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Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass

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Abstract

The spatial distribution of forest biomass is closely related with carbon cycle, climate change, forest productivity, and biodiversity. Efficient quantification of biomass provides important information about forest quality and health. With the rising awareness of sustainable development, the ecological benefits of forest biomass attract more attention compared to traditional wood supply function. In this study, two nonparametric modeling approaches, random forest (RF) and support vector machine were adopted to estimate above ground biomass (AGB) using widely used Landsat imagery in the region, especially within the ecological forest of Fuyang District in Zhejiang Province, China. Correlation analysis was accomplished and model parameters were optimized during the modeling process. As a result, the best performance modeling method RF was implemented to produce an AGB estimation map. The predicted map of AGB in the study area showed obvious spatial variability and demonstrated that within the current ecological forest zone, as well as the protected areas, the average of AGB were higher than the ordinary forest. The quantification of AGB was proven to have a close relationship with the local forest policy and management pattern, which indicated that combining remote-sensing imagery and forest biophysical property would provide considerable guidance for making beneficial decisions.

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Acknowledgements

The authors are thankful to the USGS and NASA for the open archives of Landsat imagery, and would like to give sincere thanks to the R Development Team for the open-source package for statistical analysis. The authors also thank the Editor and anonymous reviewers for their constructive comments, suggestions, and help in enhancing the manuscript.

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Authors and Affiliations

  1. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China

    Chaofan Wu, Hongxiang Tao, Manyu Zhai, Yi Lin, Ke Wang, Jinsong Deng, Muye Gan & Jun Li

  2. Zhejiang Forestry Academy, Hangzhou, 310023, China

    Aihua Shen

  3. School of Civil Engineering and Environmental Sciences and School of Meteorology, University of Oklahoma, Norman, OK, 73019, USA

    Jinsong Deng & Hong Yang

  4. State Key Laboratory of Hydro Science and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China

    Hong Yang

Authors
  1. Chaofan Wu
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  2. Hongxiang Tao
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  3. Manyu Zhai
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  4. Yi Lin
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  5. Ke Wang
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  6. Jinsong Deng
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  7. Aihua Shen
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  8. Muye Gan
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  9. Jun Li
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  10. Hong Yang
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Corresponding author

Correspondence to Ke Wang.

Additional information

Project funding: The authors are grateful for the support of Chinese Ministry of Environmental Protection (No. STSN-05-11), Ministry of Science and Technology of the People’s Republic of China (No. 2015BAC02B00) and Science Technology Department of Zhejiang Province (No. 2015F50056).

The online version is available at http://www.springerlink.com.

Corresponding editor: Hu Yanbo.

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Wu, C., Tao, H., Zhai, M. et al. Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass. J. For. Res. 29, 151–161 (2018). https://doi.org/10.1007/s11676-017-0404-9

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  • DOI: https://doi.org/10.1007/s11676-017-0404-9

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