  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).
