scholarly journals Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

2016 ◽  
Vol 13 (5) ◽  
pp. 1571-1585 ◽  
Author(s):  
Pierre Ploton ◽  
Nicolas Barbier ◽  
Stéphane Takoudjou Momo ◽  
Maxime Réjou-Méchain ◽  
Faustin Boyemba Bosela ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Reference Model ◽  
Original Data ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Biomass Estimation ◽  
Systematic Bias ◽  
Data Set ◽  
Allometric Models ◽  
Large Trees

Abstract. Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees  ≥  45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

scholarly journals Closing a gap in tropical forest biomass estimation: accounting for crown mass variation in pantropical allometries

2015 ◽  
Vol 12 (23) ◽  
pp. 19711-19750 ◽  
Author(s):  
P. Ploton ◽  
N. Barbier ◽  
S. T. Momo ◽  
M. Réjou-Méchain ◽  
F. Boyemba Bosela ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Reference Model ◽  
Central Africa ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Biomass Estimation ◽  
Systematic Bias ◽  
Allometric Models ◽  
Original Dataset ◽  
Large Trees

Abstract. Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from −23–16 to 0–10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.

Examining High-Resolution PiSAR-L2 Textures for Estimating Tropical Forest Carbon Stocks

2016 ◽  
Vol 9 (7) ◽  
pp. 3202-3209 ◽  
Author(s):  
Rajesh Bahadur Thapa ◽  
Manabu Watanabe ◽  
Masanobu Shimada ◽  
Takeshi Motohka
Keyword(s):  
High Resolution ◽  
Tropical Forest ◽  
Carbon Stocks ◽  
Forest Carbon

scholarly journals Monitoring and estimating tropical forest carbon stocks: making REDD a reality

2007 ◽  
Vol 2 (4) ◽  
pp. 045023 ◽  
Author(s):  
Holly K Gibbs ◽  
Sandra Brown ◽  
John O Niles ◽  
Jonathan A Foley
Keyword(s):  
Tropical Forest ◽  
Carbon Stocks ◽  
Forest Carbon

scholarly journals High-resolution Mapping of Forest Carbon Stocks in the Colombian Amazon

2012 ◽  
Vol 9 (3) ◽  
pp. 2445-2479 ◽  
Author(s):  
G. P. Asner ◽  
J. K. Clark ◽  
J. Mascaro ◽  
G. A. Galindo García ◽  
K. D. Chadwick ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Forest ◽  
Large Scale ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Airborne Lidar ◽  
High Resolution Mapping ◽  
Resolution Mapping ◽  
Carbon Mapping ◽  
Colombian Amazon

Abstract. High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (>40 %) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

scholarly journals Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar

2016 ◽  
Vol 104 (3) ◽  
pp. 703-715 ◽  
Author(s):  
Ghislain Vieilledent ◽  
Oliver Gardi ◽  
Clovis Grinand ◽  
Christian Burren ◽  
Mamitiana Andriamanjato ◽  
...  
Keyword(s):  
Climate Change ◽  
Tropical Forest ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Bioclimatic Envelope Models ◽  
Bioclimatic Envelope

scholarly journals Tree height and tropical forest biomass estimation

2013 ◽  
Vol 10 (6) ◽  
pp. 10491-10529 ◽  
Author(s):  
M. O. Hunter ◽  
M. Keller ◽  
D. Vitoria ◽  
D. C. Morton
Keyword(s):  
Tropical Forest ◽  
Tropical Forests ◽  
Reference Values ◽  
Tree Height ◽  
Carbon Stocks ◽  
Estimation Accuracy ◽  
Biomass Estimation ◽  
Height Measurement ◽  
Individual Tree ◽  
Allometric Relations

Abstract. Tropical forests account for approximately half of above-ground carbon stored in global vegetation. However, uncertainties in tropical forest carbon stocks remain high because it is costly and laborious to quantify standing carbon stocks. Carbon stocks of tropical forests are determined using allometric relations between tree stem diameter and height and biomass. Previous work has shown that the inclusion of height in biomass allometries, compared to the sole use of diameter, significantly improves biomass estimation accuracy. Here, we evaluate the effect of height measurement error on biomass estimation and we evaluate the accuracy of recently published diameter : height allometries at four sites within the Brazilian Amazon. As no destructive sample of biomass was available at these sites, reference biomass values were based on allometries. We found that the precision of individual tree height measurements ranged from 3 to 20% of total height. This imprecision resulted in a 5–6% uncertainty in biomass when scaled to 1 ha transects. Individual height measurement may be replaced with existing regional and global height allometries. However, we recommend caution when applying these relations. At Tapajós National Forest in the Brazilian state of Pará, using the pantropical and regional allometric relations for height resulted in site biomass 26% to 31% less than reference values. At the other three study sites, the pan-tropical equation resulted in errors of less that 2%, and the regional allometry produced errors of less than 12%. As an alternative to measuring all tree heights or to using regional and pantropical relations, we recommend measuring height for a well distributed sample of about 100 trees per site. Following this methodology, 95% confidence intervals of transect biomass were constrained to within 4.5% on average when compared to reference values.

scholarly journals Tree height and tropical forest biomass estimation

2013 ◽  
Vol 10 (12) ◽  
pp. 8385-8399 ◽  
Author(s):  
M. O. Hunter ◽  
M. Keller ◽  
D. Victoria ◽  
D. C. Morton
Keyword(s):  
Tropical Forest ◽  
Tropical Forests ◽  
Reference Values ◽  
Tree Height ◽  
Carbon Stocks ◽  
Estimation Accuracy ◽  
Biomass Estimation ◽  
Height Measurement ◽  
Individual Tree ◽  
Allometric Relations

Abstract. Tropical forests account for approximately half of above-ground carbon stored in global vegetation. However, uncertainties in tropical forest carbon stocks remain high because it is costly and laborious to quantify standing carbon stocks. Carbon stocks of tropical forests are determined using allometric relations between tree stem diameter and height and biomass. Previous work has shown that the inclusion of height in biomass allometries, compared to the sole use of diameter, significantly improves biomass estimation accuracy. Here, we evaluate the effect of height measurement error on biomass estimation and we evaluate the accuracy of recently published diameter–height allometries at four areas within the Brazilian Amazon. As no destructive sample of biomass was available at these sites, reference biomass values were based on allometries. We found that the precision of individual tree height measurements ranged from 3 to 20% of total height. This imprecision resulted in a 5–6% uncertainty in biomass when scaled to 1 ha transects. Individual height measurement may be replaced with existing regional and global height allometries. However, we recommend caution when applying these relations. At Tapajos National Forest in the Brazilian state of Pará, using the pantropical and regional allometric relations for height resulted in site biomass 21% and 25% less than reference values. At the other three study sites, the pantropical equation resulted in errors of less that 2%, and the regional allometry produced errors of less than 12%. As an alternative to measuring all tree heights or to using regional and pantropical relations, we recommend measuring height for a well-distributed sample of about 100 trees per site. Following this methodology, 95% confidence intervals of transect biomass were constrained to within 4.5% on average when compared to reference values.

The imprint of logging on tropical forest carbon stocks: A Bornean case-study

2018 ◽  
Vol 417 ◽  
pp. 154-166 ◽  
Author(s):  
Andes Hamuraby Rozak ◽  
Ervan Rutishauser ◽  
Karsten Raulund-Rasmussen ◽  
Plinio Sist
Keyword(s):  
Tropical Forest ◽  
Carbon Stocks ◽  
Forest Carbon

scholarly journals High-resolution mapping of forest carbon stocks in the Colombian Amazon

2012 ◽  
Vol 9 (7) ◽  
pp. 2683-2696 ◽  
Author(s):  
G. P. Asner ◽  
J. K. Clark ◽  
J. Mascaro ◽  
G. A. Galindo García ◽  
K. D. Chadwick ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Forest ◽  
Large Scale ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Airborne Lidar ◽  
High Resolution Mapping ◽  
Resolution Mapping ◽  
Carbon Mapping ◽  
Colombian Amazon

Abstract. High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

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