Call for Papers "Forest Carbon Monitoring and Artificial Intelligence" Frontiers in Environmental Science (IF: 4.581) （情報掲載2022/3/5）

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Frontiers in Environmental Science (IF: 4.581)です。

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Call for Papers "Forest Carbon Monitoring and Artificial Intelligence"
Frontiers in Environmental Science (IF: 4.581)
Editors: Nophea Sasaki, David Thau, and Toshiaki Owari
Papers can be submitted at:
https://www.frontiersin.org/research-topics/26080/forest-carbon-monitoring-and-artificial-intelligence

About this Research Topic
Forest carbon monitoring is the collection and archiving of all data such as land use and land cover change, forest inventory, carbon stocks, carbon emission factors, and other related data necessary for measuring, reporting, and verifying carbon emissions or sources, emission reductions, carbon removals or sinks, within the targeted locations at any scale temporarily and spatially. As part of the climate change mitigation measures, for achieving the target of the Paris Agreement, a key focus is on reducing emissions from; deforestation, forest degradation, conservation of forests, sustainable management of forests, and enhancement of forest carbon stocks. This is collectively known as REDD+ of the United Nations Framework Convention on Climate Change and is a result-based payment for developing countries, where the implementation of the REDD+ activities results in carbon emission reductions and/or carbon removals. Therefore, forest carbon monitoring plays a vital role in ensuring that carbon emissions or removals can be measured, reported, and verified.

This Research Topic focuses on applications of AI, machine learning, and deep learning for solving the forest carbon monitoring, measurement, reporting, and verification challenges in various disciplines such as land cover, land-use change, illegal logging, forest degradation, deforestation, and forest restoration. This collection also considers papers on data-driven approaches and the development of online applications for real-time or near real-time detection of carbon emissions or removals due to land use and land cover change and forest management (the latter includes management, conservation, and restoration).

This Research Topic welcome submissions that mainly include but are not limited to the following aspects:
Automation of science discovery related to forest carbon monitoring,measurement, reporting, and verification.
Detection of illegal logging and related carbon loss.
Detection of land cover and land-use changes and related carbon loss.
Detection of forest fires and carbon loss.
Detection of plant insects and carbon loss.
Computer vision for detecting and preventing illegal timber.
Prediction of carbon emissions and removals across scales.
Real-time or near-real-time detection of carbon sources or sinks.
Mobile applications for forest carbon monitoring, measurement, reporting,and verification.
Supply chain management in forestry and AI.
Life cycle assessment in forestry and AI.

This Research Topic aims to provide a platform for a broader audience to publish and obtain the latest innovative research and technology advancement in the cross-cutting areas of forest carbon monitoring and the applications of artificial intelligence, machine learning, and deep learning in forest carbon monitoring spatially and temporarily at all scales. We welcome research articles, short reports, and review articles.