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Sergey Bartalev
A Space Research Institute , Russian Academy of Sciences , Moscow

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Preprint content
Published: 04 March 2021
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Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported (FAO, 2014) almost no changes in growing stock (+1.8%) and biomass (+0.6%). Yet remote sensing products indicate increased vegetation productivity (Guay et al., 2014), tree cover (Song et al., 2018) and above-ground biomass (Liu et al., 2015). Here, we challenge the official national statistics with a combination of recent National Forest Inventory and remote sensing data products to provide an alternative estimate of the growing stock of Russian forests and assess the relative changes in the post-Soviet era. Our estimate for the year 2014 is 118.29±1.3 109 m3, which is 48% higher than the official value reported for the same year in the State Forest Register. The difference is explained by increased biomass density in forested areas (+39%) and larger forest area estimates (+9%). Using the last Soviet Union report (1988) as a reference, Russian forests have accumulated 1163×106 m3 yr-1 of growing stock between 1988–2014, which compensates for forest growing stock losses in tropical countries (FAO FRA, 2015). Our estimate of the growing stock of managed forests is 94.2 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory (National Inventory Report, 2020).

Acknowledgement: The research plots data collection was performed within the framework of the state assignment of the Center for Forest Ecology and Productivity of the Russian Academy of Sciences (no. АААА-А18-118052590019-7), and the ground data pre-processing were financially supported by the Russian Science Foundation (project no. 19-77-30015).

ACS Style

Dmitry Schepaschenko; Elena Moltchanova; Stanislav Fedorov; Victor Karminov; Petr Ontikov; Maurizio Santoro; Linda See; Vladimir Kositsyn; Anatoly Shvidenko; Anna Romanovskaya; Vladimir Korotkov; Sergey Bartalev; Steffen Fritz; Maria Shchepashchenko; Florian Kraxner. New estimate of growing stock volume and carbon sequestration of Russian forests based on national forest inventory and remote sensing data. 2021, 1 .

AMA Style

Dmitry Schepaschenko, Elena Moltchanova, Stanislav Fedorov, Victor Karminov, Petr Ontikov, Maurizio Santoro, Linda See, Vladimir Kositsyn, Anatoly Shvidenko, Anna Romanovskaya, Vladimir Korotkov, Sergey Bartalev, Steffen Fritz, Maria Shchepashchenko, Florian Kraxner. New estimate of growing stock volume and carbon sequestration of Russian forests based on national forest inventory and remote sensing data. . 2021; ():1.

Chicago/Turabian Style

Dmitry Schepaschenko; Elena Moltchanova; Stanislav Fedorov; Victor Karminov; Petr Ontikov; Maurizio Santoro; Linda See; Vladimir Kositsyn; Anatoly Shvidenko; Anna Romanovskaya; Vladimir Korotkov; Sergey Bartalev; Steffen Fritz; Maria Shchepashchenko; Florian Kraxner. 2021. "New estimate of growing stock volume and carbon sequestration of Russian forests based on national forest inventory and remote sensing data." , no. : 1.

Preprint content
Published: 23 March 2020
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Knowledge of dynamics of forest productivity, expressed in terms of Growing Stock Volume (GSV), Net Primary Production (NPP), such derivatives like current increments (net and gross growth), is crucial for understanding the impacts of forest ecosystems on the major global biogeochemical cycles and eventually – on the Earth climate system. This knowledge is not satisfactory in Russia currently (the country’s forests cover >20% of the global forest area) because 1) data of official forest inventory are obsolete and substantially biased due to the fact that about 50% of Russian forests were inventoried more than 30 years ago; 2) of the above indicators, Russian forest inventory directly defines only GSV, but by the methods, which have substantial systematic errors of unknown size; 3) remote sensing methods themselves still cannot reliably provide some necessary details, like species composition, age and age structure of stands, below ground live biomass etc. In this presentation, we attempted to provide a systematic reanalysis of the estimates of the above indicators. To this end, a special system was developed to update the data of forest inventory for periods after the latest inventory by forest enterprises (about 1700) based on all available ground-based information and a multi-sensor concept of remote sensing. Hybrid forest cover was presented as an aggregation of 12 satellite products at spatial resolution of 150m. The updating of the main biometric indicators of Russian forests was based on the models of the growth and bioproductivity of modal stands. The results of the actualization have showed substantial overestimation of areas by official inventory and underestimation (up to 20%) of GSV. Comparison of obtained results with an independent assessment of the dynamics of areas and GSV, which was made by the Space Research Institute of the Russian Academy of Sciences for the period 2000-2017, showed a high level of compatibility. Using the results of actualization, live biomass was assessed based on a new system of conversion coefficients (Schepaschenko et al. 2018), NPP - on a method described in Shvidenko et al. (2007); and current increments – using a regionally distributed modelling system on increment dynamics of modal stands. Climate were analyzed for 3 periods: “historical” (1948-1975), “current”(1975-2017) and “future” (using all 4 scenarios RCP (2020-2100)). NPP and increments were estimated for the two last periods using a model, which takes into account selected climatic indicators and fertilization effect of enhanced CO2 concentration. It is shown that use of the obtained results presents substantial possibility for improvement of estimates of the carbon budget of Russian forests, particularly those received by inventory methods, and eliminate the existing discrepancies in estimates of the carbon budget of Russian forests reported in different publications. Projections for future suppose that significant part of Russian forests under “critical” scenarios (RCP6.0 and RCP 8.5) have a high probability to reach the tipping point by end of this century.

ACS Style

Anatoly Shvidenko; Dmitry Schepaschenko; Sergey Bartalev; Andrey Krasovskii; Anton Platov. Recent and future productivity of Russian forests under climate change. 2020, 1 .

AMA Style

Anatoly Shvidenko, Dmitry Schepaschenko, Sergey Bartalev, Andrey Krasovskii, Anton Platov. Recent and future productivity of Russian forests under climate change. . 2020; ():1.

Chicago/Turabian Style

Anatoly Shvidenko; Dmitry Schepaschenko; Sergey Bartalev; Andrey Krasovskii; Anton Platov. 2020. "Recent and future productivity of Russian forests under climate change." , no. : 1.

Preprint content
Published: 23 March 2020
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Russian forest is a factor of global importance for implementation of international conventions on climate considering its potential for absorption and accumulation of the atmospheric carbon at an impressive scale. Considering recently adopted Paris agreement on climate the comprehensive and accurate estimation of Russian forests’ carbon budget became a top priority research and development issue on national agenda. However existing quantitative estimates of Russian forests’ carbon budget are of significant level of uncertainty. One of the most obvious reasons for such uncertainty is not sufficiently reliable and up-to-date information on characteristics of forests and their dynamics.

The Russian Science Foundation has supported an ambitious research megaproject titled “Space Observatory for Forest Carbon” (SOFC) started in year 2019 and aimed at the development of a new concept and comprehensive methods for forest carbon budget monitoring using Earth observation data and forest growth and dynamics models. The main SOFC project objectives are as follows:

- Development of a new concept and methodology for Russian forests and their carbon budget monitoring based on the integration of remote sensing and ground data along with improved models of forest structure and dynamics;

- Development of new annually updated GIS databases on the characteristics and multi-annual dynamics of Russian forests;

- Development of an informational system and technology for the continuous monitoring of Russian forests’ carbon budget.

Information necessary for carbon budget estimation includes data on various land cover types, forest characteristics (growing stock volume, species composition, age, site-index) and ecological parameters (Net Primary Production, heterotrophic respiration). Data on natural (fires, diseases and pests, windstorm, droughts) and anthropogenic (felling, pollution) forest disturbances causing deforestation, as well as information on subsequent reforestation processes are also vital.

The existing remote sensing methods can provide significant part of missing country-wide information about the land cover types and forest characteristics for the national-scale carbon budget estimation and monitoring. Multi-year time series of this data since the beginning of the century allow modelling the forest dynamics and its biophysical characteristics. The Earth observation data derived information on forest fires’ impact includes burnt area mapping over various land cover types as well as forest fire severity assessment allowing characterisation of fire induced carbon emissions. Furthermore, developed methods for processing and analysis of multi-year satellite data time series enable detection of forest cover changes caused by various destructive factors making it possible to substantially improve the accuracy of carbon budget estimation.

Obtained information on forest ecosystems’ parameters is used to improve existing and develop new approaches to forest carbon budget estimation, as well as to simulate various scenarios of Russian economy development depending on forest management practices and climate change trajectories.

This work was supported by the Russian Science Foundation [grant number 19-77-30015].

ACS Style

Sergey Bartalev. Space Observatory for carbon budget monitoring in Russian forests using Earth observations and modelling. 2020, 1 .

AMA Style

Sergey Bartalev. Space Observatory for carbon budget monitoring in Russian forests using Earth observations and modelling. . 2020; ():1.

Chicago/Turabian Style

Sergey Bartalev. 2020. "Space Observatory for carbon budget monitoring in Russian forests using Earth observations and modelling." , no. : 1.

Journal article
Published: 28 June 2016 in International Journal of Remote Sensing
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Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring JECAM sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer MODIS mean composites red and near-infrared channels. Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data from 10% to 30%. This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.

ACS Style

Francois Waldner; Diego De Abelleyra; Santiago R. Verón; Miao Zhang; Bingfang Wu; Dmitry Plotnikov; Sergey Bartalev; Mykola Lavreniuk; Sergii Skakun; Nataliia Kussul; Guerric le Maire; Stéphane Dupuy; Ian Jarvis; Pierre Defourny. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. International Journal of Remote Sensing 2016, 37, 3196 -3231.

AMA Style

Francois Waldner, Diego De Abelleyra, Santiago R. Verón, Miao Zhang, Bingfang Wu, Dmitry Plotnikov, Sergey Bartalev, Mykola Lavreniuk, Sergii Skakun, Nataliia Kussul, Guerric le Maire, Stéphane Dupuy, Ian Jarvis, Pierre Defourny. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. International Journal of Remote Sensing. 2016; 37 (14):3196-3231.

Chicago/Turabian Style

Francois Waldner; Diego De Abelleyra; Santiago R. Verón; Miao Zhang; Bingfang Wu; Dmitry Plotnikov; Sergey Bartalev; Mykola Lavreniuk; Sergii Skakun; Nataliia Kussul; Guerric le Maire; Stéphane Dupuy; Ian Jarvis; Pierre Defourny. 2016. "Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity." International Journal of Remote Sensing 37, no. 14: 3196-3231.

Data descriptor
Published: 19 March 2016 in Data
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Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.

ACS Style

François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data 2016, 1, 3 .

AMA Style

François Waldner, Steffen Fritz, Antonio Di Gregorio, Dmitry Plotnikov, Sergey Bartalev, Nataliia Kussul, Peng Gong, Prasad Thenkabail, Gerard Hazeu, Igor Klein, Fabian Löw, Jukka Miettinen, Vinay Kumar Dadhwal, Céline Lamarche, Sophie Bontemps, Pierre Defourny. A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data. 2016; 1 (1):3.

Chicago/Turabian Style

François Waldner; Steffen Fritz; Antonio Di Gregorio; Dmitry Plotnikov; Sergey Bartalev; Nataliia Kussul; Peng Gong; Prasad Thenkabail; Gerard Hazeu; Igor Klein; Fabian Löw; Jukka Miettinen; Vinay Kumar Dadhwal; Céline Lamarche; Sophie Bontemps; Pierre Defourny. 2016. "A Unified Cropland Layer at 250 m for Global Agriculture Monitoring." Data 1, no. 1: 3.

Original articles
Published: 05 January 2016 in Remote Sensing Letters
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The sustainable agriculture requires a regular country-wide update of information on the status and extension of arable land in Russia. The arable land mapping method is developed based on multi-year time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The method exploits differences between the intra- and inter-annual changes in the spectral reflectance of arable land and the corresponding changes for other land cover types. It involves a set of satellite data-derived phenological metrics generated using a 6 years long time series of the perpendicular vegetation index (PVI). The approach utilizes the Locally Adaptive Global Mapping Algorithm (LAGMA), which is a supervised classification technique accounting for the spatial variability of intra-classes spectral properties. The method has been applied to produce a uniform time series of comparable annual arable land maps for Russia at 250 m spatial resolution for the years 2005–2013. Countrywide arable land area trends over the above time series were found to be consistent with official statistics (ROSSTAT).The mapping result has been evaluated using reference data providing F-score exceeding 80% for the most productive regions.

ACS Style

Sergey A. Bartalev; Dmitry Plotnikov; Evgeny A. Loupian. Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique. Remote Sensing Letters 2016, 7, 269 -278.

AMA Style

Sergey A. Bartalev, Dmitry Plotnikov, Evgeny A. Loupian. Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique. Remote Sensing Letters. 2016; 7 (3):269-278.

Chicago/Turabian Style

Sergey A. Bartalev; Dmitry Plotnikov; Evgeny A. Loupian. 2016. "Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique." Remote Sensing Letters 7, no. 3: 269-278.

Journal article
Published: 01 January 2016 in Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
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ACS Style

E.A. Loupian; Space Research Institute RAS; S.A. Bartalev; I.V. Balashov; M.A. Bourtsev; V.A. Egorov; V.Yu. Efremov; V.O. Zharko; A.V. Kashnitskiy; P.A. Kolbudaev; L.S. Kramareva; A.A. Mazurov; O.Yu. Oksyukevich; Dmitry Plotnikov; A.A. Proshin; K.S. Senko; I.A. Uvarov; S.A. Khvostikov; T.S. Khovratovich; Far Eastern Center of Planeta Research Center for Space Hydrometeorology; Llc "ikiz". Vega-Primorie: complex remote forest monitoring information system. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 1 .

AMA Style

E.A. Loupian, Space Research Institute RAS, S.A. Bartalev, I.V. Balashov, M.A. Bourtsev, V.A. Egorov, V.Yu. Efremov, V.O. Zharko, A.V. Kashnitskiy, P.A. Kolbudaev, L.S. Kramareva, A.A. Mazurov, O.Yu. Oksyukevich, Dmitry Plotnikov, A.A. Proshin, K.S. Senko, I.A. Uvarov, S.A. Khvostikov, T.S. Khovratovich, Far Eastern Center of Planeta Research Center for Space Hydrometeorology, Llc "ikiz". Vega-Primorie: complex remote forest monitoring information system. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (5):1.

Chicago/Turabian Style

E.A. Loupian; Space Research Institute RAS; S.A. Bartalev; I.V. Balashov; M.A. Bourtsev; V.A. Egorov; V.Yu. Efremov; V.O. Zharko; A.V. Kashnitskiy; P.A. Kolbudaev; L.S. Kramareva; A.A. Mazurov; O.Yu. Oksyukevich; Dmitry Plotnikov; A.A. Proshin; K.S. Senko; I.A. Uvarov; S.A. Khvostikov; T.S. Khovratovich; Far Eastern Center of Planeta Research Center for Space Hydrometeorology; Llc "ikiz". 2016. "Vega-Primorie: complex remote forest monitoring information system." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 5: 1.

Journal article
Published: 01 January 2016 in Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
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ACS Style

S.A. Khvostikov; Sergey Bartalev; E.A. Loupian. Stochastic wildfire model based on Monte-Carlo method and remote sensing data integration. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 145 -156.

AMA Style

S.A. Khvostikov, Sergey Bartalev, E.A. Loupian. Stochastic wildfire model based on Monte-Carlo method and remote sensing data integration. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (5):145-156.

Chicago/Turabian Style

S.A. Khvostikov; Sergey Bartalev; E.A. Loupian. 2016. "Stochastic wildfire model based on Monte-Carlo method and remote sensing data integration." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 5: 145-156.

Journal article
Published: 01 January 2016 in Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
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ACS Style

E.A. Loupian; Sergey Bartalev; Yu.S. Krasheninnikova. Observing an abnormally early development of crops in the southern regions of Russia in spring 2016 using remote monitoring data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 240 -243.

AMA Style

E.A. Loupian, Sergey Bartalev, Yu.S. Krasheninnikova. Observing an abnormally early development of crops in the southern regions of Russia in spring 2016 using remote monitoring data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (2):240-243.

Chicago/Turabian Style

E.A. Loupian; Sergey Bartalev; Yu.S. Krasheninnikova. 2016. "Observing an abnormally early development of crops in the southern regions of Russia in spring 2016 using remote monitoring data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 2: 240-243.

Journal article
Published: 01 January 2016 in Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
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ACS Style

T.S. Miklashevich; Space Research Institute RAS; Sergey Bartalev. Method for estimating vegetation cover phenological characteristics from satellite data time series. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 1 .

AMA Style

T.S. Miklashevich, Space Research Institute RAS, Sergey Bartalev. Method for estimating vegetation cover phenological characteristics from satellite data time series. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (1):1.

Chicago/Turabian Style

T.S. Miklashevich; Space Research Institute RAS; Sergey Bartalev. 2016. "Method for estimating vegetation cover phenological characteristics from satellite data time series." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 1: 1.

Journal article
Published: 01 January 2016 in Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
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ACS Style

F.V. Eroshenko; Sergey Bartalev; I.G. Storchak; Dmitry Plotnikov. The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 99 -112.

AMA Style

F.V. Eroshenko, Sergey Bartalev, I.G. Storchak, Dmitry Plotnikov. The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (4):99-112.

Chicago/Turabian Style

F.V. Eroshenko; Sergey Bartalev; I.G. Storchak; Dmitry Plotnikov. 2016. "The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 4: 99-112.

Journal article
Published: 01 January 2016 in Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
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ACS Style

V.A. Egorov; Sergey Bartalev. Radiometric correction for topography-induced distortions in land cover reflectance derived from satellite data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 2016, 13, 192 -201.

AMA Style

V.A. Egorov, Sergey Bartalev. Radiometric correction for topography-induced distortions in land cover reflectance derived from satellite data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa. 2016; 13 (5):192-201.

Chicago/Turabian Style

V.A. Egorov; Sergey Bartalev. 2016. "Radiometric correction for topography-induced distortions in land cover reflectance derived from satellite data." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 13, no. 5: 192-201.

Letter
Published: 01 December 2015 in Environmental Research Letters
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The dynamic global vegetation model (DGVM) SEVER has been regionally adapted using a remote sensing data-derived land cover map in order to improve the reconstruction conformity of the distribution of vegetation functional types over Russia. The SEVER model was modified to address noticeable divergences between modelling results and the land cover map. The model modification included a light competition method elaboration and the introduction of a tundra class into the model. The rigorous optimisation of key model parameters was performed using a two-step procedure. First, an approximate global optimum was found using the efficient global optimisation (EGO) algorithm, and afterwards a local search in the vicinity of the approximate optimum was performed using the quasi-Newton algorithm BFGS. The regionally adapted model shows a significant improvement of the vegetation distribution reconstruction over Russia with better matching with the satellite-derived land cover map, which was confirmed by both a visual comparison and a formal conformity criterion.

ACS Style

S Khvostikov; S Venevsky; Sergey Bartalev. Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia. Environmental Research Letters 2015, 10, 125007 .

AMA Style

S Khvostikov, S Venevsky, Sergey Bartalev. Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia. Environmental Research Letters. 2015; 10 (12):125007.

Chicago/Turabian Style

S Khvostikov; S Venevsky; Sergey Bartalev. 2015. "Regional adaptation of a dynamic global vegetation model using a remote sensing data derived land cover map of Russia." Environmental Research Letters 10, no. 12: 125007.

Journal article
Published: 01 November 2014 in Herald of the Russian Academy of Sciences
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Natural resources are a major source of wealth of Russia and a guarantee of its prosperity and special role in the world. In addition, we should not forget that they refer not only to mineral deposits but also to the great diversity of bioresources. The vast territory and a large number of ecosystems not only distinguish Russia among other countries but also lay a great responsibility on it to maintain the sustainability of the biosphere at the national, regional, and planetary levels. The problem of forestland protection, which is especially urgent because of its importance for biospheric interrelations and, consequently, for the survival of humanity, is hard to overestimate.

ACS Style

Aleksandr Sergeevich Isaev; Sergei Aleksandrovich Bartalev; Evgenii Arkad’Evich Lupyan; Natal’Ya Vasil’Evna Lukina. Earth observations from satellites as a unique instrument to monitor Russia’s forests. Herald of the Russian Academy of Sciences 2014, 84, 413 -419.

AMA Style

Aleksandr Sergeevich Isaev, Sergei Aleksandrovich Bartalev, Evgenii Arkad’Evich Lupyan, Natal’Ya Vasil’Evna Lukina. Earth observations from satellites as a unique instrument to monitor Russia’s forests. Herald of the Russian Academy of Sciences. 2014; 84 (6):413-419.

Chicago/Turabian Style

Aleksandr Sergeevich Isaev; Sergei Aleksandrovich Bartalev; Evgenii Arkad’Evich Lupyan; Natal’Ya Vasil’Evna Lukina. 2014. "Earth observations from satellites as a unique instrument to monitor Russia’s forests." Herald of the Russian Academy of Sciences 84, no. 6: 413-419.

Journal article
Published: 22 July 2014 in Forests
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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.

ACS Style

Christian Huttich; Mikhail Korets; Sergey Bartalev; Vasily Zharko; Dmitry Schepaschenko; Anatoly Shvidenko; Christiane Schmullius. Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia. Forests 2014, 5, 1753 -1776.

AMA Style

Christian Huttich, Mikhail Korets, Sergey Bartalev, Vasily Zharko, Dmitry Schepaschenko, Anatoly Shvidenko, Christiane Schmullius. Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia. Forests. 2014; 5 (7):1753-1776.

Chicago/Turabian Style

Christian Huttich; Mikhail Korets; Sergey Bartalev; Vasily Zharko; Dmitry Schepaschenko; Anatoly Shvidenko; Christiane Schmullius. 2014. "Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia." Forests 5, no. 7: 1753-1776.

Journal article
Published: 27 March 2014 in International Journal of Remote Sensing
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ACS Style

Svyatoslav S. Bartalev; Ouns Kissiyar; Frédéric Achard; Sergey A. Bartalev; Dario Simonetti. Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps. International Journal of Remote Sensing 2014, 35, 2671 -2692.

AMA Style

Svyatoslav S. Bartalev, Ouns Kissiyar, Frédéric Achard, Sergey A. Bartalev, Dario Simonetti. Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps. International Journal of Remote Sensing. 2014; 35 (7):2671-2692.

Chicago/Turabian Style

Svyatoslav S. Bartalev; Ouns Kissiyar; Frédéric Achard; Sergey A. Bartalev; Dario Simonetti. 2014. "Assessment of forest cover in Russia by combining a wall-to-wall coarse-resolution land-cover map with a sample of 30 m resolution forest maps." International Journal of Remote Sensing 35, no. 7: 2671-2692.

Original articles
Published: 02 January 2014 in Remote Sensing Letters
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A new locally-adaptive image classification method LAGMA (Locally-Adaptive Global Mapping Algorithm) has been developed to meet requirements of land cover mapping over large areas using remote-sensing data. The LAGMA involves the grid-based supervised image classification using classes’ features estimated locally in classified pixels’ surrounding from spatially distributed reference data. The LAGMA considers inherently spatial variations of classes’ features and is capable of exploiting discriminative properties of local classes’ signatures without any preliminary stratification of mapping area. The LAGMA has been applied for country-wide land cover classification over Russian Federation using the Vegetation instrument data on board of the SPOT (Satellite Pour l’Observation de la Terre) satellite and has demonstrated advantages in terms of recognition accuracy.

ACS Style

S.A. Bartalev; V.A. Egorov; E.A. Loupian; S.A. Khvostikov. A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data. Remote Sensing Letters 2014, 5, 55 -64.

AMA Style

S.A. Bartalev, V.A. Egorov, E.A. Loupian, S.A. Khvostikov. A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data. Remote Sensing Letters. 2014; 5 (1):55-64.

Chicago/Turabian Style

S.A. Bartalev; V.A. Egorov; E.A. Loupian; S.A. Khvostikov. 2014. "A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data." Remote Sensing Letters 5, no. 1: 55-64.

Journal article
Published: 31 October 2012 in Environmental Research Letters
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ACS Style

Matthias Forkel; Kirsten Thonicke; Christian Beer; Wolfgang Cramer; Sergey Bartalev; Christiane Schmullius. Extreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia. Environmental Research Letters 2012, 7, 1 .

AMA Style

Matthias Forkel, Kirsten Thonicke, Christian Beer, Wolfgang Cramer, Sergey Bartalev, Christiane Schmullius. Extreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia. Environmental Research Letters. 2012; 7 (4):1.

Chicago/Turabian Style

Matthias Forkel; Kirsten Thonicke; Christian Beer; Wolfgang Cramer; Sergey Bartalev; Christiane Schmullius. 2012. "Extreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia." Environmental Research Letters 7, no. 4: 1.

Proceedings article
Published: 01 July 2012 in 2012 IEEE International Geoscience and Remote Sensing Symposium
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ZAPÁS investigates and cross validates methodologies using both Russian and European Earth observation data to develop procedures and products for forest resource assessment and monitoring. Products include biomass change maps for the years 2007 to 2009 on a local scale, a biomass and improved land cover map on the regional scale as input to a carbon accounting model. The geographical focus of research and development is Central Siberia, which contains two administrative districts of Russia, namely Krasnoyarsk Kray and Irkutsk Oblast. The results of the terrestrial ecosystem full carbon accounting are addressed to the Federal Forest Agency as federal instance. The high resolution products comprise biomass and change maps for selected local sites. These products are addressed to support the UN FAO Forest Resources Assessment as well as the requirements of the local forest inventories.

ACS Style

C. Hüttich; C.C. Schmullius; C. J. Thiel; Sergey Bartalev; K. Emelyanov; M. Korets; Anatoly Shvidenko; Dmitry Schepaschenko. Assessment and monitoring of Siberian forest resources in the framework of the EU-Russia ZAPÁS project. 2012 IEEE International Geoscience and Remote Sensing Symposium 2012, 7208 -7211.

AMA Style

C. Hüttich, C.C. Schmullius, C. J. Thiel, Sergey Bartalev, K. Emelyanov, M. Korets, Anatoly Shvidenko, Dmitry Schepaschenko. Assessment and monitoring of Siberian forest resources in the framework of the EU-Russia ZAPÁS project. 2012 IEEE International Geoscience and Remote Sensing Symposium. 2012; ():7208-7211.

Chicago/Turabian Style

C. Hüttich; C.C. Schmullius; C. J. Thiel; Sergey Bartalev; K. Emelyanov; M. Korets; Anatoly Shvidenko; Dmitry Schepaschenko. 2012. "Assessment and monitoring of Siberian forest resources in the framework of the EU-Russia ZAPÁS project." 2012 IEEE International Geoscience and Remote Sensing Symposium , no. : 7208-7211.

Book chapter
Published: 24 May 2010 in Advances in Global Change Research
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Envisat-MERIS and SPOT Vegetation satellite data were tested for estimation of vegetation cover disturbances caused by fire and industrial pollution in central and northern Siberian test sites, respectively. MERIS data were used to assess forest disturbance levels on burned sites in Angara region. Chlorophyll indexes (REP and MTCI) were found to allow identifying up to five forest disturbance levels due to high space-borne sensor resolution and sensitivity to chlorophyll content of vegetation. A comparison of these chlorophyll indexes revealed that MTCI to show chlorophyll contents fairly precisely and to be useful for quantifying and mapping forest damage levels on burns. The current vegetation condition was assessed using MTCI index in the northern (Norilsk) test region. The lowest index values calculated for the most severely disturbed vegetation near Norilsk were found to correlate with sulphur concentrations in larch and spruce needles. Another approach to estimating spatial and temporal trends of vegetation condition used the 1998–2005 SPOT-Vegetation satellite data. The relationships obtained between MTCI, NDVI values, and forest mortality were based upon to map the1998–2005 forest degradation zone dynamics in the northern test site.

ACS Style

M. A. Korets; V. A. Ryzhkova; I. V. Danilova; A. I. Sukhinin; S. A. Bartalev. Forest Disturbance Assessment Using Satellite Data of Moderate and Low Resolution. Advances in Global Change Research 2010, 40, 3 -19.

AMA Style

M. A. Korets, V. A. Ryzhkova, I. V. Danilova, A. I. Sukhinin, S. A. Bartalev. Forest Disturbance Assessment Using Satellite Data of Moderate and Low Resolution. Advances in Global Change Research. 2010; 40 ():3-19.

Chicago/Turabian Style

M. A. Korets; V. A. Ryzhkova; I. V. Danilova; A. I. Sukhinin; S. A. Bartalev. 2010. "Forest Disturbance Assessment Using Satellite Data of Moderate and Low Resolution." Advances in Global Change Research 40, no. : 3-19.