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Posts Tagged ‘imagery’

Historical Imagery for the entire world now available via Wayback Service in ArcGIS from Esri

I know that many of you regularly want to examine changes-over-space-and-time with imagery and GIS for research or instruction purposes.   As of last week, 81 different dates of historical imagery for the past 5 years now reside in ArcGIS via the World Imagery Wayback service.   For more information, see: https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/imagery/wayback-81-flavors-of-world-imagery/

You can access this imagery in ArcGIS Online, ArcMap, and ArcGIS Pro.  A great place to start is the World Imagery Wayback app – just by using a web browser  – https://livingatlas.arcgis.com/wayback/    A fascinating and an incredible resource for examining land use and land cover change, changes in water levels of reservoirs, coastal erosion, deforestation, regrowth, urbanization, and much more.  This resource covers the entire globe.

However, in keeping with the theme of our book The GIS Guide to Public Domain Data and this blog of being critical of the data, caution is needed.  The dates represent the update of the Esri World Imagery service.  This service is fed by multiple sources, private and public, from local and global sources.  Thus, the date does not mean that every location that you examine on the image is current as of that date.  I verified this in several locations where my ground observations in my local area show construction as of June 2018, for example, but that construction does not appear on the image.  In addition, several other places I examined from wintertime in the Northern Hemisphere were clearly “leaf-on” and taken during the summer before, or even from the summer before that.  Therefore, as always, know what you are working with.  Despite these cautions, the imagery still represents an amazing and useful resource.

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Sample from this imagery set for 30 July 2014 (top) and four years later, 27 June 2018 (bottom) for an area outside Denver, Colorado USA. 

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Access data and analytical tools online from EOS Data Analytics

A new platform from EOS Data Analytics (https://eos.com/platform/) allows data users not only to access data, but even perform online image processing in a web browser.  It is a set of mutually integrated cloud products for searching, analyzing, storing, and visualizing geospatial data.  This is a representation of what we have been discussing on this blog, namely, the increasing adoption of Software as a Service, and in addition, the combination of SaaS with data services and analytical services.  Thus, GIS professionals can search for, analyze, store, and visualize large amounts of geospatial data in one platform.  Doing all this in one system and also in a browser is, quite frankly, quite amazing.

With the EOS Platform, GIS users have access to an ecosystem of four mutually integrated EOS products, which together provide a powerful toolset for geospatial analysts. Image data is stored in cloud-based storage and is available for image processing or remote sensing analysis at any time; this can be a raw user file, an imagery obtained from their LandViewer data portal, or an output file from their online EOS Processing tools.  The EOS Platform is currently available for free during an open Beta.   The LandViewer tool has been freely available for some time and will continue to be.

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There are at least two reasons why image processing is the platform’s major asset: the processing of large data amounts runs online and offers as many as 16 workflows with even more coming soon. On top of that, users can get the best cartographic features of EOS Vision for vector data visualization and soon to come, analysis.

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A list of EOS Processing workflows that can be filtered by industry and input data type.

I found the LandViewer tool easy to use, with a wide variety of data sets to choose from, including Landsat, MODIS, NAIP, and others.

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The LandViewer tool interface.

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Choosing one’s own area of interest via the LandViewer interface.

Imaging Spectrometer data from NASA AVIRIS

The NASA AVIRIS mission has generated imaging spectrometer data for many areas of the USA since the 1990s.  The AVIRIS download portal for data from 2006 onward is on a node at NASA, here.  The AVIRIS sensor collects data that can be used for characterization of the Earth’s surface and atmosphere from geometrically coherent spectroradiometric measurements. This data can be applied to studies in the fields of oceanography, environmental science, snow hydrology, geology, volcanology, soil and land management, atmospheric and aerosol studies, agriculture, and limnology.  Applications under development include the assessment and monitoring of environmental hazards such as toxic waste, oil spills, and land/air/water pollution. With proper calibration and correction for atmospheric effects, the measurements can be converted to ground reflectance data which can then be used for quantitative characterization of surface features. In short, AVIRIS can collect in over 200 bands and therefore it can help analysts work out details such as vegetation health, or even species type, from the data.

The AVIRIS portal, presented in a Google map with popups with download links, as well as the  metadata file (in plain text format, available here)  both look very dated.  But this is a case where we encourage the user to give it a try–the portal may not look modern, but the data behind the portal is incredibly useful.   One can toggle data layers in the right hand corner of map to show All AVIRIS data or the Attrib. Filtered data (data that meets the attribute criteria but ignores the spatial filter).  One can also bound a box on the map, which has long been a favorite feature of mine on data portals, using the red rectangle to activate.  To update the spatial filter, click the “Update Map” button below the map.   The files are not streamed, but must be downloaded; perhaps because of their large size (typically over 1 GB).  Again, think “old school” formats – zip files and TAR files, but again, the data are plentiful and useful.   A set of previews are available, for example, here, and shown below.  For more information about AVIRIS data, see this link and this link.

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The AVIRIS data portal.

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A sample AVIRIS image. 

A Review of the Geoportal

March 19, 2018 1 comment

The GEOSS is a portal run by the European Space Agency (ESA) Group and the Group on Earth Observations (GEO) that provides one way to access earth observation data from around the world.   The site focuses on satellite imagery–Sentinel and Landsat data.  One helpful feature about the site is the ability to send search results to social media or via email.  A list of Popular Searches is a good place to start with the site.  The site is definitely worth investigating as it features a wealth of data.  I found myself wishing that there were more predefined searches listed there (currently 4).  The site also offers a login option and the ability to save your “workspace” which is an intriguing idea; using this feature, you could come back to the site and continue searching and downloading with the knowledge of what you have done previously.  There are different formats available, although at many points in my work with the site, I was confused as to how to proceed, or what format my file would be in, and if I was truly downloading the extent shown in the interface.

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Interface for the Geoportal.

Like other portals, this one allows search terms, but without knowing what is there, the user is left with some confusion knowing what search terms to use.  I found myself really wanting a tutorial and a list of data sets I could browse through.  Searching is good but the users also need to know what the possibilities are.  I am intrigued by the data offerings on the site but had trouble navigating and discovering resources; I frequently encountered this message below and even had trouble drawing the bounding box for my desired search area.

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Given the data holdings that are available via this portal, I think it is worth investigating further.  The about page on Geoportal lists enhancements to the site that are continually being made, and the mission of the page states that one goal is to make the site intuitive and easy to use.  I therefore have high hopes that it will be moving in this direction.  Give it a try!

Planet’s Imagery Now Viewable by the Public

January 29, 2018 2 comments

Back in 2014, we wrote about inexpensive and the miniaturization of remote sensing, as exemplified in Planet Labs then-new small satellites.  A year later, we wrote about the company’s (now called “Planet”) Open Region initiative with the United Nation to share imagery under a Creative Commons license.  As described in this National Geographic post, Planet has now created a web mapping tool that allows users to examine two million images, updated monthly.  The tool, called Planet Explorer Beta, contains images dating back to 2016, at anywhere from 3 to 40 meters.  My favorite feature so far on the Explorer Beta is the ability to drag-and-drop two images to create a swipe map, to compare changes over time for any given area.  If you create an account and log in, you can explore daily, rather than just monthly, imagery.  Whether logged in or not, the tool is an excellent and amazing resource for teaching and research.  It could also serve as a great way to introduce students and faculty to imagery and encourage them to go further and deeper with remote sensing.

As most of the readers of this blog are work in the field of GIS, they will want to know how to use this imagery in a GIS.  The viewer described above is just that–a viewer.  You can only view the images online.  To actually access the data for use in your GIS or remote sensing work, begin with Planet’s Documentation.  As Planet is a professional satellite image company, it comes as no surprise that users have a multitude of options from which to choose–bands, date and time, cloud cover, sun elevation and azimuth, rectification, data format, and much more.  The imagery is available via a Planet Explorer interface and a Data API, which requires installing a Python client.

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Comparing imagery from two time periods in Colorado, USA, using Planet’s Planet Explorer Beta.

Accessing and Using Lidar Data from The National Map

January 8, 2018 Leave a comment

We have written about the USGS data portal NationalMap numerous times in this blog and in our book, but since the site keeps getting enhanced, a re-examination of the site is warranted.  One of the enhancements over the past few years is the addition of Lidar data to the site.  I did some recent testing of searching for and downloading Lidar data on the site and wanted to report on my findings.  For videos of some of these procedures, go to the YouTube Channel geographyuberalles and search on Lidar.

From a user perspective, in my view the site is still a bit challenging, where the user encounters moments in the access and download process where it is not clear how to proceed.  However, (1) the site is slowly improving; (2) the site is worth investigating chiefly because of its wealth of data holdings:  It is simply too rich of a resource to ignore.  One challenging thing about using NationalMap is, like many other data portals, how to effectively narrow the search from the thousands of search results.  This in part reflects the open data movement that we have been writing about, so it is a good problem to have, albeit still cumbersome in this portal.  Here are the procedures to access and download the Lidar data from the site:

  1. To begin:  Visit the National Map:  https://nationalmap.gov/ > Select “Elevation” from this page.
  2. Select “Get Elevation Data” from the bottom of the Elevation page.  This is one of several quirks about the site – why isn’t this link in a more prominent position or in a bolder font?
  3. From the Data Elevation Products page left hand column:   Select “1 meter DEM.”
  4. Select the desired format.  Select “Show Availability”.   Zoom to the desired area using a variety of tools to do so.  In my example, I was interested in Lidar data for Grand Junction, in western Colorado.
  5. Note that the list of  available products will appear in the left hand column.  Lidar is provided in 10000 x 10000 meter tiles.  In my example, 108 products exist for the Grand Junction Lidar dataset.  Use “Footprint” to help you identify areas in which you need data–the footprints appear as helpful polygon outlines.  At this point, you could save your results as text or CSV, which I found to be quite handy.
  6. You can select the tiles needed one by one to add to your cart or select “Page” to select all items.  Select the Cart where you can download the tiles manually or select the “uGet Instructions” for details about downloading multiple files.  Your data will be delivered in a zip format right away, though Lidar files are large and may require some time to download.

 

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The National Map interface as it appeared when I was selecting my desired area for Lidar data.

Unzip the LAS data for use in your chosen GIS package.  To bring the data into ArcGIS Pro, create a new blank project and name it.  Then, Go to Analysis > Tools > Create LAS dataset from your unzipped .las file, noting the projection (in this case, UTM) and other metadata.  Sometimes you can bring .las files directly into Pro without creating a LAS dataset, but with this NationalMap Lidar data, I found that I needed to create a LAS dataset first.

Then > Insert:  New Map > add your LAS dataset to the new map. Zoom in to see the lidar points.  View your Lidar data in different ways using the Appearance tab to see it as elevation, slope, aspect (shown below), and contours.  Use LAS dataset to raster to convert the Lidar data to a raster.  In a similar way, I added the World Hydro layer so I could see the watersheds in this area, and USA detailed streams for the rivers.

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Aspect view generated from Lidar data in ArcGIS Pro.

There are many things you can do with your newly downloaded Lidar data:  Let’s explore just a few of those.  First, create a Digital Elevation Model (DEM) and a Digital Surface Model (DSM).  To do this, in your .lasd LAS dataset > LAS Filters > Filter to ground, and visualize the results, and then use LAS Dataset to Raster, using the Elevation as the value field.  Your resulting raster is your digital elevation model (DEM).  Next, Filter to first return, and then convert this to a raster:  This is your digital surface model (DSM).  After clicking on sections of each raster to compare them visually, go one step further and use the Raster Calculator to create a comparison raster:  Use the formula:  1streturn_raster – (subtract) the ground_raster.  The first return result is essentially showing the objects or features on the surface of the Earth–the difference between “bare earth” elevation and the “first return”–in other words, the buildings, trees, shrubs, and other things human built and natural.  Symbolize and classify this comparison surface to more fully understand your vegetation and structures.  In my study area, the difference between the DEM and the DSM was much more pronounced on the north (northeast, actually) facing slope, which is where the pinon and juniper trees are growing, as opposed to the barren south (southwest) facing slope which is underlain by Mancos Shale (shown below).

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Comparison of DEM and DSM as a “ground cover” raster in ArcGIS Pro.

My photograph of the ridgeline, from just east of the study area, looking northwest.  Note the pinon and juniper ground cover on the northeast-facing slopes as opposed to the barren southwest facing slope.

Next, create a Hillshade from your ground raster (DEM) using the hillshade tool.   Next, create a slope map and an aspect map using tools of these respective names.  The easiest way to find the tools is just to perform a search.  The hillshade, slope, and aspect are all raster files.  Once the tools are run, these are now saved as datasets inside your geodatabase as opposed to earlier—when you were simply visualizing your Lidar data as slope and aspect, you were not making separate data files.

Next, create contours, a vector file, from your ground raster (DEM), using the create contours tool.  Change the basemap to imagery to visualize the contours against a satellite image.  To create index contours, use the Contour with Barriers tool.  To do this, do not actually indicate a “barriers” layer but rather use the contour with barrier tool to achieve an “index” contour, as I did, shown below.  I used 5 for the contour interval and 25 (every fifth contour) for the index contour interval.  This results in a polyline feature class with a field called “type”.  This field receives the value of 2 for the index contours and 1 for all other contours.  Now, simply symbolize the lines as unique value on the type field, specifying a thicker line for the index contours (type 2) and a thinner line for all the other contours.

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Next, convert your 2D map to a 3D scene using the Catalog pane.  If you wish, undock the 3D scene and drag it to the right side of your 2D map so that your 2D map and 3D scene are side by side.  Use View > Link Views to synchronize the two.  Experiment with changing the base map to topographic or terrain with labels.  Or, if your area is in the USA like mine is, use the Add Data > USA topographic > add the USGS topographic maps as another layer.  The topographic maps are at 1:24,000 scale in the most detailed view, and then 1:100,000 and 1:250,000 for smaller scales.

 

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2D and 3D synced views of the contours symbolized with the Contours with Barriers tool in ArcGIS Pro. 

At this point, the sky’s the limit for you to conduct any other type of raster-based analysis, or combine it with vector analysis.  For example, you could run the profile tool to generate a profile graph of a drawn line (as I did, shown below) or an imported shapefile or line feature class, create a viewshed from your specified point(s), trace downstream from specific points, determine which areas in your study site have slopes over a certain degree, or use the Lidar and derived products in conjunction with vector layers to determine the optimal site for a wildfire observation tower or cache for firefighters.

Profile graph of the cyan polyline that I created from the Lidar data from the National Map in ArcGIS Pro.

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Tracing downstream using the rasters derived from the lidar data in ArcGIS Pro.

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Slopes over 40 degrees using the slope raster derived from the lidar data in ArcGIS Pro.

I hope these procedures will be helpful to you.

 

 

 

Possible Changes to NAIP Imagery Licensing Model

November 27, 2017 Leave a comment

As this blog and our book make clear, the world of geospatial data is in a continual state of change.  Much of this change has been toward more data in the public domain, but sometimes, the change may move in the opposite direction. The National Agriculture Imagery Program (NAIP) has been a source for aerial imagery in the USA since 2003 and has been in the public domain, available here.  But recently, the Farm Services Agency (FSA) has proposed to move the data model from the public domain to a licensing model.  The collection of this imagery has been under an innovative model wherein state governments and the federal government share the costs.

One reason for the proposed change is that the states have been $3.1 million short over the past several years, and FSA cannot continue “picking up the tab.”  Furthermore, delays in releasing funding from cost-share partners forces contract awards past “peak agriculture growth” season, which thwarts one key reason why the imagery is collected in the first place–to assess agricultural health and practices.  We have discussed this aspect of geospatial data frequently in this blog–that geospatial data comes at a cost.  Someone has to pay, and sometimes, those payment models need to be re-considered with changing funding and priorities.  In this case, agencies and data analysts that rely on NAIP imagery would suffer adverse consequences, but with the expansion of the types and means by which imagery can be acquired nowadays, perhaps these developments will enable those other sources to be explored more fully.  And, possibly, the model could be adjusted so that the data could be paid for and that all could benefit from it.

For more information, see the report by our colleagues at GIS Lounge, and the presentation housed on the FGDC site, here.

Two samples of NAIP imagery, for Texas, left, and North Dakota, right.