Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation . Measuring how much carbon is stored in forests is, however, still largely done via expensive, time-consuming, and sometimes unaccountable field measurements . To overcome limitations, many verification bodies are leveraging machinelearning (ML) algorithms to estimate forest carbon from satellite or aerialimagery . Initial results show that forest carbon estimates from satelliteimagery can overestimate above-ground biomass by more than 10-times fortropical reforestation projects . The significant difference between aerial and .satellite-derived forest carbon measurements shows the potential for aerial imagery-based ML algorithms and raises the importance to extend this study to aglobal benchmark between options for carbon measurements, says the author . The author . Back to the page you came from: http://www.

Author(s) : Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Xiaoxiang Zhu, Ce Zhang

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Keywords : carbon - forest - measurements - aerial - author -

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