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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2019-202
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2019-202
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Jun 2019

Research article | 11 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

Improving non-representative-sample prediction of forest aboveground biomass maps: A combined machine learning and spatial statistical approach

Shaoqing Dai1,2, Xiaoman Zheng1,2, Lei Gao3, Shudi Zuo1,2,4, Qi Chen5, Xiaohua Wei6, and Yin Ren1,4 Shaoqing Dai et al.
  • 1Key Laboratory of Urban Environment and Health, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, CN 361021, China
  • 2University of Chinese Academy of Sciences, CN 100049, China
  • 3CSIRO, Waite Campus, Urrbrae, SA 5064, Australia
  • 4Ningbo Urban Environment Observation and Research Station-NUEORS, Chinese Academy of Sciences, CN 315800, China
  • 5Department of Geography, University of Hawai'i at Mānoa, Honolulu, HI 96822, USA
  • 6Department of Earth and Environmental Sciences, University of British Columbia, Kelowna, BC V1V 1V7, Canada

Abstract. High-precision prediction of large-scale forest aboveground biomass (AGB) is important but challenging on account of the uncertainty involved in the prediction process from various sources, especially the uncertainty due to non-representative sample units. Usually caused by inadequate sampling, non-representative sample units are common and can lead to geographic clusters of localities. But they cannot fully capture complex and spatially heterogeneous patterns, in which multiple environmental covariates (such as longitude, latitude, and forest structures) affect the spatial distribution of AGB. To address this challenge, we propose herein a low-cost approach that combines machine learning with spatial statistics to construct a regional AGB map from non-representative sample units. The experimental results demonstrate that the combined methods can improve the accuracy of AGB mapping in regions where only non-representative sample units are available. This work provides a useful reference for AGB remote-sensing mapping and ecological modelling in various regions of the world.

Shaoqing Dai et al.
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Short summary
We propose a low-cost approach that combines machine learning with spatial statistics to construct a regional forest C sequestration map from non-representative sample units. The experimental results demonstrate that the combined methods can improve the accuracy of the C sequestration map. This work provides a useful reference for climate change mitigation and other cases that used non-representative sample units.
We propose a low-cost approach that combines machine learning with spatial statistics to...
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