Predictive modelling is being used to facilitate decision-making by allowing authorities to easily detect areas highly threatened by deforestation.
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Image source
AI can use historical data and environmental factors to predict where deforestation is likely to occur in the future. This helps prioritise conservation efforts, target interventions and allocate resources effectively.
How It Works: AI models analyse data such as historical deforestation patterns, land use, population density, and economic activities. By identifying trends and correlations, AI generates models to predict which areas are most vulnerable to future deforestation.
Examples:
PrevisIA: An AI tool developed by Imazon (Amazon Institute of People and the Environment) that predicts deforestation hotspots in the Amazon.
Microsoft’s AI for Earth: Supports projects that use AI to predict environmental changes, including deforestation.
Activity: Play the video about using AI to protect the Amazon
Discussion Point: How can predictive analytics help governments and NGOs plan better conservation strategies?
Supporting Law Enforcement and Conservation Efforts
AI can assist authorities in detecting and preventing illegal activities, such as logging, mining, and land encroachment, by providing actionable insights and evidence.
How It Works: AI analyses data from satellites, drones, or ground sensors to identify suspicious activities.
Activity: Play the video of how the rainforest is being defended AI
Examples:
Rainforest Connection: Uses AI-powered audio sensors to detect sounds of chainsaws or trucks, alerting authorities to illegal logging.
Brazil’s Amazon Protection System (SIPAM): Uses AI and satellite data to monitor and combat illegal activities in the Amazon from IPAM Amazônia
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Image source: Rainforest Connection
AI can also help track the movement of vehicles or equipment used in illegal deforestation. This data is shared with law enforcement agencies to support investigations and prosecutions.
Discussion Point:
What are the challenges of using AI to support law enforcement in remote areas? (Hint: Consider infrastructure, funding, and coordination.)