June 26, 2017
by chris

Is smaller better? – Where the rubber meets the road with earth observation microsatellites

First here an introductory note on my policy for reviewing geodata products – since i occasionally receive questions along the lines of why don’t you review product X by company Y. I review things i find interesting and useful or which i consider a significant innovation. In case of satellite image mosaic products i looked at the work of Mapbox and Google in depth because when these were introduced they were something new and innovative no one had done before. I did however not discuss any of the various me too products introduced since then based on Landsat or Sentinel-2 data because none of them so far shows either a significant step up in terms of quality of the results or bringing any notable technical innovation with it.

I also focus on products that are open data or are based on open data of course. This is both because this is where i am most knowledgeable in and because these i think are of most interest for my readers.

With that in mind the products of Planet Labs would not normally be subject of a review by me. While they use open data satellite imagery and offer products based on this to their customers Planet Labs does not currently offer any open data products apparently. They have a product called Open California which is supposed to be under CC-BY-SA but this is not actually publicly available (which makes it look quite strongly like openwashing).

Planet Labs is the most prominent company that has grown out of the microsatellite hype of the past years and undoubtedly the most serious player in that field for earth observation purposes. They have developed and launched a significant number of very small satellites of just a few kilograms in weight over the last years but so far the only public service available from them is a program called Planet Explorer which is a short time (one or three month) near global satellite image mosaic based on data from these and their other larger satellites (coming from a purchase back in 2015). I am reviewing this here not because of the practical usefulness of the product itself (which seems rather limited) nor because it is technically innovative (which it might be on a basic data processing level but which it certainly is not in terms of image processing). I review this here as a contribution to a fact oriented public discourse on currently produced and available satellite imagery which obviously has to include Planet Labs images.

I want to clarify that this is not a review of the Planet Labs imagery itself. Planet Explorer does not even offer a full resolution view for the unregistered user and there are currently apparently no raw sample images available for Rapideye and Plantscope imagery so a real review is not possible, at least not without signing an NDA. This only discusses the corpus of imagery as shown in Planet Explorer and which i assume to be the bulk of useful imagery currently produced by the company (in this resolution range, Planet Labs recently also purchased higher resolution satellites data from which is not included there).

Not off for a good start

To get this out of the way first – Planet Explorer uses OpenStreetMap data for their map for labels, boundaries and other stuff in clear violation of the ODbL. They mention OpenStreetMap hidden under Terms which is kind of the internet equivalent of having it placed “in the bottom of a locked filing cabinet stuck in a disused lavatory with a sign on the door saying Beware of the Leopard”. The user does not commonly get to see this which is exactly what would be required by the ODbL.

You could also call it shooting yourself in the foot with regards to public relations with the open data community. Most people can and do respect if a company business model is based on licensing data and they therefore offer none of their data as open data but tolerance ends when the very same company is too cheap to even properly acknowledge use of data they use which has been generously made available by others as open data.

I will ignore that for the purpose of this review but everyone should keep this in mind when making use of Planet Labs services of course.

What does it tell us about the data?

The map shows us aggregates of the Planet Labs imagery in either monthly or three month intervals. This comprises three different types of images at the moment apparently:

  • Images from the Rapideye constellation which can be identified in the mosaic based on their relatively wide recording swath (77 km).
  • Images from the very small Planetscope satellites in sun synchroneous orbit with 24 km recording width and for which Planet Labs is mostly known.
  • Images from the Planetscope satellites in ISS orbit which can be distinguished from the former based on the slightly less wide swath (20 km) and the lower orbit inclination.

I won’t comment much on the assembly strategy they use – this is fairly non-sophisticated. Cloud detection and masking seems to be used for Rapideye imagery but not for the Planetscope data.

The more interesting part is coverage. Planet Labs has for a long time advertised their goal is daily coverage of the whole planet – which is of course meant for the land masses only. Their claims about to what extent they are actually doing this are always a bit fuzzy though – numbers usually appear to be theoretical goals and they speak of the ability to record a full daily coverage but do not say this is actually being recorded.


Coverage seems to be constrained between 57°S and 67°N, the northern limit however is quite clearly not a recording limit but a processing limit. These limits by the way happen to be approximately the extent of the known world until the 17th to 18th century. Of the low latitude areas where they record they achieve approximately 90-95 percent coverage in the monthly interval at the moment (with May 2017 being the last month covered so far). It is possible that part of this is because they do not include images in processing because they are completely cloud covered and their actual coverage is slightly better. This is still pretty far away from a daily global coverage but does not preclude the possibility that their daily coverage in terms of recorded area is close to or above the total land area on earth. For the latter you just need sufficient recording capacity or in other words: enough satellites. For actual full coverage you would need to have these recordings distributed uniformly across the earth land surfaces which is a whole other problem.

Their recording strategy at the moment looks rather odd with seemingly random gaps in the recordings. I of course don’t know what technical constraints exist with these very small satellites, how specifically they can task the recordings and how far they depend on having a receiving station close-by. And keep in mind that they can’t really maneuver these satellites so they have very limited control over where the satellites look at a certain time. Imagine playing darts with a very low skill level and having the task to hit all regions of the dart board at least once. You need many more darts to accomplish this than there are regions on the board because you end up hitting many parts several time before achieving full coverage.

As an example here a view of their May 2017 image of southern Germany.

This has 100 percent coverage but as you can clearly see there is a strip of cloud affected images in the center part of the area indicating they don’t have any May 2017 recordings of this area without clouds (or a really bad image quality assessment as a basis of mosaic assembly – which however seems unlikely). If i look over the weather in May (based on MODIS and VIIRS imagery for example) there are at least four days with good weather in the morning to noon time frame that would have allowed for better images in the area (May 10, 17, 26 and 27). Here a quick assembly of the same area based on Sentinel-2 data from May 10 and May 27.

That i can produce this from Sentinel-2 data with a ten day recording interval is pure luck. But it shows that while the number of images recorded by Planet Labs in the area maybe could cover it completely every day based on sheer numbers the images actually recorded clearly do not by a fairly big margin. And this is at a latitude where due to the orbit geometry you already have on average a much higher potential recording density than at the equator.

Is smaller better?

From this specific analysis of the current Planet Labs offerings and capabilities i now get to the ultimate more general question – is a large number of small satellites recording a relatively narrow field of view better or worse than a small number of larger satellites with a wider field of view?

Note although the way i phrased this question is independent of the recording resolution in reality this is not the case of course – higher resolution satellites tend to have a more narrow field of view. Here a few examples:

Satellite mass recording width resolution width in pixel (approximate)
Landsat 1500 kg 190 km 15 m 13000
Sentinel-2 1100 kg 290 km 10 m 30000
Rapideye 156 kg 77 km 6.5 m 12000
Planetscope 6 kg 24.6 km 3.7 m 6600
Skysat 83 kg 8 km 0.9 m 8000
Pleiades 970 kg 20 km 0.7 m 30000
WorldView-4 2500 kg 13.1 km 0.31 m 42000

For comparing resolutions note the Planetscope satellites are the only ones with a Bayer pattern sensor (so the specified resolution is only for all spectral bands in combination).

A very wide recording width like with Sentinel-2 causes additional problems with positional accuracy and varying illumination and viewing conditions across the field of view of course, this is not what i want to talk about here though. Without these problems there are mainly the following factors:

  • Satellites recording a smaller view (in terms of viewing angle as well as for the width in pixel) can be built cheaper and more light-weight. This is the main reason for the small field of view of Planetscope.
  • Smaller individual images allow more fine grained targeting of recordings – either on specific places or on good weather windows. In other words: if your recording planning is good smaller images will have a smaller average cloud coverage.
  • Smaller images mean more edges between images and more problems with discontinuities in the data and assembly.
  • Developing and building a larger number of smaller satellites can be significantly less expensive than building a single large satellite. Management of risks of failures during launch and operations is also easier.
  • Recording in high resolution requires a certain minimum size of the optics which puts a hard constraint on the size of the satellite.
  • Recording in the longer wavelength infrared (SWIR/TIR) requires cooling equipment that cannot be easily miniaturized.

As you can see there are pros and cons for both. Additional factors play in if you want to specifically target certain areas – which is what all current very high resolution systems do. I only look at things for the purpose of routine large area coverage.

If you had 16 Landsat satellites and you would properly line up their orbits for this purpose you could record a solid daily coverage (yes, you would of course need to significantly extend the ground based infrastructure for this as well). Based on just the field of view (which is a strongly simplified way to look at it) you could do the same with with just about 16*190/24.6 = 124 Planetscope satellites if you (a) can operate them on the same duty cycle (the same recording duration per orbit) – which could be realistic although current operations do not demonstrate this and (b) if you could perfectly and permanently align their orbits relative to each other – which you can’t because they lack propulsion and the options they have with controlling atmospheric drag probably do not give you sufficient control for that. Hence they would need a significantly larger number of satellites, probably several times this number – for a true daily global coverage.

My prediction is that if Planet Labs stays in business for a longer time with their current business model and the aim to provide continuous coverage of larger areas world wide on a daily basis they will probably add some form of propulsion to their satellites sooner or later.


June 16, 2017
by chris

Mapping coasts and the tidal zone

With the recent introduction of additional imagery layers for the purpose of mapping in OpenStreetMap by DigitalGlobe significantly more source material is now available for remote mapping in OSM. However in many, especially remote areas my OSM images for mapping still provide the most recent image source readily available for mappers and in quite a few areas also the best overall. And even in areas where recency of images is not that important and where Bing and DigitalGlobe offer good quality images an additional independent image source can be very useful for interpretation.

I added a few additional images now with a focus on coastal areas and tidal flats. Areas with changing water levels are something where open data imagery is of particular use even if higher resolution images are available from other sources because you can specifically select high and low water levels and are thereby able to accurately map the coastal features while in higher resolution image sources you tend to have more or less random water levels in the images and essentially need to map based on guesswork if you do not have additional sources of information.

Mapping coasts and the tidal zone in OpenStreetMap is not that difficult, here the basics:

Much of this is of course rather difficult to assess without local knowledge so be careful when mapping just from the distance and familiarize yourself with the area in question before you do so. In many of the areas i show in the following at least the basics, delineating the coastline and mapping the tidal flats, are not that difficult to do though.

You can find some more details on coastal mapping in another blog post about beaches and reefs.

Bahía Blanca

Bahía Blanca is the name of a city as well as a bay in Argentina and features one of the largest tidal wetlands in South America which is currently quite poorly mapped in OpenStreetMap. I added images featuring a low and high water level.

Bahía Blanca low tide

Bahía Blanca high tide

Note these are from different times of the year so differences in color are not exclusively due to the tidal cycle. The whole area is also covered in high resolution image sources but with randomly varying water levels so accurate mapping is quite difficult just based on these.

Cook Inlet

Cook Inlet is the large bay in southern Alaska which features quite large tidal flats at the northern end near Anchorage.

This late summer image also allows mapping in the mountains around the bay. The area is partly covered by high resolution image sources but largely from less than optimal recording dates.

Bogoslof Island

Also located in Alaska is Bogoslof Island where a volcanic eruption recently changed the shape of the island quite significantly. See also here.

Northern Dvina delta

The situation for the Northern Dvina delta near Arkhangelsk is similar to that of the Cook Inlet although existing mapping on land is already much better here. I also provide a low tide image for this area that should allow adding details of the tidal zone.

Aral Sea

Finally i also have two images of low and high water levels of the Aral Sea which is of course not a sea but a lake. Exact water levels vary significantly from year to year but these images will at least roughly indicate which are permanent and and which are intermittent water areas at the moment.

Aral Sea low water

Aral Sea high water

There is some residual ice on the water in the northern part of the high water level image that should not be mistaken for something else. Note the right tagging for seasonally water covered areas is natural=water + intermittent=yes or seasonal=yes, not natural=wetland – even if intermittency of water areas is not currently shown in the standard map style.

June 16, 2017
by chris

Public request for input on future Landsat requirements

The NASA and USGS are now seeking input on requirements for future Landsat missions from all data users. Quoting from the RFI:

The U.S. Geological Survey (USGS) Land Remote Sensing Program has collected a diverse set of U.S. Federal civil user measurement needs for moderate-resolution land imaging to help formulate future Landsat missions. The primary objective of this RFI is to determine if these needs are representative of the broader Landsat user community, including, but not limited to, private sector, government agencies, non-governmental organizations and academia, both domestic and foreign. Responses to this RFI will be considered along with other inputs in future system formulation.

This is quite remarkable. Usually parameters of such projects are decided on almost exclusively between public institutions. If input is sought from the general public this tends to be in the form of multiple choice questionnaire which are often set up to lead to a specific result and are then interpreted to that goal as well. This however looks a bit different, they are asking for free form answers to a number of specific but open questions and specifically ask not only what you want but also for your reasons why you think this would be good to have.

There is no guarantee of course that any of this will actually have an effect on future Landsat plans but i would still urge anyone routinely using Landsat data, who understands the questions and feels qualified to give answers to them to send in their thoughts. So far the interests that went into future Landsat planning were probably almost exclusively from government and scientific institutions as well as likely a few larger corporations. And interests of these are not necessarily the same as those from the broad range of smaller private sector users, independent scientists, community projects or similar things. If you belong to any of these underrepresented groups, are a Landsat data user and are reading my posts here on satellite image related topics with interest and not just skim them for interesting images there is a good chance you could provide useful input here.

Answers should be sent before July 14.


June 4, 2017
by chris

Open data satellite image news

Here a few more news from the field of open data satellite images:

  • in reference to my recent report and the missing recordings of Sentinel-2 imagery – ESA seems to have “found” some images and the formulation they use in the status reports is something i am going to save for future use:

    The ground segment has suffered a sporadic anomaly between March and May, leading to an incomplete dissemination of the production with about 11% products missing throughout the period.

    I mean like: what do you mean by tax evasion, my bookkeeping suffered a sporadic anomaly last year… I updated the coverage illustrations a few days ago including what is newly available now.

  • the USGS has updated their EarthNow! live Landsat viewer – not to be confused with Mapbox Landsat Live (which is not really live). The new version finally shows true color renderings. While this rarely shows a true live feed – you mostly get recordings from a few hours back – it is a nice illustration how satellites actually record imagery and the only place AFAIK where you can actually see current Landsat Level 0 data.
  • the USGS is now distributing some images from the Indian IRS-P6/Resourcesat 1 satellite and followup mission Resourcesat 2. Images are mostly for the US only and from two instruments: LISS-3 and AWiFS. Quality of this data is pretty good but features relatively limited spectral ranges with only red, green NIR and a single SWIR band. AWiFS is quite interesting as an intermediate between the higher resolution and low revisit frequency systems like Landsat and Sentinel-2 and the low resolution high revisit systems like MODIS, VIIRS and Sentinel-3.

    Here examples from the western United States in approximated true color with estimated blue (as i have shown previously for ASTER).

ISRO Resourcesat 2 AWiFS example

ISRO Resourcesat 2 LISS-3 example

LISS-3 full resolution crop


June 3, 2017
by chris

Arctic places in spring

A few more satellite images from spring in the Arctic – this time featuring the northmost settlements on Earth.

The northmost permanent settlement on the planet is and has been for a long time the weather station and military base at Alert in northern Canada on the northeastern end of Ellesmere Island at 82.5 degrees north.

Alert, Ellesmere Island

Next, about one degree further south, is Station Nord in northeastern Greenland, a Danish military post. In contrast to all other places shown which are near the coast and accessible by ship in summer this is inaccessible all year round in most years due to sea ice and all supplies need to be brought in by air.

Station Nord, Greenland

Again nearly a degree further south at 80.8 degrees north is Nagurskoye on Alexandra Land, Franz Josef Land. This military outpost was significantly extended in recent years – i showed an image of supply operations two years ago.

Nagurskoye, Franz Josef Land

All these three northmost settlements have been established in the 1950s during the cold war. They are all military stations with restricted access. The northmost settlement open to everyone to visit is Ny-Ålesund on Svalbard which is mostly used for scientific research.

Ny-Ålesund, Svalbard

Also on Svalbard slightly further south is Longyearbyen which is the northmost bigger settlement on Earth with a population of more than 2000. On the plateau south of the airport on the left side of the image you can see the antennas of the Svalbard Satellite Station which receives a significant fraction of the satellite images i show here.

Longyearbyen, Svalbard

Both these Svalbard settlements are more than 100 years old, much older than the ones further north established during the cold war. And in contrast to the other places where all ressources need to be brought in from abroad the Svalbard settlements are powered by locally mined coal.

Last – and no more competing for a record latitude in any way but kind of significant for balance around the pole here another image of the Russian military base on Kotelny Island which – like Nagurskoye – has been extended significantly in recent years.

Темп, Kotelny Island

Locations of all these places can also be found on the following map.

All images based on Copernicus Sentinel-2 data from April and May 2017.


May 26, 2017
by chris

Grounded sea ice in the Arctic

I mentioned the phenomenon of grounded sea ice in the Kara Sea a few years back. Here a recent image of the same area in a wider view of the whole northeastern Kara Sea showing this still happens in 2017 at the same places.

But this is not the only area in the Arctic Ocean where sea ice gets in contact with the ocean floor at places away from the coast and is thereby fixed in place and does not move with the general ice drift in the area any more. The most famous area of this kind is the Norske Øer Ice Barrier off the East Greenland coast. The special thing about this is that the ice in parts is semi-permanent here, it only breaks up completely in some years.

At this time the ice barrier forms a continuous solid area of ice together with the freely floating land fast sea ice closer to the coast. How this typically looks like in summer can be seen in my Greenland mosaic. Another places where grounded sea ice is well visible at the moment is the East Siberian Sea. Here an image from March of the area north of the Medvezhyi Islands.

And here the same area a few days ago.

All images based on Copernicus Sentinel-3 OLCI data.


May 16, 2017
by chris

Some satellite image news

It has been some time since i wrote about news in the world of open data satellite images and quite a few things have happened since then some of which i want to comment here.


Recently ESA published a report on the access to satellite data from the Copernicus program. This is mostly boring read with a fairly bad signal-to-noise ratio but there are also quite a few interesting things buried under a lot of meaningless all numbers have increased during the last year stuff. The most important thing to keep in mind if you read this is that the distribution form of the Sentinel-2 data changed to single granule packages during the reporting time frame. This is not properly taken into account so most numbers referring to package counts are nonsense combining apples and cherries so to speak. But to avoid misunderstandings – it is already quite positive that such a report is at least written and especially that it is published in the first place.

What is particularly funny is the following illustration meant to indicate the spatial distribution of published Sentinel-2 images.

For comparison here the accurate coverage map (which i published earlier for a different reporting timeframe).

The ESA illustration is not only combining the single and multiple granule packages apparently, it also senselessly blurs the illustration – probably they had the same troubles producing an accurate visualization from their own inconsistent metadata as me. Overall great example how not to do a visualization.

Some interesting facts that can be extracted from the report are:

  • a fairly open admission that availability of the open data access sucked beyond belief during the last part of the reporting time frame – something i already pointed out here. This is rationalized in the text of course. From current use of the service i predict that this will improve in the report for 2017 but the bar is not that high – they consider everything above 95% as good. And formal availability of the service as a whole of course does not necessarily means it is practically usable.
  • some interesting numbers regarding the actual use of the download services. I will get to these in the following.

Use of Sentinel-2 data access

I will only discuss Sentinel-2 numbers here while the report also covers the other Sentinel satellites of course. The report also covers both the Open Access Hub available to everyone and the other data hub instances available only to certain privileged groups:

  • the Copernicus Services Hub which is for organizations performing services inside the Copernicus program, i.e. stuff directly financed within the program.
  • the Collaborative Hub which is for partner organizations within the European Union, in other words independently tax financed stuff.
  • the International Hub which is for partner organizations outside the EU – which is currently one from Australia and two from the US (of either NASA, USGS and NOAA – which is not specified)

Now the main numbers: A total of 0.46PB of Sentinel-2 data has been published. Downloaded via the Open Access Hub by the general public were 1.53PB and via the other non-public access hubs were 1.14PB.

Of the downloads by the general public about 75 percent were data that was less than one week old, there were about 6500 registered users accessing Sentinel-2 data of which less than 100 downloaded more than 100 packages.

Now my interpretation of these numbers:

  • The routine very large volume data users (think: Google, Amazon – but also various smaller ones probably) apparently do not get their data via these channels, they must have separate arrangements which are not publicly reported on.
  • Independent data users with a larger volume of data use like myself are extremely rare. Almost all of the use via Open Access Hub is just testing a few recent images and no routine use. Of course this is the first year of operations and people just start getting interested. And naturally the frequent changes in data format and the bad reliability of the systems does not really encourage use and especially smaller data users will likely wait if this stabilizes and use alternative data sources in the meantime.
  • If there is significant large volume access to the Sentinel-2 data from the partner organizations through these channels it must have started relatively late in 2016 since overall numbers likewise suggest mostly limited volume use.

Some additional data

Here some additional illustrations based on the publicly available metadata which are not in the report and which are also not widely discussed otherwise. First the development of published image volume in terms of covered area with Landsat in comparison.

average daily acquisition volumes of open data satellites

Note this is not a hundred percent accurate due to the difficulties of accurately calculating it from the available metadata. But it is pretty close. The volume is always calculated per orbital period, the repeat cycle of the orbit pattern which is 10 days for Sentinel-2 and 16 days for Landsat. The numbers are then normalized to a daily volume for comparability.

What you can see is that Landsat 8 has been recording on a relatively stable level since 2014 between about 20 and 23 million square kilometers per day. There is a seasonal pattern due to the variation in illumination of the earth land masses. Landsat 7 follows a different pattern on a lower overall level with a minimum in the northern hemisphere winter since it does not record the Antarctic. If you look closely you can also see a drop in last winter in the numbers of Landsat 8 which comes from the Antarctic coverage being limited in the 2016/2017 season mostly to coastal areas for some reason. I could not find any documentation of this in the USGS materials and I hope this is a one time incident and will not mark a permanent change in the recording patterns.

Sentinel-2 numbers are meanwhile on the same level as Landsat 8 in terms of overall coverage volume but the numbers are much less stable with many irregularities and gaps. This brings me to the next thing i prepared – which is a visualization of the coverage by orbital period indicating the images available as well as those missing – images which were planned to be recorded according to the published plans but which are not available for download – either because they have not been acquired or because they are not processed.

It should be noted that the way acquisitions are planned differs strongly between Landsat 8 and Sentinel-2. Landsat 8 has a dynamic acquisition system based on predefined scene priorities that leads to an automatic short term selection which scenes are recorded based on a large number of influencing factors and the resulting acquisition plan is usually very close to what is actually recorded and then also available. Sentinel-2 in contrast to that is operated with a fixed acquisition plan set long in advance.

In any case what can be concluded from this is that there are significant derivations of actual operations of Sentinel-2 from the published plans and the recording numbers (in minutes per orbit) listed in the mission status reports are not actual recording numbers but just what has been planned. I am not sure if those gaps which are clearly missing recordings are due to the acquisition plans overbooking the satellite and data transfer systems or if there are outages in components of the data transfer system or operational errors. The missing recordings are too frequent to be purely due to operational contraints like orbital maneuvers, calibrations etc.

In addition gaps in processing with individual granules missing are fairly common and these do not get filled after a few days as you might expect. If these would be filled by later reprocessings of the images is an open question. This seems to be a side effect of the move to single granule packages – i never experienced individual granules missing with the larger packages – paired with a lack of fault tolerance and error checks in the processing system.

Last i also fixed the daily scene numbers page which had not been working correctly for quite some time. These numbers are now also normalized area coverage numbers.

With the whole thing it is of course important to keep in mind that the spatial resolution of Sentinel-2 imagery is higher than that of Landsat so the data volume for the same coverage area is naturally significantly larger.


I kind of fear that with ESA getting all screen time here with critical commentary the USGS might get jealous so here also a few words on Landsat (although i already had a bit of analysis in the previous paragraphs).

Transit to the new ”Collection” distribution form for Landsat data is now complete. As mentioned previously this is a fairly superficial change for most data users. The USGS also continues to use the old scene IDs in their data management system at several places – for example the metadata pages still have the form

The most significant shortcoming from my perspective is however that the bulk metadata they make avilable is apparently incomplete – at least for Landsat 8 there seem to be more than 3000 scenes missing in the Collection data set which are in the old legacy database. This appears to be a problem of the metadata file though – the scenes appear to be available in EarthExplorer.


May 1, 2017
by chris

OSM map update cycles

Recently i made some effort updating the Antarctic coastline in OpenStreetMap regarding the most significant changes due to the movement of the ice. This essentially means bringing the position of the outer edges of the three largest ice shelf plates to the current state. These ice edges move by about 1-2 km per year, i had last updated them about two years ago and in between no one else has significantly modified the data. There is not that much additional value in mapping these in fine detail even if suitable images are available so i used fairly low resolution images as basis. Other parts of the Antarctic coastline have not been updated in recent years so are fairly outdated but probably more than 80 percent of the area change happens in these sections.

Amery Ice Shelf coastline change in OpenStreetMap

For illustration above the history of the coastline at the Amery Ice Shelf edge in OpenStreetMap. This kind of regular change with updates from time to time in the database makes these areas a great test case of evaluating the update cycle of maps. The shown changes happened early 2013, mid 2013, early 2015 and the last one about two weeks ago. Most OSM based maps show changes in the base data pretty fast, sometimes after minutes. But this only applies to normal small geometries and only to the higher zoom levels. Low zoom updates tend to be less frequent and coastline updates likewise. The map on usually updates the coastline data on a daily basis but the low zoom levels up to z12 are only rendered once per month. Many other map services are even slower. Here a Map Compare view of various maps of the area discussed.

Right now the German OSM style on seems to be the only widely used one that is up-to-date with the coastline at the low zoom levels two weeks after the edits have been made. The map on is currently behind in updates due to hardware problems but would normally also show the current data at least on the higher zoom levels. As you can see many of the other map styles do not even show the 2015 update although on the positive side it should be noted that no map style i checked with some importance is using a pre-ADD (before mid-2013) data basis. In the coming weeks and months you will be able to observe if and when the various map providers update their coastline data basis.

If you render maps based on OpenStreetMap data you can find recent coastline data on


April 22, 2017
by chris

Autumn colors in the south

After the recent work on autumn color satellite image mosaics here for comparison also an image of autumn colors on the southern hemisphere.

This shows the Andes Mountains in Patagonia, South America at the border between Chile and Argentina with the Northern Patagonian Ice Field in the lower part of the image on April 16 this year. In general autumn color changes of the vegetation are much less widespread on the southern hemisphere than in the north. In this case the most widespread color change occurs with deciduous Nothofagus forests which grow at higher altitudes just below the tree line in the drier, eastern parts of the mountains. These trees feature a color change to strong red in autumn which can be prominently seen in this image. The western sides of the mountains and the fjords at the coast however are dominated by evergreen trees and do not change color that much. Here a few detail crops.

You can compare the last one also to the second image i showed with a talk announcement recently which was taken almost exactly a month earlier.

This image based on Copernicus Sentinel-2 data can be found in the catalog on


April 18, 2017
by chris

Images for mapping update 11

I just uploaded another update for the OSM images for mapping containing the following new imagery:

First there is a new low tide image of the North Sea Coast of Germany, Denmark and the Netherlands from September last year suited for updating the tidal flats, beaches and shoals in the area. These change quite rapidly so even if you have full coverage in high resolution images not extremely old it is a good idea to consult recent images when you map there to avoid investing in mapping features that might no more exist or look very different now.

Then there is an image of the Pechora Sea Coast in northern Russia – likewise from last September. This allows improving the mapping of the coast and tidal areas and adding missing lakes but also shows quite a few roads not currently mapped in OSM.

I also decided to add recent coverage of a small part of the Antarctic coast, mostly to demonstrate that the LIMA images which are shown by all other image sources there are not really useful to improve of the existing data in the area because they are hopelessly outdated where ice is moving. But even if the images here are recent and from late summer with minimal seasonal ice you should familiarize yourself with the area before remotely mapping based on images alone – it is not always easy to distinguish between permanent and non-permanent ice for example. In case of doubt just concentrate on obvious features, in particular updating the glacier front positions and correcting and detailing ice free rock outcrops.

And finally i also added the recently introduced Svalbard mosaic as an up-to-date and accurate basis of mapping Svalbard. So far glacier mapping on the islands is essentially missing in OpenStreetMap and large parts of the coastline are still extremely crude. This image will give anyone interested plenty of up-to-date information for accurate mapping of the area.


April 10, 2017
by chris

Taming the Chameleon

To illustrate the difficulties of assembling cloud free satellite image mosaics i tend to use the analogy of trying to photograph an elephant running through a dense forest at a distance and changing its color like a chameleon while doing that.

This metaphor is meant to illustrate the two primary difficulties of image assembly – the dense forest stands for the clouds while the chameleon nature illustrates the changing appearance of the earth surface over time. Now most efforts in satellite image assembly and most research and discussion going into that tends to focus on the clouds – they are an universal and unavoidable problem you have to deal with when working in this field. The changing appearance of the earth surface on the other hand is not usually given much consideration. A quality satellite image mosaic needs to deal with that as well but usually this tends to be treated as a side effect within the bigger cloud related problems and people just try to handle this but don’t care much about how they handle this.

There are a number of strategies that are commonly used when specifically dealing with this matter. Most of them tend to select a certain state within the spectrum of appearances and target what i like to call a stable extreme in the earth surface appearance. Targeting the vegetation maximum is an obvious and widely used approach here. Stable extreme means that that (a) the appearance you target is an extreme state with regards to some kind of definable property and (b) that this state is stable to some extent, i.e. nature has some persistence in staying this way. The vegetation maximum in most parts of the earth qualifies as such a state. Another frequently targeted stable extreme, especially in regions with a distinct rain season, is the dry season because clouds are often less of a problem then. A less stable extreme would – at least in temperate climate – be the snow maximum. Snow cover in low land areas in central Europe tends to be fairly volatile.

The most difficult state to target are the unstable transient states. One of the most interesting examples for this are autumn colors. The difficulty of assembling image mosaics showing this kind of situation is that

  • you essentially target a single point in a fairly rapid development.
  • what you target is not easily quantifiable because the change in colors of the vegetation during autumn is a multidimensional effect, different species of plant change their colors at different times across different color ranges and depend on the local setting in this regard as well.
  • how this happens and when exactly this happens often varies strongly from one year to the other and the variance in time is often not small compared to the duration of the whole process. In other words: when the leaves start to change their color in one year they might already be falling in another year.

Because of these difficulties image mosaics of larger areas showing autumn colors are very rare. Trying to find out what can be done in that field i produced the following image of northeastern Russia showing the lower Lena River and the Verkhoyansk Range with autumn colors.

This by the way is the region on Earth with the most extreme temperature differences between winter and summer with a total range of naturally occurring temperatures of more than 100 degrees Celsius in some parts. It also is a region with extensive and quite diverse color changes of the vegetation in autumn – which is why i choose this area here.

Compared to the usual image mosaics i produce creating such an image is not a well established and routine process yet. As you can see i did not create full coverage of the rectangular area and consistency as well as the level of cloud free-ness of the image are not quite on my usual levels. Due to the difficulties i described above many of the techniques i normally use for image assembly that help ensure the quality level do not work that well when i target an unstable transient state. None the less i think the results are pretty decent considering the difficulties. Here a few magnified crops:

Data basis is mostly Copernicus Sentinel-2 imagery supplemented with about 20-30 percent Landsat data.


April 8, 2017
by chris

Spring again

Spring is coming again around here while winter is approaching on the southern hemsiphere. And as last year a few images from the Antarctic before the night and from the Arctic from the beginning of spring:

The first one shows the western part of James Ross Island on the east side of the Antarctic Peninsula with the ice free Prince Gustav Channel between the island and the mainland.

The second image features Mount Takahe in West Antarctica, one of the largest of the extinct volcanoes of Marie Byrd Land peeking through the clouds in the last rays of the autumn sun.

And then from the northern Hemisphere i have a kind of Yin and Yang image of northwestern Svalbard

In this area ocean currents are transporting warmer water to the north so the area at the western coast is mostly ice free. Winds from the north lead to clouds forming over the warm water but air temperatures are fairly high already so there is not much formation of new sea ice. The north of Svalbard however is still tightly enclosed in ice.

And finally here an image from the southern Kamchatka peninsula of the eruption of Kambalny:

For comparison see also my former images of Kamchatka volcanoes.

First and third image are made from Copernicus Sentinel-2 data, second and fourth from Landsat 8.