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• W2193261635 abstract "The global forest ecosystem, which acts as a large carbon sink, plays an important role in modeling the global carbon balance. An accurate estimation of the total forest carbon stock in the aboveground biomass (AGB) is therefore necessary for improving our understanding of carbon dynamics, especially against the background of global climate change. The forest area of China is among the top five globally. However, because of limitations in forest AGB mapping methods and the availability of ground inventory data, there is still a lack in the nationwide wall-to-wall forest AGB estimation map for China. In this study, we collected over 8000 ground inventory records from published literatures, and developed an AGB mapping method using a combination of these ground inventory data, Geoscience Laser Altimeter System (GLAS)/Ice, Cloud, and Land Elevation Satellite (ICESat) data, optical imagery, climate surfaces, and topographic data. An uncertainty field model was introduced into the forest AGB mapping procedure to minimize the influence of plot location uncertainty. Our nationwide wall-to-wall forest AGB mapping results show that the forest AGB density in China is 120 Mg/ha on average, with a standard deviation of 61 Mg/ha. Evaluation with an independent ground inventory dataset showed that our proposed method can accurately map wall-to-wall forest AGB across a large landscape. The adjusted coefficient of determination (R2) and root-mean-square error between our predicted results and the validation dataset were 0.75 and 42.39 Mg/ha, respectively. This new method and the resulting nationwide wall-to-wall forest AGB map will help to improve the accuracy of carbon dynamic predictions in China." @default.
• W2193261635 created "2016-06-24" @default.
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• W2193261635 date "2016-02-01" @default.
• W2193261635 modified "2023-10-17" @default.
• W2193261635 title "Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data" @default.
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• W2193261635 doi "https://doi.org/10.1016/j.rse.2015.12.002" @default.
• W2193261635 hasPublicationYear "2016" @default.
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