English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse
  1. Basket / 
  2. Item Summary

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary
 PreviousNext  

Released
Journal Article

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Authors

Araza,  Arnan
External Organizations;

de Bruin,  Sytze
External Organizations;

/persons/resource/herold

Herold,  Martin
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Quegan,  Shaun
External Organizations;

Labriere,  Nicolas
External Organizations;

Rodriguez-Veiga,  Pedro
External Organizations;

Avitabile,  Valerio
External Organizations;

Santoro,  Maurizio
External Organizations;

Mitchard,  Edward T.A.
External Organizations;

Ryan,  Casey M.
External Organizations;

Phillips,  Oliver L.
External Organizations;

Willcock,  Simon
External Organizations;

Verbeeck,  Hans
External Organizations;

Carreiras,  Joao
External Organizations;

Hein,  Lars
External Organizations;

Schelhaas,  Mart-Jan
External Organizations;

Pacheco-Pascagaza,  Ana Maria
External Organizations;

da Conceição Bispo,  Polyanna
External Organizations;

Laurin,  Gaia Vaglio
External Organizations;

Vieilledent,  Ghislain
External Organizations;

Slik,  Ferry
External Organizations;

Wijaya,  Arief
External Organizations;

Lewis,  Simon L.
External Organizations;

Morel,  Alexandra
External Organizations;

Liang,  Jingjing
External Organizations;

Sukhdeo,  Hansrajie
External Organizations;

Schepaschenko,  Dmitry
External Organizations;

Cavlovic,  Jura
External Organizations;

Gilani,  Hammad
External Organizations;

Lucas,  Richard
External Organizations;

External Ressource
No external resources are shared
Fulltext (public)

5009944.pdf
(Publisher version), 6MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Araza, A., de Bruin, S., Herold, M., Quegan, S., Labriere, N., Rodriguez-Veiga, P., Avitabile, V., Santoro, M., Mitchard, E. T., Ryan, C. M., Phillips, O. L., Willcock, S., Verbeeck, H., Carreiras, J., Hein, L., Schelhaas, M.-J., Pacheco-Pascagaza, A. M., da Conceição Bispo, P., Laurin, G. V., Vieilledent, G., Slik, F., Wijaya, A., Lewis, S. L., Morel, A., Liang, J., Sukhdeo, H., Schepaschenko, D., Cavlovic, J., Gilani, H., Lucas, R. (2022): A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. - Remote Sensing of Environment, 272, 112917.
https://doi.org/10.1016/j.rse.2022.112917


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5009944
Abstract
Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.