Mapping Russian forest biomass with data from satellites and forest inventories

被引:101
|
作者
Houghton, R. A. [1 ]
Butman, D. [2 ]
Bunn, A. G. [3 ]
Krankina, O. N. [4 ]
Schlesinger, P. [1 ]
Stone, T. A. [1 ]
机构
[1] Woods Hole Res Ctr, Falmouth, MA 02540 USA
[2] Yale Univ, Yale Sch Forestry & Environm Sci, New Haven, CT 06511 USA
[3] Western Washington Univ, Huxley Coll Environm, Dept Environm Sci, Bellingham, WA 98225 USA
[4] Oregon State Univ, Dept Forest Sci, Corvallis, OR 97331 USA
关键词
biomass; carbon; forests; forest inventory; MODIS; Russia;
D O I
10.1088/1748-9326/2/4/045032
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The forests of Russia cover a larger area and hold more carbon than the forests of any other nation and thus have the potential for a major role in global warming. Despite a systematic inventory of these forests, however, estimates of total carbon stocks vary, and spatial variations in the stocks within large aggregated units of land are unknown, thus hampering measurement of sources and sinks of carbon. We mapped the distribution of living forest biomass for the year 2000 by developing a relationship between ground measurements of wood volume at 12 sites throughout the Russian Federation and data from the MODIS satellite bidirectional reflectance distribution function (BRDF) product (MOD43B4). Based on the results of regression-tree analyses, we used the MOD43B4 product to assign biomass values to individual 500 m x 500 m cells in areas identified as forest by two satellite-basedmaps of land cover. According to the analysis, the total living biomass varied between 46 and 67 Pg, largely because of different estimates of forest area. Although optical data are limited in distinguishing differences in biomass in closed canopy forests, the estimates of total living biomass obtained here varied more in response to different definitions of forest than to saturation of the optical sensing of biomass.
引用
收藏
页数:7
相关论文
共 50 条
  • [21] Achieving accuracy requirements for forest biomass mapping: A spaceborne data fusion method for estimating forest biomass and LiDAR sampling error
    Montesano, P. M.
    Cook, B. D.
    Sun, G.
    Simard, M.
    Nelson, R. F.
    Ranson, K. J.
    Zhang, Z.
    Luthcke, S.
    REMOTE SENSING OF ENVIRONMENT, 2013, 130 : 153 - 170
  • [22] Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data
    Zhang, Yuzhen
    Liang, Shunlin
    Sun, Guoqing
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (01) : 140 - 152
  • [23] Mapping Forest Aboveground Biomass Using Multisource Remotely Sensed Data
    Ehlers, Dekker
    Wang, Chao
    Coulston, John
    Zhang, Yulong
    Pavelsky, Tamlin
    Frankenberg, Elizabeth
    Woodcock, Curtis
    Song, Conghe
    REMOTE SENSING, 2022, 14 (05)
  • [24] RELIABILITY, PRESENTATION, AND RELATIONSHIPS AMONG DATA FROM INVENTORIES OF FOREST CONDITION
    INNES, JL
    BOSWELL, RC
    CANADIAN JOURNAL OF FOREST RESEARCH-REVUE CANADIENNE DE RECHERCHE FORESTIERE, 1990, 20 (06): : 790 - 799
  • [25] Mapping secondary tropical forest and forest age from SPOT HRV data
    Kimes, DS
    Nelson, RF
    Salas, WA
    Skole, DL
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1999, 20 (18) : 3625 - 3640
  • [26] A map of African humid tropical forest aboveground biomass derived from management inventories
    Pierre Ploton
    Frédéric Mortier
    Nicolas Barbier
    Guillaume Cornu
    Maxime Réjou-Méchain
    Vivien Rossi
    Alfonso Alonso
    Jean-François Bastin
    Nicolas Bayol
    Fabrice Bénédet
    Pulchérie Bissiengou
    Georges Chuyong
    Benoît Demarquez
    Jean-Louis Doucet
    Vincent Droissart
    Narcisse Guy Kamdem
    David Kenfack
    Hervé Memiaghe
    Libalah Moses
    Bonaventure Sonké
    Nicolas Texier
    Duncan Thomas
    Donatien Zebaze
    Raphaël Pélissier
    Sylvie Gourlet-Fleury
    Scientific Data, 7
  • [27] A map of African humid tropical forest aboveground biomass derived from management inventories
    Ploton, Pierre
    Mortier, Frederic
    Barbier, Nicolas
    Cornu, Guillaume
    Rejou-Mechain, Maxime
    Rossi, Vivien
    Alonso, Alfonso
    Bastin, Jean-Francois
    Bayol, Nicolas
    Benedet, Fabrice
    Bissiengou, Pulcherie
    Chuyong, Georges
    Demarquez, Benoit
    Doucet, Jean-Louis
    Droissart, Vincent
    Kamdem, Narcisse Guy
    Kenfack, David
    Memiaghe, Herve
    Moses, Libalah
    Sonke, Bonaventure
    Texier, Nicolas
    Thomas, Duncan
    Zebaze, Donatien
    Pelissier, Raphael
    Gourlet-Fleury, Sylvie
    SCIENTIFIC DATA, 2020, 7 (01)
  • [28] National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China
    Chi, Hong
    Sun, Guoqing
    Huang, Jinliang
    Guo, Zhifeng
    Ni, Wenjian
    Fu, Anmin
    REMOTE SENSING, 2015, 7 (05) : 5534 - 5564
  • [29] MAPPING FOREST ABOVE-GROUND BIOMASS AND ITS CHANGES FROM LVIS WAVEFORM DATA
    Huang, Wenli
    Sun, Guoqing
    Dubayah, Ralph
    Zhang, Zhiyu
    Ni, Wenjian
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 6561 - 6564
  • [30] Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data
    Xinchuang Wang
    Shidong Wang
    Limin Dai
    Journal of Forestry Research, 2018, 29 : 797 - 811