Communications of the ACM
Estimating Amazon Carbon Stock Using AI-based Remote Sensing
Credit: Cadaf Project, LMF/INPA
Forests are the major terrestrial ecosystem responsible for carbon sequestration and storage. The Amazon rainforest is the world's largest tropical rainforest encompassing up to 2,124,000 square miles, covering a large area in South America including nine countries. The majority of that area (69%) lies in Brazil. Thus, Amazonia holds about 20% of the total carbon contained in the world's terrestrial vegetation.1,5,7 But the rampant deforestation due to illegal logging, mining, cattle ranching, and soy plantation are examples of threats to the vast region. Biodiversity loss, ecosystem imbalance, and higher concentration of carbon dioxide in the atmosphere are related consequences.9
This work presents advances in the way of a more accurate estimate of the carbon captured by forest areas, in particular the Amazon rainforest and its peculiarities.
Based on the directives given by the Intergovernmental Panel on Climate Change (IPCC), there is an urgent need to provide additional guidance on the design of forest monitoring systems. This involves issues such as forest inventory design, stratification, sampling, pools, accuracy/uncertainty assessment, and the combination of ground-based inventories with remote sensing and modeling approaches. Computing approaches can provide valuable tools to support the development of efficient solutions for this environmental problem. In this article, we present some ongoing research initiatives to address the carbon stock estimation problem.
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