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Archive for July, 2016


Sun-synchronous orbits with Google Earth

by Timothy Whitehead on Jul.26, 2016, under 3D Models, Argentina, Australia, Brazil, California, Denmark, England, Germany, Google Earth News, Google Earth Tips, Google Sky, Google maps, Hawaii, Indonesia, Ireland, Italy, Japan, Kenya, Mexico, Natural Landmarks, Netherlands, Sightseeing, Street Views, USA

After our recent posts on rainbow plane offsets and the list of imaging satellites we thought it would be interesting to see what a sun-synchronous orbit actually looks like in Google Earth. We have previously written a post about sun-synchronous orbits and why most imaging satellites use them, but we only showed an approximate single orbit of the earth assuming the earth was not rotating. In reality, the earth rotates on its axis as well as going around the sun. A sun synchronous orbit is designed to drift slowly so as to keep in sync with the earth’s orbit around the sun. All this starts to get complicated when you want to plot an actual orbit. But we believe we have succeeded.


What the orbit of WorldView-3 looks like.

We used the equations from Wikipedia, which allow us to use the altitude and period of a given satellite to work out its orbit. For simplicity we start at latitude zero and longitude zero. We show the orbit for a period of approximately 24 hours. Some satellites, such as Landsat 8, have their orbits arranged so that they repeat the same path on a regular basis. Others do not.

All sun-synchronous orbits look very similar, with the differences in altitude being hardly noticeable. The most obvious difference is in the period, which affects the spacing of the orbits.


Red: Landsat 8 orbit. White: WorldView-3 orbit.

WorldView-3 has an altitude of 617 km and a period of 97 minutes.
Landsat 8 has an altitude of 703 km and a period of 98.8 minutes.

The diagonals from the north east to south west ( / ) are on the daylight side of the earth and the diagonals from south east to north west ( \ ) are on the night side.

To see the above orbits in Google Earth download this KML file. Alternatively, you can create your own orbit by entering the altitude and period below:


Sun-synchronous orbit creator.

Altitude: km

Period: minutes

The post Sun-synchronous orbits with Google Earth appeared first on Google Earth Blog.

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Discover the action around you with the updated Google Maps

by Maps on Jul.26, 2016, under 3D Models, Argentina, Australia, Brazil, California, Denmark, England, Germany, Google Earth News, Google Earth Tips, Google Sky, Google maps, Hawaii, Indonesia, Ireland, Italy, Japan, Kenya, Mexico, Natural Landmarks, Netherlands, Sightseeing, Street Views, USA

The real world is changing every second and Google Maps is changing with it. Most often these changes happen behind the scenes in the form of road closures and new businesses. But today we’re making a few visual changes and additions to Google Maps on desktop, Android and iOS to help you better explore the world around you.

SS1.png

A cleaner look 

The world is full of information, which means highlighting necessary info on the map without overcrowding it is a balancing act. So as part of this update, we’ve removed elements that aren’t absolutely required (like road outlines). The result is a cleaner look that makes it easier to see helpful and actionable information like traffic and transit. And we’ve improved the typography of street names, points of interest, transit stations, and more to make them more distinguishable from other things on the map, helping you navigate the world with fewer distractions.

SS2.png

Areas of interest

The cleaner canvas also lets us show local information in entirely new ways. As you explore the new map, you’ll notice areas shaded in orange representing “areas of interest”—places where there’s a lot of activities and things to do. To find an “area of interest” just open Google Maps and look around you. When you’ve found an orange-shaded area, zoom in to see more details about each venue and tap one for more info. Whether you’re looking for a hotel in a hot spot or just trying to determine which way to go after exiting the subway in a new place, “areas of interest” will help you find what you’re looking for with just a couple swipes and a zoom.

We determine “areas of interest” with an algorithmic process that allows us to highlight the areas with the highest concentration of restaurants, bars and shops. In high-density areas like NYC, we use a human touch to make sure we’re showing the most active areas.
Google Maps_Color.jpg

A more subtle and balanced color scheme

The new Maps has a subtle color scheme to help you easily differentiate between man-made or natural features, and quickly identify places like hospitals, schools or highways. In case you’re curious, here’s a key showing what each color on the map represents.


Google Maps already provides you everything you need to get around the world in one place — including business information, ratings and reviews, and more than 100+ million distinct places. And with these updates, it's now even easier to navigate to where you want to go.

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Imaging satellite list

by Timothy Whitehead on Jul.25, 2016, under 3D Models, Argentina, Australia, Brazil, California, Denmark, England, Germany, Google Earth News, Google Earth Tips, Google Sky, Google maps, Hawaii, Indonesia, Ireland, Italy, Japan, Kenya, Mexico, Natural Landmarks, Netherlands, Sightseeing, Street Views, USA

Last week we mentioned that it would be useful to have a table of imaging satellites with some of the specifications of most interest to users of Google Earth. We couldn’t find any site with a single list in table form, so we decided to create our own.

The information comes from three main sources: Wikipedia, DigitalGlobe’s website and Sat Imaging Corporation’s website. We make no guarantees about the accuracy of this data.

Satellite Company Panchromatic
Resolution
Multispectral
Resolution
Launch
Date
Decommissioned Altitude
GeoEye-1 DigitalGlobe 46cm 1.64m 2008-09-06 681 / 770
WorldView-1 DigitalGlobe 50cm 2007-09-18 496
WorldView-2 DigitalGlobe 46cm 1.85m 2009-10-08 770
WorldView-3 DigitalGlobe 31cm 1.24m 2014-08-13 614
WorldView-4 DigitalGlobe 31cm 1.24m 2016-09 617
IKONOS DigitalGlobe 80cm 3.2m 1999-09-24 2015-04 681
QuickBird DigitalGlobe 55cm 2.16m 2001-10-18 2014-12-17 400
Pleiades-1A CNES 50cm 2m 2011-12-17 695
Pleiades-1B CNES 50cm 2m 2012-12-02 695
KOMPSAT-3A KARI 55cm 2.2m 2015-03-25 543
KOMPSAT-3 KARI 70cm 2.8m 2012-05-17 533
Gaofen-2 CAST 80cm 3.2m 2014-08-19 631
TripleSat (3 satellites) SSTL 80cm 3.2m 2015-07-10
SkySat-1 Terra Bella 90cm 2.0m 2013-11-21 450
SkySat-2 Terra Bella 90cm 2.0m 2014-07-08 450
SPOT-1 CNES 10m 20m 1986-02-22 1990-12-31 832
SPOT-2 CNES 10m 20m 1990-01-22 2009-07-01 832
SPOT-3 CNES 10m 20m 1993-09-26 1997-11-14 832
SPOT-4 CNES 10m 20m 1998-03-24 2013-07-01 830
SPOT-5 CNES 2.5m 10m 2002-05-04 2015-03-31 832
SPOT-6 CNES 1.5m 6.0m 2012-09-09 832
SPOT-7 CNES 1.5m 6.0m 2014-06-30 832
Landsat 1 USGS 1972-07-23 1978-01-06
Landsat 2 USGS 1975-01-22 1982-02-25
Landsat 3 USGS 1978-03-05 1983-03-31
Landsat 4 USGS 1982-07-16 1993-12-14
Landsat 5 USGS 1984-03-01 2013-06-05
Landsat 6 USGS 1993-10-05 1993-10-05
Landsat 7 USGS 15m 30m 1999-04-15 702
Landsat 8 USGS 15m 30m 2013-02-11 702
RapidEye (5 satellites) Planet Labs 5m 630
Doves (many satellites) Planet Labs approx 400
ASTER Japan METI 15m 15m 1999-12-18 705
Sentinel 2A ESA 10m 10m 2016-06-23 768
Sentinel 2B ESA 10m 10m 2016 768

Red: No longer active.
Blue: Not yet launched.

Notes on the above data:

Most of the satellites capture imagery in high resolution in grayscale (panchromatic) and lower resolution in each of the three primary colours and the images are combined to produce a high resolution colour image.

Orbits are typically not perfectly round and the earth itself is not perfectly spherical, so altitudes listed are averages. GeoEye-1 apparently had its orbit changed to a higher orbit in 2013.

Details not listed.

As far as we know, all the satellites can capture imagery in various infra-red bands that are typically lower resolution than the colour bands. There is significant variation between satellites as to which infrared bands they can capture.

There are differences in how the satellites capture imagery, such as whether they use a single sensor, a row of sensors or a sensor array.

Most satellites capture imagery in strips, which have a specific ‘swath width’ for a given satellite.

The best quality image is usually captured straight down from the satellite (nadir) but most satellites are capable of rotating so as to be able to capture imagery to either side of their orbit. How fast they can rotate varies by satellite.

Most satellites have a limit on how much imagery they can capture at a time. This is due to data storage and communication limitations.

Satellites vary as to how accurately they can report exactly where they were and what direction they were pointing when they took the photo.

The mass of the satellites varies considerably. In some cases the ‘satellite’ listed is actually an instrument on a larger satellite (ASTER for example).

Each satellite crosses the equator at a specific time.

Satellite design.

The various different requirements for satellites are
1. Resolution.
2. Acquisition speed. (How quickly a satellite can image a given area when required such as after a natural disaster).
3. Repeat acquisition. (The frequency with which it can image a given location).
4. Area covered. (Higher resolution typically means smaller coverage footprints, so lower resolution can be an advantage at times).
5. Consistency. (This is important for long term environmental monitoring).
6. Global coverage. (The ability to image the whole globe on a regular basis).
7. Price.

Commercial satellites tend to be more interested in 1. to 4., whereas government programs are more interested in 5. and 6. DigitalGlobe’s satellites are mostly expensive high resolution satellites whereas Planet Labs has sacrificed resolution in order to be able to make large numbers of cheap satellites, which allows them to cover more area quicker and repeatedly. The Landsat, SPOT and Sentinel satellites are designed for long term, regular, consistent coverage for environmental monitoring. The Sentinel satellites are essentially a continuation of the SPOT satellites with their orbits chosen to match.

Where does Google Earth imagery come from?

The main suppliers of satellite imagery to Google Earth at the current point in time are Digital Globe and CNES/Astrium, so almost all satellite imagery comes from their satellites. The low resolution global mosaic is Landsat 7 / 8 imagery, which is also used to fill in the gaps where there is no higher resolution imagery. Imagery from the SPOT satellites is also used for filling in gaps and can be recognised from the copyright notice “CNES/Spot Image”.

South America has a lot of imagery dated 1970. We are not sure which satellite it comes from, but we guess it may be from one of the early Landsat satellites and the date is only approximate.

Landsat, ASTER and Sentinel imagery is free to the public and can be downloaded and viewed in Google Earth.

Google Earth includes aerial imagery (captured from aircraft not satellites) from a number of sources.


Landsat 8. Image from Wikimedia commons.

The post Imaging satellite list appeared first on Google Earth Blog.

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Rainbow plane offsets

by Timothy Whitehead on Jul.22, 2016, under 3D Models, Argentina, Australia, Brazil, California, Denmark, England, Germany, Google Earth News, Google Earth Tips, Google Sky, Google maps, Hawaii, Indonesia, Ireland, Italy, Japan, Kenya, Mexico, Natural Landmarks, Netherlands, Sightseeing, Street Views, USA

When we had a look at the ‘rainbow effect’ of planes in flight we mentioned that the offsets of the different images were a result of both the plane’s movement and the movement of the satellite taking the photos. We thought it would be worth having a look at that in more detail.

We know that most imaging satellites follow a sun-synchronous orbit. It is fairly easy to approximate that orbit relative to a plane in flight by drawing a line from the location of the plane to form a tangent on the right hand side of the North pole with the circle of latitude 82° N. For increased accuracy we will try to follow the tail of the plane.

In the image above, the satellite was travelling north to south in the direction of the red line although not necessarily directly overhead.

The satellite captured 4 photos: one high resolution grey scale image which we see at (1), and then after a short delay, blue, red and green images in quick succession which we see overlapping at (3).

If the plane was completely stationary, we would have expected the rainbow images to appear at (2) due to parallax and the motion of the satellite. If the satellite was stationary and the plane moving, then we would expect to see them at (4).

Using Google Earth’s measuring tools the distance from (1) to (2) is about 40 m. The distance from (2) to (3) is about 70 m.

This is enough information such that if we knew which satellite took the image, and how long it pauses between the monochrome and colour images, we could work out the approximate altitude and velocity of the plane. Alternatively, if we knew the altitude and velocity of the plane, we could, work out which satellites could have taken the image.

One useful fact is that all the possible satellites have very similar velocities which we can approximate at 7.5 km/s.

Wikipedia suggests that a typical plane cruises at 878–926 km/h at an altitude of around 12km and that a much higher altitude is not possible.

So, if we start by guessing the planes altitude at 12 km, its velocity at 900 km/h then we get the satellite altitude at about 550 km. Now we look through this list to try and find a matching satellite from DigitalGlobe – keeping in mind the image was captured in 2012 so satellites launched after that date must be excluded. Our best guess is that the image was probably captured by a satellite in a slightly lower orbit such as World-View-1 at an altitude of 496 Km and to make our calculations match up, the plane was probably at a slightly lower altitude of 11 km above sea level. (The ground at this location is 1.3 km above sea level.)

The time between the the monochrome and colour images is about 0.27 seconds.

If any of our readers knows of a reference with satellite orbit data in tabular form for a wide variety of imaging satellites please let us know in the comments.

We also came across this image:

The images of the plane appear to have been sheared and offset slightly in a horizontal direction, but the image of the ground does not seem to have been affected. We don’t know how this happened. Do our readers have any suggestions? One of the images in our earlier post on the ‘rainbow effect’ also includes an plane which seems to have a double tail which may be a related effect.

To find the above planes in Google Earth download this KML file.

We found the above planes using the Google Earth Community aircraft in flight list.

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Watching sand dunes move with Google Earth

by Timothy Whitehead on Jul.21, 2016, under 3D Models, Argentina, Australia, Brazil, California, Denmark, England, Germany, Google Earth News, Google Earth Tips, Google Sky, Google maps, Hawaii, Indonesia, Ireland, Italy, Japan, Kenya, Mexico, Natural Landmarks, Netherlands, Sightseeing, Street Views, USA

We recently got the idea of trying to see if we could see sand dunes moving using Google Earth historical imagery. The biggest problem is that for the best results we needed frequent satellite imagery over as long a time period as possible, but most deserts have very little satellite imagery. Google Earth imagery tends to focus on populated areas, so we looked for towns that have sand dunes on the outskirts. We started with Nouakchott, the capital of Mauritania. We had come across this article, which suggests that Nouakchott may be slowly obliterated by creeping sand dunes. But what we found, in the places we looked, was that the opposite was the case – the city was slowly taking over the sand dunes.

We recommend watching the videos full screen.

We tried a little to the east of Nouakchott and, since the image jumps around quite a lot due to poor image alignment in Google Earth, we cannot definitively say which way the dunes are moving, if at all. They do change shape quite considerably in the first few frames:

Next we tried Namibia and chose some sand dunes just east of Lüderitz. This time there is no doubt that the dunes are moving northwards.

We also looked at sand dunes east of Oranjemund, also in Namibia. There isn’t much imagery and it gets updated in sections, but the overall movement is still clear. If it wasn’t for the town of Oranjemund staying in one place you might think the sand was stationary and the imagery was just being moved.

Still in Namibia, we go to Walvis Bay and here we can see dunes slowly moving north-west.

You can find some more videos we made of dunes near Dubai, UAE, that were less successful here and here.

You can download the Google Earth tours used to create the above videos.

To create the above videos we used our advanced Google Earth historical imagery tour maker and Google Earth Pro’s built-in Movie Maker.

We presume that how fast sand dunes move depends on many factors, including wind speeds, dune size and grain size.

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