Scientific Data (Oct 2024)

Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation

  • Neha Hunka,
  • Laura Duncanson,
  • John Armston,
  • Ralph Dubayah,
  • Sean P. Healey,
  • Maurizio Santoro,
  • Paul May,
  • Arnan Araza,
  • Clement Bourgoin,
  • Paul M. Montesano,
  • Christopher S. R. Neigh,
  • Hedley Grantham,
  • Peter Potapov,
  • Svetlana Turubanova,
  • Alexandra Tyukavina,
  • Jessica Richter,
  • Nancy Harris,
  • Mikhail Urbazaev,
  • Adrián Pascual,
  • Daniela Requena Suarez,
  • Martin Herold,
  • Benjamin Poulter,
  • Sylvia N. Wilson,
  • Giacomo Grassi,
  • Sandro Federici,
  • Maria J. Sanz,
  • Joana Melo

DOI
https://doi.org/10.1038/s41597-024-03930-9
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 19

Abstract

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Abstract Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration’s (NASA’s) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency’s (ESA’s) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)). The approaches leverage complementary strengths of various EO-derived datasets that are compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP). This transparency and flexibility enables the adoption of any new incoming datasets in the framework in the future. The EO-based AGBD estimates are expected to be an independent contribution to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change.