Remote Sensing: Tracking Damage and Helping Find Solutions As Well
May 3rd, 2022
Via The Conversation, a look at how satellites over the Amazon capture the choking of the ‘house of God’ by the Belo Monte Dam – and how they can help find solutions too:
The Xingu River is revered as the “house of God” by the Indigenous people living along its Volte Grande, or Big Bend, in the Brazilian Amazon. The river is essential to their culture and religion, and a crucial source of fish, transportation and water for trees and plants.
Five years ago, the Big Bend was a broad river valley interwoven with river channels teaming with fish, turtles and other wildlife. Today, as much as 80% of the water flow is gone.
That’s because in late 2015, the massive Belo Monte Dam project began redirecting water from the Xingu River upstream from the Big Bend, channeling it through a canal to a giant new reservoir. The reservoir now powers one of the largest hydropower dams in the world, designed with enough capacity to power around 20 million households, though it has been producing far less.
Most of the river’s flow now bypasses the Big Bend, and the Indigenous peoples who live there are watching their livelihoods and way of life become endangered. Some of the most devastating effects are during the rainy season, when wildlife and trees rely heavily on having high water. The consortium of utilities and mining companies that runs the dam has pushed back on government orders to allow more water to reach the Big Bend, claiming it would cut their generation and profits. The group has argued in the past that there was no scientific proof that the change in water flow harmed fish or turtles.
There is proof of the Belo Monte Dam project’s impact on the Big Bend, though – from above. Satellite data shows how dramatically the dam has altered the hydrology of the river there.
The same satellite data can also point to potential solutions and ways that operators of the Belo Monte Dam could revise the dam’s operations to keep both its renewable power and the Xingu River flowing at the most important times of the year.
As scientists who work with remote sensing, we believe satellite observations can empower populations around the world who face threats to their resources. The fact that satellite observations of surface water of the Xingu River can be clearly tied to the construction and operation of the Belo Monte Dam offers hope that this kind of knowledge can no longer be hidden.
50 years of Earth observation
Satellites have been monitoring changes in Earth’s landscapes for 50 years, ever since the U.S. launched the first Landsat satellite in July 1972. By piecing together data from the Landsat program and other satellites, scientists can reconstruct historical patterns of change in the landscape and predict current and future trends. They can monitor forest cover, drought, wildfire damage and desert expansion, as well as river flows and reservoir operations around the world.
An example of how that data can be used to help threatened communities is the global Reservoir Assessment Tool, which was created by colleagues and one of us at the University of Washington. It monitors how much water is in about 1,600 reservoirs around the world.
Screenshot of the tool showing a map of Brazil and an example dam’s chart of water outflow.
The Reservoir Assessment Tool allows communities to track river flow changes caused by nearby dams and locate proposed dams. It currently tracks dams built before 2000. University of Washington
Dam operators already collect thorough on-site data about water flow, but their datasets are rarely shared with the public. Remote sensing doesn’t face the same restrictions. Making that data public can help hold operators to account for and protect local communities and their rivers.How satellites could pressure Belo Monte to share
Satellite monitoring can provide unprecedented insight into the operations of dams like the Belo Monte and their impact on downstream populations.
Existing satellite data can be used to monitor recent historical behavior of a dam’s operations, track the state of the river and patterns of inflow and outflow at the dam, and even forecast the likely state of the reservoir. Much of that data is easily accessible and free. For example, a tool created for the regional governing body of the Mekong River Commission is empowering communities along the river in Southeast Asia by giving them access to satellite data about water flow at each dam – data that cannot be hidden or modified by those in power.
While estimates based on remote sensing have higher uncertainty than on-site measurements, unfettered access to such information can provide local populations with evidence to argue, in court if necessary, for more water releases.
Long-term observations of dams and hydroclimate records show it is possible to revise the standard operating procedures of dams so they allow more water to flow downstream when needed. A compromise with the Belo Monte Dam could ensure that enough water flows to the Xingu’s Big Bend region while also providing hydropower benefits.
By making the impact of the Belo Monte Dam and others like it public to the world, agencies and the general public can put pressure on the dam’s operators and its investors to release more water. Public pressure will become increasingly important, as water disputes in the Amazon are expected to worsen as the planet warms and deforestation continues. Climate change will affect river flow patterns in the Amazon and likely increase droughts, leaving less water during some periods.
A tool for social justice
The Amazonian native population has declined, and dams and nearby mining operations, like those threatening the Xingu’s Big Bend region, play a role. The current Brazilian government under president Jair Bolsonaro has generally sided with wealthy landowners and industry over Indigenous peoples, making access to independent data crucial for protecting these communities.
Monitoring dams is a powerful way satellites can make a difference. Nearly two-thirds of Brazil’s electricity comes from more than 200 large and 400-plus small hydropower plants, and more large dams are expected to be built in the Amazon this decade. Many are in areas with Indigenous populations.
Remote sensing may not directly solve the problem of social injustice, but it offers the tools needed to recognize the problems and explore solutions. Being able to monitor changes in near-real time and compare them with historical operations can help maintain the checks and balances required for equitable growth.
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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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Read More »Using Data From Space To Develop A Global View Of Animal Movements
March 21st, 2022
Via Yale News, an article on how data from space unveil a global view of animals on the move:
A global team of researchers, including Yale scientists, is using advanced tagging to track the movement of individual animals across the world, an ambitious research project that is opening a new frontier in efforts to monitor biodiversity change and pinpoint areas for conservation intervention.
For the project, individual animals from 15 species worldwide were tagged with lightweight sensors that transmit data to the International Space Station, which then transmits the information to a single system for integration and interpretation. Since late 2021, the new technology has captured the movements of hundreds of small animals, such as blackbirds, artic terns, and even bats. Eventually they hope to track many species of reptiles, mammals, and insects.
The first findings are described March 8 in the journal Trends in Ecology and Evolution.
“For the first time, we are able to have a finger on the pulse of life worldwide,” said Walter Jetz, lead author of the study, co-director of the Max Planck-Yale Center for Biodiversity Movement and Global Change (MPYC), and a professor of ecology and evolutionary biology and of the environment at Yale. “This now operational technology blazes the trail for a biological earth observation with animal sensors.”
Given the low cost and small tag size researchers hope to scale the effort to thousands of species and deliver data about animal lives globally in real time, said Jetz.
The project is called ICARUS or International Cooperation for Animal Research Using Space, a collaboration of international scientists led by the Max Planck Society.
The effort relies upon trained volunteers to place the miniature sensors, which weigh less than one-tenth of an ounce, on individual animals. The sensors not only record GPS data but can also supply other information on conditions experienced by animals, such as temperature.
Data from the sensors are collected from the International Space Station and then transmitted to computers on the ground. While most of the species tagged so far are birds, future tagging could include many species of land animals, researchers say.
“Rather than globe-orbiting sensors capturing images of the planet’s surface for subsequent interpretation, animals, through countless individual movement decisions, seek out their preferred conditions, sensing the quality and health of ecosystems in real time,” said Martin Wikelski, co-director of MPYC, research director at the Max Planck Institute for Animal Behavior in Germany, and originator of ICARUS.
For instance, scientists will not only be able to identify areas essential for survival of the animals, but identify areas where biodiversity may be threatened by human encroachment or poaching when anticipated migration routes are blocked.
With the technology now in place, the Max Planck-Yale Center is currently raising funds to purchase more sensors, which cost about $300 each, support researchers worldwide to use and scale up the system, train volunteers, and integrate information sharing platforms. The group is also negotiating with NASA and the German space agencies to place new data collection devices on satellites.
“The dream is an ongoing cohort of say 100,000 animal sentinels that help us humans measure, understand, and mitigate our changes to this planet,” Jetz said.
Movements across space and environments, home ranges and migration corridors from these new data can be explored at the website https://animallives.org, an initiative of the international Max Planck-Yale Center for Biodiversity Movement and Global Change (MPYC).
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Read More »Detecting Wildlife Crime in Real Time
March 7th, 2022
Via Medium, an article on three innovations that deliver real-time data to official combatting wildlife crime:
Wildlife crime is a multifaceted threat. It not only endangers thousands of species, it also threatens global security and robs vulnerable populations of income and food sources. Driven by consumer demand and corruption, wildlife crime is enabled by complex, hard-to-monitor transit routes and weak on-site species detection at border crossings.
Such complex challenges require innovative solutions. In 2016, through the Wildlife Crime Tech Challenge, USAID and partners awarded prize funding and technical assistance to 16 innovators developing technology to tackle the illegal wildlife trade. More than five years later, see what they have achieved in this story map.
Three of the prize winners — the Zoological Society of London, the University of Technology Sydney, and the University of Leicester — created affordable, accessible, and scalable products that deliver real-time data to authorities who combat wildlife crime. Read on to learn more about these innovations.
Catching Poachers in Real Time
Wildlife patrols often struggle to monitor vast and remote protected areas with limited technology and information on the activity of poachers. The Zoological Society of London, an international conservation organization, is developing a technology that combines motion-sensing cameras with military-grade sensors that can detect lightweight metal. The goal is to overcome a shortcoming of traditional motion detection systems, which cannot discern between animals passing by and poachers armed with weapons. The Instant Detect 2.0 could save wildlife patrols time and resources by quickly locating likely threats.
“Before, you had no idea what was triggering that alert, if it was 10 poachers or someone walking by with a machete,” said Sam Seccombe, technical project manager for the Zoological Society of London’s Monitoring and Technology Programme.
With this wireless, battery-operated, and camouflaged device, data collected through the cameras and motion-detecting sensors would automatically be transferred to secure cloud storage for sharing among authorities.
Since winning the Tech Challenge, the Zoological Society of London has worked to refine its product to make it accessible and affordable to those who need it most. Despite delays in field testing due to COVID-19, the product is scheduled to be deployed by the end of this year. In the future, the team hopes to add artificial intelligence for even faster image processing and greater accuracy in detecting specific species. They are currently assessing different business models to lower the price for consumers and encourage uptake of the product.
Identifying Illegal Wildlife Meat at Border Crossings
While the Zoological Society of London’s innovation aimed to help authorities catch poachers in protected areas, the University of Technology Sydney saw the need for detection at another juncture for wildlife crime: border crossings. With illegal wildlife often hidden in legal shipments, enforcement authorities, even those with sniffer dogs, struggle to rapidly distinguish legally-traded wildlife meat from illegal wildlife meat, highlighting a need for on-site species identification.
Thus, a team at the University of Technology Sydney developed a portable, electronic “nose” that customs officials can use to smell “fingerprints” of trafficked wildlife and wildlife parts. With four rounds of prototypes completed, the nose’s new sensor has shown high identification reliability in testing and the team is nearing commercialization.
The original schematic drawing for the University of Technology Sydney’s electronic ‘nose.’ / University of Technology Sydney
Once on the market, the portable “nose” will enable authorities to rapidly identify and confiscate illegal wildlife products, and quickly build evidence to prosecute offenders — without the need for laboratory analysis or the expertise required to operate similar equipment.Through the Tech Challenge’s networking opportunities, the university developed a partnership with the Australia National Museum to use the technology in addressing the illegal reptile trade. The University of Technology Sydney team is developing a business plan and will seek additional support to scale-up the electronic nose.
“The Tech Challenge has led to a lot of really good collaborations with people that we might not necessarily have crossed paths with otherwise,” said Maiken Ueland, deputy director of the The Australian Facility for Taphonomic Experimental Research and ARC DECRA fellow at the University of Technology Sydney.
Automating DNA-Based Species Identification
Another innovation that benefited from support through the Tech Challenge and could transform how authorities respond to wildlife crime is the MinION DNA sequencer. Recognizing that enforcement authorities are unable to run rapid, field-level DNA-based species identification to use as evidence of a crime, a team at the University of Leicester in the United Kingdom built the sequencer to analyze wildlife samples on the spot and deliver results in one hour, avoiding expensive equipment and lengthy testing processes.
“We can pretty much identify any vertebrate species.” said Dr. Jon Wetton, co-director of the Alec Jeffreys Forensic Genomics Unit at the University of Leicester.
MinION can fully automate DNA sequencing and species identification at the crime scene, providing authorities with evidence that would allow them to detain and arrest traffickers on the spot.
The Leicester team is using their handheld technology in the lab to identify the origins of traded birds of prey and determine if they are legally captive-bred or illegally poached from the wild in the United Kingdom and destined for the Middle East. They are still testing accuracy of the field-based tool. In the meantime, the MinION also enabled the first DNA sequencing in space on the International Space Station.
Harnessing Technology to Empower People and Fight Wildlife Crime
These three Tech Challenge winners showcase the potential for technology to transform wildlife crime detection and enforcement. By delivering information to authorities on the spot, these innovations would allow authorities to address crimes in real-time and build an evidence base to prosecute offenders. However, they also illustrate the challenges of developing a technology that is accurate, affordable, and scalable.
As demonstrated through the Wildlife Crime Tech Challenge, USAID is committed to supporting innovative approaches to combat wildlife crime, including empowering people with technology that allows them to work more efficiently and effectively.
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Read More »Five Ways AI Is Saving Wildlife
February 27th, 2022
Courtesy of The Guardian, a look at how AI is being used in conservation:
There’s a strand of thinking, from sci-fi films to Stephen Hawking, that suggests artificial intelligence (AI) could spell doom for humans. But conservationists are increasingly turning to AI as an innovative tech solution to tackle the biodiversity crisis and mitigate climate change.
A recent report by Wildlabs.net found that AI was one of the top three emerging technologies in conservation. From camera trap and satellite images to audio recordings, the report notes: “AI can learn how to identify which photos out of thousands contain rare species; or pinpoint an animal call out of hours of field recordings – hugely reducing the manual labour required to collect vital conservation data.”
AI is helping to protect species as diverse as humpback whales, koalas and snow leopards, supporting the work of scientists, researchers and rangers in vital tasks, from anti-poaching patrols to monitoring species. With machine learning (ML) computer systems that use algorithms and models to learn, understand and adapt, AI is often able to do the job of hundreds of people, getting faster, cheaper and more effective results.
Here are five AI projects contributing to our understanding of biodiversity and species:
1. Stopping poachers
Zambia’s Kafue national park is home to more than 6,600 African savanna elephants and covers 22,400 sq km, so stopping poaching is a big logistical challenge. Illegal fishing in Lake Itezhi-Tezhi on the park’s border is also a problem, and poachers masquerade as fishers to enter and exit the park undetected, often under the cover of darkness.Stopping poachers at Kafue national park.
Automated alerts mean that just a handful of rangers are needed to provide around-the-clock surveillance. Photograph: Game Rangers International
The Connected Conservation Initiative, from Game Rangers International (GRI), Zambia’s Department of National Parks and Wildlife and other partners, is using AI to enhance conventional anti-poaching efforts, creating a 19km-long virtual fence across Lake Itezhi-Tezhi. Forward-looking infrared (FLIR) thermal cameras record every boat crossing in and out of the park, day and night.Installed in 2019, the cameras were monitored manually by rangers, who could then respond to signs of illegal activity. FLIR AI has now been trained to automatically detect boats entering the park, increasing effectiveness and reducing the need for constant manual surveillance. Waves and flying birds can also trigger alerts, so the AI is being taught to eliminate these false readings.
“There have long been insufficient resources to secure protected areas, and having people watch multiple cameras 24/7 doesn’t scale,” says Ian Hoad, special technical adviser at GRI. “AI can be a gamechanger, as it can monitor for illegal boat crossings and alert ranger teams immediately. The technology has enabled a handful of rangers to provide around-the-clock surveillance of a massive illegal entry point across Lake Itezhi-Tezhi.”
2. Tracking water loss
Brazil has lost more than 15% of its surface water in the past 30 years, a crisis that has only come to light with the help of AI. The country’s rivers, lakes and wetlands have been facing increasing pressure from a growing population, economic development, deforestation, and the worsening effects of the climate crisis. But no one knew the scale of the problem until last August, when, using ML, the MapBiomas water project released its results after processing more than 150,000 images generated by Nasa’s Landsat 5, 7 and 8 satellites from 1985 to 2020 across the 8.5m sq km of Brazilian territory. Without AI, researchers could not have analysed water changes across the country at the scale and level of detail needed. AI can also distinguish between natural and human-created water bodies.The Negro River, a major tributary of the Amazon and one of the world’s 10 largest rivers by volume, has lost 22% of its surface water. The Brazilian portion of the Pantanal, the world’s largest tropical wetland, has lost 74% of its surface water. Such losses are devastating for wildlife (4,000 species of plants and animals live in the Pantanal, including jaguars, tapirs and anacondas), people and nature.
“AI technology provided us with a shockingly clear picture,” says Cássio Bernardino, WWF-Brasil’s MapBiomas water project lead. “Without AI and ML technology, we would never have known how serious the situation was, let alone had the data to convince people. Now we can take steps to tackle the challenges this loss of surface water poses to Brazil’s incredible biodiversity and communities.”
3. Finding whales
Knowing where whales are is the first step in putting measures such as marine protected areas in place to protect them. Locating humpbacks visually across vast oceans is difficult, but their distinctive singing can travel hundreds of miles underwater. At National Oceanic and Atmospheric Association (Noaa) fisheries in the Pacific islands, acoustic recorders are used to monitor marine mammal populations at remote and hard-to-access islands, says Ann Allen, Noaa research oceanographer. “In 14 years, we’ve accumulated around 190,000 hours of acoustic recordings. It would take an exorbitant amount of time for an individual to manually identify whale vocalisations.”Google AI that recognises Humpback Whale Song bars.
AI is helping researchers in the Pacific islands recognise whale song from acoustic recordings. Photograph: Noaa
In 2018, Noaa partnered with Google AI for Social Good’s bioacoustics team to create an ML model that could recognise humpback whale song. “We were very successful in identifying humpback song through our entire dataset, establishing patterns of their presence in the Hawaiian islands and Mariana islands,” says Allen. “We also found a new occurrence of humpback song at Kingman reef, a site that’s never before had documented humpback presence. This comprehensive analysis of our data wouldn’t have been possible without AI.”4. Protecting koalas
Australia’s koala populations are in serious decline due to habitat destruction, domestic dog attacks, road accidents and bushfires. Without knowledge of their numbers and whereabouts, saving them is challenging. Grant Hamilton, associate professor of ecology at Queensland University of Technology (QUT), has created a conservation AI hub with federal and Landcare Australia funding to count koalas and other endangered animals. Using drones and infrared imaging, an AI algorithm rapidly analyses infrared footage and determines whether a heat signature is a koala or another animal. Hamilton used the system after Australia’s devastating bushfires in 2019 and 2020 to identify surviving koala populations, particularly on Kangaroo Island.“This is a gamechanger project to protect koalas,” says Hamilton. “Powerful AI algorithms are able to analyse countless hours of video footage and identify koalas from many other animals in the thick bushland. This system will allow Landcare groups, conservation groups and organisations working on protecting and monitoring species to survey large areas anywhere in Australia and send the data back to us at QUT to process it.
“We will increasingly see AI used in conservation,” he adds. “In this current project, we simply couldn’t do this as rapidly or as accurately without AI.”
5. Counting species
Saving species on the brink of extinction in the Congo basin, the world’s second-largest rainforest, is a huge task. In 2020, data science company Appsilon teamed up with the University of Stirling in Scotland and Gabon’s national parks agency (ANPN) to develop the Mbaza AI image classification algorithm for large-scale biodiversity monitoring in Gabon’s Lopé and Waka national parks.Conservationists had been using automated cameras to capture species, including African forest elephants, gorillas, chimpanzees and pangolins, which then had to be manually identified. Millions of pictures could take months or years to classify, and in a country that is losing about 150 elephants each month to poachers, time matters.
The Mbaza AI algorithm was used in 2020 to analyse more than 50,000 images collected from 200 camera traps spread across 7,000 sq km of forest. Mbaza AI classifies up to 3,000 images an hour and is up to 96% accurate. Conservationists can monitor and track animals and quickly spot anomalies or warning signs, enabling them to act swiftly when needed. The algorithm also works offline on an ordinary laptop, which is helpful in locations with no or poor internet connectivity.
“Many central African forest mammals are threatened by unsustainable trade, land-use changes and the global climate crisis,” says Dr Robin Whytock, post-doctoral research fellow at the University of Stirling. “Appsilon’s work on the Mbaza AI app enables conservationists to rapidly identify and respond to threats to biodiversity. The project started with 200 camera traps in Lopé and Waka national parks in Gabon but, since then, hundreds more have been deployed by different organisations across west and central Africa. In Gabon, the government and national parks agency are aiming to deploy cameras across the entire country. Mbaza AI can help all these projects speed up data analysis.”
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Read More »How AI Is Helping Combat Poaching in Africa and Asia
December 19th, 2021
Via Men’s Journal, an article on the use of AI to combat poaching in Africa and Asia:
Early in August, movement in a remote part of Kenya’s Maasai Mara National Reserve woke a sleeping trail camera. In seconds a processor chip took four photos, ran an artificial intelligence program that recognized a human shape and sent the highest-quality image via satellite to park headquarters. Rangers saw it was one of their own on patrol, not a poacher on an illegal hunt, dismissed the alert and got back to work. False alarm, sure, but for biologist Eric Dinerstein it was proof a new technology called TrailGuard AI works.
“Nothing beats a photo for telling if it’s a cattle herder or someone carrying an AK-47,” says Dinerstein, who helped develop the system for RESOLVE, a conservation nonprofit. “TrailGuard is a camera, but I think of it more as an AI-supported poacher alarm.”
Now being distributed to parks across Africa and Asia, the devices are part of a growing pool of innovative technology aimed at protecting habitat and wildlife. Rates of poaching are increasing, particularly in Africa where poachers kill an elephant every 15 minutes, according to the World Animal Foundation.
“Technology is going to play a critical roll in saving wildlife,” says Eric Becker, a conservation engineer with the WWF, a wildlife-focused NGO. “The right tools can be a force multiplier for rangers.”
Rangers typically hunt for signs of poaching during the day and conduct stakeouts at night. Efforts are often futile, wasting scarce resources, and, when successful, dangerous. At least 1,000 rangers have died at work in the last decade, as many as half those deaths at the hands of poachers.
New tech is helping save wildlife and make the ranger’s job safer. In Kenya, Becker, a former Air Force Research lab engineer, helped deploy thermal-imaging cameras to spot poachers from afar. “Rangers no longer patrol randomly,” he says. “They can set up to completely overwhelm the poachers and make arrests.”
Other groups are working on different solutions: traceable chips embedded in rhino horns and elephant tusks to bust trading networks; tracking collars that monitor wildlife movement for signs of stress; analytical tools that identify patterns, like poaching hot spots; and the CAKE Kalk AP, an electric dirt bike designed to help rangers sneak up on poachers. “We usually have to hack or custom develop conservation tools,” Becker says. “Off-the-shelf solutions aren’t usually baboon-proof.”
Preventative and noninvasive measures, like TrailGuard, are the most valuable, says Dinerstein. An early version of the device alerted rangers to a group of poachers entering Serengeti National Park in Tanzania. With the help of tracking dogs, the rangers arrested 30 poachers.
“Several million years of canine olfactory evolution and the latest tech and AI,” says Dinerstein. “It’s a killer combination.”
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