Using satellite estimates of aboveground biomass to assess carbon stocks in a mixed-management, semi-deciduous tropical forest in the Yucatan Peninsula

Stephanie P. George-Chacón*, David Milodowski, Juan Manuel Dupuy, Jean-François Max, Mathew Williams, Miguel Castillo-Santiago, José Luis Hernández-Stefanoni

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Information on the spatial distribution of forest aboveground biomass (AGB) and its uncertainty is important to evaluate management and conservation policies in tropical forests. However, the scarcity of field data and robust protocols to propagate uncertainty prevent a robust estimation through remote sensing. We upscaled AGB from field data to LiDAR, and to landscape scale using Sentinel-2 and ALOS-PALSAR through machine learning, propagated uncertainty using a Monte Carlo framework and explored the relative contributions of each sensor. Sentinel-2 outperformed ALOS-PALSAR (R2 = 0.66, vs 0.50), however, the combination provided the best fit (R2 = 0.70). The combined model explained 49% of the variation comparing against plots within the calibration area, and 17% outside, however, 94% of observations outside calibration area fell within the 95% confidence intervals. Finally, we partitioned the distribution of AGB in different management and conservation categories for evaluating the potential of different strategies for conserving carbon stock.
Original languageEnglish
JournalGeocarto International
Early online date13 Sep 2021
DOIs
Publication statusPublished - 22 Sep 2021

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