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Estimating Aboveground Forest Biomass Using Radar Methods

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Contemporary Problems of Ecology Aims and scope

Abstract

The forest biomass dynamics in boreal forests has a significant effect on global carbon cycles. Biomass estimates provide insight into the carbon balance of forest vegetation in Siberia. This paper discusses the methods used in modern studies (2010–2021) to estimate aboveground forest biomass on the basis of radar remote sensing data. Biomass estimation methodologies are described, including field data collection, data preprocessing, and modeling of relationships between remote sensing (RS) data and biomass. In terms of forest biomass estimation, radar sensing has limited capabilities determined by the characteristics of the survey equipment and parameters of studied forest stands. Modern studies combine optical and radar RS data to estimate forest biomass more accurately using regression models, machine learning, and special techniques (BIOMASAR, SWCM, and MaxEnt). Vegetation optical depth values estimated on the basis of microwave surveys make it possible to solve the saturation problem hindering the estimation of large amounts of biomass. It is difficult to compare the accuracy of biomass estimation methods due to the lack of uniform approaches to experimental and error computation procedures. Errors in biomass estimates produced on the basis of optical and radar data vary considerably (~25% on average). The small amount of reference field data complicates biomass estimations in boreal forests of Siberia. It is believed that the application of machine learning algorithms to remote sensing data collected by the Sentinel-1 and ALOS-PALSAR satellites will make it possible to estimate the biomass of boreal forests with a high spatial resolution.

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Funding

This study was supported in part by the Russian Science Foundation, project no. 22-17-20012 (https://rscf.ru/project/22-17-20012), and supported on a parity basis by the government of the Republic of Khakassia.

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Authors and Affiliations

  1. Sukachev Institute of Forest, Siberian Branch, Russian Academy of Sciences, 660036, Krasnoyarsk, Russia

    S. T. Im & V. I. Kharuk

  2. Siberian Federal University, 660041, Krasnoyarsk, Russia

    S. T. Im & V. I. Kharuk

  3. Reshetnev Siberian State University of Science and Technology, 660014, Krasnoyarsk, Russia

    I. A. Babiy & S. T. Im

  4. Katanov Khakassian State University, 655017, Abakan, Russia

    S. T. Im

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Babiy, I.A., Im, S.T. & Kharuk, V.I. Estimating Aboveground Forest Biomass Using Radar Methods. Contemp. Probl. Ecol. 15, 433–448 (2022). https://doi.org/10.1134/S1995425522050031

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