Diameter Estimation of Eucalyptus spp. Plantations in Southern Brazil Using Global Ecosystem Dynamics Investigation Data and Support Vector Regression

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Date

2022-06-23

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Virginia Tech

Abstract

Forest plantations make up a large percentage of managed forest land globally. Assessing plantation productivity is vital from both commodity production and carbon management standpoints. Measuring the productivity of these areas is essential given their rapid growth and turnover. Transparent metrics to compare reported carbon storage with estimated values are required for internationally transferred mitigation outcomes under Article 6.2 of the Paris Agreement. Data from the Global Ecosystems Dynamics Investigation (GEDI) provide an excellent opportunity to measure plantation forests over large areas. We focused our efforts on Eucalyptus in southern Brazil and used data from an industrial partner to investigate plantation metrics (height, diameter, volume, stems per hectare, etc.) and to create a model of plantation diameter using Support Vector Regression (SVR). SVR enabled a robust model of tree diameter even given the heteroskedasticity and spatial auto correlation present in the GEDI data, which deleteriously impacted attempts at linear modeling. We could predict tree diameter in these plantations to within 1 cm using space-borne lidar, with broad implications for using space-borne lidars to monitor carbon accretion in secondary forest plantation.

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Keywords

Remote Sensing, Eucalyptus, lidar, Machine Learning, forest plantations

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