Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees

TL;DR

This paper presents a novel scientific workflow integrating spatial data processing, machine learning classification, and carbon storage assessment of urban trees, specifically applied to an African region, advancing climate science and urban ecology.

Contribution

It introduces a new workflow combining data preparation, machine learning, and carbon assessment, and is the first to estimate urban tree carbon storage in Africa following IPCC guidelines.

Findings

Workflow successfully classifies satellite imagery.

Estimates carbon storage in urban trees in Africa.

Enhances reproducibility and interoperability in climate-related studies.

Abstract

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).