Models of Pattern Recognition and Forest State Estimation Based on Hyperspectral Remote Sensing Data
- Authors: Kozoderov V.V.1, Dmitriev E.V.2
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Affiliations:
- Moscow State University
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences
- Issue: Vol 54, No 9 (2018)
- Pages: 1291-1302
- Section: Methods and Means of Satellite Data Processing and Interpretation
- URL: https://journals.rcsi.science/0001-4338/article/view/148660
- DOI: https://doi.org/10.1134/S0001433818090220
- ID: 148660
Cite item
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.
About the authors
V. V. Kozoderov
Moscow State University
Author for correspondence.
Email: vkozod@mail.ru
Russian Federation, Moscow, 119234
E. V. Dmitriev
Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences
Email: vkozod@mail.ru
Russian Federation, Moscow