Forest degradation is a global phenomenon contributing to climate change as potential source of CO2 emission, as well as to loss of natural ecosystems (e.g., forests, savannahs, natural grasslands and wetlands) and declining biodiversity (IPCC, 2019).

Beyond a critical point of species removal or diminishment, the ecosystem can become destabilized and collapse. Although several methods have been developed to detect and map forest degradation, for example by using optical, synthetic aperture radar (SAR) and/or LiDAR data, there is no single method that can be applied to monitor forest degradation. This is largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. Typical indicators of deforestation are decrease of vegetation indices and biomass, which can be derived from high resolution optical (such as MetImage on Post-EPS) and scatterometer data (such as MetOp Advanced Scatterometer (ASCAT)), and increase in surface albedo. Furthermore deforestation leads to short-term and long-term changes in the emissions of the three most abundant greenhouse gases (CO2, CHG, and NOx). AI may help to relate the exiting indicators and develop a more smart deforestation monitoring system.

IIASA and SAS have collaborated to create a citizen science project. They launched a crowdsource-driven app to gather the collective intelligence of the crowd. They asked volunteers to review and judge images of the rainforest and tag areas that have been affected by human development.

The crowdsourcing helps to improve AI algorithms, speeding up what used to take years to analyze, and therefore help to drive vital policy responses to protect the Earth’s rainforests more quickly. The project has 807,000 square kilometers of the Amazon. To date [8th Feb 2021] – they have evaluated 731,547 square kilometers.

Featured Educator

• Olga Danylo

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