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

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.

Keywords:

• Remote sensing
• machine learning
• LiDAR
• error propagation
• Sentinel-2
• ALOS PALSAR

Acknowledgements

The authors would like to thank Fernando Tun Dzul and Reserva Biocultural Kaxil Kiuic, for their support during fieldwork. The first author would like to thank Consejo Nacional de Ciencia y Tecnologia (CONACyT) for the Ph D. scholarship awarded.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Availability of data and materials

The ALOS PALSAR data used in this study was downloaded from (https://www.eorc.jaxa.jp/ALOS/en/top/obs_top.htm). The LiDAR data can be accessed at (https://gliht.gsfc.nasa.gov/). Data from national forest inventory in Mexico can be obtained by request to CONAFOR (Comisión Nacional Forestal, https://www.gob.mx/conafor).

Additional information

Funding

This work was funded by Ecometrica LTD and the UK space agency under project Forests 2020.