History Comes Around

Blue Marble image of the Earth taken by the crew of Apollo 17 on Dec. 7 1972. Image Credit: NASA

Blue Marble image of the Earth taken by the crew of Apollo 17 on Dec. 7 1972.
Image Credit: NASA

Remote sensing is a relatively young industry, but it doesn’t mean we don’t have history. We do. We shouldn’t it, and were reminded why this week as we bounced back through time.

We noticed an introductory tweet yesterday from the Earth Resources Observation and Science Centre (EROS) History Project established by the US Geological Survey. This project has created an amazing online archive of information about its involvement in remote sensing that contains documents, and videos, from 1960s/70s to the current day. A few of the archive items that caught our attention were:

News Release from United States Department of the Interior on the 21st September 1966 with the title ‘Earths Resources To Be Studied From Space’. What struck us was how the phrases could be from today.

  • ‘gathering facts about the natural resources of earth’
  • ‘the time is now right and urgent to apply space technology towards the solution of many pressing natural resources problems being compound by population and industrial growth’
  • ‘An opportunity to collect valuable resource data and use it to improve the quality of our environment’

Equally, the sessions from 1973 Management & Utilization of Remote Sensing Data Symposium, organized by the American Society of Photogrammetry, could easily be describing a current conference:

  • Role of Remote Sensing in Resource Management & Planning
  • Hydrological and Environmental Applications
  • Future Sensor and Information Handling Systems
  • Agricultural and Forestry Applications

We loved the 1980 User Frustrations with Landsat, which noted data quality issues like:

  • Desert scenes have no contrast
  • There’s no underwater detail in the image
  • The image is striped!

A reminder in the news release from 15 March 1989 on how close the world came to losing the Landsat archive. This release rescinded the order, made two weeks earlier, to shutdown Landsat 4 & 5 and to provide funding until a policy review of Landsat could be completed.

The archive is a wealth of interesting details about the history of US remote sensing, including the amount of data collected over the years to the more mundane, but no less fascinating, descriptions of the furniture required to set up EROS in the first instance! We’d highly recommend you have a look at this archive – although be warned, I lost a few hours in there whilst writing this blog!!

This week is also a big anniversary for Landsat-1 which was launched on the 23rd July 1972, and the first satellite image from it was received on the 26th July 1972 beginning the 44 year archive. It’s also the Landsat Science Team’s 2016 Summer meeting this week in South Dakota, and amongst the topics of discussion are future sensor capabilities for Landsat 10 – showing not much has changed from 1973!

Although remembering the past is important, it’s vital that we also look forward to the future. At the Landsat Science Team meeting, it was noted the target launch date for Landsat 9 is the 15th December 2020, and as discussed above they are already talking about Landsat 10!

Ocean Colour Partnership Blooms

Landsat 8 Natural Colour image of Algal Blooms in Lake Erie acquired on 01 August 2014. Image Courtesy of NASA/USGS.

Landsat 8 Natural Colour image of Algal Blooms in Lake Erie acquired on 01 August 2014. Image Courtesy of NASA/USGS.

Last week NASA, NOAA, USGS and the US Environmental Protection Agency announced a $3.6 million partnership to use satellite data as an early warning system for harmful freshwater algae blooms.

An algae bloom refers to a high concentration of micro algae, known as phytoplankton, in a body of water. Blooms can grow quickly in nutrient rich waters and potentially have toxic effects. Shellfish filter large quantities of water and can concentrate the algae in their tissues, allowing it to enter the marine food chain and potentially causing a risk to human consumption. Blooms can also contaminate drinking water. For example, last August over 40,000 people were banned from drinking water in Toledo, Ohio, after an algal bloom in Lake Erie.

The partnership will use the satellite remote sensing technique of ocean colour as the basis for the early warning system.  Ocean colour isn’t a new technique, it has been recorded as early as the 1600s when Henry Hudson noted in his ship’s log that a sea pestered with ice had a black-blue colour.

Phytoplankton within algae blooms are microscopic, some only 1,000th of a millimetre in size, and so it’s not possible to see individual organisms from space. Phytoplankton contain a photosynthetic pigment visible with the human eye, and in sufficient quantities this material can be measured from space. As the phytoplankton concentration increases the reflectance in the blue waveband decreases, whilst the reflectance in the green waveband increases slightly. Therefore, a ratio of blue to green reflectance can be used to derive quantitative estimates of the concentration of phytoplankton.

The US agency partnership is the first step in a five-year project to create a reliable and standard method for identifying blooms in US freshwater lakes and reservoirs for the specific phytoplankton species, cyanobacteria. To detect blooms it will be necessary to study local environments to understand the factors that influence the initiation and evolution of a bloom.

It won’t be easy to create this methodology as inland waters, unlike open oceans, have a variety of other organic and inorganic materials suspended in the water through land surface run-off, which will also have a reflectance signal. Hence, it will be necessary to ensure that other types of suspended particulate matter are excluded from the prediction methodology.

It’s an exciting development in our specialist area of ocean colour. We wish them luck and we’ll be looking forward to their research findings in the coming years.

Five Landsat Quirks You Should Know

South West England from the 8th December 2014. Landsat 7 imagery courtesy of NASA Goddard Space Flight Center and U.S. Geological Survey

South West England from the 8th December 2014. Landsat 7 imagery courtesy of NASA Goddard Space Flight Center and U.S. Geological Survey

If you’ve started using Landsat after our five simple steps blog last week, or perhaps you’ve used its imagery for awhile, you may have come across, what we’ll call, quirks of Landsat. These may be things you didn’t understand, things that confused you or where you thought you’d done something wrong. This week we’re going to try to demystify some of the common quirks and questions with using Landsat data and imagery.

Quirk One: What do the WRS path and row numbers mean?
The Worldwide Reference System (WRS) is what Landsat uses to map its orbits around the world, and is defined by sequential path and row numbers. Despite its name, there are in fact two versions of the WRS; WRS-1 that’s used for Landsat’s 1-3, and WRS-2 for the rest of the missions.

The paths are a series of vertical-ish tracks going from east to west, where Path 001 crosses the equator at 65.48 degrees west Longitude. In WRS-1, there are 251 tracks, whereas the instruments in Landsat 4 and beyond have a wider swath width and only require 233 tracks to cover the globe. Both WRS-1 and WRS-2 use the same 119 Rows, where Row 001 starts near the North Pole at Latitude 80 degrees, 1 minute and 12 seconds north , Row 60 coincides with the Equator at Latitude 0, and row 119 mirrors the start at Latitude 80 degrees, 1 minute and 12 seconds south. A combination of path and row numbers gives a unique reference within Landsat, the path number always comes first, followed by the row number. For example, 204-025 is the WRS-2 path and row for Plymouth.

There are maps available of the paths and rows. However, there is also handy website from USGS that converts path and row numbers to Latitude and Longitude and vice versa; it’s accompanied by a map so you can tell you’ve got the area you want!

Quirk Two: My image has a minus one percent cloud cover
This one can be confusing! On the GloVis image selector you have the option to specify the maximum percentage of cloud cover on your image. Selecting 50% means up to 50% of the image could be cloud, and selecting 0% means no cloud at all.

Cloud cover is calculated using both the optical and thermal bands, and therefore as any Landsat imagery taken using the Multispectral Scanner System (MSS) does not include a thermal band, the cloud cover percentage is not easily calculated. Where a calculation does not occur the cloud cover percentage is set to -1%.

At the bottom of the Scene Information Box, there is line for Sensor/Product. Although, the title changes it effectively displays similar information. If the sensor/product line includes TM, ETM+ or OLI-TIRS, meaning Thematic Mapper, Enhanced Thematic Mapper Plus or Operational Land Imager-Thermal InfraRed Sensor respectively, the cloud cover will usually be calculated as all these sensors have a thermal band. Whereas, if the sensor/product is MSS, then the cloud cover percentage will be -1%.

Landsat 8 uses the OLI-TIRS sensor, Landsat 7 has the ETM+ sensor, whereas Landsat’s 4 & 5 have both TM and MSS sensors, and Landsat’s 1, 2 & 3 only have MSS.

Quirk Three: What are all the other files alongside the LandsatLook Natural Colour Image?
When you select an image from Landsat, you’re given all available Landsat products associated with it. The most common additional products you’ll be offered are:

  • LandsatLook Thermal Image – This is usually a jpeg of the thermal band, which shows the variations in temperature, where the darker areas are colder, and the lighter areas are warmer.
  • LandsatLook Quality Image – Currently only available with Landsat 8, and is a jpeg which shows the positions of the clouds and other features such as snow and ice on your image.
  • LandsatLook Images with Geographic Reference – These are a series of compressed data files which can be uploaded into a Geographical Information System, allowing the application of image processing techniques. These are big files compressed, an even bigger uncompressed, and so you need a lot of storage space if you start downloading them!

Quirk Four: Why do some Landsat 7 images have black stripes on them?

South West England from the 8th December 2014, showing black stripes.  Landsat 7 imagery courtesy of USGS/NASA.

South West England from the 8th December 2014, showing black stripes.
Landsat 7 imagery courtesy of USGS/NASA.

This is due to the failure of Landsat 7’s Scan Line Corrector on the 31st May 2003. The Scan Line Corrector’s role is to compensate for the forward movement of the satellite as it orbits, and the failure means instead of mapping in straight lines, a zigzag ground track is followed. This causes parts of the edge of the image not to be mapped; hence giving you the black stripe effect – it can be seen clearly to the right with a zoomed in version of the image at the top of the blog. The location of the black stripes varies, and each stripe represents between 390 – 450m of the image; therefore US Geological Survey (USGS) estimates that affected images lose about 22% of their data.

The centre of the image can still be used, however it’s more complicated to use Landsat 7 data after May 2003. It’s worth noting that on the sensor/product line in the Scene Information Box, it uses the notation SLC-off to indicate that the image was taken after the Scan Line Corrector failed.

Quirk Five: My image has brightly coloured single pixels

Landsat 5 MSS image acquired on 16 January 1997 via ESA receiving station. Image courtesy of USGS/NASA/ESA.

Landsat 5 MSS image acquired on 16 January 1997 via ESA receiving station. Image courtesy of USGS/NASA/ESA.

Brightly coloured single pixels that don’t match the surrounding area, is phenomena known as Impulse Noise; which is also seen with dark or missing pixels. An example of an image with this phenomena is shown on the right. Technical issues during the downlink from the satellite or during the transcription from tape to digital media are the most frequent causes. However, small fires on the ground can also show up as bright pixels that cause the same effect, although these are less frequent. As Landsat has a 30m spatial resolution, these aren’t campfires or barbecues; but are high temperature features such as brush burning, wildfires or gas flares.

Images heavily affected by Impulse Noise aren’t released into the USGS archive. Also it’s only visible when zoomed it, and selecting another image from a different date will mostly likely cure the phenomena.

We hope this quintet of quirks has explained some of the queries and questions you might have about using Landsat data, and if you’ve not come across any of these yet this should give you a heads up for when you do come across them.

Mastering Landsat Images in 5 Simple Steps!

Landsat 8 image of South West England from the 25th July 2014. Landsat imagery courtesy of NASA Goddard Space Flight Center and U.S. Geological Survey

Landsat 8 image of South West England from the 25th July 2014. Landsat imagery courtesy of NASA Goddard Space Flight Center and U.S. Geological Survey

Always wanted to use satellite imagery, but weren’t sure where to start? This blog shows you the five simple steps to find, download and view free imagery from the United States Geological Survey (USGS) Landsat satellite. Within fifteen minutes of reading this post you could have images from Landsat’s 40 year global archive on your computer, like the one at the top of this blog of Plymouth Hoe from the 25th July 2014. So what are we waiting for, let’s get started …

Step One: Register!
Register for a user account with the USGS who, along with NASA, manages the Landsat data archive. It’s free to create an account, although you will need an email address and answer a quick few questions to help USGS assess their users. Once the account is activated, you’re ready to go and you can download as much data as you need.

Step Two: Selecting your data download tool
USGS offers three tools for downloading data: Landsat LookViewer, Global Visualisation Viewer (GloVis) and EarthExplorer. Whilst all three offer options to view Landsat data, we’d suggest you use GloVis as it’s the easiest tool for new users to navigate. GloVis has a main screen and a left sidebar; the sidebar controls which Landsat image is displayed in main screen.

Step Three: Selecting the image
At the top of the sidebar is a map centred on the US, and the red dot indicates the position of the displayed image. To choose another location use the map’s scroll bars to wander the world, and simply click on the area you want to see. The four arrow buttons on the sidebar allow you to fine-tune the precise location.

Finally, select the month and year you’re interested in, and the Landsat image that most closely matches your selection will appear in the main window. As Landsat is an optical sensor, it cannot see through clouds. If the chosen image has clouds obscuring the view, use the Previous Scene and Next Scene buttons to move easily around the closet images to your preferred date.

It is worth noting, the Max Cloud dropdown option, which allows you to choose the maximum percentage of the image you are willing to have covered by cloud. For example, if you select 40%, GloVis will only give you images that have 40% or less cloud coverage.

Step Four: Downloading the Landsat image
Once you have an image you like, simply click on Add at the bottom of the sidebar, and then click Send to Cart. This will take you to the download screen.

Your image will have entity ID, which was also visible in the Scene Information Box on the previous screen, consisting of 21 characters such as LC82040252014206LGN00, where:

  • The first three characters describe the Landsat satellite the image is from and LC8 refers to Landsat 8.
  • The next six (204025) are a Landsat catalogue number known as the Worldwide Reference Systems. If you remember the numbers for your area of interest, entering them in GloVis can be a quick way of navigating to that location.
  • The following seven characters give the year (2014) and the day of year (206) the image was taken; the day of the year is a numerical count starting with 001 on 1st January, and so 206 is 25th July.
  • A three-digit ground station identifier is next, in this case LGN indicates that the USGS Landsat Ground Network received this data.
  • Finally, the last two-digits are a version number (00).

Clicking the download button, gives you options to download any of the Landsat products available for the image you’ve selected. The LandsatLook Natural Colour Image is a jpeg version of the image you were looking at in GloVis, and is the easiest one to use. Click on download and the image you’ve chosen will be downloaded to your computer.

Step Five: Viewing, and using, the Landsat image

Plymouth Sound on 25th July 2014 from Landsat 8: Image courtesy of USGS/NASA Landsat

Plymouth Sound on 25th July 2014 from Landsat 8: Image courtesy of USGS/NASA Landsat

The easiest way to view the image is to use the Windows Photo Viewer tool, where you will be able to see the image and zoom in and out of it. You can also open the image in Windows Paint, and use its basic tools to resize and crop the image. For example, the image on the right is a zoomed in version of the image at the top of this post.

Landsat images are free, and they carry no copyright; however, NASA does request you attribute them appropriately – “Landsat imagery courtesy of NASA Goddard Space Flight Center and U.S. Geological Survey” or “USGS/NASA Landsat” – which means you, can use Landsat images on your website or other materials. The full information on Landsat copyright can be found here.

Next week, we’ll talk more about the other products you can download from Landsat. We hope these five simple steps have inspired you to find, download and use some Landsat data.

The Satellite Earth Observation Industry Began …

The satellite Earth observation (EO) industry, arguably, began 37 years ago yesterday. Now, before everyone starts tweeting and emailing hear me out. Although by this date EO satellites were in orbit, data successfully collected and imagery produced, the concept of a sustainable industry really began on the 6th January 1978 with the deactivation of Landsat-1.

Landsat 1 Image of East Anglia June 1976

Image of East Anglia, UK taken by Landsat 1 in June 1976; data courtesy of the European Space Agency / U.S. Geological Survey.

Landsat-1, also known as ERTS-1 (Earth Resources Technology Satellite), was launched by NASA into a sun-synchronous near polar orbit on the 23rd July 1972. It carried two sensors:

  • Return Beam Vidicon (RBV) which only operated for 14 days and recorded only 1692 images; and
  • Multispectral Scanner operating in four bands with a non-square sampling interval (pixel size) of 57m x 79m, that’s now resampled to 60m resolution imagery.

Landsat-2 was launched on 22nd January 1975 and carried exactly the same sensors as its predecessor; and it is this continuity of data that gave birth to the Earth observation industry. It paved the way for the development of an archive of over forty years worth of additional data provided by Landsat-3, Landsat-4 and Landsat-5; unfortunately, Landsat-6 did not reach its orbit. The archive continues to grow through the currently active Landsat-7 and Landsat-8, but it all began with Landsat-1.

The concept of a global archive gives satellite remote sensing its unique selling point. No other method of measurement or imagery has the ability to provide global coverage, almost real time data, time-series data analysis and the opportunity to go back and retrieve data before you knew you needed it! These elements, together with scientific knowledge and computing power, are the backbone of the products and services that form the modern EO industry.

The second Landsat driver to enhance the EO industry occurred thirty years after the deactivation of Landsat-1, when a data policy change in 2008 meant that all new and archived Landsat data held by the United States Geological Survey (USGS) was made freely available, via the internet, to anyone in the world.

In addition, in researching this post I also discovered that Landsat-1 has an island named after it. A Canadian coastal survey was carried out in 1976 using Landsat-1 data, and a number of unchartered features were discovered off the northeast coast of Labrador. Landsat Island is 20km off the coast and has a landmass of only 25m x 45m, with the only known inhabitant a polar bear! The island marks the easternmost point of the Canadian land mass; and its discovery increased Canada’s territorial waters by 68km.

Landsat first day cover

Landsat first day cover

Since the first Landsat was launched, many more EO satellites have gone into orbit; our blog post last year noted 192 EO satellites in orbit at the start of 2014. However, it’s worth remembering that although Landsat was not the first EO satellite, the Landsat missions are the founding fathers of the EO industry through their foresight of data continuity.