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New technologies to map environmental crime in the Amazon Basin (commentary)

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Environmental crime slows climate action, deters investment in nature-based solutions, and undermines the green transition. Around the world, land grabbing, illegal deforestation, illicit mining, poaching and a rash of other crimes are ravaging tropical forests, eroding biodiversity, and reversing sustainable development. Despite growing awareness of the problem, an ecosystem of criminality persists in most major biomes, from the Amazon Basin to the African Great Lakes region and the rainforests of Southeast Asia. Preventing and reducing environmental crime is challenging due to the logistical constraints posed by policing vast geographic territories. Dismantling criminal markets is frustrated by the weak rule of law in areas affected by metastasizing organized crime and meager economic alternatives to the unsustainable extraction of natural resources.  Notwithstanding these and other obstacles, new technologies are helping to optimize detection, deterrence and disruption of environmental crime, including in the Amazon Basin. Governments, companies and non-governmental organizations are starting to leverage geospatial and predictive analytics to anticipate where certain types of environmental crime are more likely to occur. In Brazil, for example, several promising initiatives are combining remote sensing data with machine learning tools to monitor and predict everything from selective logging to wildcat gold mining. Many of these innovations are being designed and developed through public-private partnerships, including with state agencies, technology firms, universities, and specialized non-governmental organizations.  Take the case of MapBiomas, a multi-partner initiative tracking phenomena such as land use, fire scars, soil carbon stocks, industrial mining, and deforestation. Launched in 2015, it receives funds from multiple sources to power a collaborative network of universities, non-governmental organizations and…This article was originally published on Mongabay

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