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

/ 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SAR-based GSV estimates helped to detect inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands.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.

Scopus

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.

WOS,
Scopus

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]

[Текст] : статья / Дмитрий Геннадьевич Щепащенко [и др.] // Сибирский лесной журнал. - 2017. - № : 4. - С. 3-11, DOI 10.15372/SJFS20170401 . - ISSN 2311-1410
   Перевод заглавия: Forest biomass observation: current state and prospective

УДК

528.8

581.5

Аннотация: Дан обзор современных методов, инструментов и перспектив мониторинга лесной фитомассы в глобальном масштабе. Рассмотрены преимущества и недостатки различных дистанционных методов космического базирования, включая оптические, радарные (C-, L-, P-диапазонов) и лазерные, а также соответствующие им инструменты, находящиеся на орбите (MODIS, Proba-V, Landsat, Sentinel-1, Sentinel-2, ALOS PALSAR, Envisat ASAR) или готовящиеся к запуску (BIOMASS, GEDI, NISAR, SAOCOM-CS). Подчеркнута роль наземных методов в разработке моделей фитомассы, обеспечении калибровки и проверки дистанционных данных. Описаны имеющиеся в свободном доступе карты, базы данных и эмпирические модели (как подеревные - аллометрические, так и на уровне насаждений) лесной фитомассы. Описаны функциональные возможности интернет-портала Biomass.Geo-Wiki.org, который предоставляет доступ к коллекции глобальных и региональных карт фитомассы в полном разрешении с унифицированной легендой, наложенных на снимки высокого разрешения. Анонсирована международная кооперация ученых, проводящих измерения на постоянных пробных площадях (Forest Observation System), и рассмотрены ее перспективы в развитии сети наземных наблюдений во взаимодействии с дистанционным сообществом. Кратко рассмотрены перспективы беспилотных летательных аппаратов в инвентаризации лесов. Авторы адресуют данный обзор специалистам лесного хозяйства и научным работникам в области лесоведения и экологии, которые не являются экспертами в дистанционном зондировании, но хотят получить представление о современных тенденциях в этой области знания. Также статья нацелена на уменьшение разобщенности научных коллективов и более широкий обмен данными и знаниями между дистанционным и экологическим сообществами.
With this article, we provide an overview of the methods, instruments and initiatives for forest biomass observation at global scale. We focus on the freely available information, provided by both remote and in-situ observations. The advantages and limitation of various space borne methods, including optical, radar (C, L and P band) and LiDAR, as well as respective instruments available on the orbit (MODIS, Proba-V, Landsat, Sentinel-1, Sentinel-2, ALOS PALSAR, Envisat ASAR) or expecting (BIOMASS, GEDI, NISAR, SAOCOM-CS) are discussed. We emphasize the role of in-situ methods in the development of a biomass models, providing calibration and validation of remote sensing data. We focus on freely available forest biomass maps, databases and empirical models. We describe the functionality of Biomass.Geo-Wiki.org portal, which provides access to a collection of global and regional biomass maps in full resolution with unified legend and units overplayed with high-resolution imagery. The Forest-Observation-System.net is announced as an international cooperation to establish a global in-situ forest biomass database to support earth observation and to encourage investment in relevant field-based observations and science. Prospects of unmanned aerial vehicles in the forest inventory are briefly discussed.

РИНЦ

Держатели документа:
Ботанический сад УрО РАН
Всероссийский институт повышения квалификации руководящих работников и специалистов лесного хозяйства
Институт биологии Коми научного центра УрО РАН
Институт леса им. В. Н. Сукачева СО РАН
Международный институт прикладного системного анализа
Московский государственный технический университет им. Н. Э. Баумана
Национальный университет биоресурсов и природопользования Украины

Доп.точки доступа:
Щепащенко, Дмитрий Геннадьевич; Schepaschenko D.G.; Осипов, Андрей Федорович; Osipov A.F.; Мартыненко, Ольга Вениаминовна; Martynenko O.V.; Карминов, Виктор Николаевич; Karminov V.N.; Онтиков, Пётр Вячеславович; Ontikov P.V.; Щепащенко, Мария Владимировна; Shchepashchenko M.V.; Кракснер, Флориан; Kraxner F.; Швиденко, Анатолий Зиновьевич; Shvidenko A.Z.; Пергер, Кристоф; Perger C.; Дресел, Кристофер; Dresel C.; Фриц, Штефен; Fritz S.; Лакида, Петр Иванович; Lakyda P.I.; Мухортова, Людмила Владимировна; Mukhortova L.V.; Усольцев, Владимир Андреевич; Usoltsev V.A.; Бобкова, Капитолина Степановна; Bobkova K.S.

/ 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]