Past decade above-ground biomass change comparisons from four multi-temporal global maps

0572439 - ÚVGZ 2024 RIV NL eng J - Journal Article
Araza, A. - Herold, M. - de Bruin, S. - Ciais, P. - Gibbs, D. A. - Harris, N. - Santoro, M. - Wigneron, J. - Yang, H. - Malaga, N. - Nesha, K. - Rodriguez-Veiga, P. - Brovkina, Olga - Brown, H. C. A. - Chanev, M. - Dimitrov, Z. - Filchev, L. - Fridman, J. - Garcia, M. - Gikov, A. - Govaere, L. - Dimitrov, P. - Moradi, F. - Muelbert, A. E. - Novotný, Jan - Pugh, T. A. M. - Schelhaas, M.J. - Schepaschenko, D. - Sterenczak, K. - Hein, L.
Past decade above-ground biomass change comparisons from four multi-temporal global maps.
International Journal of Applied Earth Observation and Geoinformation. Roč. 118, APR (2023), č. článku 103274. ISSN 1569-8432. E-ISSN 1872-826X
Research Infrastructure: CzeCOS IV - 90248
Institutional support: RVO:86652079
Keywords : Above-ground biomass * Above-ground biomass change * Carbon flux * Map assessment * Global carbon cycle * Earth observation
OECD category: Forestry
Impact factor: 7.5, year: 2022
Method of publishing: Open access
https://www.sciencedirect.com/science/article/pii/S1569843223000961?via%3Dihub

Above-ground biomass (AGB) is considered an essential climate variable that underpins our knowledge and information about the role of forests in mitigating climate change. The availability of satellite-based AGB and AGB change (Delta AGB) products has increased in recent years. Here we assessed the past decade net Delta AGB derived from four recent global multi-date AGB maps: ESA-CCI maps, WRI-Flux model, JPL time series, and SMOS-LVOD time series. Our assessments explore and use different reference data sources with biomass re-measurements within the past decade. The reference data comprise National Forest Inventory (NFI) plot data, local Delta AGB maps from airborne LiDAR, and selected Forest Resource Assessment country data from countries with well-developed monitoring capacities. Map to reference data comparisons were performed at levels ranging from 100 m to 25 km spatial scale. The comparisons revealed that LiDAR data compared most reasonably with the maps, while the comparisons using NFI only showed some agreements at aggregation levels <10 km. Regardless of the aggregation level, AGB losses and gains according to the map comparisons were consistently smaller than the reference data. Map-map comparisons at 25 km highlighted that the maps consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall AAGB map cross-correlation between maps varied in the range 0.11-0.29 (r). Reported AAGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that AAGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, AAGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.
Permanent Link: https://hdl.handle.net/11104/0343468