RUSSIAN JOURNAL OF FOREST SCIENCE, 2021, No. 6, P. 609–626

GEOSPATIAL MODELING OF BIOMETRIC AND STRUCTURAL FOREST ATTRIBUTES IN THE BRYANSK OBLAST BASED ON SATELLITE IMAGERY AND SELECTIVE INVENTORY DATA
E. A. Gavrilyuk, N. V. Koroleva, D. A. Karpukhina, E. N. Sochilova, D. V. Ershov
Center for Forest Ecology and Productivity of the Russian Academy of Sciences (CEPF RAS) Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997 Russia
E-mail: egor@ifi.rssi.ru

Received 31 March 2021
The geospatial modeling capabilities of biometric (stand age, height, stem diameter, growing stock volume) and structural (coniferous and deciduous species stock ratio) forests attributes based on Landsat multi-seasonal satellite imagery and selective forest inventory data at regional scale, specifically for the Bryansk oblast of Russian Federation, were assessed. The reference sample for models training and testing was obtained from taxation descriptions for approximately 10000 forest sites with a total area of about 35 thousand hectares. Used satellite data were temporary synchronized with a period of taxation works (2002-2005). The main stages of geospatial modeling, including the compositing of multi-seasonal mosaic images, the calculation of spectral variables and the extraction of their values in the locations of reference sites, the training of Random forest regression models and the prediction over the study area, was performed on the Google Earth Engine cloud platform. The best results were obtained for coniferous and deciduous species stock ratio models – coefficient of determination R² = 0.7 with a scaled root-mean-square error RMSE = 22%. For biometric attributes R² varied from 0.4 for stand age to 0.5 for growing stock volume, and RMSE was in the range of 26–37%. Such accuracy levels are fully consistent with the results of similar Russian and foreign studies. The obtained thematic products demonstrated high convergence with official statistics data at forestry scale: squared correlation coefficient r² = 0.98 with a scaled mean deviation MD = 5% for the forested area and r² = 0.96 with MD = 8.6% for the total growing stock. To conclude, the combination of the initial data and methods of their processing described in our work can be used for obtaining reliable estimates of the biometric and structural forest attributes, at least, at the federal subject’s scale.
Keywords: forest stand age, forest stand height, tree stem diameter, growing stock volume, coniferous and deciduous species ratio, remote sensing, Landsat, Random Forest, Google Earth Engine.
Acknowledgements: This study was performed within the framework of the state assignment of the CEPF RAS no. АААА-А18-118052590019-7 (data processing), and was financially supported by the Russian Science Foundation project no. 19-77-30015 (data collection and scripting).
DOI: 10.31857/S002411482106005X

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