Latest Contributions by Keith-VanGraafeiland

This is a great resource that uses multidimensional data to better understand global wind speeds and potential for wind farm siting.   ... View more

With ArcGIS Image Analyst or ArcGIS Spatial Analyst, you can perform complex analysis on multidimensional raster data to explore scientific trends and anomalies. Multidimensional data represents geospatial data captured at multiple times and multiple depths or heights. These data types are commonly used in atmospheric, oceanographic, and earth sciences. Multidimensional raster data can be captured by satellite observations in which data is collected at certain time intervals or generated from numerical models in which data is aggregated, interpolated, or simulated from other data sources. Multidimensional analysis in ArcGIS Pro  ... View more

Multidimensional data represents data captured at multiple times or multiple depths or heights. This data type is commonly used in atmospheric, oceanographic, and earth sciences. Multidimensional raster data can be captured by satellite observations in which data is collected at certain time intervals, or generated from numerical models in which data is aggregated, interpolated, or simulated from other data sources. Multidimensional raster data  ... View more

This is a great resource to understand a netcdf file and if it is CF compliant or not... Describes the nature and content of an input netCDF dataset. List all the variables along with their dimensions and their attributes. Describe NetCDF File (Multidimension)  ... View more

StoryMap: Multidimensional workflows in ArcGIS   ... View more

Various forms of remote sensing data are collected to help understand our planet. Satellite imagery, which captures the reflectance of the Earth’s surface, is a familiar asset within the GIS community and serves as a vital data source for GIS analysis and visualization. Altimetry data is a relatively new addition to the world of GIS and ArcGIS Pro. Altimetry data is collected via satellite sensors that record the elevation of surface features such as canopy height, sea surface height, and ice thickness.  This new data and capability present an opportunity for enhanced analysis and understanding. Thanks to NASA’s commitment to open science initiatives—characterized by transparency, accessibility, inclusivity, and reproducibility—these altimetry data are freely and readily accessible to all researchers. ArcGIS aims to be a comprehensive system capable of handling various forms of remote sensing data, including the processing and analysis of data from altimetry sensors.   In this example, CryoSat-2 altimetry data is used for analysis in ArcGIS Pro to quantify the amount of change/loss in the Greenland ice sheet over time.  This is a great example of how altimetry data can help support analysis on land.  Altimetry data also has effective applications in the ocean environment. The following examples demonstrate how this can be achieved. The ocean is vast and dynamic. Currently, the number of instruments collecting near-real time in-situ physical oceanographic observations makes it difficult to measure change in the open-ocean with high-accuracy. Satellite altimeters provide a global and precise measurement of ocean surface topography. These instruments operate by transmitting radar pulses to the ocean surface, determining the sensor’s height above the surface by measuring the time taken for the radar pulse to return. The sea surface height is then calculated as the difference between the sensor’s height and the sensor’s height above the surface.  These valuable insights allow for monitoring changes in sea surface height, ocean circulation, and weather prediction for tropical cyclones, among other uses. ArcGIS Pro offers support for various altimetry sensors, including Sentinel 3 SRAL, Sentinel 6, Jason 2 and Jason 3, with the latter two being added recently in the ArcGIS Pro 3.2 Release. The trajectory dataset Altimetry sensors gather point data along flight paths, which we refer to as trajectory data. ArcGIS uses a trajectory dataset to effectively manage this type of data. This is like the mosaic dataset model, which organizes a collection of images, the trajectory dataset serves a similar purpose, but managing a compilation of trajectory files containing point measurements. The point sublayer of the trajectory dataset can be utilized as a feature class layer during processing and analysis. You can generate a trajectory dataset from a folder of trajectory files using two geoprocessing tools: The Create Trajectory Dataset tool creates a trajectory dataset in a File geodatabase The Add Data to Trajectory Dataset tool to is used to load the trajectory files/data into the trajectory dataset. There are different options when importing the trajectory data that allow users to specify the sensor types and the associated variables to be loaded. Model Sea surface topography using SSH variable In this example altimetry data from Sentinel-3 is used to map Sea Surface Height (SSH).  Sentinel 3 has a two-satellite configuration that offers coverage with a rapid revisit period compared to other altimetry sensors.  Sentinel 3 altimetry data can be accessed from EUMETSAT Data Service. Using a week of level-2 altimetry data a trajectory dataset was created.  The trajectory dataset contains an SSH variable that is calculated on-the-fly from the data variables “mean sea surface level” and “sea surface height anomaly”.  The SSH variable is computed when “Add Data to Trajectory Dataset” is used to load the altimetry data into the trajectory dataset and the data type “Sentinel 3 SRAL” is selected. Below is the trajectory dataset created from Sentinel 3 SRAL data between 1/2/2022 and 1/8/2022. It contains 394 tracks (green lines) which reference 817,204 observation points. Next, using the “Interpolate From Spatiotemporal Points” tool a SSH raster was produced.  To enhance the visualization, the triangulation option for interpolating data was used. Geoprocessing tool    A land mask is also used to clean the pixels on land. The resulting SSH raster dataset was then utilized as an elevation source in 3D, while also functioning as a raster layer draped over the elevation surface. This provides a clear depiction of the peaks and valleys present during this period in the ocean topography.     Analyzing the sea surface height change In this example data from the Jason 2 and Jason 3 sensors is used to show changes in SSH. The Jason 2 and 3 sensor combination offers continuous temporal coverage from 2008 to the present (Jason 2 – 2008 to 2016 and Jason 3 2016 to present). Access to data from Jason 3 and Jason 2 is available from several sources; in this case, the NOAA NCEI ftp site was accessed due to ease of batch downloading. To conduct an analysis of sea surface height over an extended period, we utilized two variables: SSH (Sea Surface Height) and SSHA (Sea Surface Height Anomaly).  Two independent trajectory datasets for Jason 2 and Jason 3 data respectively were created, each containing the SSH and SSHA variables.  Features were then exported to individual feature classes using the “Copy Feature” tool, maintaining them as two separate feature classes for subsequent analysis. Calculate the average monthly SSHA In these steps we use the SSHA data from the Jason 2 and Jason 3 feature classes to create independent multidimensional (time-series) rasters.  To do this, we will again use the “Interpolate From Spatiotemporal Points” tool – this time using the IDW option and calculate a monthly SSHA variable for each dataset independently.  Next, we merge the outputs from Jason 2 and Jason 3 using the “Merge Multidimensional Raster” tool.  The result, animated below,  is a monthly time series compilation of both Jason 2 and 3 ranging from 2008 to 2022.   Global average sea surface height change In this example, the goal is to compute the global average sea surface height utilizing the altimetry data points using both the Jason 2 and Jason 3 feature classes created in the previous step. Once again, the “Interpolate from Spatiotemporal Points” tool is used, this time selecting the “Mean” interpolation method to calculate the monthly averages for each feature class independently. The Mean option computes the average using the observation points within the designated pixel size and time interval, avoiding pixel extrapolation in no data areas and removes the introduction of errors.  Below is Jason monthly interpolated mean example created. The valid data area is between -180 and 180 and -60 and 60 (skewed in the polar regions).  In analyzing the 2016 raster data from Jason 2 and Jason 3 (the one-year overlap maintained for data calibration purposes), we observed a discrepancy of 0.7 meters. Consequently, we applied a correction factor of 0.7 meters to the Jason 2 data prior to merging it with the Jason 3 dataset. Next, global averages for both monthly and yearly SSH were computed using the “Zonal Statistics as Table” tool; the results are represented as graphs below.  This provides the capability to examine seasonal and annual variations, change, and trends in global SSH.   Monitoring significant wave height Several of these altimetry sensors offer near real-time measurements of sea surface height, significant wave height, and wind speed, facilitating operational oceanography and climate monitoring. The new Trajectory profile chart capability provides a valuable tool for quickly visualizing near real-time data. The following depicts a comparison of significant wave height from Sentinel 6 over two consecutive days along two distinct tracks.   These examples are intended to help provide insight on how these trajectory tools in ArcGIS Pro can be used for oceanographic analysis.  Please feel free to reach out to the blog authors if you have questions, comments, or feedback. Link to ArcGIS Blog: https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/oceanographic-analysis-using-trajectory-data/  ... View more

WOW! 20 Years of GeoTools!  We have two presentations and a booth presence at the conference this year.  I'm presenting on Local Ecological Marine Units in the Mapping Benthic Habitats session on Tuesday morning at 10:30am in Windsor A.  My colleague Charmel Menzel‌ is presenting In the Tools Showcase from 3-5pm on Tuesday afternoon. Charmel Menzel, Esri GIS Solution Engineer, will discuss and demonstrate ArcGIS Citizen Science and Crowdsourcing Configurable Apps. We're excited to continue to be a part of the GeoTools community.  Please stop by so we can talk about GIS and the Science of Where.  See you in Myrtle Beach! coastalgeotools‌ scienceofwhere‌ ... View more

Calculating the extent and coverage for your bathymetry data just got easier. Bathymetry data typically comes as a processed image (.tif or .asc) or maybe even a Bathymetric Attributed Grid (BAG) file.  This allows access to a single band of values that represent elevation.  This data is commonly stored as 32 bit floating point.  While that’s all very informative it’s sometimes difficult to understand how many of the pixels contain data and how many don’t and just how much area was mapped. This tool reclassifies the bathymetry data and then generates polygon footprint for the areas where data exists. The tool started as a model in ArcGIS Pro and is now  available as a geoprocessing package (.gpkx) so you can use it in your ArcGIS Pro projects. What is a geoprocessing package? A geoprocessing package is a convenient way to share geoprocessing workflows by packaging one or more tools and the data used by the tools into a single compressed file (.gpkx). Geoprocessing packages are created from one or more successfully run geoprocessing tools. You can add the geoprocessing package to ArcGIS Pro by downloading it and copying the file (.gpkx) to your ArcGIS Pro Project folder in windows explorer. Then browse to that location in ArcGIS Pro using your Catalog and add the tool to the current project by right clicking on it. Try it out! ... View more

Have you ever seen green algal swirls in surface waters or dozens of dead fish washed up on the shoreline? Do you know what likely was the cause? These phenomena are typically related to the amount of chlorophyll in our oceans. Mapping chlorophyll concentrations in the ocean can be accomplished through remote sensing. Chlorophyll in water changes the way it reflects and absorbs sunlight, allowing scientists to map the amount and location of phytoplankton using optics. These measurements give valuable insights into the health of the ocean environment, and help researchers study the ocean carbon cycle. The same critters that cause red tides and harmful algal blooms are also the reason for success of aquaculture and fisheries. Tiny microscopic organisms called Phytoplankton contain chlorophyll and conduct photosynthesis (using light at the surface of the ocean) to produce energy, this is how they survive. These phytoplankton are at the base of the food chain for marine life and play a vital role in the carbon cycle by converting carbon dioxide to organic matter. Chlorophyll concentration data shown here are obtained from global satellite measurements by the MODIS-Aqua projects of the National Aeronautics and Space Administration (NASA). The image on the left shows the true-color satellite view and the image on the right shows the chlorophyll concentrations. Phytoplankton are tiny microscopic organisms that survive by converting photosynthetic pigments such as chlorophyll into organic matter. The amount of phytoplankton in the ocean can be quantified by measuring chlorophyll concentrations. In the occurrence of a phytoplankton bloom, when populations are large, phytoplankton congregate, feed on the nutrients and form layers at the surface of the ocean. This causes the water to visually appear greener because of high concentrations of chlorophyll being observed from the phytoplankton. The name Phytoplankton comes from the Greek words phyton, meaning “plant”, and planktos, meaning “wanderer” or “drifter”. Why is this important? Changes in phytoplankton populations can impact fish and other marine life, which can in turn affect food availability. Scientist use phytoplankton as an indicator to understand the health and productivity of ecosystems both in the ocean and on land. Chlorophyll is a new layer added by Esri to the Living Atlas of the World. It’s one of the many layers that is available within the Environmental collection of content. A few things that make this layer unique: it’s updated daily (automated) to reflect the previous days collection, the collection includes the entire archive spanning back to 2002, you can view the common time intervals (daily, 8 day, monthly) for the entire span of the archive, and the data are fully capable for analytics. The layer is updated routinely using the Aggregated Live Feed tool. Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop. Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in mg/m-2. See this Esri blog post for more information on how to use this layer in your analysis. Do not use this layer for analysis while the Cartographic Renderer processing templates are applied. Time: This is a time-enabled layer. It shows the average chlorophyll-a concentration during the map’s time extent, or if time animation is disabled, a time range can be set using the layer’s multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month. Supporting images generated in ArcGIS Pro using Chlorophyll concentrations combined with the MODIS True Color Imagery available from Esri’s Living Atlas of the World. North Sea Bloom | Phytoplankton Bloom off the north coast of Norway - July 23, 2017. MODIS Tasman Bay Bloom | November 9th, 2017 - Tasman Bay phytoplankton Bloom Gulf of Alaska | Phytoplankton Bloom in the Gulf of Alaska on May 12, 2017. MODIS. If you’re interested in learning more about Chlorophyll, Phytoplankton and the Ocean here are some additional resources: NASA | Earth Science Week: The Ocean’s Green Machines https://www.nasa.gov/content/goddard/nasa-ocean-data-shows-climate-dance-of-plankton/ https://oceanservice.noaa.gov/facts/habharm.html https://www.globalchange.gov/browse/indicators/indicator-ocean-chlorophyll-concentrations References: Thurman, H. V. (2007). Introductory Oceanography. Academic Internet Publishers. ISBN 978-1-4288-3314-2. NASA Earth Observatory (2010). Importance of phytoplankton. Web Article. the living atlas‌ living atlas layers esri ocean ocean science‌ ... View more