Although there are a large number of software products available for calculating landscape metrics (e.g. FRAGSTATS, landscapemetrics package in R) no tools are currently available (to my knowledge) that calculate landscape metrics directly in ArcGIS Pro. Moreover, many, if not most, landscape metrics were designed with vector data in mind, but most software calculates landscape metrics from raster data due to processing time and complexity. Scaling landscape metrics can also be tedious in some instances. This toolbox was designed to calculate attributes of patches that are easily calculated on polygons in ArcGIS (i.e. area, number of patches, Landscape Shape Index, edge density, patch size, distance to the nearest patch) and scales those calculations to coarser resolutions using Block Statistics. The tool also summarizes the relationships among metrics by using Principal Component Analysis and correlation matrices to assess relationships among variables. All variables are output to a single folder.

The Terrain Ruggedness Index (TRI) is used to express the amount of elevation difference between adjacent cells of a DEM. This raster function template is used to generate a visual representation of the TRI with your elevation data. The results are interpreted as follows:0-80m is considered to represent a level terrain surface81-116m represents a nearly level surface117-161m represents a slightly rugged surface162-239m represents an intermediately rugged surface240-497m represents a moderately rugged surface498-958m represents a highly rugged surface959-4367m represents an extremely rugged surfaceWhen to use this raster function templateThe main value of this measurement is that it gives a relatively accurate view of the vertical change taking place in the terrain model from cell to cell. The TRI provides data on the relative change in height of the hillslope (rise), such as the side of a canyon.How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual TRI representation of your imagery. This index supports elevation data.References:Raster functionsApplicable geographiesThe index is a standard index which is designed to work globally.

Calculating the total volume of water stored in a landscape can be challenging. In addition to lakes and reservoirs, water can be stored in soil, snowpack, or even inside plants and animals, and tracking the all these different mediums is not generally possible. However, calculating the change in storage is easy - just subtract the water output from the water input. Using the GLDAS layers we can do this calculation for every month from January 2000 to the present day. The precipitation layer tells us the input to each cell and runoff plus evapotranspiration is the output. When the input is higher than the output during a given month, it means water was stored. When output is higher than input, storage is being depleted. Generally the change in storage should be close to the change in soil moisture content plus the change in snowpack, but it will not match up exactly because of the other storage mediums discussed above.Dataset SummaryThe GLDAS Change in Storage layer is a time-enabled image service that shows net monthly change in storage from 2000 to the present, measured in millimeters of water. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: Change in Water StorageUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.By applying the "Calculate Anomaly" raster function, it is possible to view these data in terms of deviation from the mean, instead of total change in storage. Mean change in storage for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max change in storage over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

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

Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The combined processes of evaporation and transpiration, known as evapotranspiration (ET), plays a key role in the water cycle. Precipitation that falls on land can either run off in streams and rivers, soak into the ground, or return to the atmosphere through evapotranspiration. Water that evaporates returns directly to the atmosphere while water that is transpired is taken up by plant roots and lost to the atmosphere through the leaves.Evapotranspiration data can be used to calculate regional water and energy balance and soil water status and provides key information for water resource management. Potential evapotranspiration, the amount of ET that would occur if soil moisture were not limited, is a purely meteorological characteristic, based on air temperature, solar radiation, and wind speed. Actual evapotranspiration also depends on water availability, so it might occur at very close to the potential rate in a rainforest, but be much lower in a desert despite the higher potential there.Dataset SummaryPhenomenon Mapped: EvapotranspirationUnits: Millimeters per yearCell Size: 927.6623821756539 metersSource Type: ContinuousPixel Type: 16-bit unsigned integerData Coordinate System: Web Mercator Auxiliary SphereExtent: Global Source: University of Montana Numerical Terradynamic Simulation GroupPublication Date: March 10, 2015ArcGIS Server URL: https://landscape6.arcgis.com/arcgis/This layer provides access to a 1km cell sized raster of average annual evaporative loss from the land surface, measured in mm/year. Data are from the MOD16 Global Evapotranspiration Product, which is derived from MODIS imagery by a team of researchers at the University of Montana. This algorithm, which involves estimating land surface temperature and albedo and using them to solve the Penman-Monteith equation, is not valid over urban or barren land so these are shown as NoData, as is any open water. For all other pixels, the algorithm was used to estimate evapotranspiration for every 8-day period from 2000 to 2014 and these estimates have been averaged together to come up with the annual normal. You can also get access to the monthly totals using the MODIS Toolbox.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "evapotranspiration" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "evapotranspiration" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

File-based data for download:https://www.sciencebase.gov/catalog/item/6556549dd34ee4b6e05c4822This layer calculated changes between the first and last time steps from the Sagebrush Conservation Design dataset. Calculations were done by adding the first and second time step rasters using the Raster Calculator tool in ArcGIS Pro. The later raster was reclassified with the following values Non-Rangeland Areas = 0, Core Sagebrush Areas = 10, Growth Opportunity Areas = 20, Other Rangeland Areas = 30. This created a raster showing change with the following values. Non-Rangeland to Non-Rangeland = 0Core to Non-Rangeland =1, Growth to Non-Rangeland = 2,Other to Non-Rangeland = 3Non-Rangeland to Core = 10Core to Core = 11Growth to Core = 12Other to Core = 13Non-Rangeland to Growth = 20Core to Growth = 21Growth to Growth = 22Other to Growth = 23Non-Rangeland to Other = 30Core to Other = 31Growth to Other = 32Other to Other = 33The purpose of these data are to provide a biome-wide, consistent, quantitative information about changes in sagebrush core habitat and growth areas. These data may be used to enable better prioritization of landscapes for conservation, and to inform which treatments or other conservation actions are appropriate in specific areas.Original Data cited as:Doherty, K., Theobald, D.M., Holdrege, M.C., Wiechman, L.A., and Bradford, J.B., 2022, Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design: U.S. Geological Survey data release, https://doi.org/10.5066/P94Y5CDV.Supporting literature for original dataset:Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2922, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.

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

This layer uses sand, silt, and clay most likely values from soilgrids.org to create texture classes. Soilgrids.org sand, silt, and clay datasets are integers that give a weight in grams in each particle class. The weight we are converting directly into percent, for example soilgrids value of 500g of sand means 50% sand ((500g/1kg) * 100 = 50%).A 60cm depth was chosen because it matches many of the world's most important crops' rooting depths. A 0 to 100cm version of this is also available.Variable mapped: Predominant USDA texture class as derived from predicted percent sand, silt, and clay.Data Projection: Goode's Homolosine (land) WKID 54052Mosaic Projection: Goode's Homolosine (land) WKID 54052Extent: World, except AntarcticaCell Size: 250 mSource Type: ThematicVisible Scale: All scales are visibleSource: SoilGrids.orgPublication Date: June 14, 2021NOTE: This layer uses the USDA texture classification system with international soil datasets, which use different particle size definitions than the USDA. Very little silt shows up in this layer, this could be a reason why.To determine the predominant soil texture we first classified texture for the following layer depths:0-5cm5-15cm15-30cm30-60cmThen we used focal statistics with the majority option to find the majority texture class of each pixel from the five layers, weighted as follows:0-5cm * 15-15cm * 215-30cm * 330-60cm * 5 (not 6, something had to break the tie and I reduced the multiplier by 1 to break ties, thinking of all soil depths the depth from 55-60cm may be the least significant in the stack overall.)-----------------------------------------------------------------Raster functions were created to classify sand, silt, and clay using the following statements in raster calculator:Sand Con((( Silt + ( 1.5 * Clay )) < 150 ), 1, 0)Loamy Sand Con(((Silt + (1.5 * Clay)) >= 150) & ((Silt + (2 * Clay)) < 300),2, 0)Sandy Loam Con(((Clay

=70)&(Clay<200)&(Sand>520)&((Silt + (2 * Clay)) = 300))|((Clay<70)&(Silt<500)&((Silt + (2 * Clay)) = 300)),4, 0)Loam Con(((Clay>=70) & (Clay<270) & (Silt>=280) & (Silt<500) & (Sand<=520)),8 ,0)Silt LoamCon((((Silt>=500) & (Clay>=120) & (Clay<270)) | ((Silt>=500) & (Silt<800) & (Clay<120))),16 , 0)SiltCon(((Silt >= 800)&(Clay<120)),32 ,0)Sandy Clay LoamCon(((Clay>=200) & (Clay < 350) & (Silt < 280) & (Sand > 450)),64 ,0)Clay LoamCon(((Clay >= 270) & (Clay<400) & (Sand > 200) & (Sand <= 450)), 128, 0)Silty Clay LoamCon(((Clay >= 270) & (Clay < 400) & (Sand <= 200)),256 ,0)Sandy ClayCon(((Clay >= 350) & (Sand > 450)) ,512 , 0)Silty Clay Con(((Clay >= 400) & (Silt >= 400)), 1024, 0)Clay Con(((Clay>=400) & (Sand <= 450) & (Silt < 400)) , 2048 , 0 )These conditionals were used on the "mean" soilgrids.org rasters for silt, sand, and clay on rasters representing the following depths:0-5 cm below the land surface5-15cm below the land surface15-30cm below the land surface30-60cm below the land surfaceThe conditionals were just summed together to create check rasters for each depth. All analysis was done in soilgrids.org own Goode's Homolosine projection (land) in ArcGIS Pro. The data were served in this same projection in ArcGIS Image for ArcGIS Online.---------------------------------------------------------------------------------------------------At first, the classes were given a value of 1, 2, 4, 8, 16, 32 and so on, then were added together. This is so we could see if some classes were overlapping others. We continued to troubleshoot the above definitions until there were no overlaps and as few values of 0 as possible. Once the overlaps and misses were fixed, the dataset was reclassed into values of 1-13. An attribute table was built to drive popups and a legend.