What is it about?
Data on land-use change matrix is almost unavailable for most developing countries under the Paris Agreement. This article uses satellite imagery analysis and machine learning to estimate and classify all the six landforms and their change according to IPCC guidelines
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Why is it important?
Our findings show that the gap in the availability of activity data on land use and land-use changes can be addressed by analyzing satellite imageries combine with machine learning techniques. Thus, emissions from the AFOLU sector in general, and the land use subsector can easily be calculated.
Perspectives
I hope this article helps Parties from the developing countries especially, under the Paris agreement to be able to fill the gap in their activity data for greenhouse gas estimations and reporting.
Bright Aboh
African Institute for Mathematical Sciences
Read the Original
This page is a summary of: Satellite imagery analysis for Land Use, Land Use Change and Forestry, June 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3378393.3402268.
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