Vega-Les Information System. Actual Features and Future Evolution

Balashov, И.В.; Bartalev, Sergey; Burtsev, М.А.; Vorushilov, I; Egorov, V.A.; Kashnitskii, A.V.; Khovratovich, T.S.; Khvostikov, S.A.; Kobets, D.A.; Loupian, Е.А.; Saigin, I.A.; Senko, K.S.; Stytsenko, F.V.; Sychugov, I.G.; Zharko, Vasily

doi:10.1088/1755-1315/507/1/012002

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…Land-cover classifications were produced for the two study areas from the available MSI images using the Semi-Automatic Classification plugin (version 6.4.5: [71]) for QGIS version 3.14 [72]. Training data for these classifications were produced by generating classified point objects from the MODIS-based Russian land cover map [73,74] available through the VEGA system of the Space Research Institute of the Russian Academy of Sciences [75]. The 'Point object' tool in the VEGA system was used to generate the classified point objects.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

et al. 2021

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Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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Section: Methodsmentioning

confidence: 99%

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

et al. 2021

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“…The work was carried out using the capabilities of the "IKI-Monitoring" center for collective use [1,2] with the support of the Ministry of Science and Higher Education of the Russian Federation (theme "Monitoring", state registration No.01.20.0.2.00164).…”

Section: Discussionmentioning

confidence: 99%

“…Vega-Les information system (IS) [1] was developed by the Space Research Institute of the Russian Academy of Sciences (IKI) in 2019 based on the "IKI-Monitoring" center for collective use [2,3] infrastructure. The main purpose of this system is to provide researchers with the capabilities for remote complex study of forest resources in Russia using Earth observation data.…”

Section: Introductionmentioning

confidence: 99%

Regional monitoring of forests using the Vega-Les system: case study for Tungussko-Chunskoye forest management unit and Tunguska reserve in the Russian Krasnoyarsk region

et al. 2020

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This paper demonstrates the capabilities of the Vega-Les (“Les” is the Russian word meaning “forest”) information system (IS) for forest monitoring. A brief assessment and characteristics of the Earth observation data and main available thematic products about Russian forests available in the system are given. An assessment of the capabilities of the Vega-Les IS for studying local scale forest changes was carried out. The Tungussko-Chunskoye forest management unit (FMU) and the Tunguska nature reserve in the Russian Krasnoyarsk Krai region were chosen as the test area. The analysis of forest cover changes over this area since the beginning of the 21st century, including the changes in the number and extent of wildfires, is presented. As a result, it is concluded that the Vega-Les IS is applicable for remote assessment and monitoring of various characteristics of Russian forests.

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“…We linked each record from the wildfire database to forest type available for Siberia from the VEGA geoinformation resource [16] using GIS procedure of intersection of vector layers. Forest types characteristics and pre-fire fuel loads were summarized from field obtained data [1,6,7,18,19].…”

Section: Methodsmentioning

confidence: 99%

“…), and aspen (Populus tremula) species. Forests dominated (based on data from Vega-service, http://pro-vega.ru/maps/ [16]) by larch range over an area of 2.7-3.0 × 10 6 km 2 ; an area of Scots pine stands extends over 1.2 × 10 6 km 2 , dark coniferous stands occupy 1.0 × 10 6 km 2 , and mixed forest covers about 7.7 × 10 6 km 2 .…”

Section: Study Areamentioning

confidence: 99%

Estimation of Direct Fire Emissions from Forests Burning in Siberia

Пономарев

2020

The 3rd International Electronic Conference on Atmospheric Sciences

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Using a database on wildfires recorded by remote sensing for 1996-2020, we assessed the seasonal variation of direct carbon emissions from the burning in Siberian forests. We have implemented an approach that takes into account the combustion parameters and the changing intensity of the fire (in terms of Fire Radiative Power, FRP), which affects the accuracy of the emission estimate. For the last two decades, the range of direct carbon emissions from wildfires was 20-250 Тg С per year. Sporadic maxima were fixed in 2003 (> 150 Тg С/yr), in 2012 (> 220 Тg С/yr), and in 2019 (> 190 Тg С/yr). Preliminary estimation of emissions for 2020 (on 30th of September) was ~180 Tg/year. Fires in the larch forests of the flat-mountainous taiga region (Central Siberia) made the greatest contribution (>50%) to the budget of direct fire emission, affecting the quality of the atmosphere in vast territories during summer period. According to the temperature rising and forest burning trend in Siberia, the fire emissions of carbon may double (220 Тg С/yr) or even increase by an order of magnitude (>2000 Тg С/yr) at the end of the 21st century, which was evaluated depending on IPCC scenario.

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Vega-Les Information System. Actual Features and Future Evolution

Cited by 9 publications

References 8 publications

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Regional monitoring of forests using the Vega-Les system: case study for Tungussko-Chunskoye forest management unit and Tunguska reserve in the Russian Krasnoyarsk region

Estimation of Direct Fire Emissions from Forests Burning in Siberia

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