Lesson 1. An Introduction to working with multispectral satellite data in ArcGIS Pro In which we learn: • How to unpack tar and gz files from USGS EROS • The basic map interface in ArcGIS • How to add image files • What each individual band of Landsat spectral data looks like • The difference between: o Analysis-ready data: surface reflectance and surface temperature o Landsat Collection 1 Level 3 data: burned area and dynamic surface water o Sentinel2data o ISRO AWiFS and LISS-3 data Lesson 2. Basic image preprocessing In which we learn: • How to composite using the composite band tool • How to represent composite images • All about band combinations • How to composite using raster functions • How to subset data into a rectangle • How to clip to a polygon Lesson 3. Working with mosaic datasets In which we learn: o How to prepare an empty mosaic dataset o How to add images to a mosaic dataset o How to change symbology in a mosaic dataset o How to add a time attribute o How to add a time dimension to the mosaic dataset o How to view time series data in a mosaic dataset Lesson 4. Working with and creating derived datasets In which we learn: • How to visualize Landsat ARD surface temperature • How to calculate F° from K° using ARD surface temperature • How to generate and apply .lyrx files • How to calculate an NDVI raster using ISRO LISS-3 data • How to visualize burned areas using Landsat Level 3 data • How to visualize dynamic surface water extent using Landsat Level 3 data

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.

File-based data for download:https://www.sciencebase.gov/catalog/item/65564cbad34ee4b6e05c47fcThis 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.

The Travel Time Tool was created by the MN DNR to use GIS analysis for calculation of hydraulic travel time from gridded surfaces and develop a downstream travel time raster for each cell in a watershed. This hydraulic travel time process, known as Time of Concentration, is a concept from the science of hydrology that measures watershed response to a precipitation event. The analysis uses watershed characteristics such as land-use, geology, channel shape, surface roughness, and topography to measure time of travel for water. Described as Travel Time, it calculates the elapsed time for a simulated drop of water to migrate from its source along a hydraulic path across different surfaces of the replicated watershed landscape, ultimately reaching the watershed outlet. The Travel Time Tool creates a raster whereas each cell is a measure of the length of time (in seconds) that it takes water to flow across it, and then accumulates the time (in hours) from the cell to the outlet of the watershed.

The Travel Time Tool creates an impedance raster from Manning's Equation that determines the velocity of water flowing across the cell as a measure of time (in feet per second). The Flow Length Tool uses the travel time Grid for the impedance factor and determines the downstream flow time from each cell to the outlet of the watershed.

The toolbox works with ArcMap 10.6.1 and newer and ArcGIS Pro.

For step-by-step instructions on how to use the tool, please view MN DNR Travel Time Guidance.pdf

Terminal lakes are lakes with no hydrologic surface outflows and with losses of water occurring only through surface evaporation and groundwater discharge. We quantified the extent of the littoral zones (areas where 1% or more of surface irradiation reaches the lake bottom) and open water zones (areas where less than 1% of surface irradiation reaches the lake bottom) in 18 terminal lakes. Additionally, we quantified habitat usage and diets of the fish species inhabiting these lakes. This dataset contains includes seven lakes from North America (Atitlan, Crater, Eagle, Mann, Pyramid, Summit, Walker), one from South America (Titicaca), five from Eurasia (Caspian, Issyk-Kul, Neusiedl, Qinghai, Van), and five from Africa (Abijatta, Manyara, Nakuru, Shala, Turkana). Methods Measurements of the surface areas of the littoral and open water zones were performed using ArcGIS Pro Version 2.9. First, we generated year-specific digital elevation models (DEMs) of the lake’s bathymetry by a) using existing bathymetry raster data or b) by digitizing published depth contours of the lake’s bathymetry and interpolating a bathymetry raster using a natural neighbor interpolation. For several lakes that showed significant changes in lake level and where data regarding lake level change were available, we were able to produce a second year closer to the present by using the Raster Calculator function in ArcGIS Pro and then clipping the bathymetry raster to the lower lake level. This was possible for 5 of the 18 lakes (Mann Lake, Eagle Lake, Lake Abijatta, Walker Lake, and Lake Turkana), allowing us to map changes in the littoral zone size between the two years. For the lakes containing two years of data, we used only the most recent year in all subsequent analyses. We defined the portions of the littoral zone of the lake as the portions where the intensity of photosynthetically active radiation (PAR) reaching the lake bottom is 1% or greater relative to the intensity at the surface. For lakes where 1% PAR depth was not published, we calculated 1% PAR depth from published light profiles using the Lambert-Beer Law: 0.01 = e-u*z where µ is the light attenuation coefficient (meters-1) and z is 1% PAR depth (meters). For lakes where neither 1% PAR depth nor light profiles were published, we approximated the 1% PAR depth by multiplying the Secchi depth of the lake by a coefficient of 2.5. We sought the most recently collected Secchi depth to make these calculations. We then used the Raster Calculator function in ArcGIS PRO 2.9 to determine the portions of the lake where depth was less than or greater than the 1% PAR depth to map the open water and littoral zones, respectively. Fish species inventories and information regarding each species’ habitat and diet was compiled from 1) published peer-reviewed primary literature, 2) non-peer-reviewed literature (books, reports by government agencies or private firms), 3) online databases (i.e., FishBase (https://www.fishbase.de/home.htm), California Fish Website (www.calfish.ucdavis.edu)), and/or 4) experts studying the ecology of the species or lake ecosystem. We employed a conservative view regarding species taxonomy (i.e., ‘lumping’ rather than ‘splitting’). We classified species’ habitats with respect to three categories: 1) littoral zone (occurring in parts of the lake where 1% or more of the surface radiation reaches the lake bottom), 2) open water zone (occurring in parts of the lake where less than 1% of the surface radiation reaches the lake bottom), and 3) littoral & open water zone (occurring in both lake zones). These habitat classifications were based on adult habitat use only, and habitat use during larval and juvenile stages was not considered. We classified diets with respect to seven categories: 1) plankton only, 2) periphyton only, 3) periphyton and macroinvertebrates, 4) periphyton, macroinvertebrates, and plankton, 5) periphyton, macroinvertebrates, and fish, 6) fish OR fish and plankton, and 7) fish, plankton, periphyton, and macroinvertebrates.

File-based data for download:https://www.sciencebase.gov/catalog/item/65564cbad34ee4b6e05c47fcThis 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.

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.

Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

This dataset is derived from photogrammetric point cloud models of UAV imagery. It includes the Above ground models of vegetation as well as the isolated scrub vegetation.

We illustrate the method with two case studies from the UK. The scrub cover at Daneway Banks, a calcareous grassland site in Gloucestershire was calculated at 21.8% of the site. The scrub cover at Flat Holm Island, a maritime grassland in the Severn Estuary was calculated at 7%. This approach enabled the scrub layer to be readily measured and if required, modelled to provide a visual guide of what a projected management objective would look like. This approach provides a new tool in reserve management, enabling habitat management strategies to be informed, and progress towards objectives monitored.

Methods Two readily available consumer-grade UAVs were used in this study. Daneway Banks was surveyed using a DJI T600 Inspire 1 quadcopter equipped with a 12MP Zenmuse X3 RGB sensor. Flat Holm was surveyed using the DJI T900 Inspire 2 with 20MP Zenmuse X4S RGB sensor. Both UAVs have comparable outputs, and choice of their use was driven by equipment availability and the greater stability of the Inspire 2 in more exposed conditions of the marine environment.

Transects were planned and conducted using Pix4D Capture (Pix4D China Technology Company) application running on a Sony Experia android smartphone. The transects were programmed using the ‘double grid for 3D model’ template, covering all areas of the site with front and side overlap setting of 80%. The cameras were angled at 70̊ (not nadir). The drone flying speed was set at ‘normal’ which was approximately 5m/s. The shutter speed, ISO and aperture for the both the Inspire 1 (Zenmuse X3) and Inspire 2 (Zenmuse X4S) was 1/2000, 200 and f2.8, with focus set at infinity. At Daneway Banks flights were undertaken on July 3rd 2017 at an elevation above ground level of 50m, giving a ground sample distance (GSD) of 2.49cm/px. The image capture at Daneway Banks collected 1127 images in five flights which took 78 minutes of flight time. At Flat Holm flights were conducted on 31st May 2019 at an elevation of 75m, giving a GSD of 2.34cm/px. The flight height was chosen on the basis of minimising disturbance to the colony of lesser black-backed gulls. The image capture at Flat Holm collected 1417 images in 7 flights, which took 106 minutes of flight time. Images were saved on SD-Cards as tagged image file format (tiff) including the GPS position, camera orientation, and time.

Workflow

The data analysis workflow involved: Generation of a photogrammetric point cloud and associated elevation models, Generation of an Above Ground Model (AGM); Defining the study boundary, Classification of height bands, and; Measurement of the scrub layer.

Generation of a photogrammetric point cloud and associated elevation models; All UAV image files contain metadata of flight information (coordinates of UAV) and camera parameters (orientation, ISO, shutter speed and aperture). All images were uploaded to Pix4d Mapper V 4.5.6 which automatically produces geo-referenced orthomosaics and digital elevation models. Matching points are identified across all uploaded images and their 3D coordinates are calculated using Structure from Motion algorithms. The points are interpolated to form a triangulated irregular network, which generates a dense point cloud. This point cloud enables all image pixels to be positioned in the same scale on an ortho-rectified mosaic (or ‘orthomosaic’; Küng et al. 2012). In this study we use the ‘3D Maps’ standard template, which retains the full keypoints image scale in the initial processing. The point cloud densification was created at the original scale (1), at ‘optimal’ point density, and a minimum of three matches for each point. As well as the orthomosaic, Pix4d generates a Digital Surface Model (DSM) and a Digital Terrain Model (DTM) as exportable raster tiff files.

Generation of the Above Ground Model (AGM); To isolate vegetation from the ground and eliminate the effect from topographical variation, the DTM was subtracted from the DSM (DSM-DTM) using the raster calculator tool in ArcGIS Pro 2.5.2 (Esri Ltd.) to produce the Above Ground Model (AGM). The AGM comprises positive values of all pixels above the ground, representing ground vegetation, scrub, trees and any other structures.

Defining the study boundary; The site boundary and any other excluded features onsite (e.g. blocks of woodland) are manually defined as polygons and used to clip the AGM.

Classification of height bands; The AGM is classified into three height bands including ground vegetation and flat surfaces (minimum pixel value to 1m), mature scrub (1 to 5 m), and all vegetation and structures exceeding the scrub height (5 m to maximum pixel value). The values used to distinguish the scrub layers was verified on the ground at each site to ensure patches of low-lying scrub, were actually scrub and not tall ruderal vegetation like bracken Pteridium aquilinum, nettle urtica dioca or willowherb Epilobium sp.. The scrub layer at Daneway Banks was between 1.5m and 5.75m and between 1m and 5m at Flat Holm. The minimum size stand of vegetation to be classified as scrub was 0.5m2 (0.25cm x0.25cm). The accuracy of the classification was confirmed by physically visiting the stands on the ground with the scrub layer map.

Measurement of the scrub layer; The scrub layer was isolated and converted into a polygon for area measurement. The area of each polygon is calculated in m2, which can be summarised as a total measurement for the whole site.

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.

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 100cm depth was chosen because it matches many of the world's most important crops' rooting depths. A 0 to 60cm 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:0-5cm5-15cm15-30cm30-60cm60-100cmThen 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 * 660-100cm * 7 (not 8, something had to break the tie and I reduced the multiplier by 1 to break ties, thinking of all soil depths the depth from 95-100cm 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 surface60-100cm 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.

Pacific lamprey (Entosphenus tridentata) are native fish to the Columbia River Basin. Over the past 60 years, anthropogenic disturbances have contributed to a 95% decline of historical population numbers. Member-tribes of the Columbia River Inter-Tribal Fish Commission have acknowledged the importance of Pacific lamprey to the Columbia River ecosystem and expressed concern about the loss of an essential tribal cultural resource. As a result, the Columbia River Inter-Tribal Fish Commission created the Tribal Pacific Lamprey Restoration Plan to halt their decline, re-establish the species, and restore the population to sustainable, harvestable levels throughout their historical range. Limited knowledge about the movement and preferred habitat of larval Pacific lamprey, such as optimal habitat conditions, demographic information, and species resilience, results in challenges to monitor and protect the species. Pacific lamprey is known to use the mainstem Columbia River to migrate between their spawning grounds and the Pacific Ocean. However, dams, levees, and culverts within the Columbia River Estuary and adjacent tributaries have restricted the lamprey’s access to spawning grounds and other upstream habitats. These restrictions have prompted conservation and restoration efforts to better understand how Pacific lamprey utilizes the Columbia River Estuary. Here, we address these knowledge gaps in an effort to aid restoration initiatives by completing a Habitat Suitability Analysis to determine where optimal larval Pacific lamprey habitat may exist in the Columbia River Estuary. The project identified the spatial and temporal distribution of suitable habitat for larval Pacific lamprey and generated recommendations to address habitat-related knowledge gaps and further evaluate anthropogenic threats to their recovery. The results of the Habitat Suitability Analysis suggest that habitat conditions in the Columbia River itself are unable to support larval lamprey year-round, but may provide suitable habitat on a seasonal basis due to spatial and temporal limitations. However, we stress that our analyses were necessarily limited to aquatic conditions and that the temperature of the water column used in our analyses may differ from the temperature within fine sediments, where larval lamprey burrow. Our results imply that suitable lamprey habitat is present at times throughout the year in the Columbia River Estuary, and these locations can be used to support habitat restoration and conservation strategies for improving the species’ recovery. Anthropogenic threats to the Columbia River continue to alter habitat conditions, including average water temperature, salinity, and sedimentation. Laboratory experiments have provided insight into the potential impacts of changing temperature and salinity on larval Pacific lamprey, where elevated water temperatures can affect their development and elevated salinity levels can result in larval mortality. In addition, anthropogenic disturbances such as dams, levees, and culverts have cut off the Columbia River Estuary’s floodplain habitats from the mainstem Columbia River, decreased sedimentation rates, and separated adult lamprey from the floodplains and tributaries that they use to spawn. The presence of these barriers in the region can inhibit the distribution of fine sediments in the river, limiting where larval lamprey burrow and develop. The burrowing behavior of larval lamprey has yet to fully be investigated in the Columbia River Estuary. Limited research may be due to the lack of resources for studying Pacific lamprey’s life cycle, habitat, and population dynamics since they are not federally designated as an endangered species, like resident salmonid species. This has further added to the challenge of understanding the species and restoring its population to sustainable numbers.

To the best of our knowledge, this project is the first to explore spatial and temporal trends of suitable larval Pacific lamprey habitat conditions in the Columbia River Estuary. The Habitat Suitability Analysis provides technical information about the presence and distribution of suitable conditions to address habitat-related uncertainties. The member-tribes of the Columbia River Inter-Tribal Fish Commission and their collaborators can incorporate the information into current and future Pacific lamprey restoration, conservation, and education programs to enhance general understanding of lamprey populations throughout the Columbia River Basin. Key recommendations are provided to address additional knowledge gaps and prioritize future restoration projects in the Columbia River Basin including the refinement of the Habitat Suitability Analysis, evaluation of barrier effects on Pacific lamprey passage, and assessment of climate change scenarios on larval lamprey habitat. Methods The Habitat Suitability Analysis uses salinity, temperature, and geomorphology data to identify suitable larval Pacific lamprey habitat in the Columbia River Estuary. In addition, the analysis uses hydrogeomorphic reach data of the Columbia River Estuary. The monthly salinity and temperature data was obtained from a Oregon Health & Science University's Center for Coastal Margin Observation & Prediction hindcast simulation database known as db33. This simulation's outputs were projections that were based on 20-year averages between 1999 and 2018 and resulted in daily summary statistic files; these files were binned by month to produce GeoTIFF files, consisting of 12 individual raster files for each month. In total, there are 12 salinity GeoTIFFs (units are in Practical Salinity Units, which are roughly equivalent to Parts Per Thousnd) and 12 temperature GeoTIFFs (units are in degrees Celsius). Each GeoTIFF summarized salinity or temperature conditions for that month of the year. For example, one raster file contains the summary statistics for all Aprils between 1999 and 2018. The geomorphology data and hydrogeomorphic reach data are layers from a Columbia River Estuary Ecosystem Classification geodatabase from the Lower Columbia Estuary Partnership's website. The geomorphology data (also known as geomorphic catena) is a vector layer that contains individual landforms within the Columbia River's ecosystem complexes that were created over the past 2,000 years. Examples include natural levees, bedrock, and floodplains. The hydrogeomorphic reach data is a vector layer that divides the Columbia River Estuary into eight separate regions based on the region's biophysical characteristics. This dataset also uses a shapefile layer of the Columbia River Basin called "Columbia Basin Streams" to define the research project's region of study. This shapefile layer was obtained from NOAA Fisheries' Columbia Basin Historical Ecology Project Data, though it was replaced by the hydrogeomorphic reach data during the analysis process All of the datasets were processed using the ArcGIS Pro 2.6.0 ModelBuilder by using a binary classification system to reclassify the salinity, temperature, and geomorphology data. This project had researched environmental parameters that were critical for larval Pacific lamprey survival and identified specific salinity and temperature ranges using scientific literature. Salinity and temperature values that fell within their respective ranges were assigned a 1, while salinity and temperature values that did not fall within the range were assigned a 0. This process was completed for each month of the year. The geomorphology data was assigned a binary classification based on whether the habitat within the layer was predominantly aquatic; layers that were predominantly aquatic would be suitable for larval Pacific lamprey were assigned a 1 while layers that were not predominantly aquatic would be unsuitable for larval Pacific lamprey and were assigned a 0. The researchers then used ArcGIS Pro's Raster Calculator tool to sum the reclassified output for each month, and then multiplying the monthly salinity results by the monthly temperature results and the geomorphic catena results. This resulted in 12 outputs per month where suitable habitat was either met or not met. The last step of the Habitat Suitability Analysis combined the resulting 12 output layers of monthly suitable habitat into a single Raster Calculator to add the number of months where suitable habitat was met.

This paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas - the Santana district in S ̃ao Paulo, Brazil, and the Heino rural area in Raalte, the Netherlands - were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector-based simulation models offer better results than raster-based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector-based methods.

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.

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.