XTBG OpenIR  > 其他
Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks
Rejou-Mechain, M; Muller-Landau, HC; Detto, M; Thomas, SC; Le Toan, T; Saatchi, SS; Barreto-Silva, JS; Bourg, NA; Bunyavejchewin, S; Butt, N; Brockelman, WY; Cao, M; Cardenas, D; Chiang, JM; Chuyong, GB; Clay, K; Condit, R; Dattaraja, HS; Davies, SJ; Duque, A; Esufali, S; Ewango, C; Fernando, RHS; Fletcher, CD; Gunatilleke, IAUN; Hao, Z; Harms, KE; Hart, TB; Herault, B; Howe, RW; Hubbell, SP; Johnson, DJ; Kenfack, D; Larson, AJ; Lin, L; Lin, Y; Lutz, JA; Makana, JR; Malhi, Y; Marthews, TR; McEwan, RW; McMahon, SM; McShea, WJ; Muscarella, R; Nathalang, A; Noor, NSM; Nytch, CJ; Oliveira, AA; Phillips, RP; Pongpattananurak, N; Punchi-Manage, R; Salim, R; Schurman, J; Sukumar, R; Suresh, HS; Suwanvecho, U; Thomas, DW; Thompson, J; Uriarte, M; Valencia, R; Vicentini, A; Wolf, AT; Yap, S; Yuan, Z; Zartman, CE; Zimmerman, JK; Chave, J
2014
Source PublicationBIOGEOSCIENCES
ISSN1726-4170
Volume11Issue:23Pages:6827-6840
AbstractAdvances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.
KeywordAlos Palsar Data Aboveground Biomass Error Propagation Amazonian Forest Tropical Forests Airborne Lidar Live Biomass Models Deforestation Regression
Language英语
Document Type期刊论文
Identifierhttp://ir.xtbg.org.cn/handle/353005/8393
Collection其他
森林生态研究组
Recommended Citation
GB/T 7714
Rejou-Mechain, M,Muller-Landau, HC,Detto, M,et al. Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks[J]. BIOGEOSCIENCES,2014,11(23):6827-6840.
APA Rejou-Mechain, M.,Muller-Landau, HC.,Detto, M.,Thomas, SC.,Le Toan, T.,...&Chave, J.(2014).Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks.BIOGEOSCIENCES,11(23),6827-6840.
MLA Rejou-Mechain, M,et al."Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks".BIOGEOSCIENCES 11.23(2014):6827-6840.
Files in This Item: Download All
File Name/Size DocType Version Access License
Local spatial struct(481KB) 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Rejou-Mechain, M]'s Articles
[Muller-Landau, HC]'s Articles
[Detto, M]'s Articles
Baidu academic
Similar articles in Baidu academic
[Rejou-Mechain, M]'s Articles
[Muller-Landau, HC]'s Articles
[Detto, M]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Rejou-Mechain, M]'s Articles
[Muller-Landau, HC]'s Articles
[Detto, M]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.