Day 4

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Paper title Russian case study: validation and calibration of the global biomass maps
Authors
  1. Dmitry Schepaschenko International Institute for Applied Systems Analysis (IIASA) Speaker
  2. Elena Moltchanova University of Canterbury
  3. Stanislav Fedorov Federal Forestry Agency
  4. Victor Karminov Center for Forest Ecology and Productivity of the Russian Academy of Sciences
  5. Petr Ontikov FSBI Roslesinforg, Federal Forestry Agency
  6. Maurizio Santoro Gamma Remote Sensing
  7. Linda See International Institute for Applied Systems Analysis (IIASA)
  8. Vladimir Kositsyn Federal Forestry Agency
  9. Anatoly Shvidenko International Institute for Applied Systems Analysis (IIASA)
  10. Anna Romanovskaya Yu. A. Izrael Institute of Global Climate and Ecology
  11. Vladimir Korotkov Yu. A. Izrael Institute of Global Climate and Ecology
  12. Myroslava Lesiv International Institute for Applied Systems Analysis (IIASA)
  13. Sergey Bartalev Space Research Institute of the Russian Academy of Sciences
  14. Steffen Fritz International Institute for Applied Systems Analysis (IIASA)
  15. Maria Shchepashchenko Russian Institute of Continuous Education in Forestry
  16. Florian Kraxner International Institute for Applied Systems Analysis (IIASA)
Form of presentation Poster
Topics
  • A3. Biosphere
    • A3.06 Biomass monitoring
Abstract text Since the collapse of the Soviet Union and being in transition to a new forest inventory system, Russia has reported almost no change in growing stock (+1.3%) and biomass (+0.6%). The Forest and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA) national report 2020 presented 81.1 billion m3 of the growing stock volume (GSV) or 63.0 billion tons in above ground biomass (73.3 t/ha). FAO FRA national report is based on outdated State Forest Register. The first cycle of National Forest Inventory (NFI) was accomplished in Russia in 2020. The results of the new NFI were announced at the UN Climate Change Conference of the Parties (COP26) in Glasgow. The total GSV of Russian forest is 111.7 billion m3, or 38% higher than in the FAO FRA report. This discrepancy explained by the transition to a new inventory system – NFI and the gap in updating forest information.

In Russia, the long intervals between consecutive surveys and the difficulty in accessing very remote regions in a timely manner by an inventory system make satellite remote sensing (RS) an essential tool for capturing forest dynamics and providing a comprehensive, wall-to-wall perspective on biomass distribution. However, observations from current RS sensors are not suited for producing accurate biomass estimates unless the estimation method is calibrated with a dense network of measurements from ground surveys (Chave et al., 2019). Here we calibrated models relating two global RS biomass data products (GlobBiomass GSV (Santoro, 2018) and CCI Biomass GSV (Santoro & Cartus, 2019)) and additional RS data layers (forest cover mask (Schepaschenko et al., 2015), the Copernicus Global Land Cover CGLS‐LC100 product (Buchhorn et al., 2019)) with ca 10,000 ground plots to reduce nuances in the individual input maps due to imperfections in the RS data and approximations in the retrieval procedure (Santoro, 2019; Santoro et al., 2021). The combination of these two sources of information, i.e., ground measurements and RS, utilizes the advantages of both sources in terms of: (i) highly accurate ground measurements and (ii) the spatially comprehensive coverage of RS products and methods. The amount of ground plots currently available may be insufficient for providing an accurate estimate of GSV for the country when used alone, but they are the key to obtaining unbiased estimates when used to calibrate RS datasets (Næsset et al., 2020).

Our estimate of the Russian forest GSV is 111±1.3 billion m3 for the official forested area (713.1 million ha) for the year 2014, which is very close to the NFI aggregated results. An additional 7.1 billion m3 were found due to the larger forested area (+45.7 million ha) recognized by RS (Schepaschenko et al., 2015), following the expansion of forests to the north (Schaphoff et al., 2016), to higher elevations, in abandoned arable land (Lesiv et al., 2018), as well as the inclusion of parks, gardens and other trees outside of forest, which were not counted as forest in the State Forest Register. Based on cross-validation, our estimate at the province level is unbiased. The standard error varied from 0.6 to 17.6% depending on the province. The median error was 1.6%, while the area weighted error was 1.2%. The predicted GSV with associated uncertainties is available here (https://doi.org/10.5281/zenodo.3981198) as a GeoTiff at a spatial resolution of 3.2 arc sec. (ca 0.5 ha).

Acknowledgements
This study was partly supported by the European Space Agency via projects IFBN (4000114425/15/NL/FF/gp). The NFI data preparation and pre-processing were financially supported by the Russian Science Foundation (project no. 19-77-30015). FOS data preparation and processing for the Central Siberia were supported by the RSF (project no 21-46-07002).

References
Buchhorn, M., Bertels, L., Smets, B., Lesiv, M., & Tsendbazar, N.-E. (2019). Copernicus Global Land Service: Land Cover 100m: version 2 Globe 2015: Algorithm Theoretical Basis Document. Zenodo. https://doi.org/10.5281/zenodo.3606446
Chave, J., Davies, S. J., Phillips, O. L., et al. (2019). Ground Data are Essential for Biomass Remote Sensing Missions. Surveys in Geophysics, 40(4), 863–880. https://doi.org/10.1007/s10712-019-09528-w
Lesiv, M., Schepaschenko, D., Moltchanova, E., et al. (2018). Spatial distribution of arable and abandoned land across former Soviet Union countries. Scientific Data, 5, 180056. https://doi.org/10.1038/sdata.2018.56
Næsset, E., McRoberts, R. E., Pekkarinen, A., et al. (2020). Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania. International Journal of Applied Earth Observation and Geoinformation, 102138. https://doi.org/10.1016/j.jag.2020.102138
Santoro, M. (2018). GlobBiomass—Global datasets of forest biomass [Data set]. https://doi.org/10.1594/PANGAEA.894711
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Santoro, M., & Cartus, O. (2019). ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1 [Application/xml]. Centre for Environmental Data Analysis (CEDA). https://doi.org/10.5285/BEDC59F37C9545C981A839EB552E4084
Santoro, M., Cartus, O., Carvalhais, N., et al. (2021). The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth System Science Data, 13, 3927–3950. https://doi.org/10.5194/essd-13-3927-2021
Schaphoff, S., Reyer, C. P. O., Schepaschenko, D., Gerten, D., & Shvidenko, A. (2016). Tamm Review: Observed and projected climate change impacts on Russia’s forests and its carbon balance. Forest Ecology and Management, 361, 432–444. https://doi.org/10.1016/j.foreco.2015.11.043
Schepaschenko, D., Shvidenko, A. Z., Lesiv, M. Yu., et al. (2015). Estimation of forest area and its dynamics in Russia based on synthesis of remote sensing products. Contemporary Problems of Ecology, 8(7), 811–817. https://doi.org/10.1134/S1995425515070136