scholarly journals High-resolution forest carbon stocks and emissions in the Amazon

2010 ◽  
Vol 107 (38) ◽  
pp. 16738-16742 ◽  
Author(s):  
G. P. Asner ◽  
G. V. N. Powell ◽  
J. Mascaro ◽  
D. E. Knapp ◽  
J. K. Clark ◽  
...  
Keyword(s):  
High Resolution ◽  
Carbon Stocks ◽  
Forest Carbon

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 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 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.

scholarly journals A 100-year conservation experiment: Impacts on forest carbon stocks and fluxes

2013 ◽  
Vol 310 ◽  
pp. 242-255 ◽  
Author(s):  
Tara Sharma ◽  
Werner A. Kurz ◽  
Graham Stinson ◽  
Marlow G. Pellatt ◽  
Qinglin Li
Keyword(s):  
Carbon Stocks ◽  
Forest Carbon

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 Forests: Carbon sequestration, biomass energy, or both?

2020 ◽  
Vol 6 (13) ◽  
pp. eaay6792 ◽  
Author(s):  
Alice Favero ◽  
Adam Daigneault ◽  
Brent Sohngen
Keyword(s):  
Carbon Sequestration ◽  
Carbon Balance ◽  
Carbon Stocks ◽  
Forest Carbon ◽  
Biomass Energy ◽  
Climate Mitigation ◽  
Intensive Management ◽  
Natural Forests ◽  
Timber Management ◽  
Forest Carbon Sequestration

There is a continuing debate over the role that woody bioenergy plays in climate mitigation. This paper clarifies this controversy and illustrates the impacts of woody biomass demand on forest harvests, prices, timber management investments and intensity, forest area, and the resulting carbon balance under different climate mitigation policies. Increased bioenergy demand increases forest carbon stocks thanks to afforestation activities and more intensive management relative to a no-bioenergy case. Some natural forests, however, are converted to more intensive management, with potential biodiversity losses. Incentivizing both wood-based bioenergy and forest sequestration could increase carbon sequestration and conserve natural forests simultaneously. We conclude that the expanded use of wood for bioenergy will result in net carbon benefits, but an efficient policy also needs to regulate forest carbon sequestration.

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