Carbon Sequestration Assessment in Napier Grass Plantations Using UAV-Based Imagery and Blockchain-Enabled Carbon Trading

Information from the abstract

Quantifying carbon sequestration in energy-grass plantations at the frequency demanded by voluntary carbon markets remains challenging, since destructive sampling and bomb calorimetry are analytically reliable but labour-intensive. This paper presents a pilot-scale workflow that pairs UAV imagery and image processing with a blockchain ledger to estimate and record above- and below-ground carbon mass in Pak Chong 1 Napier grass (Pennisetum purpureum × P. americanum) over a single four-month growing season at a 5-rai site in Pong Daeng, Nakhon Ratchasima, Thailand. Monthly UAV orthomosaics were segmented into per-hill tiles, from which canopy area and height were extracted and used as inputs to allometric regression models calibrated against laboratory bomb-calorimetry data (BS EN 14918:2009; ASTM D7582 ); the resulting carbon-mass estimates were committed to an Ethereum-compatible test ledger to provide tamper-evident provenance. Predicted carbon mass agreed with laboratory measurements within 2–9 % mean absolute percentage error. The canopy-area-to-biomass and stem carbon-fraction regressions were comparatively stable (R = 0.914 and R = 0.816 respectively), whereas a second-order polynomial model for root carbon fraction (R = 0.9015) proved unstable under leave-one-out cross-validation and bootstrap resampling, a consequence of the small calibration set (n = 4 monthly time-points) that is discussed as a primary limitation. Total above- and below-ground carbon mass at month 4, expressed as CO₂-equivalent using a standard stoichiometric conversion, reached 0.441 × 10⁻³ t CO₂-eq. Because the study draws on a single cultivar, site and season, these findings are presented as a proof-of-concept demonstration of UAV-based estimation combined with blockchain-anchored record-keeping, rather than as a generalised or market-ready carbon-accounting method.

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Related topics: Remote Sensing in Agriculture · Plant Water Relations and Carbon Dynamics · Remote Sensing and LiDAR Applications