Труды сотрудников ИЛ им. В.Н. Сукачева СО РАН

/ C. Huttich [et al.] // Forests. - 2014. - Vol. 5, Is. 7. - P1753-1776, DOI 10.3390/f5071753 . - ISSN 1999-4907
Аннотация: Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25 m from ALOS-PALSAR and 1 km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands. © 2014 by the authors.licensee MDPI, Basel, Switzerland.

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Держатели документа:
Department for Earth Observation, Friedrich-Schiller-University Jena, Lobdergraben 32, 07743 Jena, Germany
Sukachev Institute of Forest, Siberian Branch of the Russian Academy of Sciences, Krasnoyarsk, 660036, Russian Federation
Space Research Institute of the Russian Academy of Sciences, Moscow 117997, Russian Federation
International Institute for Advanced System Analyses, Laxenburg 2361, Austria

Доп.точки доступа:
Huttich, C.; Korets, M.; Bartalev, S.; Zharko, V.; Schepaschenko, D.; Shvidenko, A.; Schmullius, C.

/ V. V. Shishov [et al.] // Dendrochronologia. - 2016. - Vol. 39. - P42-50, DOI 10.1016/j.dendro.2015.10.001 . - ISSN 1125-7865
Аннотация: It is generally assumed in dendroecological studies that annual tree-ring growth is adequately determined by a linear function of local or regional precipitation and temperature with a set of coefficients that are temporally invariant. However, various researchers have maintained that tree-ring records are the result of multivariate, often nonlinear biological and physical processes. To describe critical processes linking climate variables with tree-ring formation, the process-based tree-ring Vaganov-Shashkin model (VS-model) was successfully used. However, the VS-model is a complex tool requiring a considerable number of model parameters that should be re-estimated for each forest stand. Here we present a new visual approach of process-based tree-ring model parameterization (the so-called VS-oscilloscope) which allows the simulation of tree-ring growth and can be easily used by researchers and students. The VS-oscilloscope was tested on tree-ring data for two species (Larix gmeliniiand Picea obovata) growing in the permafrost zone of Central Siberia. The parameterization of the VS-model provided highly significant positive correlations (p < 0.0001) between simulated growth curves and original tree-ring chronologies for the period 1950-2009. The model outputs have shown differences in seasonal tree-ring growth between species that were well supported by the field observations. To better understand seasonal tree-ring growth and to verify the VS-model findings, a multi-year natural field study is needed, including seasonal observation of the thermo-hydrological regime of the soil, duration and rate of tracheid development, as well as measurements of their anatomical features. © 2015 Elsevier GmbH.

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Держатели документа:
Siberian Federal University, Math Methods and IT Department, Krasnoyarsk, Russian Federation
V.N Sukachev Institute of Forest SB RAS, Laboratory of Tree-Ring Structure, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Shishov, V. V.; Tychkov, I. I.; Popkova, M. I.; Ilyin, V. A.; Bryukhanova, M. V.; Kirdyanov, A. V.

/ V. N. Ustinova [et al.] // IOP Conference Series: Earth and Environmental Science : Institute of Physics Publishing, 2018. - Vol. 211: International Conference and Early Career Scientists School on Environmental Observations, Modeling and Information Systems, ENVIROMIS 2018 (5 July 2018 through 11 July 2018, ) Conference code: 143586, Is. 1, DOI 10.1088/1755-1315/211/1/012025 . -
Аннотация: A probabilistic-statistical parameterization of time series characterizing geological and climatic processes allows determining some regularities by an autocorrelation analysis of signals which differ in nature. The use of the autocorrelation method for analyzing data related to solar and tectonic activity and characterizing the level of stratospheric ozone (total ozone content), hydrothermal regimes (De Martonne aridity index), and wood structure (maximum density of annual rings) allows us to find regularities in time series of various natural processes. Data on the maximum density of Siberian larch trees growing in the Altai Mountains made it possible to calculate the past changes in total ozone content and the aridity index in the Altai Mountains from 1900 to 2014 based on some similarities in the series and a separation of a dendrochronological signal into its main components. © Published under licence by IOP Publishing Ltd.

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Держатели документа:
TUSUR, RETEM Russia, 40, Lenin Avenue, Tomsk, 634050, Russian Federation
Institute of Monitoring of Climatic and Ecological Systems SB RAS, 10/3, Akademicheskii Ave., Tomsk, 634055, Russian Federation
School of Engineering Entrepreneurship, National Research Tomsk Polytechnic University, 30, Lenina Avenue, Tomsk, 634050, Russian Federation
Sukachev Institute of Forest SB RAS, Akademgorodok No50,.28, Krasnoyarsk, 660036, Russian Federation

Доп.точки доступа:
Ustinova, V. N.; Zuev, V. V.; Bondarenko, S. L.; Ustinova, I. G.; Ovchinnikov, D. V.; Kirdjanov, A. V.