Are inventory based and remotely sensed above-ground biomass estimates consistent?

Carbon emissions resulting from deforestation and forest degradation are poorly known at local, national and global scales. In part, this lack of knowledge results from uncertain above-ground biomass estimates. It is generally assumed that using more sophisticated methods of estimating above-ground...

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Main Authors: Timothy C Hill, Mathew Williams, A Anthony Bloom, Edward T A Mitchard, Casey M Ryan
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3777937?pdf=render
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spelling doaj-7d23681ae61146f2aa09e5e39e7675bb2020-11-25T02:53:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0189e7417010.1371/journal.pone.0074170Are inventory based and remotely sensed above-ground biomass estimates consistent?Timothy C HillMathew WilliamsA Anthony BloomEdward T A MitchardCasey M RyanCarbon emissions resulting from deforestation and forest degradation are poorly known at local, national and global scales. In part, this lack of knowledge results from uncertain above-ground biomass estimates. It is generally assumed that using more sophisticated methods of estimating above-ground biomass, which make use of remote sensing, will improve accuracy. We examine this assumption by calculating, and then comparing, above-ground biomass area density (AGBD) estimates from studies with differing levels of methodological sophistication. We consider estimates based on information from nine different studies at the scale of Africa, Mozambique and a 1160 km(2) study area within Mozambique. The true AGBD is not known for these scales and so accuracy cannot be determined. Instead we consider the overall precision of estimates by grouping different studies. Since an the accuracy of an estimate cannot exceed its precision, this approach provides an upper limit on the overall accuracy of the group. This reveals poor precision at all scales, even between studies that are based on conceptually similar approaches. Mean AGBD estimates for Africa vary from 19.9 to 44.3 Mg ha(-1), for Mozambique from 12.7 to 68.3 Mg ha(-1), and for the 1160 km(2) study area estimates range from 35.6 to 102.4 Mg ha(-1). The original uncertainty estimates for each study, when available, are generally small in comparison with the differences between mean biomass estimates of different studies. We find that increasing methodological sophistication does not appear to result in improved precision of AGBD estimates, and moreover, inadequate estimates of uncertainty obscure any improvements in accuracy. Therefore, despite the clear advantages of remote sensing, there is a need to improve remotely sensed AGBD estimates if they are to provide accurate information on above-ground biomass. In particular, more robust and comprehensive uncertainty estimates are needed.http://europepmc.org/articles/PMC3777937?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Timothy C Hill
Mathew Williams
A Anthony Bloom
Edward T A Mitchard
Casey M Ryan
spellingShingle Timothy C Hill
Mathew Williams
A Anthony Bloom
Edward T A Mitchard
Casey M Ryan
Are inventory based and remotely sensed above-ground biomass estimates consistent?
PLoS ONE
author_facet Timothy C Hill
Mathew Williams
A Anthony Bloom
Edward T A Mitchard
Casey M Ryan
author_sort Timothy C Hill
title Are inventory based and remotely sensed above-ground biomass estimates consistent?
title_short Are inventory based and remotely sensed above-ground biomass estimates consistent?
title_full Are inventory based and remotely sensed above-ground biomass estimates consistent?
title_fullStr Are inventory based and remotely sensed above-ground biomass estimates consistent?
title_full_unstemmed Are inventory based and remotely sensed above-ground biomass estimates consistent?
title_sort are inventory based and remotely sensed above-ground biomass estimates consistent?
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2013-01-01
description Carbon emissions resulting from deforestation and forest degradation are poorly known at local, national and global scales. In part, this lack of knowledge results from uncertain above-ground biomass estimates. It is generally assumed that using more sophisticated methods of estimating above-ground biomass, which make use of remote sensing, will improve accuracy. We examine this assumption by calculating, and then comparing, above-ground biomass area density (AGBD) estimates from studies with differing levels of methodological sophistication. We consider estimates based on information from nine different studies at the scale of Africa, Mozambique and a 1160 km(2) study area within Mozambique. The true AGBD is not known for these scales and so accuracy cannot be determined. Instead we consider the overall precision of estimates by grouping different studies. Since an the accuracy of an estimate cannot exceed its precision, this approach provides an upper limit on the overall accuracy of the group. This reveals poor precision at all scales, even between studies that are based on conceptually similar approaches. Mean AGBD estimates for Africa vary from 19.9 to 44.3 Mg ha(-1), for Mozambique from 12.7 to 68.3 Mg ha(-1), and for the 1160 km(2) study area estimates range from 35.6 to 102.4 Mg ha(-1). The original uncertainty estimates for each study, when available, are generally small in comparison with the differences between mean biomass estimates of different studies. We find that increasing methodological sophistication does not appear to result in improved precision of AGBD estimates, and moreover, inadequate estimates of uncertainty obscure any improvements in accuracy. Therefore, despite the clear advantages of remote sensing, there is a need to improve remotely sensed AGBD estimates if they are to provide accurate information on above-ground biomass. In particular, more robust and comprehensive uncertainty estimates are needed.
url http://europepmc.org/articles/PMC3777937?pdf=render
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