scholarly journals High-resolution forest carbon modelling for climate mitigation planning over the RGGI region, USA

2021 ◽  
Vol 16 (4) ◽  
pp. 045014
Author(s):  
L Ma ◽  
G Hurtt ◽  
H Tang ◽  
R Lamb ◽  
E Campbell ◽  
...  
2019 ◽  
Vol 14 (4) ◽  
pp. 045013 ◽  
Author(s):  
G Hurtt ◽  
M Zhao ◽  
R Sahajpal ◽  
A Armstrong ◽  
R Birdsey ◽  
...  

2020 ◽  
Author(s):  
Lei Ma ◽  
George Hurtt ◽  
Hao Tang ◽  
Elliott Campbell ◽  
Ralph Dubayah ◽  
...  

Author(s):  
Rachel L Lamb ◽  
George Hurtt ◽  
Tee Jay Boudreau ◽  
Elliott Campbell ◽  
Edil Sepúlveda Carlo ◽  
...  

Author(s):  
Hao Tang ◽  
Lei Ma ◽  
Andrew Lister ◽  
Jarlath O'Neil-Dunne ◽  
Jiaming Lu ◽  
...  

2020 ◽  
Author(s):  
Rachel Lamb ◽  
George Hurtt ◽  
TeeJay Boudreau ◽  
Elliott Campbell ◽  
Edil A. Sepúlveda Carlo ◽  
...  

Author(s):  
Rajesh Bahadur Thapa ◽  
Manabu Watanabe ◽  
Masanobu Shimada ◽  
Takeshi Motohka

2020 ◽  
Vol 6 (13) ◽  
pp. eaay6792 ◽  
Author(s):  
Alice Favero ◽  
Adam Daigneault ◽  
Brent Sohngen

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.


2012 ◽  
Vol 9 (3) ◽  
pp. 2445-2479 ◽  
Author(s):  
G. P. Asner ◽  
J. K. Clark ◽  
J. Mascaro ◽  
G. A. Galindo García ◽  
K. D. Chadwick ◽  
...  

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.


2009 ◽  
pp. 105-111 ◽  
Author(s):  
G. J. Nabuurs ◽  
D. C. van der Werf ◽  
A. H. Heidema ◽  
I. J. J. van den Wyngaert

Author(s):  
Steven Bott ◽  
Ryan Sporns

The benefits and limitations of using both deterministic and probabilistic analysis are demonstrated using two case studies. The incorporation of High Consequence Area (HCA) data with deterministic and probabilistic analysis is an effective way to track the corrosion risk of a pipeline through time. Both types of analysis of high-resolution in line inspection corrosion data can be used to plan excavation and repair programs and set safe re-assessment intervals. Back to back high resolution corrosion inspections are presented to validate the use of these techniques along with a discussion on how to properly allocate resources to manage corrosion uncertainty.


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