An Aedes aegypti mosquito in the process of acquiring a blood meal. Image Credit: CDC/James Gathany.
Though mosquitoes are small, they are also deadly. Mosquito bites result in the deaths of more than 725,000 people each year—more than any other animal. That compares to about 50,000 deaths from snakes, 25,000 from dogs, 20,000 from tsetse flies, 1,000 from crocodiles, and 500 from hippos.
More than 50 percent of the world’s population lives in areas where mosquito-borne diseases are common, according to the World Health Organization. Malaria is the deadliest disease spread by mosquito, but the bugs also serve as vectors for Chikungunya, Zika, Dengue, West Nile Virus, and Yellow Fever.
NASA and the Global Learning and Observation to Benefit the Environment (GLOBE) Program have a new way of fighting back. The GLOBE Observer Mosquito Habitat Mapper is an app that makes it possible for citizen scientists to collect data on mosquito range and habitat and then feed that information to public health and science institutions trying to combat mosquito-borne illnesses. The app also provides tips on fighting the spread of disease by disrupting mosquito habitats. Specifically, it will help you find potential breeding sites, identify and count larvae, take photos, and clean away pools of standing water where mosquitoes reproduce.
Each month, we try to stump you with a new EO Puzzler. If you missed the latest one, or if you just want more challenges, we have a new one from our colleagues at the Jet Propulsion Laboratory.
The science and outreach team from the Multi-angle Imaging SpectroRadiometer (MISR) is inviting you to become a geographical detective and solve their latest mystery quiz. But this is more than just a “where is this place?” kind of quiz. You need to know some history — natural and human — to correctly answer all of the questions. Check out the quiz — based on the image below — at http://climate.nasa.gov/quizzes/misr_quiz_29.
Quiz answers will be accepted on the MISR page through 4:00 p.m. Pacific Daylight Time on June 28.
A June snowstorm just topped off the already thick layer of white stuff atop the Sierra Nevadas. California’s snow water equivalent rose to a heaping 170 percent of normal. But not so long ago, the state was in the midst of a deep drought; its mountains were bare and brown, and water levels plummeted in reservoirs.
Throughout, satellites were watching. Check out the California drought and its aftermath in a video from NASA Earth Observatory:
The DC-8’s four engines burned either JP-8 jet fuel or a 50-50 blend of JP-8 and renewable alternative fuel made from camelina plant oil.Credits: NASA/SSAI Edward Winstead
Taking Some of the Search Out of ‘Search and Rescue’
NASA engineers are developing prototypes of second-generation locator beacons. The little devices have been used by pilots, mariners, and hikers since the 1970s to relay distress signals in times of emergency. Until now, those beacons have had a 2-kilometer (1 mile) radius. The new beacons will pinpoint location within a 140-meter radius—that’s more than 10 times more precise.
Small Satellites Will Track Big Storms
Atlantic hurricane season has just begun—and the CYGNSS mission has it covered. The constellation of eight mini-satellites, launched into low-Earth orbit in December 2016, measures surface winds using GPS signals reflected from the ocean surface. The data will help track storms as they grow, giving forecasters a better sense of storm intensity.
Long a source of wonder (and occasional conspiracy theories), the white plumes that trail behind aircraft are a focus of study for NASA scientists testing the effects of biofuels. A new study shows that alternative fuels made from plant oils can cut down on particle emissions in jet exhaust by as much as 50 to 70 percent. From the news release:
Contrails are produced by hot aircraft engine exhaust mixing with the cold air that is typical at cruise altitudes several miles above Earth’s surface, and are composed primarily of water in the form of ice crystals…Researchers are most interested in persistent contrails because they create long-lasting, and sometimes extensive, clouds that would not normally form in the atmosphere, and are believed to be a factor in influencing Earth’s environment.
A Different Kind of Scat
Scientists have a new tool for measuring both ocean winds and water currents. Tested on airborne missions this spring, DopplerScatt is a cousin of QuickSCAT and RapidScat, which used a scatterometer to measure the “roughness” of the ocean surface and determine the direction and intensity of wind. DopplerScat adds a doppler radar to the package, allowing scientists to measure the speed and direction of the moving water. The instrument is another potential tool to measure currents along shipping routes or predict the direction that oils and other slicks might move.
Landsat imagery has become a staple of the Earth observation community. By acquiring as many as 1,400 new images a day, Landsat 7 and 8 have given us an unprecedented look at our planet. But it’s not just the quantity of the imagery that is so exciting. Within each scene, a trove of data awaits those eager to explore the planet, pixel-by-pixel, from the vantage point of space.
A true-color Landsat 8 image of Mount Jefferson, Oregon, at a resolution of 15 meters per pixel.
It’s almost like having your own camera in orbit, 700 kilometers (430 miles) above the Earth. But Landsat is quite different from the camera in your mobile phone or dSLR. The sensors onboard Landsat capture imagery in several different “bands.” Each band represents a unique chunk of the electromagnetic spectrum, defined by wavelength. This allows for multispectral imagery that goes beyond the visible light our eyes can see. It also enables us to combine these bands in order to see the unseen, make important calculations, or assemble just the data from visible light to produce a natural-color image.
But there’s another great feature of the different bands in Landsat 7 and 8 data: one of them can be used to effectively double the resolution of your image. Yes, you read that correctly!
Panchromatic sharpening (pan-sharpening) is a technique that combines the high-resolution detail from a panchromatic band with the lower-resolution color information of other bands (usually only the visible bands).
The spectral bands of Landsat 7 and 8 have a resolution of 30 meters per pixel, while the panchromatic band has a resolution of 15 meters per pixel. By itself, the panchromatic band would look somewhat uninteresting; like other individual bands, it appears black and white. But unlike other bands, it captures a much wider range of light, allowing it to be significantly sharper. As a result, panchromatic imagery is twice as detailed as the individual spectral bands.
An image that is twice as detailed is a lot more valuable and can reveal features or phenomena that would otherwise be difficult to see:
Left: a natural-color Landsat 8 image of Mt. Jefferson, by Robert Simmon. Right: A pan-sharpened version of the same data, by Joshua Stevens.
Let’s make a pan-sharpened image with Landsat 8
The first step in creating a pan-sharpened image is to acquire the data. If you need help, we have an Earth Explorer tutorial that will guide you through the process. You may also use this sample data of Mt. Jefferson (960 MB zip archive).
The following steps assume you have acquired and unzipped the necessary data. You will also need an image editor capable of working with 16-bit data, the ability to combine and edit color channels, and support for the Lab color space. Adobe Photoshop and GIMP, a free and open source editor, are both great tools that meet this criteria.
I use Photoshop, and the examples below will reflect the Photoshop tools and interface.
First things first: create a natural color RGB composite
1. At this point, you should have a single, uncorrected RGB composite. Mine looks like this:
This unsharpened image will have a height and width in the ballpark of 7000 to 8000 pixels.
2. To prepare the image to be twice as sharp, we first need to double its size. In the Photoshop menu, go to Image and then Image Size. In the dialog, set the height and width to 200% and ensure that resampling is set to bilinear (this will prevent any unwanted, extra sharpening from occurring).
Your image will now be twice as large (15000-16000 pixels wide and tall). But you will notice it is not very sharp; in fact, it will look quite fuzzy. Let’s change that.
3. Recall that the panchromatic band has a resolution of 15 m in black and white. What if we could use the color from this fuzzy image, but add detail from the lightness and darkness of the panchromatic data? That is exactly what we are going to do.
Currently, our image is composed of three channels: red, green, and blue. We want to change that so one of the channels reflects lightness.
That is where the Lab color space comes in. Lab color space depicts color in terms of lightness and two other channels known as a and b (which control the red-green and yellow-blue of an image, respectively).
In the Photoshop menu, go to Image –> Mode -> Lab Color. You won’t see the image change, but if you look at your image channels, you will see that you now have Lightness, a, and b.
4. We need to replace the image in the Lightness channel with the panchromatic data. Open your panchromatic band (the filename should end with _B8.tif in your data).
5. With the panchromatic image open, select everything (Photoshop menu Select -> All). Then copy the image (cmd-c on Mac, ctrl-c on Windows).
Go back to your RGB composite image. In the Channels window click on Lightness to isolate that channel. Your image should change to black and white to indicate that only the selected channel is active:
Paste the panchromatic data (cmd-v on Mac, ctrl-v on Windows). You should see the black and white image become significantly sharper as the higher resolution imagery replaces the previous data:
6. Convert the image back to RGB space. In Photoshop go to Image -> Mode -> RGB Color.
Color has returned and you now have a pan-sharpened, RGB composite!
You can now color correct the image and apply any additional sharpening desired.
Pan-sharpening is a common technique, and there’s more than one way to do it. The method shown here is called HSV Sharpening. It is the simplest of the techniques and relies only on swapping the value (the ‘v’ of HSV) data with a higher resolution counterpart. The hue and saturation (the ‘h’ and ‘s’) provide the color from the lower resolution imagery.
Another more sophisticated method known as Gram-Schmidt pan-sharpening is frequently used in GIS software. Gram-Schmidt generates a closer representation of the panchromatic data by simulating it from the other spectral bands.
On Landsat 8, the panchromatic band doesn’t go as far into the blue wavelengths as the blue band, and it goes slightly beyond red. If you followed this tutorial and noticed a slight change in tone when you pasted in the panchromatic data, this is why. Panchromatic data from Landsat 7 is even more extreme and includes more near infrared light, so it will change the tone of the image even more.
A third technique, known as a Brovey transform, or color normalization, attempts to balance the color information with the panchromatic data. It can be useful for pan-sharpening false color imagery, the success of which depends on the wavelengths captured by the panchromatic data.
Although this tutorial covered the process on a single image manually, much of this can be automated from the command line with relative ease. A few tools I recommend:
Every month on Earth Matters, we offer a puzzling satellite image. We usually ask you to identify the location of a scene, but that’s not enough this time. For the June 2017 puzzler (above) your challenge is to tell us what the image shows and what makes it interesting.
How to answer. Your can use a few words or several paragraphs. (If possible, try to keep it shorter than 300 words). Tell us the location, but realize we want to hear more than just the location. What features in the image say something about the geological, meteorological, or historical processes that shaped the landscape? What does it teach us about physics or chemistry or biology? How do you think scientists or others might use imagery like this to solve problems and make life better here on Earth? And what do you find notable about this image from a purely visual perspective?
The prize. We can’t offer prize money or a trip to Mars, but we can promise you credit and glory. Well, maybe just credit. Roughly five days after a puzzler image appears on this blog, we will post an annotated and captioned version as our Image of the Day. After we post the answer, we will also update this post and recognize readers who offered the best responses. Please include your preferred name or alias with your comment. If you work for or attend an institution that you want us to recognize, please mention that as well.
Recent winners. If you’ve won the puzzler in the past few months, or if you work in geospatial imaging, please sit on your hands for at least a day to give others a chance to play.
The Gulf of Mexico, like any sea, is rich in dissolved salts. Unlike most seas, the Gulf also sits atop a big mound of salt. Left behind by an ancient ocean, salt deposits lie beneath the Gulf seafloor and get pressed and squeezed and bulged by the heavy sediments laying on top of them. The result is pock-marked, almost lunar-looking seafloor. The many mounds and depressions came into clearer relief this spring with the release of a new seafloor bathymetry map compiled from oil and gas industry surveys and assembled by the U.S. Bureau of Ocean Energy Management.
3D Water Babies
NASA’s Scientific Visualization Studio took a look back at conditions in the Pacific Ocean in 2015-16, which included the arrival and departure of both El Nino and La Nina. The 3D visualizations were derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset, a global climate modeling effort that is built from remote sensing data.
In other Nino news, a research team led by NASA Langley scientists found that the strong 2015-2016 El Niño lofted abnormal amounts of cloud ice and water vapor unusually high into the atmosphere, creating conditions similar to what could happen on a larger scale in a warming world.
Not the Kind of Brightening You Want to See
For past few years, warm ocean temperatures in the western Pacific Ocean have wrecked havoc on the Great Barrier Reef. Extreme water temperatures can disrupt the symbiotic partnership between corals and the algae that live inside their tissues. This leads the colorful algae to wash out of the coral, leaving them bright white in what scientists refer to as “bleaching” events. The health of coral reefs is usually monitored by airborne and diver-based surveys, but the European Space Agency recently reported that scientists have been able to use Sentinel 2 data to identify a bleaching event on the Great Barrier Reef. Such satellite monitoring could prove especially useful for monitoring reefs that are more remote and not as well studied as those around Australia.
Eyeing the Fuel for Hurricane Season
On June 1, the beginning of Atlantic Hurricane Season, the National Oceanic and Atmospheric Administration released a map of sea surface temperatures in the Caribbean, the Gulf of Mexico, and the tropical North Atlantic Ocean. The darkest orange areas indicate water temperatures of 26.5°C (80°F) and higher — the temperatures required for the formation and growth of hurricanes. Forecasters are expecting a hurricane season that is a bit more active than average.
(Finger)Prints of Tides
In a new comprehensive analysis published in Geophysical Research Letters, a French-led research team found that global mean sea level is rising 25 percent faster now than it did during the late 20th century. The increase is mostly due to increased melting of the Greenland Ice Sheet. A big part of the study was a reanalysis and recalibration of data acquired by satellites over the past 25 years, which are now better correlated to surface-based measurements. The study found that mean sea level has been increasing by 3 millimeters (0.1 inches) per year. The American Geophysical Union published a popular summary of the study.
It takes a certain amount of devotion to reach Miyar Glacier. The glacier sits high in the Indian Himalayas, well away from towns and roads, but it rewards explorers with stunning scenery and mountain peaks that rise above 6,000 meters (20,000 feet). Many of the peaks have little or no record of previous ascents. Satellites, however, can explore with considerably greater ease.
The Operational Land Imager (OLI) on Landsat 8 acquired this image on October 19, 2016. Summer warmth had melted off snow from the previous winter, leaving only the permanent snow and ice cover. Notice the debris field spread across the width of the glacier. The landslide that left it predates this image by some time; we know this because the debris has been carried downstream by the flow of the ice.
A little exploring with the Google Earth Engine timelapse tool shows Landsat 8’s high dynamic range — that is, its ability to discern both dim and bright features. In images prior to 2013, much of the glacier is featureless and white because it was too bright for the older Thematic Mapper (Landsat 5) and Enhanced Thematic Mapper Plus (Landsat 7) instruments to make out details. Images from 2013 onwards, which use the newer OLI data, show more detail. Still, it is fairly clear that there was no landslide feature as recently as 2007, and the slide definitely had taken place by 2010. Indian researchers used other satellite resources to pin the landslide date down to some time in 2009.
The Miyar Glacier has a relatively smooth surface in this image, with long linear streaks through the center of the glacier. These are medial moraines, features that form when two or more glaciers merge. The confluence of the tributary and the glacier shows how new material gets carried in to create medial moraines.
The tributary merging from the east (in the image above) shows choppy features from the confluence all the way upstream. This very rough surface is an icefall, a feature somewhat akin to rapids or a waterfall in a river. The glacier at the bend is roughly 700 meters (2,100 feet) higher than at the Miyar confluence (approximately 5,200 meters and 4,500 meters above sea level respectively). The ice is flowing over a rough and steep rock surface, causing matching rippling in the ice surface.
The wider image shows the terminus of the Miyar Glacier as well as a number of other tributary glaciers. The names shown here are based on the American Alpine Journal (2009), which notes that many of these glaciers have different names in trekking journals and maps.
Tropical forests, such as those in Gabon, Africa, are an important reservoir of carbon. (Photography courtesy of Sassan Saatchi, NASA/JPL-Caltech.)
Old-growth forests are vital because they capture large amounts of carbon and provide homes to hundreds of species. In the Eastern United States, trees in these minimally disturbed ecosystems tend to be more than 120 years old.
Can satellites help pinpoint this “old-growth” and quantify its value? That was the question Joan Maloof posed to a group of researchers during a talk at NASA Goddard Space Flight Center in May 2017. As head of the Old-Growth Forest Network and a professor at Salisbury University, Maloof aims to identify stands of old-growth forests for conservation. A large part of her job involves explaining why these areas are important—something satellite data can help show.
As it turns out, satellites have already told us much about trees. A 2012 story from NASA Earth Observatory described some of the remote sensing methods researchers use:
Scientists have used a variety of methods to survey the world’s forests and their biomass. […] With satellites, they have collected regional and global measurements of the “greenness” of the land surface and assessed the presence or absence of vegetation, while looking for signals to distinguish trees from shrubs from ground cover.
In January 2017, a paper in Science Advances tracked intact forest landscapes between 2000 and 2013. (Intact forest landscapes were defined as areas larger than 500 square kilometers with no signs of human activity in Landsat imagery). This new research underscores the importance of such landscapes. The study’s authors identified several key findings:
Dividing up forest landscapes with roads and development can hinder their ability to store carbon
Forest wildlands (forests least affected by human activity) have the highest conservation value
Large forest wildlands store more carbon than small forest wildlands; they are at risk of deforestation
The global extent of intact forests declined by 7 percent 2000 and 2013.