Five Revolutionary Technologies Helping Scientists Study Polar Bears
January 13th, 2023
Via Smithsonian Magazine, a look at how researchers are using novel technologies to study polar bears, which live in the rapidly warming Arctic:
When they’re born, polar bears are toothless and blind, and they weigh roughly a pound. But over time—thanks to lots of fat-rich milk and protection from their mother—these helpless cubs grow to become large, powerful predators that are perfectly adapted for their Arctic environment. Though temperatures can dip to minus 50 degrees Fahrenheit in the winter, the massive marine mammals—which live in Canada, Norway, Russia, Greenland and Alaska—stay warm with a thick layer of body fat and two coats of fur. Their huge paws help them paddle through the icy water and gently walk across sea ice in search of their favorite meal, seals.
Their size, power, intelligence and environmental adaptions have long intrigued humans living in the north, including many Indigenous communities, such as the Inuit, the Ket and the Sámi. Biologists are curious about Ursus maritimus for many of the same reasons.
“Bears are fascinating,” says B.J. Kirschhoffer, director of conservation technology at Polar Bears International. “For me, when standing on a prominent point overlooking sea ice, I want to know how any animal can make a living in that environment. I am curious about everything that makes them able to grow to be the biggest bear by living in one of the harshest places on this planet. There is still so much to learn about the species—how they use energy, how they navigate their world and how they are responding to a rapidly changing environment.”
Today, researchers and conservationists want to know about these highly specialized marine mammals because human-caused climate change is reshaping their Arctic habitat. The bears spend much of their time on sea ice hunting for seals. But as temperatures in the Arctic rise, sea ice is getting thinner, melting earlier in the spring and forming later in the fall. Pollution and commercial activity also threaten the bears and their environment. An estimated 26,000 polar bears roam the northern reaches of the world, and conservationists worry they could disappear entirely by 2100 because of global warming.
But investigating mostly solitary creatures who spend much of their time wandering around sea ice, in some of the most remote and rugged places on the planet, is expensive, logistically challenging and dangerous to researchers. For help, scientists are turning to technology. These five innovations are changing the way they study polar bears.
Sticky tracking devices
Researchers can twist three black bottle brushes into a sedated bear’s fur to attach a triangular plate equipped with a tracking device. 3M
Much of what scientists know about polar bears comes from tracking female members of the species. This is largely due to anatomical differences between the sexes: Males have small heads and thick necks, which means tracking collars can easily slip right off. Females, on the other hand, have larger heads and thinner necks.Neck collars are out of the question for males, and they’re not ideal for young bears, which can quickly outgrow the devices. Other options—like implants—require the bears to undergo minor surgery, which can be potentially risky to their health. Ear tags don’t require surgery, but they are still invasive. They’re also permanent, and polar bear researchers strive to make as minimal an impact on the bears as possible. How, then, can scientists attach tracking devices to young bears and male polar bears?
This was the challenge put to innovators at 3M, the Minnesota-based company that makes everything from medical devices to cleaning supplies to building materials. 3M is particularly good at making things sticky—its flagship products include Post-it Notes and Scotch Tape.
Jon Kirschhoffer spent his nearly 40-year career at 3M as an industrial designer, developing novel solutions to complex problems just like this one. So when B.J. Kirschhoffer, his son, started chatting about the need for a new, noninvasive way of attaching trackers to polar bears, Jon’s wheels started turning. He brought the problem to his colleagues, who set to work studying polar bear fur and building prototypes.
Crimping Device For Polar Bear Fur
One of the most promising designs draws inspiration from the human process of attaching hair extensions. 3M
In the end, they landed on two promising “burr on fur” approaches. One device uses three bottle brushes—small, tubular brushes with a long handle made of twisted metal wire that could fit inside the neck of a skinny bottle—to grab onto clumps of a sedated bear’s fur. They also have the option of applying a two-part epoxy to the bottle brushes to help hold the bear’s fur more securely. Scientists and wildlife managers can use the brushes to firmly attach a triangular plate that contains a tracking device between the animal’s shoulder blades. In tests, the researchers have sedated the animals before attaching the trackers, but some zoos are training their bears to accept the tags while fully alert.“It’s like a burr: You twist and entangle the fur in the bottle brush, then bend over the handle so it doesn’t untwist,” Jon says. “We do that on three sides and put a little protective cap over it so it’s less likely to get snagged on willows and brush and other things that bears walk through.”
The other option draws inspiration from the process hair stylists use to attach hair extensions to their human clients’ heads. This pentagonal design involves extending a loop of a fishing leader down through five metal ferrules, or tubes; lassoing some hair on a sedated polar bear; and pulling it back through. Scientists can then use pliers to squeeze and crimp the hair in place.
Researchers are testing both devices on wild bears in Churchill, Manitoba, and on bears housed at zoos and aquariums. The verdict is still out on which option is better, and Polar Bears International expects the testing phase to last several more years. Ultimately, by making design modifications based on their experimental learnings, they hope to tweak the devices so they will stick to the bears’ fur for at least 270 days, which is the lifespan of the tracking devices themselves.
But even if they can’t get the sticky devices to stay attached to bears for the full 270 days, the gadgets will still be useful for gathering some amount of data on males and young bears, which is currently lacking. They’re also promising for short-term tracking situations, such as “when a bear has entered a community, been captured and released, and we want to monitor the animal to ensure it doesn’t re-enter the community,” says B.J.
“Bear-dar” detection systems
Radar Tower For Detecting Polar Bears
Scientists are testing several radar systems designed to detect approaching polar bears. Erinn Hermsen / Polar Bears International
When humans and polar bears meet, the encounters can often end in tragedy—for either the bear, the human or both. Conflict doesn’t happen often, but global warming is complicating the issue. Because climate change is causing sea ice to form later in the fall and melt earlier in the spring, the bears are fasting longer. And, with nowhere else to go, they’re also spending more time on land in the Arctic, where an estimated four million humans live. Some are even seeking out easy calories from garbage dumps or piles of butchered whale remains.Scientists counted 73 reports of wild polar bears attacking humans around the world between 1870 to 2014, which resulted in 20 human deaths and 63 human injuries. (They didn’t include bear outcomes in the study.) After analyzing the encounters, researchers determined that thin or skinny adult male bears in below-average body condition posed the greatest threats to humans. Female bears, meanwhile, rarely attacked and typically only did so while defending their cubs.
To prevent human-bear encounters, scientists are developing early-warning radar detection systems they’ve nicknamed “bear-dar” to help alert northern communities when a bear is getting close. A handful of promising prototypes are in the works: Some teams of researchers are building the systems from scratch, while others are riffing off technologies that are already in use by the military. They all use artificial intelligence models that may be able to discern approaching bears. Scientists have tested the systems in Churchill, Manitoba, and are now tweaking the A.I. models to be more accurate.
“We’ve already established that the radar sees everything,” B.J. Kirschhoffer says in a statement. “Being able to see is not the problem. Filtering out the noise is the problem. … Ideally, we can train them to identify polar bears with a high degree of certainty.”
As the systems are still in testing, they do not alert members of the community or professional responders. But, eventually, communities may develop custom responses depending on the alerts, says Kirschhoffer.
“For instance, if a bear-like target is identified 200 meters out, send a text message,” he says. “If a bear-like target is identified 50 meters out, blink a red light and sound a siren.”
Synthetic aperture radar
Scientists are highly interested in polar bear dens—that is, the cozy nooks female bears dig under the snow to give birth to cubs—for several reasons. Denning, which occurs each year from December to early April, is the most vulnerable time in the life of youngsters and mothers. Though they’re accustomed to covering huge amounts of territory to find prey, mother bears hunker down for the entire denning period to protect their cubs from the Arctic elements and predators. Studying bears at den sites allows researchers to gather important behavioral and population insights, such as the body condition of mothers and cubs or how long they spend inside the den before emerging.
Scientists also want to know where dens are located because oil and gas companies can inadvertently disturb the dens—and, thus, potentially harm the bears—when they search for new sources of fossil fuels. If researchers and land managers know where polar bear dens are located, they can tell energy companies to steer clear.
But finding polar bear dens on the snowy, white, blustery tundra is a lot like finding a needle in a haystack. Historically, scientists have used low-tech methods to find dens, such as heading out on cross-country skis with a pair of binoculars or using dogs to sniff them out. But those options were often inefficient and ineffective, not to mention rough on the researchers. For the last few years, scientists have been using a technology known as forward-looking infrared imagery, or FLIR, which involves using heat-sensing cameras attached to an aircraft to detect the warm bodies of bears under the snow. But FLIR is finicky and only works in near-perfect weather—too much wind, sun or blowing snow basically renders it useless. What’s more, if the den roof is too thick, the technology can’t pick up the heat inside. Tom Smith, a plant and wildlife scientist at Brigham Young University, estimates that aerial FLIR surveys are 45 percent effective, which is far from ideal.
But a promising new technology is on the horizon: synthetic aperture radar (SAR). Affixed to an aircraft, SAR is a sophisticated remote-sensing technology that sends out electromagnetic waves, then records the bounce back, to produce a radar image of the landscape below. SAR is not constrained by the same weather-related issues as FLIR, and it can capture a huge swath of land, up to half a mile wide, at a time, according to Smith.
Scientists are still testing SAR, but, in theory, they hope to use it to create a baseline map of an area during the summer or early fall, then do another flyover during denning season. They can then compare the two images to see what’s changed.
“You can imagine, with massive computing power, it goes through and says, ‘These objects were not in this image before,’” says Smith.
Artificial intelligence
Getting an accurate headcount of polar bears over time gives scientists valuable insights into the species’ well-being amid environmental changes spurred by climate change. But polar bears roam far and wide, traveling across huge expanses of sea ice and rugged, hard-to-reach terrain in very cold environments, which makes it challenging, as well as potentially dangerous and expensive, for scientists to try to count them in the field. As a result, researchers have taken to the skies, looking for the bears while aboard aircraft or via satellites flying over their habitat. After snapping thousands of aerial photos or satellite images taken from space, they can painstakingly pore over the pictures in search of bears.
A.I. may eventually help them count the animals. Scientists are now training A.I. models to quickly and accurately recognize polar bears, as well as other species of marine mammals, in photos captured from above. For researchers who conduct aerial surveys, which produce hundreds of thousands of photos that scientists sift through, this new technology is a game-changer.
“If you’re spending eight hours a day looking through images, the amount of attention that a human brain is going to pay to those images is going to fluctuate, whereas when you have a computer do something … it’s going to do that consistently,” Erin Moreland, a research zoologist with the National Oceanic and Atmospheric Administration, told Alaska Public Media’s Casey Grove in 2020. “People are good at this, but they’re not as good at it as a machine, and it’s not necessarily the best use of a human mind.”
To that same end, researchers are also now testing whether drones work to capture high-resolution images and gather other relevant data. Since they don’t require onboard human pilots, drones are a safer, more affordable alternative to helicopters; they’re also smaller and nimbler, and tend to be less disruptive to wildlife.
Treadmill and swim chamber
Researchers want to understand how much polar bears exert themselves while walking across the tundra or swimming through the Arctic Ocean. To get a handle on the marine mammals’ energy output on land, Anthony Pagano, a biologist with the United States Geological Survey, built a special heavy-duty polar bear treadmill. Study collaborators at the San Diego Zoo and the Oregon Zoo then trained captive polar bears to walk on it. Using shatterproof plastic and reinforced steel, the team constructed a 10-foot-long chamber that encased a treadmill typically used by horses. The 4,400-pound contraption also included a circular opening where researchers could tempt the bears into walking with fish and other tasty treats.
As a follow-up to the walking study, Pagano and biologists at the Oregon Zoo also measured the energy output of the bears while swimming. To do so, they developed a polar bear-sized swim chamber, complete with a small motor that generated waves to simulate the conditions the bears might encounter in the ocean.
Together, the two technologies helped scientists learn that bears expend more energy swimming than walking. Polar bears are good swimmers, but they’re not very efficient ones, thanks to their relatively short arms, their non-aerodynamic body shape and their propensity for swimming at the water’s surface, where drag is greatest. In a world with shrinking sea ice, polar bears likely need to swim more to find food and, thus, will burn precious calories, which could cause them to lose weight and lower their chances of reproducing—decreasing the species’ chances of survival.
Together, these and other technologies are helping researchers learn how polar bears are faring as the climate evolves. This knowledge, in turn, informs conservation decisions to help protect the bears and their environment—and the health of the planet more broadly.
“We need to understand more about how the Arctic ecosystem is changing and how polar bears are responding to loss of habitat if we are going to keep them in the wild,” says B.J. Kirschhoffer. “Ultimately, our fate is tied to the polar bear’s. Whatever actions we take to help polar bears keep their sea ice habitat intact are actions that will help humans protect our own future.”
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Read More »How A.I. Helps Protect Ecosystems In The Galápagos Islands
November 26th, 2022
Via Fortune, a look at how A.I. is helping protect ecosystems in the Galápagos Islands:
Within the Galápagos Marine Reserve, one of the world’s largest and most biologically diverse marine protected areas, more than 2,900 species survive off the interconnectedness of healthy ecosystems. Globally, humans also live off the many benefits of a thriving ocean.
Technology underwater is increasing to safeguard the environments needed for the ocean to release the earth’s oxygen, sequester carbon, provide sustenance, and so much more. And data is essential to track the development of the ecosystems as threats, such as illegal fishing and climate change, fluctuate. Collecting and analyzing ocean data at a large scale—including that of rare and endangered species—gives researchers useful insight to help conserve the vital diversity that makes up such a valued part of the earth.
“Wildlife research has, until the past few years, been a world of ‘small data,’” says Jason Holmberg, executive director at Wildbook Image Analysis (WBIA), a tech company that builds open software and A.I. solutions for conservationists. “Observations of rare or endangered species, whether via collars, camera traps, DNA samples, or photos, have been expensive to obtain and often required research teams to go directly into the field for short durations to obtain even small volumes of data.”
But with the use of A.I., the data and capabilities are expanding.
Observing whale sharks through passive tracking
In 2016, the International Union for Conservation of Nature (IUCN) downgraded whale sharks from vulnerable to endangered because of the many anthropogenic impacts they face, including industrial fisheries (both targeted and bycatch), vessel strikes, marine pollution, and climate change.Whale sharks, which can grow up to 60 feet long, provide significant ecological importance, according to Sofia Green, marine biologist and research scientist at the Galápagos Whale Shark Project, who studies the behavior, ecology, and migration patterns of the species. As predators on the top of the food chain, whale sharks feed off the weak and sick. By doing so, they keep the food chain in order. They also serve as carbon sequesters and nutrient-dense fertilizers by bringing nutrients (via defecation) to unhealthy ecosystems.
“If you protect sharks, you protect the ocean. And if you protect the ocean, you have a healthy planet,” she explains, while emphasizing the incredible importance of preserving the existence of all sharks. “And you cannot save a species if you don’t understand how they behave.”
In the northernmost point of the Galápagos, near the island of Darwin, Green and her team tag, video, photograph, do ultrasounds and blood draws, and take tissue samples to track the health and whereabouts of whale sharks that migrate through the Galápagos reserve. Observations like this are extremely valuable. But when made from limited studies, such as these, they may provide information at a small scale.
This is where technology like “passive tracking”—via photo identification and A.I.—broadens the data set. Whale sharks have unique spots, like human thumbprints, that can be referenced as identification. The Galápagos Whale Shark Project uses sharkbook.ai, a system run by WBIA that taps into A.I. to aggregate images of individual whale sharks uploaded by the likes of Green as well as images and videos posted on social media platforms around the world.
“Consider how whale sharks, the world’s biggest fish, were once rarely observed and even less frequently photographed,” Holmberg says. With the advent of cell phones, numerous sightings show up on YouTube and Instagram. “With this new wealth of data emerging from more cost-effective technology and public observations of wildlife, there is now an overwhelming amount of wildlife data available.”
A.I. helps sort and curate images and videos. Then sharkbook.ai classifies each whale shark based on the spot patterns and uses the photos for “capture/recapture.” For Green, this information shows where sharks appear elsewhere on the planet, when the individual returns to Galápagos, or if one has ended up on land or in an area it typically wouldn’t go (usually a result of being illegally fished).
“Modified growth algorithms like this are used by NASA to track stars,” Green explains. “It’s now being used to track the underwater constellations found on the bodies of these whale sharks.” Before this technology, researchers would obtain identification only from sharks seen on their expeditions. Through new, more expansive data collection, they now track almost 700 identified whale sharks that have migrated through the Galápagos.
When a photo is inputted for the first time, it goes into a newly created profile page. There, a summary of its sightings will build over time based on contributions. “All of this is aimed at a single goal,” Holmberg says: “Identifying the best ways to prevent extinction.”
A.I. to resolve the Galápagos “longline dilemma”
A threat to whale sharks and other species within the Galápagos Marine Reserve is the illegal longline fishing of tuna, which was banned in 2000 as a precautionary measure to prevent unintentional bycatch of endangered, threatened, and protected (ETP) species. However, it is still a common practice. Over the past two decades, people in the Galápagos have debated longline as the biggest threat to biodiversity. As a result, management authorities and environmental organizations hope to maintain the ban. On the other hand, local fishers argue that longline fishing should be authorized because it is the most cost-efficient way to catch tuna.According to a study done by marine biologist Mauricio Castrejón, Ph.D., as the local population (approximately 30,000 people spread across three habitable islands) grows, so does tuna consumption. Between 1997 and 2017, yellowfin tuna landings increased from 41.1 tons to 196.8 tons per year. Castrejón, who has led small-scale fisheries research and development projects in the Galápagos and the Eastern Tropical Pacific region (Costa Rica, Panama, Colombia, and Ecuador) for more than 18 years, dubs this fishing debate the “longline dilemma.”
This technique of fishing trails a long line with numerous hooks behind a boat. If not properly monitored, it can result in the unintended bycatch of ETP species. Many believe the rate of bycatch isn’t as high as reported or believed. And as of now, Galápagos doesn’t have proper data collection to track and survey the true rate of bycatch. Through science, technology, and innovation, Castrejón is hoping to build better monitoring and practices, and end the debate.
One such pathway is installing video cameras created by Shellcatch onto fishing vessels, an initiative that began in 2021 after being chosen by the Charles Darwin Foundation and WildAid. The cameras capture high-resolution information of fishing activities (technique, bycatch rate, and more) to hold fishers accountable for how and what they are catching, which creates market incentives, like selling their catch at a premium.
Shellcatch uses an A.I. algorithm through its electronic monitoring sensor to quickly detect bycatch of nontarget species. “A.I. is critical in validating sustainable fishing practices and connecting supply and demand in a cost-effective way,” says Alfredo Sfeir, founder and president of Shellcatch. He also cofounded Frescapesca, a seafood marketplace that also uses A.I. With Shellcatch, fishers input data into their logbooks (per usual practice), and scientists/A.I. use the video and accumulated data to validate the information. This proves to consumers that their product is fresh, legal, sustainable, and has a low incidental catch of protected species. Hence, earning a premium market price.
Sfeir believes these technologies, including Frescapesca, will be a game changer. “With A.I., the online seafood market platform can convert a growing number of fishing events into data that optimizes logistics and creates higher margin transactions,” he says. “The end result is a growing number of fishermen and women supplying seafood and buyers ready to purchase when vessels arrive with product anywhere along the shoreline.”
Furthermore, this new data can be integrated into science-based evidence and advice to better validate the best technique, risk-averse gear, and solutions to protect other species and keep tuna fishing sustainable for the people of Galápagos and beyond.
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Read More »Earning Its Stripes: Tech Used To Crack Tiger Trade
August 18th, 2022
Via Terra Daily, an article on the use of artificial intelligence to help stop poaching:
In a town in northeastern Scotland, Debbie Banks looks for clues to track down criminals as she clicks through a database of tiger skins.
There are thousands of photographs, including of rugs, carcasses and taxidermy specimens.
Banks, the crime campaign leader for the Environmental Investigation Agency (EIA), a London-based charity, tries to identify individual big cats from their stripes.
Once a tiger is identified, an investigator can pinpoint where it comes from.
“A tiger’s stripes are as unique as human fingerprints,” Banks told AFP.
“We can use the images to cross-reference against images of captive tigers that might have been farmed.”
Currently this is slow painstaking work.
But a new artificial intelligence tool, being developed by The Alan Turing Institute, a centre in the UK for data science and artificial intelligence, should make life much easier for Banks and law enforcement officials.
The project aims to develop and test AI technology that can analyse the tigers’ stripes in order to identify them.
“We have a database of images of tigers that have been offered for sale or have been seized,” Banks said.
“When our investigators get new images, we need to scan those against the database.
“At the moment we are doing that manually, looking at the individual stripe patterns of each new image that we get and cross-referencing it against the ones we have in our database.”
It is hoped that the new technology will help law enforcement agencies determine where tiger skins come from and allow them to investigate the transnational networks involved in trafficking tigers.
Once the officials know the origins of confiscated tiger skins and products, they will be able to tell whether the animal was farmed or poached from a protected area.
Poaching, fuelled by consumer demand, remains a major threat to the survival of the species, according to the EIA.
Tiger skins and body parts are sought after, partly due to their use in traditional Chinese medicine.
An estimated 4,500 tigers remain in the wild across Asia.
“Tigers faced a massive population decline in the last 120 years, so we want to do everything we can to help end the trade in their parts and products, including tiger skins,” Banks said.
Anyone with photographs of tigers is invited to submit them to the EIA to help bolster the AI database.
“We are inviting individuals — whether they are photographers or researchers and academics — who may have images of tigers where their stripe patterns are clear,” Banks said.
“They could be live tigers, dead tigers or tiger parts.
“If they can share those with us, the data scientists can then develop, train and test the algorithm,” she said.
“We need thousands of images just to do that phase of the project.”
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Read More »Google’s Dynamic World Uses AI To Analyze Satellite Data
June 10th, 2022
Via Fast Company, an article on a new tool from Google that shows how the planet is changing in near real time:
The planet changes quickly: More than half a million acres are burning in New Mexico. A megadrought is shrinking Lake Mead. The Alps are turning from white to green. Development continues to expand, from cities to massive solar farms. All of these changes impact the Earth’s climate and biodiversity. But in the past, such changes have been difficult to track in detail as they’re happening.
A new tool from Google Earth Engine and the nonprofit World Resources Institute pulls from satellite data to build detailed maps in near real time. Called Dynamic World, it zooms in on the planet in 10-by-10-meter squares from satellite images collected every two to five days. The program uses artificial intelligence to classify each pixel based on nine categories that range from bare ground to trees, crops, and buildings.
Researchers, nonprofits, and other users can “explore and track and monitor changes in these terrestrial ecosystems over time,” says Tanya Birch, senior program manager for Google Earth Outreach. As the tool was being built last year, Birch used it in the days after the Caldor Fire, a wildfire that burned more than 200,000 acres in California. The pixels in satellite images quickly changed from being classified as “trees” to “shrub and scrub.”
Scientists used to rely on statistical tables that were sometimes released only every five years, says Fred Stolle, deputy director of the World Resources Institute’s Forests Program. “That’s clearly not good enough anymore,” he says. “We’re changing so fast, and the impact is so fast, that satellites are now the way to go.”
Researchers and planners already use satellite data in some applications—the World Resources Institute, for example, previously worked with Google to build Global Forest Watch, a tool that can track deforestation using satellite images. But the new data is much more detailed; now it’s sometimes possible to see if one or two trees are cut down in a tropical forest, even when a larger area is intact, Stolle says.
In cities, planners could use the data to easily see which neighborhoods don’t have enough green space. Researchers studying smallholder farms in Africa could use it to see the impacts of drought and when crops are being harvested. Because the data is continuously updated, it’s also possible to watch the seasons change throughout the year across the entire planet. The data goes back five years, and using the new tool, anyone can enter date ranges to see how a location has changed over time.
“I encourage people to dive into it and explore,” Birch says. “There’s a lot of depth and a lot of richness in Dynamic World. . . . I feel like this is really pushing the frontier of mapmaking powered by AI in an incredibly novel way.”
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Read More »Satellites and AI Can Help Solve Big Problems—If Given the Chance
June 6th, 2022
Via Wired, a report on some of the hurdles that stand in the way of ambitious plans to use imagery to help feed people, reduce poverty, and protect the planet:
For the past three decades, three decades, geologist Carlos Souza has worked at the Brazil-based nonprofit Imazon, exploring ways he and the teams he coordinates can use applied science to protect the Amazon rainforest. For much of that time, satellite imagery has been a big part of his job.
In the early 2000s, Souza and colleagues came to understand that 90 percent of deforestation occurs within 5 kilometers of newly created roads. While satellites have long been able to track road expansion, the old way of doing things required people to label those findings by hand, amassing what would eventually become training data. Those years of labor paid off last fall with the release of an AI system that Imazon says reveals 13 times more roadway than the previous method, with an accuracy rate of between 70 and 90 percent.
Proponents of satellite imagery and machine learning have ambitious plans to solve big problems at scale. The technology can play a role in anti-poverty campaigns, protect the environment, help billions of people obtain street addresses, and increase crop yields in the face of intensifying climate change. A UNESCO report published this spring highlights 100 AI models with the potential to transform the world for the better. But despite recent advances in deep learning and the quality of satellite imagery, as well as the record number of satellites expected to enter orbit over the next few years, ambitious efforts to use AI to solve big problems at scale still encounter traditional hurdles, like government bureaucracy or a lack of political will or resources.
Stopping deforestation, for instance, requires more than spotting the problem from space. A Brazilian federal government program helped reduce deforestation from 2004 to 2012 by 80 percent compared to previous years, but then federal support waned. In keeping with an election promise, President Jair Bolsonaro weakened enforcement and encouraged opening the rainforest to industry and cattle ranch settlers. As a result, deforestation in the Amazon reached the highest levels seen in more than a decade.
Other AI-focused conservation groups have run into similar issues. Global Fishing Watch uses machine learning models to identify vessels that turn off GPS systems to avoid detection; they’re able to predict the type of ship, the kind of fishing gear it carries, and where it’s heading. Ideally that information helps authorities around the world target illegal fishing and inform decisions to board boats for inspection at sea, but policing large swaths of the ocean is difficult. Global Fishing Watch’s tech spotted hundreds of boats engaged in illegal squid fishing in 2020, data that head of research David Kroodsma credits with increasing cooperation between China and South Korea, but it didn’t lead to any particular prosecution. Enforcement in ports, he says, is “key to making deterrence scalable and affordable.”
Back on land, the consulting company Capgemini is working with The Nature Conservancy, a nonprofit environmental group, to track trails in the Mojave Desert and protect endangered animal habitats from human activity. In a pilot program last year, the initiative mapped trails created by off-road vehicles in hundreds of square miles of satellite imagery in Clark County, Nevada, to create an AI model that can automatically identify newly created roads. Based on that work, The Nature Conservancy intends to expand the project to monitor the entirety of the desert, which stretches more than 47,000 square miles across four US states.
However, as in the Amazon, identifying problem areas only gets you so far if there aren’t enough resources to act on those findings. The Nature Conservancy uses its AI model to inform conversations with land managers about potential threats to wildlife or biodiversity. Conservation enforcement in the Mojave Desert is overseen by the US Bureau of Land Management, which only has about 270 rangers and special agents on duty.
In northern Europe, the company Iceye got its start monitoring ice buildup in the waters near Finland with microsatellites and machine learning. But in the past two years, the company began to predict flood damage using microwave wavelength imagery that can see through clouds at any time of day. The biggest challenge now, says Iceye’s VP of analytics, Shay Strong, isn’t engineering spacecraft, data processing, or refining machine learning models that have become commonplace. It’s dealing with institutions stuck in centuries-old ways of doing things.
“We can more or less understand where things are going to happen, we can acquire imagery, we can produce an analysis. But the piece we have the biggest challenge with now is still working with insurance companies or governments,” she says.
“It’s that next step of local coordination and implementation that it takes to come up with action,” says Hamed Alemohammad, chief data scientist at the nonprofit Radiant Earth Foundation, which uses satellite imagery to tackle sustainable development goals like ending poverty and hunger. “That’s where I think the industry needs to put more emphasis and effort. It’s not just about a fancy blog post and deep learning model.”
It’s often not only about getting policymakers on board. In a 2020 analysis, a cross-section of academic, government, and industry researchers highlighted the fact that the African continent has a majority of the world’s uncultivated arable land and is expected to account for a large part of global population growth in the coming decades. Satellite imagery and machine learning could reduce reliance on food imports and turn Africa into a breadbasket for the world. But, they said, lasting change will necessitate a buildup of professional talent with technical knowledge and government support so Africans can make technology to meet the continent’s needs instead of importing solutions from elsewhere. “The path from satellite images to public policy decisions is not straightforward,” they wrote.
Labaly Toure is a coauthor of that paper and head of the geospatial department at an agricultural university in Senegal. In that capacity and as founder of Geomatica, a company providing automated satellite imagery solutions for farmers in West Africa, he’s seen satellite imagery and machine learning help decision-makers recognize how the flow of salt can impact irrigation and influence crop yields. He’s also seen it help settle questions of how long a family has been on a farm and assist with land management issues.
Sometimes free satellite images from services like NASA’s LandSat or the European Space Agency’s Sentinel program suffice, but some projects require high-resolution photos from commercial providers, and cost can present a challenge.
“If decision-makers know [the value] it can be easy, but if they don’t know, it’s not always easy,” Toure said.
Back in Brazil, in the absence of federal support, Imazon is now forging ties with more policymakers at the state level. “Right now, there’s no evidence the federal government will lead conservation or deforestation efforts in the Amazon,” says Souza. In October 2022, Imazon signed cooperation agreements with public prosecutors gathering evidence of environmental crimes in four Brazilian states on the border of the Amazon rainforest to share information that can help prioritize enforcement resources.
When you prosecute people who deforest protected lands, the damage has already been done. Now Imazon wants to use AI to stop deforestation before it happens, interweaving that road-detection model with one designed to predict which communities bordering the Amazon are at the highest risk of deforestation within the next year.
Deforestation continued at historic rates in early 2022, but Souza is hopeful that through work with nonprofit partners, Imazon can expand its deforestation AI to the other seven South American countries that touch the Amazon rainforest.
And Brazil will hold a presidential election this fall. The current leader in the polls, former president Luiz Inácio Lula da Silva, is expected to strengthen enforcement agencies weakened by Bolsonaro and to reestablish the Amazon Fund for foreign reforestation investments. Lula’s environmental plan isn’t expected out for a few months, but environmental ministers from his previous term in office predict he will make reforestation a cornerstone of his platform.
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Read More »These New Technologies Could Transform Wildlife Conservation
March 22nd, 2022
Via The Hill, a look at how artificial intelligence, environmental DNA and networked sensors are among the technologies with the highest potential to improve wildlife conservation:
Published last December by conservation technology network WILDLABS, together with a group of non-profit and academic partners, the report is the first of its kind to provide a holistic assessment of the state of conservation technology.
The researchers surveyed 248 conservationists, technologists and academics across 37 countries over the 11 most commonly used conservation technologies, including camera traps, biologgers, acoustic monitoring and remote sensings.
Although it’s estimated that about 8.7 million species populate our planet, 86 percent of all species on land and 91 percent in the oceans are yet to be discovered. Multiple scientific studies suggest that if no action is taken, as many as half of all species could go extinct by the end of the century.
Traditional methods for tracking biodiversity, such as camera traps, which connect digital cameras to an infrared sensor to capture images and videos of animals moving past the sensor, or aerial surveys can be labor-intensive and costly. The technologies highlighted by the research could help reduce the time and resources required to detect wildlife, while increasing the effectiveness of conservation efforts.
Combining AI and citizen science to improve wildlife identification
Artificial intelligence (AI) is increasingly used to analyze large amounts of conservation data, such as camera trap, satellite and drone images or audio and video recordings, and improve wildlife identification and monitoring. The non-profit Wild Me created a cloud-based platform Wildbook, which uses computer vision and deep learning algorithms to scan millions of crowdsourced wildlife images to identify species and individual animals based on their unique patterns, including stripes, spots or other defining physical features such as scars.
Photos are added to the cloud by scientists and other volunteers, or are sourced from social media, and over time, the information about each species will grow as more citizen scientists and researchers contribute to the image catalogue. The aggregated data helps inform conservation actions, while the public can follow their favorite animals in the cloud.
Wildbook was started off to improve the tracking of whale sharks which was previously done by attaching plastic tags to the animals that had often never resurfaced. The platform has since grown into a vast database of various different species, including sea turtles, manta rays, sharks, whales, dolphins, big cats, giraffes and zebras.
In partnership with Microsoft’s AI for Earth initiative, Wildbook is hosted on its cloud computing service, Azure and is made available as an open-source software to encourage others to adopt this non-invasive method of species tracking.
A facial recognition tool for wildlife
The BearID Project is developing a facial recognition software that can be applied to camera trap imagery to identify and monitor brown bears, and inform subsequent conservation measures. This is especially important because camera traps are currently unable to consistently recognize individual bears due to the lack of unique natural markings for certain species.
So far, the team of biologists and software engineers have developed an AI system using personal photographs of brown bears from British Columbia, Canada and Katmai National Park, Alaska, which was able to recognize 132 individual bears with an 84 percent accuracy. While the camera trap system is currently under development, the project is already working with indigenous nations in Canada to implement the new tool within bear research and monitoring programs. The ultimate goal is to expand the scope of the facial recognition software to eventually apply to other threatened species.
Using AI to combat wildlife trafficking
AI can also help boost anti-poaching efforts. The software Protection Assistant for Wildlife Security (PAWS) takes in past poaching records and the geographic data of the protected area to predict poachers’ future behavior, and design poaching risk maps and optimal patrol routes for rangers.
During the first month of its field tests in the Srepok Wildlife Sanctuary in Cambodia, the area identified as most suitable for the reintroduction of tigers in Southeast Asia, PAWS has helped rangers double the amount of snares detected and removed during their patrols.
PAWS has since been integrated with the open-source Spatial Monitoring and Reporting Tool (SMART), which is already used by rangers in over 1,000 protected areas to log data collected during patrols. The integrated tool is currently available to national parks as a beta feature, and has been tested across Zimbabwe, Nigeria, Kenya, Malaysia, Mozambique and Zambia to generate poaching risk maps to assist with patrols.
Plans for the future include connecting the software to remote sensing tools such as satellites or drones to reduce the need for humans to enter the data, and expanding the scope of PAWS to predict other forms of environmental crime, including illegal logging or fishing.
Sampling environmental DNA for biodiversity monitoring
Environmental DNA (eDNA), meanwhile, enables conservationists to collect biodiversity data by extracting DNA from environmental samples, such as water, soil, snow or even air. All living organisms leave traces of their DNA in their environments through their feces, skin or hair, amongst others.
A single sample might carry the genetic code of tens or even hundreds of species, and can provide a detailed snapshot of an entire ecosystem. A recent study has revealed that eDNA could offer a more efficient and cost-effective method for the large-scale monitoring of terrestrial biodiversity. In the study, eDNA sampling detected 25 percent more terrestrial mammal species compared to camera traps, and for half of the cost.
eDNA can also help examine the impact of climate change, detect invisible threats such as viruses or bacteria, and assess the overall health of an ecosystem, which can be used to make the case for greater protection for the area.
NatureMetrics, for instance, partnered with the Lebanon Reforestation Initiative to use eDNA to assess the biodiversity of freshwater ecosystems, providing crucial data from a previously understudied region to inform rehabilitation and restoration work.
Increasing connectivity for better conservation outcomes
By enabling camera traps, tracking devices and other conservation hardware to connect online, networked sensors can offer a more comprehensive picture of animal behavior and provide instant alerts about imminent threats, aiding monitoring and patrolling efforts.
FieldKit and the Arribada Initiative aim to make the technology more accessible by developing low-cost, open-source sensor systems, while Smart Parks and Sensing Clues focus on using networked sensors to optimize protected area monitoring and management.
Most national parks don’t have basic internet or cellphone coverage as national telecommunications networks don’t typically extend to these protected areas. To provide low-power, long-range connectivity, Smart Parks deploys a range of sensors, including gate sensors, alarm systems, and animal, vehicle and people trackers, which run autonomously on solar power, consume little energy and are connected to a secure private network situated in the park itself.
The networked sensors track a wide range of information, and are able to detect human intrusions which can support anti-poaching efforts, or animal breakouts from the protected area into the community which could help preempt human-wildlife conflict.
The data is made available in or near real time in a web application, and can help inform operational decisions related to park management, wildlife conservation and local community protection, and could even be applied to ensure ranger and tourist safety.
Smart Parks technology has been deployed in protected areas around the world, and has helped contribute to the conservation of many endangered species, including orangutans, rhinos and elephants.
Gaming wildlife protection
Although it was not covered by the WILDLABS survey, games can also serve as a valuable tool to activate audiences with critical conservation issues, especially among a younger and more tech-savvy generation. Internet of Elephants, for example, develops a range of gaming and digital experiences based on scientific data to engage people who might not have otherwise held an interest in wildlife conservation.
Its products include Wildeverse, an augmented reality mobile game where players go on conservation missions in the jungle and learn how to keep apes safe, or Unseen Empire, which has turned one of the largest camera trap studies into a gaming experience. Players review real-life camera trap imagery to identify various wildlife species, and in the process learn more about the devastating impact of deforestation, poaching and other human developments on endangered wildlife, including the elusive clouded leopards.
Reducing inequalities in conservation tech
Besides highlighting the most promising tech innovations, the WILDLABS report has also identified some of the key challenges facing the conservation technology ecosystem, including competition for limited funding, duplication of efforts and insufficient capacity-building.
Importantly, the research revealed that financial and technical barriers might disproportionately affect women and people in developing countries.
“Many of the most critical conservation hotspots are also areas that are currently receiving the least support in terms of local tech capacity building,” shared Talia Speaker, WILDLABS Research Lead at WWF and co-author of the report.
Speaker warned about the problematic nature of “parachute science” which involves scientists and conservationists from high-income countries providing temporary support in developing nations and leaving after the project is finished, with no investment in local capacity-building. Without empowering local communities to use and develop conservation technologies themselves, the effectiveness and long-term sustainability of these solutions are put at risk.
To address these challenges, “the findings of this research are already feeding into a variety of WILDLABS programs,” added Speaker. “These range from fellowships that bridge the technology and conservation sectors to targeted community and capacity-building in regions like East Africa and Southeast Asia with high potential for conservation tech impact but historically limited resources for engagement with the field.”
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