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Environmental News Network – Big Data, Machine Learning Shed Light on Asian Reforestation Successes

October 29, 2020018
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Purdue’s Jingjing Liang found that efforts to plant trees in South Korean forests and this one in northeast China, have paid dividends for increasing carbon storage.

Since carbon sequestration is such an important factor for mitigating climate change, it’s critical to understand the efficacy of reforestation efforts and develop solid estimates of forest carbon storage capacity. However, measuring forest properties can be difficult, especially in places that aren’t easily reachable.

Purdue University’s Jingjing Liang, an assistant professor of quantitative forest ecology and co-chair of the Forest Advanced Computing and Artificial Intelligence (FACAI) Laboratory in the Department of Forestry and Natural Resources, led an international team to measure forest carbon capacity in northeast Asia. Their research, which blends remote sensing, field work and machine learning, offers the most up-to-date estimates of carbon capture potential in reclusive North Korea and details the benefits of reforestation efforts over the last two decades in China and South Korea.

“Because there is historically scant data from North Korea, people know little about how much carbon is stored in this region,” said Liang, whose findings were published in the journal Global Change Biology. “Based on the data we can collect from northeast China and South Korea, we used machine learning and big data analysis to estimate how much carbon is stored across the entire region. We mapped for the first time with accuracy the spatial trend of carbon storage in that region of the world.”

Continue reading at Purdue University

Image via Purdue University

 

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