Jul 8, 2025
Twister (1996) + Graphics
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Twister (1996) + Graphics
Time-lapse animations that once took days to create are now easy to build with publicly available satellite images and free online tools.
The increasing availability of open-access data from government satellites such as Landsat and Sentinel, and free cloud-computing resources such as Amazon Web Services, Google Earth Engine and Microsoft Planetary Computer, have made it possible for just about anyone to gain insight into environmental changes underway.
I work with geospatial big data as a professor. Here’s a quick tour of where you can find satellite images, plus some free, fairly simple tools that anyone can use to create time-lapse animations from satellite images.
More than Just Dust in the Wind
From space, we can see a swirling brown mass making its way across the Atlantic – dust from the Sahara Desert – the largest hot desert in the world. It’s a normal phenomenon. Every year, winds carry millions of tons of dust from North Africa, usually during spring and summer in the Northern Hemisphere.
June 2020 has seen a massive plume of dust crossing the ocean. It’s so large it’s visible from one million miles away in space.
Dust clouds this large can affect air quality in regions where the dust arrives. The particles can also scatter the Sun’s light, making sunrises and sunsets more vibrant.
Dust particles in the air are also known as aerosols. We can measure aerosols, including dust, sea salt and smoke, from satellites and also use computer models to study how they move with the wind.
Following the transport of dust from space shows us how one of the driest places on Earth plays a role in fertilizing the Amazon rainforest. There are minerals in Saharan dust, like phosphorous, that exist in commercial fertilizers, helping seed the rainforest.
Make sure to follow us on Tumblr for your regular dose of space: http://nasa.tumblr.com
From NASA Earth Observatory Image of the Day; September 26, 2018:
Volcanic Hell Makes for Bathing Heaven
The last big eruption at Hokkaido’s Kuttara volcano happened roughly 40,000 years ago, about the time Neanderthals were going extinct and a bit before people had begun to domesticate dogs.
Earlier eruptions at the Japanese stratovolcano had produced thick andesitic lavas that flowed into two large tongue-shaped lobes immediately north of the volcano. Silica-rich, viscous magma blocked the vent, so pressure built up in much the same way it does in a soda bottle after it is shaken. When the pressure grew intense enough, it blasted a vent at the top of the volcano and sent a large plume of ash shooting into the air. Fast-moving jumbles of ash, gas, and other debris—known as pyroclastic flows—swept down the southwestern slope of the volcano.
That explosive eruption—and the slumping of rock into the emptied-out lava chamber—sculpted the bowl-shaped caldera that now sits at the top of the mountain and holds Lake Kuttara, one of the roundest and clearest lakes in Japan. The Operational Land Imager(OLI) on Landsat 8 acquired an image on September 15, 2015. For the three-dimensional view, the Landsat 8 image was draped over a global digital elevation model built from data acquired by NASA’s Shuttle Radar Topography Mission.
Read More about the Kuttara volcano at earthobservatory.nasa.gov
Google is putting artificial intelligence (AI) and more specifically machine learning to work in many different ways. Here, we look at two of the most inspiring use cases in which Google combines their AI capabilities with public data to help make our world more sustainable.
One great example is an initiative which is already helping to protect vulnerable marine life in some of the world’s most delicate eco-systems. Using the publicly broadcast Automatic Identification System for shipping, machine learning algorithms have been shown to be able to accurately identify illegal fishing activity in protected areas.
This works in much the same way as the “cat or horse?” example for image recognition I gave above. By plotting a ship’s course and comparing it to patterns of movement where the ship’s purpose is known, computers are able to “recognize” what a ship is doing.
Brandt told “All 200,000 or so vessels which are on the sea at any one time are pinging out this public notice saying ‘this is where I am, and this is what I am going.”
This results in the broadcasting of around 22 million data points every day, and Google engineers found that by applying machine learning to this data they were able to identify the reason any vessel is at sea – whether it is a transport ferry, container ship, leisure vessel or fishing boat.
“With that dataset, and working with a couple of wonderful NGOs – Oceana and Sky Truth – we were able to create Global Fishing Watch – a real-time heat map that shows where fishing is happening,” says Brandt.
The initiative has already led to positive outcomes in the fight against illegal fishing in protected marine environments. For example, the system identified suspicious activity in waters under the jurisdiction of the Pacific island nation of Kiribati – which include the world’s largest UNESCO heritage marine site. When intercepted by Kiribati government vessels, the captain of the fishing vessels denied any wrongdoing. But after being presented with evidence gathered by Google’s machine learning algorithms, he realized he had been caught red-handed and admitted the violation of international law.
“What’s really exciting is that this creates tremendous opportunities for governments and citizens to protect our marine resources. Fishing in those marine reserves is illegal and Global Fishing Watch has been used to protect those reserves.”
0570 - Get your ass to Mars.
From NASA Earth Observatory Image of the Day; September 19, 2018:
Florence Inundates the Carolinas
Upon making landfall on the southeastern U.S. coast on September 14, 2018, Hurricane Florence brought strong winds and torrential rains that flooded roads, houses, and rivers to historic levels and left hundreds of thousands of people without power. NASA data and researchers observed and analyzed the event, providing a steady stream of information to the Federal Emergency Management Agency (FEMA) and the National Guard to help assess the hurricane's impact.
Elizabethtown and Swansboro, North Carolina, received the highest recorded rainfall totals in the region—surpassing 33 inches (83 centimeters)— from September 13 to 18, according to the National Weather Service. Trent River, about 40 miles (60 kilometers) northwest of Swanson, swelled to levels exceeding previous records. At least 28 cities and towns in North Carolina received at least 20 inches of rain. Flooded roads isolated some areas such as Wilmington, where officials were planning to airlift food and water into the city.
The images on this page show different views of hurricane-related moisture on the Carolina coast. The left image above shows soil moisture in the region on September 11, 2018, before the arrival of Hurricane Florence. The data for that map come from the Soil Moisture Active Passive (SMAP) satellite, which uses a radiometer to measure soil moisture in the top 5 centimeters of the ground.
The right image shows the rain accumulation from September 13–16. These rainfall data are remotely-sensed estimates that come from the Integrated Multi-Satellite Retrievals (IMERG), a product of the Global Precipitation Measurement (GPM) mission. Local rainfall amounts can be significantly higher when measured from the ground.
Read More about the effects of Hurricane Florence on North Carolina at earthobservatory.nasa.gov
From NASA Earth Observatory Image of the Day; September 13, 2018:
Florence Crossing Warm Waters on the Way to the Carolinas
As millions of people along the Atlantic Coast of the United States board up windows and evacuate before Hurricane Florence makes landfall, remote sensing researchers and forecasters are monitoring the environmental conditions fueling the powerful storm. They are assembling a suite of satellite images and data products that could aid storm preparedness and recovery efforts by federal and local partners.
As Florence approaches land, two key factors will help govern the severity of the storm: ocean temperatures and wind shear, the difference in wind speeds at upper and lower parts of a storm. Warm ocean water and low wind shear are required to sustain and intensify a hurricane.
The map above shows sea surface temperatures on September 11, 2018. Meteorologists generally agree that sea surface temperatures (SSTs) should be above 27.8°C (82°F) to sustain and intensify hurricanes (although there are some exceptions). In Florence’s case, National Hurricane Center forecasters expect the storm to pass over water with temperatures well above that threshold. The data for the map were compiled by Coral Reef Watch, which blends observations from the Suomi NPP, MTSAT, Meteosat, and GOES satellites and computer models. Information about the storm track and winds come from the National Hurricane Center.
Read More about Hurricane Florence and the warmer ocean waters at earthobservatory.nasa.gov