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Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks

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Abstract

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R 2) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R 2 values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.

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Acknowledgments

The authors gratefully acknowledge financial support provided for this research by the External Cooperation Program of the Chinese Academy of Sciences (GJH21123) and the National Natural Science Foundation of China (41222008).

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Authors and Affiliations

  1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100190, China

    Liangyun Liu, Dailiang Peng, Zhihui Wang & Yong Hu

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  1. Liangyun Liu
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Correspondence to Liangyun Liu.

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Liu, L., Peng, D., Wang, Z. et al. Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks. Environ Monit Assess 186, 7293–7306 (2014). https://doi.org/10.1007/s10661-014-3927-y

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