The Information Infrastructure for Collective Scientific Work in the Far East of Russia

Сорокин, А. А.; Makogonov, S. V.; Korolev, S.P.

doi:10.3103/s0147688217040153

Cited by 79 publications

(30 citation statements)

References 4 publications

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“…This research was supported in through computational resources provided by the Shared Facility Center "Data Center of FEB RAS" (Khabarovsk) [47]. The authors wish to thank three anonymous reviewers for their valuable comments and suggestions to improve this article.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Impact of Deforestation on Streamflow in the Amur River Basin

2019

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In the basin of the Amur River in the Russian Far East, the influence of watershed areas covered by forests on the river basin has a complex nature, and no strict functional dependency has been established yet between these two factors. A study of the Amur River watershed in the current conditions, between 2000 and 2016 (climate, forest coverage, fires, and felling), has been conducted using the ground and satellite observations. The purpose of the study was to identify their influence on the river behaviour (flow, flooding, and levels of water). The study of hydrological regime of rivers was conducted in conjunction with the analysis of the dynamics of forest and burns areas over the synchronised periods of time. A special attention was given to the changing nature of the species composition of the forests (coniferous and deciduous forests separately) from 2000 to 2016, and climatic parameters over thirty years (atmospheric temperature, dew point, precipitation). New facts have been obtained, which provide an explanation of the reasons for predominant prolonged trends in the dynamics of the summer streamflow. In the view of the general tendency toward increased forest coverage combining all species of forest stand, the trend in the dynamics of the coniferous species areas is negative. Therefore, a conclusion can be made, that one of the major factors in the increase of the river flood flow (alongside the atmospheric precipitation), is deforestation of primary coniferous forests on the watershed areas, in contrast with the deciduous forests, where the trend is positive. Practicability of such conclusions can be justified, as different types of forests have different root systems, which mellow the ground and facilitate partial loss of the atmospheric precipitation and its transformation into the groundwater flow. Besides, coniferous forests attract more frequent and intensive fires, more subjected to felling, have longer regeneration period, and also, use larger volumes of ground waters for growing and functioning. Consequently, with their disappearance, an increase in streamflow should be expected. No changes in surface temperature and humidity of the forest cover in the watersheds during 1980-2016 despite global warming. Therefore, annual variability of forested areas of watersheds is greatly influenced by fires and felling. There are reasons to assume, that because of the tendency for decreasing areas of coniferous forests, the conditions contributing to the increases in rivers’ flood flow and flood risks during monsoon and frontal cyclonic rainfalls will remain.

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Section: Acknowledgmentsmentioning

confidence: 99%

Impact of Deforestation on Streamflow in the Amur River Basin

2019

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“…Обработка данных производилась с использованием ресурсов Центров коллективного пользования "Центр данных ДВО РАН" (ВЦ ДВО РАН) [13] и "ИКИ-Мониторинг" (Институт космических исследований РАН) [6].…”

Section: благодарностиunclassified

The Retrospective Analysis of the 1964 Sheveluch Volcano Eruption (Kamchatka) Using the VolSatView Information System

Гирина¹,

Ras²,

Malkovsky³

et al. 2019

Information Technologies in Remote Sensing of the Earth - RORSE 2018

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Owing to modern development of information technologies and computer simulation systems, and datasets derived from open historical meteorological data archives, it appeared possible to perform retrospective analysis of large explosive volcanic eruptions. This work analyzes the results of simulation of the events associated with the eruptive cloud propagation during the catastrophic eruption of the November, 1964 Sheveluch volcano. The obtained additional eruptive cloud parameters enabled us to reproduce the dynamics of the explosive eruption and to validate its magmatic genesis.

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“…Представленные классификаторы показали достаточно высокий уровень качества и были внедрены в оперативную работу Дальневосточного центра НИЦ «Планета». При разработке были использованы вычислительные ресурсы Центра Коллективного Пользования «Центр данных ДВО РАН» [18].…”

Section: заключениеunclassified

Snow and Cloud Detection Using Convolutional Neural Network According to the Data Derived from MSU-MR Sensor of the Spacecraft Meteor-M No. 2

Kramareva¹,

Andreev²,

Simonenko³

et al. 2019

Information Technologies in Remote Sensing of the Earth - RORSE 2018

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The paper presents a method of forming cloud and snow masks using classifiers based on a convolutional neural network. The input data are the textures of six channels, obtained according to MSU-MR sensor in a resolution of 1 km. A comparison was made the Random Forest algorithm, where separate pixels, NDVI and NDSI indices were used as input data as well as texture features calculated from GLCM matrix. The resulting classifiers were evaluated by calculating f-measure as well as by comparison with the results of manual interpretation by an experienced specialist and comparison with the cloud mask derived from VIIRS sensor.

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The Information Infrastructure for Collective Scientific Work in the Far East of Russia

Cited by 79 publications

References 4 publications

Impact of Deforestation on Streamflow in the Amur River Basin

Impact of Deforestation on Streamflow in the Amur River Basin

The Retrospective Analysis of the 1964 Sheveluch Volcano Eruption (Kamchatka) Using the VolSatView Information System

Snow and Cloud Detection Using Convolutional Neural Network According to the Data Derived from MSU-MR Sensor of the Spacecraft Meteor-M No. 2

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