Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model

Xiaojing Tang; Lucy R Hutyra; Paulo Arévalo; Alessandro Baccini; Curtis E Woodcock; Pontus Olofsson

doi:10.1016/j.scitotenv.2020.137409

Spatiotemporal tracking of carbon emissions and uptake using time series analysis of Landsat data: A spatially explicit carbon bookkeeping model

Sci Total Environ. 2020 Jun 10:720:137409. doi: 10.1016/j.scitotenv.2020.137409. Epub 2020 Feb 19.

Authors

Xiaojing Tang¹, Lucy R Hutyra², Paulo Arévalo³, Alessandro Baccini⁴, Curtis E Woodcock⁵, Pontus Olofsson⁶

Affiliations

¹ Department of Earth & Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA. Electronic address: xjtang@bu.edu.
² Department of Earth & Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA. Electronic address: lrhutyra@bu.edu.
³ Department of Earth & Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA. Electronic address: parevalo@bu.edu.
⁴ Department of Earth & Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA. Electronic address: abaccini@bu.edu.
⁵ Department of Earth & Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA. Electronic address: curtis@bu.edu.
⁶ Department of Earth & Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA. Electronic address: olofsson@bu.edu.

PMID: 32145612
DOI: 10.1016/j.scitotenv.2020.137409

Abstract

Reducing terrestrial carbon emissions to the atmosphere requires accurate measuring, reporting and verification of land surface activities that emit or sequester carbon. Many carbon accounting practices in use today provide only regionally aggregated estimates and neglect the spatial variation of pre-disturbance forest conditions and post-disturbance land cover dynamics. Here, we present a spatially explicit carbon bookkeeping model that utilizes a high-resolution map of aboveground biomass and land cover dynamics derived from time series analysis of Landsat data. The model produces estimates of carbon emissions/uptake with model uncertainty at Landsat resolution. In a case study of the Colombian Amazon, an area of 47 million ha, the model estimated total emissions of 3.97 ± 0.71 Tg C yr^-¹ and uptake by regenerating forests of 1.11 ± 0.23 Tg C yr^-¹ 2001-2015, with an additional 45.1 ± 7.99 Tg of carbon remaining in the form of woody products, decomposing slash and charcoal at the end of 2015 (estimates provided with ±95% confidence intervals). Total emissions attributed to the study period (including carbon not yet released) is 6.97 ± 1.24 Tg C yr^-¹. The presented model is based on recent technological advancements in the field of remote sensing to achieve spatially explicit modeling of carbon emissions and uptake associated with land surface changes and post-disturbance landscapes that is compliant with international reporting criteria.

Keywords: Bookkeeping; Carbon; Emission; Landsat; REDD+; Time series.