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49 lines
2.5 KiB
TeX
49 lines
2.5 KiB
TeX
\section{Background}
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\label{sec:background}
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\subsection{Carbon Credit Standards}
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The VCM is dominated by four standards bodies: Verra (VCS), Gold
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Standard, American Carbon Registry, and Climate Action Reserve
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\citep{schneider2019double}. Each maintains methodologies for
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quantifying emissions reductions and procedures for third-party
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verification. Despite these frameworks, systematic over-crediting has
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been documented across multiple project types \citep{badgley2022,
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west2023action}.
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\subsection{Remote Sensing for Forest Monitoring}
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Satellite-based forest monitoring has advanced dramatically with the
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Copernicus Sentinel program \citep{drusch2012sentinel} and NASA's
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GEDI mission \citep{dubayah2020global}. Sentinel-2 provides 10\,m
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resolution multi-spectral imagery every 5 days. GEDI provides
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spaceborne LiDAR measurements of forest canopy height and structure.
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Combined, these enable wall-to-wall biomass mapping at unprecedented
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spatial and temporal resolution \citep{lang2023high}.
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\subsection{Machine Learning for Biomass Estimation}
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Traditional allometric approaches estimate biomass from plot-level
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measurements of diameter, height, and wood density \citep{chave2014improved}.
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Machine learning approaches directly regress biomass from remote sensing
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features: random forests \citep{rodriguez2021predicting}, gradient
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boosting \citep{hancock2019gradient}, and deep learning
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\citep{lang2023high}. Recent models fusing optical, radar, and LiDAR
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data achieve continental-scale AGB maps with uncertainties of 20--30
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Mg/ha \citep{xu2021changes}.
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\subsection{Blockchain for Carbon Markets}
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Blockchain applications in carbon markets include credit tokenization
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\citep{schletz2020blockchain}, registry interoperability
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\citep{franke2020blockchain}, and automated retirement tracking
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\citep{howson2019crypto}. Most existing platforms tokenize credits
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issued by traditional standards without addressing the underlying MRV
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quality problem. ZooMRV differs by generating native on-chain credits
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with embedded verification data.
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