Preprints
https://doi.org/10.5194/bg-2019-202
https://doi.org/10.5194/bg-2019-202
11 Jun 2019
 | 11 Jun 2019
Status: this preprint has been withdrawn by the authors.

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

Shaoqing Dai, Xiaoman Zheng, Lei Gao, Shudi Zuo, Qi Chen, Xiaohua Wei, and Yin Ren

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.

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Shaoqing Dai, Xiaoman Zheng, Lei Gao, Shudi Zuo, Qi Chen, Xiaohua Wei, and Yin Ren

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Shaoqing Dai, Xiaoman Zheng, Lei Gao, Shudi Zuo, Qi Chen, Xiaohua Wei, and Yin Ren
Shaoqing Dai, Xiaoman Zheng, Lei Gao, Shudi Zuo, Qi Chen, Xiaohua Wei, and Yin Ren

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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.
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