Relating radar remote sensing of biomass to modelling of forest carbon budgets

被引:104
作者
Le Toan, T [1 ]
Quegan, S
Woodward, I
Lomas, M
Delbart, N
Picard, G
机构
[1] Univ Toulouse 3, CNRS, IRD, Ctr Etud Spatiales Biosphere CNES, F-31062 Toulouse, France
[2] Ctr Terr Carbon Dynam, Sheffield, S Yorkshire, England
[3] Sheffield Ctr Earth Observat Sci, Sheffield, S Yorkshire, England
关键词
D O I
10.1007/s10584-004-3155-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper addresses the use of radar remote sensing to map forest above-ground biomass, and discusses the use of biomass maps to test a dynamic vegetation model that identifies carbon sources and sinks and predicts their variation over time. For current radar satellite data, only the biomass of young/sparse forests or regrowth after disturbances can be recovered. An example from central Siberia illustrates that biomass can be measured by radar at a continental scale, and that a significant proportion of the Siberian forests have biomass values less than 50 tonnes/ha. Comparison between the radar map and calculations by the Sheffield Dynamic Global Vegetation Model (SDGVM) indicates that the model considerably overestimates biomass; under-representation of managed areas, disturbed areas and areas of low site quality in the model are suggested reasons for this effect. A case study carried out at the Budingen plantation forest in Germany supports the argument that inadequate representations of site quality and forest management may cause model overestimates of biomass. Comparison of the calculated biomass of stands planted after 1990 with biomass estimates by radar allows identification of forest stands where the growth conditions assumed by the model are not valid. This allows a quality check on model calculations of carbon fluxes: only calculations for stands where there is good agreement between the data and the model predictions should be accepted. Although the paper only uses the SDGVM model, similar effects are likely in other dynamic vegetation models, and the results show that model calculations attempting to quantify the role of forests as carbon sources or sinks could be qualified and potentially improved by exploiting remotely sensed measurements of biomass.
引用
收藏
页码:379 / 402
页数:24
相关论文
共 49 条
[11]  
*GOFC DES TEAM, 1999, STRAT GLOB OBS FOR C
[12]   Estimating global land use change over the past 300 years: The HYDE Database [J].
Goldewijk, KK .
GLOBAL BIOGEOCHEMICAL CYCLES, 2001, 15 (02) :417-433
[13]   Global land cover classification at 1km spatial resolution using a classification tree approach [J].
Hansen, MC ;
Defries, RS ;
Townshend, JRG ;
Sohlberg, R .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2000, 21 (6-7) :1331-1364
[14]   Land cover type and biomass classification using AirSAR data for evaluation of monitoring scenarios in the Colombian Amazon [J].
Hoekman, DH ;
Quiñones, MJ .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38 (02) :685-696
[15]   The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates [J].
Houghton, RA ;
Lawrence, KT ;
Hackler, JL ;
Brown, S .
GLOBAL CHANGE BIOLOGY, 2001, 7 (07) :731-746
[16]  
*IGOSP, 2000, CARB CYCL OBS THEM R
[17]   RADAR BACKSCATTER AND BIOMASS SATURATION - RAMIFICATIONS FOR GLOBAL BIOMASS INVENTORY [J].
IMHOFF, ML .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1995, 33 (02) :511-518
[18]  
*IPCC SPEC REP, 2000, LAND US LAND US CHEM
[19]   The second Hadley Centre coupled ocean-atmosphere GCM: Model description, spinup and validation [J].
Johns, TC ;
Carnell, RE ;
Crossley, JF ;
Gregory, JM ;
Mitchell, JFB ;
Senior, CA ;
Tett, SFB ;
Wood, RA .
CLIMATE DYNAMICS, 1997, 13 (02) :103-134
[20]   Calculation of CO2 net sinks emissions in Russian forests and assessment of mitigation options [J].
Kokorin, AO ;
Lelyakin, AL ;
Nazarov, IM ;
Fillipchuk, AN .
ENVIRONMENTAL MANAGEMENT, 1996, 20 :S101-S109