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

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

The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.The color-coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation, and field operations. A color-coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes.The map also includes dynamic traffic incidents showing the location of accidents, construction, closures, and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis.Data sourceEsri’s typical speed records and live and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.Data coverageThe service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.

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