• O.O. Kavats
  • D.A. Khramov
  • K.L. Sergieieva
  • V.V. Vasyliev



forest, carbon stock, aboveground biomass, satellite data, GEDI, ICESat-2


Current approaches to carbon stock estimation in forest ecosystems are discussed. Datasets containing biomass and carbon stock estimates that can be used for training/validation in machine learning are described. Examples of applying the remote approach to assessing forest biomass over large areas are analyzed. To estimate the forest carbon stocks in Ukraine, the most promising is the remote approach, which combines ground-based and satellite measurements for forest classification and statistical modeling of carbon stocks. For training and validation of machine learning algorithms, it is proposed to use the GEDI Biomass Map covering most of the territory of Ukraine — from the southern borders to the latitude of Chernihiv in the north. A prototype of forest biomass estimating product in Ukraine can be based on publicly available MODIS NBAR data, SRTM DEM, ECMWF climate data and use the Random Forest machine learning method.


Good Practice Guidance for Land Use, Land-Use Change and Forestry / Edited by J. Penman et al. IPCC, 2003. URL:

Zamolodchikov D. Systems for estimating and forecasting carbon stocks in forest ecosystems // Sustainable Forestry. 2011. № 4 (29). С. 15-22.


Gonçalves, A. C., Sousa, A., Malico, I. (Eds.). (2021). Forest Biomass — From Trees to Energy. IntechOpen. / Patrícia Lourenço. Biomass Estimation Using Satellite-Based Data, DOI: 10.5772/intechopen.93603. URL:

Rodríguez-Veiga, P., Wheeler, J., Louis, V. et al. Quantifying Forest Biomass Car-bon Stocks From Space. Curr Forestry Rep 3, 1–18 (2017).

Baccini, A., Goetz, S., Walker, W. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Clim Change 2, 182–185 (2012).

CEOS Above Ground Biomass. Above ground biomass products: NCEO Africa Bi-omass, IceSat-2 Boreal Biomass, JPL Global Biomass, GEDI Biomass, ESA CCI Bio-mass. URL:

How We Measure the Carbon Capture Potential of Forests _ Terraformation Blog. URL:

Kolchugina, T.P. and Vinson, T.S. (1993). Carbon sources and sinks in forest bi-omes of the former Soviet Union. Global Biogeochemical Cycles 7: doi: 10.1029/93GB00571.

Kindermann G.E., McCallum I., Fritz S., Obersteiner M. (2008). A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fennica vol. 42 no. 3 article id 244.

Shvidenko, A.Z. & Schepaschenko, D.G. (2014). Carbon budget of Russian for-ests. Siberian Journal of Forest Science 1 69-92. URL:

Korzukhin, M. D., & Korotkov, V. N. (2018). Modification of the ROBUL Model for Accounting the Carbon Balance of Russian Forests. Fundamental and Applied Climatology, 3, 30–53.


Baccini, A., Goetz, S., Walker, W. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Clim Change 2, 182–185 (2012). Supplementary information:

Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S., White, L., Silman, M., & Morel, A. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences, 108(24), 9899–9904.

Bordoloi, R., Das, B., Tripathi, O. P., Sahoo, U. K., Nath, A. J., Deb, S., Das, D. J., Gupta, A., Devi, N. B., Charturvedi, S. S., Tiwari, B. K., Paul, A., & Tajo, L. (2022). Satellite based integrated approaches to modelling spatial carbon stock and carbon sequestration potential of different land uses of Northeast India. Environmental and Sustainability Indicators, 13, 100166.

Asner, G. P. et al. Targeted carbon conservation at national scales with high-resolution monitoring. Proceedings of the National Academy of Sciences 111, E5016–E5022 (2014).

Ghanbari Motlagh M., Babaie Kafaky S., Mataji A., Akhavan R. (2019): Calcula-tion of the aboveground carbon stocks with satellite data and statistical models inte-grated into the climatic parameters in the Alborz Mountain forests (Northern Iran). J. For. Sci., 65: 493-503.

Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., & Sun, Y. (2021). Combi-nation of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass. Forests, 12(2), 216.

L. Yang, S. Liang and Y. Zhang, "A New Method for Generating a Global Forest Aboveground Biomass Map From Multiple High-Level Satellite Products and Ancil-lary Information," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 2587-2597, 2020,

doi: 10.1109/JSTARS.2020.2987951

Santoro, M., Beer, C., Cartus, O., Schmullius, C., Shvidenko, A., McCallum, I., Wegmueller, U., & Wiesmann, A. (2010). The BIOMASAR algorithm: An approach for retrieval of forest growing stock volume using stacks of multi-temporal SAR data. In: Proceedings of ESA Living Planet Symposium, 28 June-2 July 2010.


Khati U, Lavalle M and Singh G (2021) The Role of Time-Series L-Band SAR and GEDI in Mapping Sub-Tropical Above-Ground Biomass. Front. Earth Sci. 9:752254. doi: 10.3389/feart.2021.752254

Ehlers, D.; Wang, C.; Coulston, J.; Zhang, Y.; Pavelsky, T.; Frankenberg, E.; Woodcock, C.; Song, C. Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data. Remote Sens. 2022, 14, 1115.