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.

Twinkle, Twinkle, Little SAR

Copyright : NASA/JPL Artist's impression of the Seasat Satellite

Copyright : NASA/JPL
Artist’s impression of the Seasat Satellite

Last week ESA released a new synthetic aperture radar (SAR) dataset from NASA’s Seasat mission; nothing unusual in that you might think, except that this data is over 36 years old. As part of its Long Term Data Preservation Programme, ESA has retrieved, consolidated and reprocessed the Seasat data it holds, and made this available to the Earth observation (EO) community.

Seasat was a landmark satellite in EO terms when it was launched on the 27th June 1978. Not only was it the first satellite specifically designed for remote sensing of the oceans, but it was also the first to carry a SAR instrument. Seasat was only in orbit for 106 days as a problem with the electrical system ended the mission just over three months later on 10th October. Although, there is a conspiracy theory that the electrical fault was just a cover story, and the military actually shut down Seasat once they discovered it could detect submerged submarines wakes!

Synthetic aperture radar (SAR) is so called as it uses a small physical antenna to imitate having a large physical antenna; to detect the long wavelengths would require a physical antenna of thousands of metres, while the same result can be achieved with a synthetic antenna of around 10 metres in length. It is an active sensing radar system which works in the microwave part of the electromagnetic spectrum, and uses pulses of radiation to map the surface of the Earth. Pulses are transmitted with wavelengths of between metres and millimetres, some of these pules are absorbed by the surface, whereas others are reflected back and recorded by the SAR. As the satellite moves, the antenna’s position relative to the area that it is mapping changes over time providing multiple observations. This movement crates a large synthetic antenna aperture, because all the recorded reflections of a particular area are processed together as if they were collected by a single large physical antenna, which gives an improved spatial resolution.

SAR is extremely sensitive to small changes in surface roughness, and can provide both day and night imagery as it works independently of visible light, and is generally unaffected by cloud cover. It is used for assessing changes in waves, sea ice features and ocean topography, and recent research is applying it to other fields such as flood mapping. Seasat blazed the trail for SAR instruments, which has since been followed by many other satellites including ESA’s ERS-1 and ERS-2, ENVISAT’s ASAR, RadarSAT, COSMO-SkyMed, TerraSAR-X; and in 2014 both the Japanese ALOS, and ESA’s Sentinel-1, satellites carried SAR instruments.

The potential value residing in Seasat data is demonstrated not only by ESA reprocessing Seasat, but last year NASA also released a reprocessed Seasat dataset. The use of historic data is one of EO most powerful tools, and it is one the remote sensing community needs to exploit more.

Temporal: The forgotten resolution

Time, Copyright: scanrail / 123RF Stock Photo

Time, Copyright: scanrail / 123RF Stock Photo

Temporal resolution shouldn’t be forgotten when considering satellite imagery; however it’s often neglected, with its partners of spatial and spectral resolution getting the limelight. The reason is the special relationship spatial and spectral has, where a higher spectral resolution has meant a lower spatial resolution and vice-versa, because of limited satellite disk space and transmission capabilities. Therefore, when considering imagery most people focus on their spatial or spectral needs and go with whatever best suits their needs, rarely giving temporal resolution a second thought, other than if immediate data acquisition is required.

Temporal resolution is the amount of time it takes a satellite to return to collect data for exactly the same location on Earth, also known as the revisit or recycle time, expressed as a function of time in hours or days. Global coverage satellites tend to have low earth polar, or near-polar, orbits travelling at around 27,000kph and taking around 100 minutes to circle the Earth. With each orbit the Earth rotates twenty-five degrees around its polar axis, and so on each successive orbit the ground track moves to the west, meaning it takes a couple of weeks to fully rotate, for example, Landsat has a 16 day absolute revisit time.

Only seeing the part of the Earth you want to image once every few weeks, isn’t very helpful if you want to see daily changes. Therefore, there are a number of techniques satellites use to improve the temporal resolution:

  • Swath Width- A swath is the area of ground the satellite sees with each orbit, the wider the swath the greater the ground coverage, but generally a wider swath means lower spatial resolution. A satellite with a wide swath will have significant overlaps between orbits that allows areas of the Earth to be imaged more frequently, reducing the revisit time. MODIS uses a wide swath and it images the globe every one to two days.
  • Constellations – If you have two identical satellites orbiting one hundred and eighty degrees apart you will reduce revisit times, and this approach is being used by ESA’s Sentinel missions. Sentinel-1A was launched in 2014, with its twin Sentinel-1B is due to be launched in 2016. When operating together they will provide a temporal resolution of six days. Obviously, adding more satellites to the constellations will continue to reduce the revisit time.
  • Pointing – High-resolution satellites in particular use this method, which allows the satellites to point their sensors at a particular point on earth, and so can map the same area from multiple orbits. However, pointing changes the angle the sensor looks at the Earth, and means the ground area it can observe can be distorted.
  • Geostationary Orbits – Although technically not the same, a geostationary satellite remains focussed on an area of the Earth at all times and so the temporal resolution is the number of times imagery is taken, for example, every fifteen minutes. The problem is that you can only map a restricted area.

Hopefully, this has given you a little oversight on temporal resolution, and whilst spectral and spatial resolution are important factors when considering what imagery you need; do spent a bit a time considering temporal needs too!

SMAP ready to map!

Artist's rendering of the Soil Moisture Active Passive satellite.  Image credit: NASA/JPL-Caltech

Artist’s rendering of the Soil Moisture Active Passive satellite.
Image credit: NASA/JPL-Caltech

On the 31st January NASA launched their Soil Moisture Active Passive satellite, generally known by the more pronounceable acronym SMAP, aboard the Delta 2 rocket. It will go into a near polar sun-synchronous orbit at an altitude of 685km.

The SMAP mission will measure the amount of water in the top five centimetres of soil, and whether the ground is frozen or not. These two measurements will be combined to produce global maps of soil moisture to improve understanding of the water, carbon and energy cycles. This data will support applications ranging from weather forecasting, monitoring droughts, flood prediction and crop productivity, as well as providing valuable information to climate science.

The satellite carries two instruments; a passive L-Band radiometer and an active L-Band synthetic aperture radar (SAR). Once in space the satellite will deploy a spinning 6m gold-coated mesh antenna which will measure the backscatter of radar pulses, and the naturally occurring microwave emissions, from off the Earth’s surface. Rotating 14.6 times every minute, the antenna will provide overlapping loops of 1000km giving a wide measurement swath. This means that whilst the satellite itself only has an eight day repeat cycle, SMAP will take global measurements every two to three days.

Interestingly, although antennas have previously been used in large communication satellites, this will be the first time a deployable antenna, and the first time a spinning application, have been used for scientific measurement.

The radiometer has a high soil moisture measurement accuracy, but has a spatial resolution of only 40km; whereas the SAR instrument has much higher spatial resolution of 10km, but with lower soil moisture measurement sensitivity. Combining the passive and active observations will give measurements of soil moisture at 10km, and freeze/thaw ground state at 3km. Whilst SMAP is focussed on provided on mapping Earth’s non-water surface, it’s also anticipated to provide valuable data on ocean salinity.

SMAP will provide data about soil moisture content across the world, the variability of which is not currently well understood. However, it’s vital to understanding both the water and carbon cycles that impact our weather and climate.

Is space a good investment?

Space is an expensive, and uncertain, environment to work in, and decisions to invest in space technology and missions are frequently questioned in the current global economic climate. Headline figures of tens of millions, or billions, do little to counter the accusations that there are more appropriate things to be investing in. Is the cost of investing in space worthwhile?

Image of East Devon, UK taken by Landsat 8 on 4th November 2013.  The River Exe flows from top to bottom and the River Teign from left to right. Plumes of suspended sediment are clearly visible following periods of heavy rainfall in late October and early November 2013.  Image courtesy of the U.S. Geological Survey

Image of East Devon, UK taken by Landsat 8 on 4th November 2013.
The River Exe flows from top to bottom and the River Teign from left to right. Plumes of suspended sediment are clearly visible following periods of heavy rainfall in late October and early November 2013.
Image courtesy of the U.S. Geological Survey

Last week the Landsat Advisory Group, a sub-committee of the US Government’s National Geospatial Advisory Committee, issued a report looking at the economic value of Landsat data to America. As Landsat data is freely available, quantifying the value of that data isn’t easy; and the Group approached it by considering the cost of providing alternative solutions for Landsat data.

They considered sixteen applications, linked to US Government departments, which use Landsat data. These ranged from flood mitigation, shoreline mapping and coastal change; through forestry management, waterfowl habitats and vineyard management; to mapping, wildfire assessment and global security support. The report estimated that these sixteen streams alone produced savings of between $350 million and $436 million to the US economy. The report concluded that the economic value of just one year of Landsat data far exceeds the multi-year total cost of building, launching, and managing Landsat satellites and sensors.

This conclusion was interesting given reports in 2014 that Landsat 8 cost around $850m to build and launch, a figure which will increase to almost $1 billion with running costs; and that NASA were estimating that Landsat 9 would cost in excess of the $650m budget they had been given. These figures are significantly in excess of the quantified figures in the Advisory Group report; however work undertaken by US Geological Survey in 2013 identified the economic benefit of Landsat data for the year 2011 is estimated to be $1.70 billion for US users, and $400 million for international users.

The discrepancy between the two figures is because the Advisory Group did not include private sector savings; nor the fact that Landsat data is also collected, and disseminated, by the European Space Agency; nor did it include unquantified societal benefits or contribution to scientific research. For example, it highlighted that humanitarian groups use Landsat imagery to monitor human rights violations at low cost and without risking staff entering dangerous, and often inaccessible, world regions.

Last week also demonstrated the uncertain side of space, with the discovery of the Beagle-2 spacecraft on the surface of Mars. The UK led probe mission was assumed to have crash landed on Christmas Day 2003, however recent images indicate it landed successfully but its solar panels did not unfurl successfully. The Beagle 2 discovery has obvious echoes with the recent shady site of the Philea comet landing, and demonstrates that space exploration is a risky business. Given the Beagle 2 mission cost £50 million and the Philea mission was estimated to cost around region of €1.4 billion, is the cost of investing in space worthwhile?

Consider satellite television, laptops, smoke detectors, tele-medicine, 3D graphics and satellite navigation – all of these developments came through the space industry, and so now think about the jobs and economic activity generated by these sectors. Working in space is expensive and challenging, but it’s precisely because of this that the space industry is innovative and experimental. The space sector works at the technological cutting edge, investment in space missions benefits and enhances our life on earth. So if anyone ever asks whether space is a good investment, tell them about the financial benefits of Landsat, the development of laptops, the number of lives saved by smoke detectors or the humanitarian support provided to Amnesty International.

Why counting animals from spaces isn’t as hard as you think

Great Migration in Maasai Mara National Park, Kenya

Great Migration in Maasai Mara National Park, Kenya; copyright alextara / 123RF Stock Photo

Last week the keepers at London Zoo were busy counting their 17,000 animals, as part of the annual headcount. Knowing numbers is vital within the wild too, but counting animals on the plains of Africa is more challenging. Traditionally wild counts are either ground surveys, which take people and time; or aerial surveys, that can spook the animals. Satellite remote sensing could offer a potential solution, but it’s not straight-forward. Three papers published in 2014 show the possibilities, and challenges, of using satellites to count animals.

The paper Spotting East African Mammals in Open Savannah from Space by Zheng Yang et al (2014) published on the 31st December, describes the use of very high-resolution GeoEye-1 satellite images to detecting large animals in the Maasai Mara National Reserve, Kenya. GeoEye-1’s 2m multispectral image resolution was not sufficient to detect large animals. However, when combined with the panchromatic image using a pan sharpening technique the resolution improved to 0.5m meaning adult wildebeests and zebras were 3 to 4 pixels long, and 1 to 2 pixels wide. Experienced Kenyan wildlife researchers initially visually reviewed images to develop a classification system, forming the basis of a hybrid image system, using both pixel-based and object-based image assessment approaches to determine which pixels belonged to animals. The results showed an average count error of 8.2% compared to manual counts, with an omission error rate of 6.6%, which demonstrates that satellites have potential for use in counting; it’s cheaper and less intrusive than existing methods.

The second paper was published by Seth Stapleton et al (2014) entitled Assessing Satellite Imagery as a Tool to Track Arctic Wildlife. It used 0.5m resolution imagery of Rowley Island in Foxe Basin, Canada, from Worldview-2 to monitor the island’s polar bear population. The images were corrected for terrain and solar irradiance, and an a histogram stretch to brighten darker, non-ice, areas to assist human analysts identify the bears. Two observers visually identified ‘presumed bears’ both individually and jointly; resulting in the identification of 92 presumed bears. This satellite derived figure was consistent with other models, again offering a potential cheaper and safer way of monitoring polar bears.

Finally, Peter Fretwell et al (2014) published Counting Southern Right Whales by Satellite. Also using WorldView-2, they used a 2m resolution image with eight colour bands and one panchromatic band. The images were analysed using ENVI5 and ArcGIS to identify potential and probable whales, and then visual inspection of these images showed they had identified objects of the right shape and size to be whales; resulting in the identification of 55 probable whales and 23 possible whales. Again, showing satellite images could be useful in calculating whale populations faster and more efficiently.

All three of these papers demonstrate that satellite remote sensing has potential to assist in the monitoring of animal species across the globe. However, there are also significant challenges still to overcome, for example:

  • Resolution: Currently available resolutions may not sufficient to distinguish the level of detail conservationists need, such as species identification in Africa or polar bear cubs in the Canada. However, it may be possible with very high resolution satellites such as the planned WordlView-4 from DigitalGlobe.
  • Cloud cover: The persistent nemesis of optical Earth observation imagery may hamper it’s use in certain areas or seasons.
  • Complicated environments: Further research is needed to ensure animals can be accurately distinguished from their surroundings.

Despite these reservations, the potential to offer regularly, more efficient and safer methods of survey animal populations from space means this will be a rapidly developing area of Earth observation.

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.

Pixalytics 2014 Blog of Blogs

Artist's rendition of a satellite - paulfleet/123RF Stock Photo

Artist’s rendition of a satellite – paulfleet/123RF Stock Photo

Have you read our most popular blog of the year? If not, you still have a chance to see How Many Satellites are Orbiting the Earth?

This time last year we decided to issue a blog every Wednesday during 2014, to increase the profile of Pixalytics both online and within the wider Earth observation community. It has been an interesting exercise in discipline; finding interesting topics, writing the words and getting a suitable image. It’s not been easy; there’ve been weeks where we‘ve had no ideas for the blog when we’ve arrived at the office on the Wednesday morning. However, we’ve done it! Every Wednesday lunchtime for the past 52 weeks we’ve released a blog.

It’s been interesting to see which posts have attracted interest, and sometimes which posts have not. Our top post by far was the one at the top of the page, with the other top four being:

The majority of our posts are focussed on Earth observation and space, although we also occasionally cover topics relevant for any small businesses. Interestingly, our sixth most popular blog was Pareto’s Principle and the Micro Business.

These top posts have been determined by the number of views according to Google Analytics, although we know timing and other factors influence these figures. Posts from the start of the year have had more time to generate views, but we’ve also developed our social media presence during the last few months, which has meant some recent posts have done very well in our list. In particular, the following two posts from the last five weeks:

Interestingly, our 2013 post on What is the reason for blooms of phytoplankton? also received a significant number of views.

There are a lot of webpages written about developing blogs that are read. We’ve tried things out, tested the blog guru’s hypothesises and made a few mistakes. However, the things we’ve learnt are:

  • Answering questions people have about space and Earth observation are our most popular posts.
  • Looking at current national or international events using an Earth observation viewpoint are also well received.
  • Blogs about conferences/meetings we’ve attended are very popular during the conference/meeting, but less so after the event.
  • Getting a blog calendar together to plan potential ideas helped a lot.
  • A blog about the Size and Health of the UK Space Industry was the least popular of the year – if you want to boost its self-esteem you can read it here!

So has all the effort been worth it?

In pure numbers, Google Analytics shows our web traffic has increased by 166% compared with 2013, although as we noted earlier this isn’t all down to our blog. We know that people read the blog as they have come up to Sam at meetings and told her; and we’ve had telephone calls and emails after people have read posts. We can’t say that our blog has directly generated business, but we absolutely believe it has raised our profile, and so for us it has been worth it. If you’d like to have a look at the all the blogs we’ve written you can find them all here.

We’ve done 2014, and can exclusively reveal our weekly blog is going to carry on into 2015. Next year we’re going to focus a bit more around specific aspects of Earth observation, with the odd small business topic, conference update and current event sprinkled in.

Thanks for reading during 2014, and we look forward to seeing you in 2015.

Happy New Year!

Merry Christmas!

Plymouth-by-night-ISS-feb13

Night-time image of Plymouth taken by an astronaut aboard the International Space Station, February 2013. Image courtesy of http://www.citiesatnight.org/

Pixalytics Ltd would like to wish everyone we worked with during the last year, and all the readers of our blog, a very happy Christmas.