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Biogeosciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Aug 2019

Submitted as: research article | 21 Aug 2019

Review status
A revised version of this preprint was accepted for the journal BG.

An analysis of forest biomass sampling strategies across scales

Jessica Hetzer1, Andreas Huth1,2,3, Thorsten Wiegand1,3, Hans J. Dobner4, and Rico Fischer1 Jessica Hetzer et al.
  • 1Department of Ecological Modelling, Helmholtz Centre for Environmental Research – UFZ, Leipzig, 04318, Germany
  • 2Institute of Environmental Systems Research, University of Osnabrück, Osnabrück, 49076, Germany
  • 3German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, 04103 Leipzig, Germany
  • 4Leipzig University of Applied Sciences-HTWK, Leipzig, 04277, Germany

Abstract. Tropical forests play an important role in the global carbon cycle, as they store a large amount of biomass. To estimate the biomass of a forested landscape, sample plots are often used, assuming that the biomass of these plots represents the biomass of the surrounding forest.

In this study, we investigated the conditions under which a limited number of sample plots conform to this assumption. Therefore, minimum sample sizes for predicting the mean biomass of tropical forest landscapes were determined by combining statistical methods with simulations of sampling strategies. We examined forest biomass maps of Barro Colorado Island (50 ha), Panama (50 000 km2), and South America, Africa and Southeast Asia (7 million–15 million km2). The results showed that 100–200 plots (1–25 ha each) are necessary for continental biomass estimations if the sampled plots are spatially randomly distributed.

The locations of the current inventory plots in the tropics and the data obtained from remote sensing often do not meet this requirement. Considering the typical aggregation of these plots considerably increase the minimum sample size required. In the case of South America, it can increase to 70 000 plots. To establish more reliable biomass predictions across South American tropical forests, we recommend more spatially randomly distributed inventory plots. If samples are generated by remote sensing, distances of more than 5 km between the measurements increase the reliability of the overall estimate, as they cover a larger area with minimum effort. The use of a combination of remote sensing data and field inventory measurements seems to be a promising strategy for overcoming sampling limitations at larger scales.

Jessica Hetzer et al.

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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

Jessica Hetzer et al.

Jessica Hetzer et al.


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Latest update: 21 Feb 2020
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Short summary
Due to limited accessibility in tropical regions, only small parts of the forest landscape can be surveyed in forest plots. Since there is an ongoing debate about how representative estimations based on samples are at larger scales, this study analyzes how many plots are needed to quantify the biomass of the entire South American tropical forest. Through novel computational and statistical investigations we show that the spatial plot positioning is crucial for continent-wide biomass estimations.
Due to limited accessibility in tropical regions, only small parts of the forest landscape can...