergency Assessment of Post-Fire Debris-Flow HazardsPost-fire debris-flow modelsUSDA/USFS site for burn area emergency responseIn this lesson, you will use ArcGIS Enterpriseconfigured for distributed raster analysis using ArcGIS Image Server. In the steps, you will create a landslide risk map to kick off more advanced debris-flow hazard assessment modeling. Creating the landslide risk map uses raster function chains to derive a burn severity map, topographic slope map, and a land-cover index map, which are combined into one processing chain for ArcGIS Enterprise to execute.The use of distributed raster analysis for this workflow demonstrates how ArcGIS Enterprise can be deployed in a rapid way to process large volumes of data across a widely affected area. However, when using raster and image analysis tools in ArcGIS Pro, a similar workflow may be developed and deployed without the benefit of distributed raster analysis. In addition, functionality in ArcGIS Image for ArcGIS Online may also be used to achieve similar results.This lesson was last tested on January 31, 2021, using ArcGIS Pro 2.9. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.View final resultRequirementsArcGIS Pro (get a free trial)ArcGIS Enterprise 10.6.1 or later: base deployment with a dedicated ArcGIS Image ServerArcGIS Image Server license for ArcGIS EnterpriseLesson Plan1. Configure the image serverSet up ArcGIS Enterprise to perform distributed raster processing.20 minutes2. Create a landslide risk mapUse raster function templates to create a map that summarizes landslide risk by watershed.1 hour3. Share a web appConfigure a web app to share your findings with your ArcGIS Enterprise account.10 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

This is a model of a wastewater treatment plant with a detailed model of the underground chamber. The data was captured via a LIDAR survey and traditional surveying techniques. From the processed point cloud, a model was created in REVIT and integrated into ArcPro, where it was then converted to GIS multipatch features. The surrounding buildings were modeled, as well as the underground utilities in the area. As you move inside the basement of the treatment plant, notice the high level of detail of the features. *Disclaimer: For privacy purposes, this model was placed in a randomly selected location and is not the actual location of the treatment plant.