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

[Text] / D. . Sulla-Menashe [et al.] // Remote Sens. Environ. - 2011. - Vol. 115, Is. 2. - P392-403, DOI 10.1016/j.rse.2010.09.010. - Cited References: 71. - The research was supported by NASA grant numbers NNG06GF54G and NNX08AE61A. An additional thanks goes to Dr. Bin Tan who was instrumental in implementing the MODIS classification algorithms, and to the rest of the NELDA team for helpful input and discussions. . - 12. - ISSN 0034-4257РУБ Environmental Sciences + Remote Sensing + Imaging Science & Photographic Technology

Аннотация: The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate-vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate-vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate-vegetation relationships for mapping land cover at continental scales. (C) 2010 Elsevier Inc. All rights reserved.

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Держатели документа:
[Sulla-Menashe, Damien
Friedl, Mark A.
Woodcock, Curtis E.
Sibley, Adam] Boston Univ, Dept Geog & Environm, Boston, MA 02215 USA
[Krankina, Olga N.] Oregon State Univ, Coll Forestry, Dept Forest Sci, Corvallis, OR 97331 USA
[Baccini, Alessandro] Woods Hole Res Ctr, Falmouth, MA 02540 USA
[Sun, Guoqing] NASA, GSFC, Biospher Sci Branch, Greenbelt, MD 20770 USA
[Kharuk, Viacheslav] Acad Gorodok Krasnoyarsk, Sukachev Forest Inst, Forest Ecol & Monitoring Branch, Krasnoyarsk 660036, Russia
[Elsakov, Vladimir] Russian Acad Sci, Inst Biol, Komi Sci Ctr, Syktyvkar 167610, Russia

Доп.точки доступа:
Sulla-Menashe, D...; Friedl, M.A.; Krankina, O.N.; Baccini, A...; Woodcock, C.E.; Sibley, A...; Sun, G.Q.; Kharuk, V...; Elsakov, V...

[Text] / E. F. Vedrova // Eurasian Soil Sci. - 2008. - Vol. 41, Is. 8. - P860-868, DOI 10.1134/S1064229308080085. - Cited References: 45. - This study was supported by the Russian Foundation for basic research, project nos. 03-04-20018 and 06-06-90596. . - 9. - ISSN 1064-2293РУБ Soil Science

Аннотация: The estimates of the carbon pool in the organic matter of gray soils of the southern taiga, the intensity of destruction of its components, and participation of the latter in the formation of the mineralized carbon flux to the atmosphere are presented for different stages of succession of deciduous (birch) and coniferous (fir) forests. The carbon pool varies from 139.7 to 292.7 t/ha. It is distributed between phytodetritus, mobile and stabile humus (32, 19, and 49%, respectively). The intensity of the mineralization carbon flux to the atmosphere amounts to 3.93-4.13 t C per year. Phytodetritus plays the main role in the formation of this flux. In the soils under the forests studied, 4-6% of the carbon flux are formed owing to mineralization of the newly formed soil humus. In birch forests, 2-6% (0.1-0.2% of the humus pool in the 0-20-cm layer) is the contribution to the flux due to mineralization of soil humus. In fir forests, the mineralized humus is compensated by humus substances synthesized in the process of humification during phytodetritus decomposition.

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Держатели документа:
Russian Acad Sci, Sukachev Inst Forest, Siberian Branch, Krasnoyarsk 660036, Russia

Доп.точки доступа:
Vedrova, E.F.

[Text] / A. G. Bunn [et al.] // Environ. Res. Lett. - 2013. - Vol. 8, Is. 3. - Ст. 35034, DOI 10.1088/1748-9326/8/3/035034. - Cited References: 36. - We thank the Northern Eurasian Earth Science Partnership Initiative for support via a grant from NASA-LCLUC-NEESPI (NNX09AK58G) to MKH and AGB and from NSF 0612341 and NSF 1044417 to AGB. VVS was supported by the Fulbright Scholar Program. Figure 1 was produced by Randal Bernhardt of the WWU Geography Department. . - 8. - ISSN 1748-9326РУБ Environmental Sciences + Meteorology & Atmospheric Sciences

Аннотация: Different methods have been developed for measuring carbon stocks and fluxes in the northern high latitudes, ranging from intensively measured small plots to space-based methods that use reflectance data to drive production efficiency models. The field of dendroecology has used samples of tree growth from radial increments to quantify long-term variability in ecosystem productivity, but these have very limited spatial domains. Since the cambium material in tree cores is itself a product of photosynthesis in the canopy, it would be ideal to link these two approaches. We examine the associations between the normalized differenced vegetation index (NDVI) and tree growth using 19 pairs of tree-ring widths (TRW) and maximum latewood density (MXD) across much of Siberia. We find consistent correlations between NDVI and both measures of tree growth and no systematic difference between MXD and TRW. At the regional level we note strong correspondence between the first principal component of tree growth and NDVI for MXD and TRW in a temperature-limited bioregion, indicating that canopy reflectance and cambial production are broadly linked. Using a network of 21 TRW chronologies from south of Lake Baikal, we find a similarly strong regional correspondence with NDVI in a markedly drier region. We show that tree growth is dominated by variation at decadal and multidecadal time periods, which the satellite record is incapable of recording given its relatively short record.

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Держатели документа:
[Bunn, Andrew G.] Western Washington Univ, Dept Environm Sci, Huxley Coll, Bellingham, WA 98225 USA
[Hughes, Malcolm K.
Losleben, Mark] Univ Arizona, Tree Ring Res Lab, Tucson, AZ 85721 USA
[Kirdyanov, Alexander V.] VN Sukachev Inst Forest SB RAS, Krasnoyarsk, Russia
[Shishov, Vladimir V.
Vaganov, Eugene A.] Siberian Fed Univ, Krasnoyarsk, Russia
[Berner, Logan T.] Woods Hole Res Ctr, Falmouth, MA USA
[Oltchev, Alexander] RAS, Severtsov Inst Ecol & Evolut, Moscow 117901, Russia

Доп.точки доступа:
Bunn, A.G.; Hughes, M.K.; Kirdyanov, Alexander V.; Кирдянов, Александр Викторович; Losleben, M.; Shishov, V.V.; Berner, L.T.; Oltchev, A.; Vaganov, E.A.; Northern Eurasian Earth Science Partnership Initiative via NASA-LCLUC-NEESPI [NNX09AK58G]; NSF [0612341, 1044417]; Fulbright Scholar Program

[Text] / J. K. Shuman [et al.] // Can. J. For. Res. - 2015. - Vol. 45, Is. 2. - P175-184, DOI 10.1139/cjfr-2014-0138. - Cited References:53. - This work was funded by NASA grants to H.H. Shugart (Terrestrial Ecology10-CARBON10-0068) and A.J. Soja (Inter-Disciplinary Science09-IDS09-116). We thank the anonymous reviewers and V.A. Seamster forhelpful comments on earlier versions of this manuscript, and RobertSmith for figure preparation. We also appreciate the software packagesthat made this work possible: IDRISI developed in 1987 by R.J. Eastmanat Clark University in Worcester, Massachusetts, USA, and ESRI 2008(ESRI ArcGIS version 9.3, ESRI, Redlands, California, USA). . - ISSN 0045-5067. - ISSN 1208-6037РУБ Forestry

Аннотация: Vegetation models are essential tools for projecting large-scale land-cover response to changing climate, which is expected to alter the distribution of biomes and individual species. A large-scale bioclimatic envelope model (RuBCliM) and an individual species based gap model (UVAFME) are used to simulate the Russian forests under current and future climate for two greenhouse gas emissions scenarios. Results for current conditions are compared between models and assessed against two independent maps of Russian forest biomes and dominant tree species. Comparisons measured with kappa statistics indicate good agreement between the models (kappa values from 0.76 to 0.69), as well as between the model results and two observation-based maps for both species presence and absence (kappa values from 0.70 to 0.43). Agreement between these multiple types of data on forest distribution provides confidence in the projected forest response to changing climate. For future conditions, both models indicate a shift in the dominant biomes from conifers to deciduous leaved species. These projections have implications for feedbacks between the energy budget, carbon cycle, and land cover in the boreal system. The distinct biome and species changes emphasize the need for continued investigation of this landmass that has the size necessary to influence regional and global climate.

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Держатели документа:
Univ Virginia, Dept Environm Sci, Charlottesville, VA 22904 USA.
Russian Acad Sci, Sukachev Inst Forest, Krasnoyarsk, Russia.
NASA, Natl Inst Aerosp, Langley Res Ctr, Climate Sci Branch, Hampton, VA 23681 USA.
NASA, Natl Inst Aerosp, Langley Res Ctr, Radiat & Aerosols Branch, Hampton, VA 23681 USA.
Russian Acad Sci, Ctr Problems Ecol & Prod Forests, Moscow, Russia.
Univ Virginia, Alliance Computat Sci & Engn, Charlottesville, VA 22904 USA.
ИЛ СО РАН

Доп.точки доступа:
Shuman, Jacquelyn K.; Tchebakova, Nadezhda M.; Parfenova, Elena I.; Soja, Amber J.; Shugart, Herman H.; Ershov, Dmitry; Holcomb, Katherine; NASA [10-CARBON10-0068, 09-IDS09-116]

/ M. Santoro, O. Cartus, N. Carvalhais [et al.] // Earth Syst. Sci. Data. - 2021. - Vol. 13, Is. 8. - P3927-3950, DOI 10.5194/essd-13-3927-2021. - Cited References:68. - This research has been supported by the European Space Agency (ESRIN contract no. 4000113100/14/I-NB) and the Russian Science Foundation (grant no. 19-77-30015). . - ISSN 1866-3508. - ISSN 1866-3516РУБ Geosciences, Multidisciplinary + Meteorology & Atmospheric Sciences

Аннотация: The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1 ha. Using an extensive database of 110 897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB 250 Mg ha(-1), where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426-571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120% of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates.

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Держатели документа:
Gamma Remote Sensing, CH-3073 Gumlingen, Switzerland.
Max Planck Inst Biogeochem, Hans Knoll Str 10, D-07745 Jena, Germany.
Univ Nova Lisboa, Fac Ciencias & Tecnol, Dept Ciencias & Engn Ambiente, FCT,DCEA, P-2829516 Caparica, Portugal.
Wageningen Univ & Res, Lab Geoinformat Sci & Remote Sensing, Droevendaalsesteeg 3, NL-6708 PB Wageningen, Netherlands.
Wageningen Univ & Res, Plant Prod Syst Grp, POB 430, NL-6700 AK Wageningen, Netherlands.
Wageningen Univ & Res, Ctr Crop Syst Anal, POB 430, NL-6700 AK Wageningen, Netherlands.
European Commiss, Joint Res Ctr, Ispra, Italy.
Univ Sheffield, Natl Ctr Earth Observat NCEO, Sheffield S3 7RH, S Yorkshire, England.
Univ Leicester, Ctr Landscape & Climate Res, Sch Geog Geol & Environm, Leicester LE1 7RH, Leics, England.
Natl Ctr Earth Observat NCEO, Leicester LE1 7RH, Leics, England.
Int Inst Appl Syst Anal, Schlosspl 1, A-2361 Laxenburg, Austria.
Russian Acad Sci, Ctr Forest Ecol & Prod, Profsoyuznaya 84-32-14, Moscow 117997, Russia.
Siberian Fed Univ, Inst Ecol & Geog, 79 Svobodny Prospect, Krasnoyarsk 660041, Russia.
Russian Acad Sci, Lab Ecophysiol Permafrost Syst, VN Sukachev Inst Forest, Siberian Branch,Separated Dept KSC SB RAS, Krasnoyarsk 660036, Russia.
Tokyo Denki Univ, Div Architectural Civil & Environm Engn, Sch Sci & Engn, Hiki, Saitama 3500394, Japan.
Remote Sensing Technol Ctr Japan, Minato Ku, Tokyu Reit Toranomon Bldg,3f,3-17-1 Toranomon, Tokyo 1050001, Japan.
Univ Valencia, Image Proc Lab IPL, Valencia, Spain.
Univ Montana, Numer Terradynam Simulat Grp NTSG, Missoula, MT 59812 USA.
Univ Zagreb, Fac Forestry & Wood Technol, Dept Forest Inventory & Management, Zagreb 10000, Croatia.
Tomsk State Univ, Biol Inst, Tomsk 634050, Russia.
Univ Manchester, Sch Environm Educ & Dev, Dept Geog, Oxford Rd, Manchester M13 9PL, Lancs, England.
Guyana Forestry Commiss, 1 Water St, Georgetown, Guyana.
UMR 5174 CNRS IRD UPS, Lab Evolut & Diversit Biol, F-31062 Toulouse 9, France.
Purdue Univ, Dept Forestry & Nat Resources, 715 State St, W Lafayette, IN 47907 USA.
Rocha Int, Cambridge, England.
RSPB Ctr Conservat Sci, Sandy, Beds, England.
Univ Edinburgh, Sch GeoSci, Crew Bldg,Kings Bldg, Edinburgh EH9 3FF, Midlothian, Scotland.
Univ Dundee, Dept Geog & Environm Sci, Dundee, Scotland.
Univ Brunei Darussalam, Fac Sci, Jln Tungku Link, BE-1410 Gadong, Brunei.
Amma Remote Sensing, CH-3073 Gumlingen, Switzerland.
Univ Tuscia, Dept Innovat Biol Agrofood & Forest Syst DIBAF, I-01100 Viterbo, Italy.
Univ Ghent, Dept Environm, CAVElab Computat & Appl Vegetat Ecol, Coupure Links 653, B-9000 Ghent, Belgium.
World Resources Inst Indonesia WRI Indonesia, Dept Res Data & Innovat, Wisma PMI, 3rd Floor,Jl Wijaya I-63, Kebayoran Baru, South Jakarta, Indonesia.
Bangor Univ, Sch Nat Sci, Bangor, Gwynedd, Wales.

Доп.точки доступа:
Santoro, Maurizio; Cartus, Oliver; Carvalhais, Nuno; Rozendaal, Danae M. A.; Avitabile, Valerio; Araza, Arnan; de Bruin, Sytze; Herold, Martin; Quegan, Shaun; Rodriguez-Veiga, Pedro; Balzter, Heiko; Carreiras, Joao; Schepaschenko, Dmitry; Korets, Mikhail; Shimada, Masanobu; Itoh, Takuya; Martinez, J.; Cavlovic, Jura; Gatti, Roberto Cazzolla; Bispo, Polyanna da Conceicao; Dewnath, Nasheta; Labriere, Nicolas; Liang, Jingjing; Lindsell, Jeremy; Mitchard, Edward T. A.; Morel, Alexandra; Pascagaza, Ana Maria Pacheco; Ryan, Casey M.; Slik, Ferry; Laurin, Gaia Vaglio; Verbeeck, Hans; Wijaya, Arief; Willcock, Simon; A., Arnan; European Space Agency (ESRIN) [4000113100/14/I-NB]; Russian Science FoundationRussian Science Foundation (RSF) [19-77-30015]