Amazon forest biomass density maps: tackling the uncertainty in carbon emission estimates

Ometto, Jean Pierre; Aguiar, Ana Paula; Assis, Talita Oliveira; Soler, Luciana; Valle, Pedro Henrique Dias; Tejada, Graciela; Lapola, David M.; Meir, Patrick

doi:10.1007/978-3-319-15901-0_7

Cited by 16 publications

(37 citation statements)

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“…However, substantial uncertainty remains in estimating tropical forest C emissions from those human activities (Clark, Roberts, Ewel, & Clark, 2011). Because land use change is a patchy process (Ometto et al, 2014), accurately mapping the spatial distribution of tropical C stock and its dynamics is vital to reduce such uncertainty (Achard et al, 2014). Remote sensing is a promising technology to achieve this goal with its ability of providing synoptic view of the whole study area (Chen, 2013;DeFries et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels

Chen

Laurin

Валентини

2015

Remote Sensing of Environment

119

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Section: Introductionmentioning

confidence: 99%

Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels

Chen

Laurin

Валентини

2015

Remote Sensing of Environment

119

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“…When different data sources are available, it is useful to compare the extent and location of the differences observed. For example, Ometto et al (2014) compared different carbon maps available for the Brazilian Amazon and identified an inter-map differences of up to 50 %, showing the substantial impact that inter-map variability can have on emission estimates.…”

Section: Dealing With Uncertainty Upstreammentioning

confidence: 97%

“…The uncertainty estimated around ER from forest degradation should be interpreted as a first attempt to provide an error assessment from this source. Empirically-based information about error is lacking and should be prioritized to orient decision-making about monitoring choices in light of overall upstream uncertainty reduction improvements (Herold and Skutsch 2011;Ometto et al 2014). This study takes place in a context where major emission reductions were produced from curbing deforestation and the costs of monitoring are relatively limited.…”

Section: Dealing With Uncertainty Upstreammentioning

confidence: 99%

Addressing uncertainty upstream or downstream of accounting for emissions reductions from deforestation and forest degradation

2015

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Uncertainty in emissions and emission changes estimates constitutes an unresolved issue for a future international climate agreement. Uncertainty can be addressed 'upstream' through improvements in the technologies or techniques used to measure, report, and verify (MRV) emission reductions, or 'downstream' through the application of discount factors to more uncertain reductions. In the context of Reducing Emissions from Deforestation and forest Degradation (REDD+), we look at the effects of upstream interventions on reductions in uncertainty, using data from Panama. We also test five downstream proposals for discounting uncertainty of the potential credits received for reducing emissions. We compare the potential compensation received for these emission reductions to the cost of alternative upstream investments in forest monitoring capabilities. First, we find that upstream improvements can noticeably reduce the overall uncertainty in emission reductions. Furthermore, the costs of upstream investments in improved forest monitoring are relatively low compared to the potential benefits from carbon payments; they would allow the country to receive higher financial compensation from more certain emission reductions. When uncertainty is discounted downstream, we find that the degree of conservativeness applied downstream has a major influence on both overall creditable emission reductions and on incentives for upstream forest monitoring improvements. Of the five downstream approaches that we analyze, only the Conservativeness Approach and the Risk Charge Approach provided consistent financial incentives to reduce uncertainty upstream. We recommend specifying Climatic Change

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“…Neigh et al 2013). There are many examples of the application of this remotely sensed imagery for forest inventory both with estimation of the precision of estimates, in one form or another, (Cohen et al 2013;Tomppo et al 2014;Kangas et al 2016;Ringvall et al 2016) and without precision estimation (Du et al 2014;Ometto et al 2014;Waser et al 2015;Clerici et al 2016;Immitzer et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Simulation studies to examine bias and precision of some estimators that use auxiliary information in design-based sampling in forest inventory

West

2017

N.Z. j. of For. Sci.

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Background: Various double sampling methods using both target and auxiliary variables have been developed over many years for use in natural resource inventory. Methods: Simulations of inventory were carried out using four different ratio estimators and model-assisted estimation in each of five rather different example forest populations. Estimates of population means and their standard errors from each of these methods were compared with those obtained using simple random sampling. Results: With all five double sampling estimators, bias in estimates of means and standard errors (the latter estimated analytically or through bootstrapping) was generally small and consistent with theoretical expectations. Their efficiency increased as either the first-or second-phase sample sizes increased. All were more efficient than estimates obtained using simple random sampling as long as there was some positive level of correlation between the target and auxiliary variable. However, none of the double sampling estimators was more efficient than any of the others. Conclusions: For many forest inventory tasks, users may well be able to use whichever of the estimators is most convenient to their purpose. However, model-assisted estimation has application in a wider range of circumstances than the other methods, which perhaps recommends it for general use.

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Amazon forest biomass density maps: tackling the uncertainty in carbon emission estimates

Cited by 16 publications

References 0 publications

Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels

Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels

Addressing uncertainty upstream or downstream of accounting for emissions reductions from deforestation and forest degradation

Simulation studies to examine bias and precision of some estimators that use auxiliary information in design-based sampling in forest inventory

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