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Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon.

Ekena Rangel PinagéMichael KellerChristopher P PeckMarcos LongoPaul DuffyOvidiu Csillik
Published in: Carbon balance and management (2023)
Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.
Keyphrases
  • climate change
  • deep learning
  • high resolution
  • machine learning
  • mass spectrometry
  • convolutional neural network
  • electronic health record
  • artificial intelligence
  • health information
  • heavy metals