Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.foreco.2018.04.054 |
Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data | |
Sagang, Le Bienfaiteur Takougoum1; Momo, Stephane Takoudjou1,2; Libalah, Moses Bakonck1,2; Rossi, Vivien4; Fonton, Noel4; Mofack, Gislain Ii1; Kamdem, Narcisse Guy1; Nguetsop, Victor Francois3; Sonke, Bonaventure1; Pierre, Ploton2; Barbier, Nicolas2 | |
2018-09-15 | |
发表期刊 | FOREST ECOLOGY AND MANAGEMENT |
ISSN | 0378-1127 |
EISSN | 1872-7042 |
出版年 | 2018 |
卷号 | 424页码:519-528 |
文章类型 | Article |
语种 | 英语 |
国家 | Cameroon; France |
英文摘要 | With the improvement of remote sensing techniques for forest inventory application such as terrestrial LiDAR, tree volume can now be measured directly, without resorting to allometric equations. However, wood specific gravity (WSG) remains a crucial factor for converting these precise volume measurements into unbiased biomass estimates. In addition to this WSG values obtained from samples collected at the base of the tree (WSG(Base)) or from global repositories such as Dryad (WSG(Dryad)) can be substantially biased relative to the overall tree value. Our aim was to assess and mitigate error propagation at tree and stand level using a pragmatic approach that could be generalized to National Forest Inventories or other carbon assessment efforts based on measured volumetric data. In the semi-deciduous forests of Eastern Cameroon, we destructively sampled 130 trees belonging to 15 species mostly represented by large trees (up to 45 Mg). We also used stand-level dendrometric parameters from 21 1-ha plots inventoried in the same area to propagate the tree-level bias at the plot level. A new descriptor, volume average-weighted WSG (WWSG) of the tree was computed by weighting the WSG of tree compartments by their relative volume prior to summing at tree level. As WWSG cannot be assessed non-destructively, linear models were adjusted to predict field WWSG and revealed that a combination of WSG(Dryad), diameter at breast height (DBH) and species stem morphology (S-m) were significant predictors explaining together 72% of WWSG variation. At tree level, estimating tree aboveground biomass using WSG(Base) and WSG(Dryad) yielded overestimations of 10% and 7% respectively whereas predicted WWSG only produced an underestimation of less than 1%. At stand-level, WSG(Base) and WSG(Dryad) gave an average simulated bias of 9% (S.D. = +/- 7) and 3% (S.D. = +/- 7) respectively whereas predicted WWSG reduced the bias by up to 0.1% (S.D. = +/- 8). We also observed that the stand-level bias obtained with WSG(Base) and WSG(Dryad) decreased with total plot size and plot area. The systematic bias induced by WSG(Base) and WSG(Dryad) for biomass estimations using measured volumes are clearly not negligible but yet generally overlooked. A simple corrective approach such as the one proposed with our predictive WWSG model is liable to improve the precision of remote sensing-based approaches for broader scale biomass estimations. |
英文关键词 | Wood specific gravity Terrestrial LiDAR Aboveground biomass Linear model Error propagation Cameroon eastern forest Remote sensing |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000437967900048 |
WOS关键词 | RADIAL VARIATION ; TROPICAL TREES ; DENSITY ; STRATEGIES ; ERROR ; STEMS ; MODEL ; LIDAR ; AGE |
WOS类目 | Forestry |
WOS研究方向 | Forestry |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/22841 |
专题 | 气候变化 |
作者单位 | 1.Univ Yaounde I, Higher Teachers Training Coll, Dept Biol, Plant Systemat & Ecol Lab LaBosystE, POB 047, Yaounde, Cameroon; 2.Univ Montpellier, CIRAD, INRA, CNRS,AMAP,IRD, Montpellier, France; 3.Univ Dschang, Fac Sci, Lab Appl Bot, Dschang, Cameroon; 4.Commiss Forets Afrique Centrale COMIFAC, BP 2572, Yaounde, Cameroon |
推荐引用方式 GB/T 7714 | Sagang, Le Bienfaiteur Takougoum,Momo, Stephane Takoudjou,Libalah, Moses Bakonck,et al. Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data[J]. FOREST ECOLOGY AND MANAGEMENT,2018,424:519-528. |
APA | Sagang, Le Bienfaiteur Takougoum.,Momo, Stephane Takoudjou.,Libalah, Moses Bakonck.,Rossi, Vivien.,Fonton, Noel.,...&Barbier, Nicolas.(2018).Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data.FOREST ECOLOGY AND MANAGEMENT,424,519-528. |
MLA | Sagang, Le Bienfaiteur Takougoum,et al."Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data".FOREST ECOLOGY AND MANAGEMENT 424(2018):519-528. |
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