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Models of Pattern Recognition and Forest State Estimation Based on Hyperspectral Remote Sensing Data

  • METHODS AND MEANS OF SATELLITE DATA PROCESSING AND INTERPRETATION
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Abstract

Model applications of airborne hyperspectral remote sensing data for the recognition of forest stand objects and parameterization of the environmental role of forests in climatic models are discussed. The article is focused primarily on a comparison of the data obtained by ground-based forest inspections and the results of processing of hyper-spectral images of a test area. The examples of such a comparison intended to determine the net primary productivity of forests and other parameters characterizing the biodiversity of forest vegetation are considered.

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ACKNOWLEDGMENTS

The study was financially supported by the Russian Science Foundation (project no. 16-11-00007) and the Russian Foundation for Basic Research (project nos. 16-01-00107 and 16-51-55019).

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Authors and Affiliations

  1. Moscow State University, 119234, Moscow, Russia

    V. V. Kozoderov

  2. Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia

    E. V. Dmitriev

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  1. V. V. Kozoderov
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  2. E. V. Dmitriev
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Correspondence to V. V. Kozoderov.

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Translated by N. Statsyuk

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Kozoderov, V.V., Dmitriev, E.V. Models of Pattern Recognition and Forest State Estimation Based on Hyperspectral Remote Sensing Data. Izv. Atmos. Ocean. Phys. 54, 1291–1302 (2018). https://doi.org/10.1134/S0001433818090220

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