Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees
This work addresses the need for accurate carbon storage assessments in urban areas to inform climate policy, though it is incremental as it applies existing methods to a new geographic context.
The paper tackles the problem of assessing carbon storage by urban trees in Africa by developing a novel workflow that integrates spatial data preparation, satellite imagery classification with machine learning, and carbon assessment, resulting in the first such estimate for an African region following IPCC guidelines.
Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making. However, climate science studies commonly require addressing complex interoperability issues between data, software, and experimental approaches from multiple fields. Scientific workflow systems provide unparalleled advantages to address these issues, including reproducibility of experiments, provenance capture, software reusability and knowledge sharing. In this paper, we introduce a novel workflow with a series of connected components to perform spatial data preparation, classification of satellite imagery with machine learning algorithms, and assessment of carbon stored by urban trees. To the best of our knowledge, this is the first study that estimates carbon storage for a region in Africa following the guidelines from the Intergovernmental Panel on Climate Change (IPCC).