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

[Text] / G. . Sun [et al.] // Remote Sens. Environ. - 2003. - Vol. 88, Is. 4. - P401-411, DOI 10.1016/j.rse.2003.09.001. - Cited References: 28 . - 11. - ISSN 0034-4257РУБ Environmental Sciences + Remote Sensing + Imaging Science & Photographic Technology

Аннотация: Spaceborne Interferometric SAR (InSAR) technology used in the Shuttle Radar Topography Mission (SRTM) and spaceborne lidar such as Shuttle Laser Altimeter-02 (SLA-02) are two promising technologies for providing global scale digital elevation models (DEMs). Each type of these systems has limitations that affect the accuracy or extent of coverage. These systems are complementary in developing DEM data. In this study, surface height measured independently by SRTM and SLA-02 was cross-validated. SLA data was first verified by field observations, and examinations of individual lidar waveforms. The geolocation accuracy of the SLA height data sets was examined by checking the correlation between the SLA surface height with SRTM height at 90 in resolution, while shifting the SLA ground track within its specified horizontal errors. It was found that the heights from the two instruments were highly correlated along the SLA ground track, and shifting the positions did not improve the correlation significantly. Absolute surface heights from SRTM and SLA referenced to the same horizontal and vertical datum (World Geodetic System (WGS) 84 Ellipsoid) were compared. The effects of forest cover and surface slope on the height difference were also examined. After removing the forest effect on SRTM height, the mean height difference with SLA-02 was near zero. It can be further inferred from the standard deviation of the height differences that the absolute accuracy of SRTM height at low vegetation area is better than the SRTM mission specifications (16 in). The SRTM height bias caused by forest cover needs to be further examined using future spaceborne lidar (e.g. GLAS) data. (C) 2003 Elsevier Inc. All rights reserved.

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Univ Maryland, Dept Geog, College Pk, MD 20742 USA
NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
VN Sukachev Inst Forest, Krasnoyarsk, Russia
Sci Syst & Applicat Inc, Lanham, MD 20706 USA

Доп.точки доступа:
Sun, G...; Ranson, K.J.; Khairuk, V.I.; Kovacs, K...

[Text] / K. M. Bergen [et al.] // J. For. - 2003. - Vol. 101, Is. 4. - P34-41. - Cited References: 28 . - 8. - ISSN 0022-1201РУБ Forestry

Аннотация: In 1997, several project teams of the NASA Land-Cover Land-Use Change Program began working with Russian organizations to try to quantify and understand the past, present, and future land-cover and land-use trends in Russian boreal forests. Selected results of completed and ongoing research projects are discussed in four categories: forest dynamics, fire and fire behavior, carbon budgets, and new remote sensing analysis methods. This research has helped pave the way for collaborations with international organizations and other networks, and collaborations at several scales are now making it possible for Russian and US scientists to work together to further our knowledge on the influence of land-cover and land-use change throughout the world.

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Univ Michigan, Sch Nat Resources & Environm, Ann Arbor, MI 48109 USA
Univ Michigan, Ctr Russian & E European Studies, Ann Arbor, MI 48109 USA
US Forest Serv, USDA, Washington, DC 20250 USA
Woods Hole Res Ctr, Woods Hole, MA 02543 USA
Univ Maryland, Dept Geog, College Pk, MD 20742 USA
Sukachev Inst Forest, Krasnoyarsk, Russia
Oregon State Univ, Dept Forest Sci, Corvallis, OR 97331 USA
Goddard Space Flight Ctr, Greenbelt, MD USA
Univ Virginia, Charlottesville, VA 22903 USA
Sukachev Forest Res Inst, Remote Sensing Facil, Krasnoyarsk, Russia
NW State Forest Inventory Enterprise, St Petersburg, Russia

Доп.точки доступа:
Bergen, K.M.; Conard, S.G.; Houghton, R.A.; Kasischke, E.S.; Kharuk, V.I.; Krankina, O.N.; Ranson, K.J.; Shugart, H.H.; Sukhinin, A.I.; Treyfeld, R.F.

[Text] / K. J. Ranson [et al.] // Can. J. Remote Sens. - 2003. - Vol. 29, Is. 2. - P271-285. - Cited References: 32 . - 15. - ISSN 0703-8992РУБ Remote Sensing

Аннотация: As part of a Siberian mapping project supported by the National Aeronautics and Space Administration (NASA), this study evaluated the capabilities of radars flown on the European Remote Sensing Satellite (ERS), Japanese Earth Resources Satellite (JERS), and Radarsat spacecraft and an optical sensor enhanced thematic mapper plus (ETM+) on-board Landsat-7 to detect fire scars, logging, and insect damage in the boreal forest. Using images from each sensor individually and combined, an assessment of the utility of using these sensors was developed. Transformed divergence analysis revealed that Landsat ETM+ images were the single best data type for this purpose. However, the combined use of the three radar and optical sensors did improve the results of discriminating these disturbances.

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Держатели документа:
NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
Sci Syst & Applicat Inc, Lanham, MD 20706 USA
Univ Maryland, Dept Geog, College Pk, MD 20742 USA
VN Sukachev Inst Forest, Krasnoyarsk, Russia

Доп.точки доступа:
Ranson, K.J.; Kovacs, K...; Sun, G...; Kharuk, V.I.

/ G. Sun, K. J. Ranson, V. I. Kharuk // Remote Sensing of Environment. - 2002. - Vol. 79, Is. 2-3. - P279-287, DOI 10.1016/S0034-4257(01)00279-6 . - ISSN 0034-4257
Аннотация: We investigated the possibility of using multiple polarization (SIR-C) L-band data to map forest biomass in a mountainous area in Siberia. The use of a digital elevation model (DEM) and a model-based method for reducing terrain effects was evaluated. We found that the available DEM data were not suitable to correct the topographic effects on the SIR-C radar images. A model-based slope correction was applied to an L-band cross-polarized (hv) backscattering image and found to reduce the topographic effect. A map of aboveground biomass was produced from the corrected image. The results indicated that multipolarization L-band synthetic aperture radar (SAR) data can be useful for estimation of total aboveground biomass of forest stands in mountainous areas. В© 2002 Elsevier Science Inc. All rights reserved.

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Department of Geography, University of Maryland, College Park, MD 20742, United States
NASA's Goddard Space Flight Center, Code 923, Greenbelt, MD 20771, United States
Sukachev Institute of Forest, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Sun, G.; Ranson, K.J.; Kharuk, V.I.

/ K. J. Ranson [et al.] // Remote Sensing of Environment. - 2001. - Vol. 75, Is. 2. - P188-200, DOI 10.1016/S0034-4257(00)00166-8 . - ISSN 0034-4257
Аннотация: This paper examines the use of space-borne radar data to map forest types and logging in the mountainous Western Sayani area in central Siberia. L- and C- band HH-, HV-, and VV-polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery, and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a digital elevation model improved classification results but required reducing the synthetic aperture radar resolution to match the digital elevation model. Using ratios of synthetic aperture radar channels that include cross-polarization improved classification and had the advantages of eliminating the need for a digital elevation model and preserving the full resolution of the synthetic aperture radar data. В© Elsevier Science Inc., 2001.

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Держатели документа:
NASA's Goddard Space Flight Center, Code 923, Greenbelt, MD, United States
Department of Geography, University of Maryland, College Park, MD, United States
V. N. Sukachev Institute of Forest, Academgorodok, Krasnoyarsk, Russian Federation
Science Systems and Applications, Inc., Lanham, MD, United States
NASA's Goddard Space Flight Center, Code 923, Greenbelt, MD 20771, United States

Доп.точки доступа:
Ranson, K.J.; Sun, G.; Kharuk, V.I.; Kovacs, K.

/ 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.

[Text] / S. . Wilhelm [et al.] // Forests. - 2014. - Vol. 5, Is. 8. - P1999-2015, DOI 10.3390/f5081999. - Cited References: 50. - The authors want to thank the employees of the Sukachev Institute of Forest in Krasnoyarsk, Russia, Siberia, who were involved in the validation of the mapping results. In addition, thanks go out to Tim Robin van Doorn for proofreading this article. The maps were produced within the FP 7 EU-Russia ZAPAS (Russian: 3anac, stands for GSV or forest stock) project on the assessment and monitoring of forest resources in central Siberia. ZAPAS was funded by the European Commission, Space, Cross-cutting Activities, International Cooperation, Grant No. SPA.2010.3.2-01 EU-Russia Cooperation in Global Monitoring for Environment and Security (GMES). . - ISSN 1999-4907РУБ Forestry

Аннотация: The forests of the Russian Taiga can be described as an enormous biomass and carbon reservoir. Therefore, they are of utmost importance for the global carbon cycle. Large-area forest inventories in these mostly remote regions are associated with logistical problems and high financial efforts. Remotely-sensed data from satellite platforms may have the capability to provide such huge amounts of information. This study presents an application-oriented approach to derive aboveground growing stock volume (GSV) maps using the annual large-area L-band backscatter mosaics provided by the Japan Aerospace Exploration Agency (JAXA). Furthermore, a multi-temporal map has been created to improve GSV estimation accuracy. Based on information from Russian forest inventory data, the maps were generated using the machine learning algorithm, RandomForest. The results showed the high potential of this method for an operational, large-scale and high-resolution biomass estimation over boreal forests. An RMSE from 55.2 to 63.3 m(3)/ha could be obtained for the annual maps. Using the multi-temporal approach, the error could be slightly reduced to 54.4 m(3)/ha.

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Держатели документа:
[Wilhelm, Sebastian] Earth Observat Serv EOS Jena GmbH, D-07743 Jena, Germany
[Huettich, Christian
Schmullius, Christiane] Univ Jena, Dept Earth Observat, D-07743 Jena, Germany
[Korets, Mikhail] Russian Acad Sci, VN Sukachev Inst Forest, Siberian Branch, Krasnoyarsk 660036, Russia
ИЛ СО РАН

Доп.точки доступа:
Wilhelm, S...; Huttich, C...; Korets, M...; Schmullius, C...; European Commission, Space, Cross-cutting Activities, International Cooperation, EU-Russia Cooperation in Global Monitoring for Environment and Security (GMES) [SPA.2010.3.2-01]

/ P. M. Montesano [et al.] // Remote Sens. Environ. - 2014. - Vol. 154. - P398-407, DOI 10.1016/j.rse.2014.01.027 . - ISSN 0034-4257
Аннотация: In this study, we examined the uncertainty of aboveground live biomass (AGB) estimates based on light detection and ranging (LiDAR) and synthetic aperture radar (SAR) measurements distributed across a low-biomass vegetation structure gradient from forest to non-forest in boreal-like ecosystems. The conifer-dominant structure gradient was compiled from ground data amassed from multiple field expeditions in central Maine (USA), Aurskog (Norway), and across central Siberia (Russia). Single variable empirical models were built to model AGB from remote sensing metrics. Using these models, we calculated a root mean square error (RMSE) and a 95% confidence interval (CI) of the RMSE from the difference between the remote sensing AGB predictions and the ground reference AGB estimates within AGB intervals across a 0-100Mgha-1 boreal forest structure gradient. The results show that the error in AGB predictions (RMSE) and the error uncertainty (the CI) from LiDAR and SAR change across a forest gradient. The errors of airborne LiDAR and SAR metrics and spaceborne LiDAR platforms show a general trend of reduced relative errors as AGB magnitudes increase, particularly from 0 to 60Mgha-1. Empirical models relating spaceborne metrics to AGB and estimates of spaceborne LiDAR error uncertainty demonstrate the difficulty of characterizing differences in AGB at the site-level with current spaceborne sensors, particularly below 80Mgha-1 with less than 50-100% error.

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Держатели документа:
University of Maryland, Department of Geographical SciencesCollege Park, MD, United States
Sigma Space Corp.Lanham, MD, United States
Code 618,Biospheric Sciences Branch, NASA/Goddard Space Flight CenterGreenbelt, MD, United States
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003As, Norway
Sukachev Institute of Forest, Siberian Branch, Russian Academy of SciencesAkademgorodok, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Montesano, P.M.; Nelson, R.F.; Dubayah, R.O.; Sun, G.; Cook, B.D.; Ranson, K.J.R.; N?sset, E.; Kharuk, V.

/ 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]