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An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China

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Abstract

Monitoring the dynamics of forest biomass at various spatial scales is important for better understanding the terrestrial carbon cycle as well as improving the effectiveness of forest policies and forest management activities. In this article, field data and Landsat image data acquired in 1999 and 2007 were utilized to quantify spatiotemporal changes of forest biomass for Dongsheng Forestry Farm in Changbai Mountain region of northeastern China. We found that Landsat TM band 4 and Difference Vegetation Index with a 3 × 3 window size were the best predictors associated with forest biomass estimations in the study area. The inverse regression model with Landsat TM band 4 predictor was found to be the best model. The total forest biomass in the study area decreased slightly from 2.77 × 106 Mg in 1999 to 2.73 × 106 Mg in 2007, which agreed closely with field-based model estimates. The area of forested land increased from 17.9 × 103 ha in 1999 to 18.1 × 103 ha in 2007. The stabilization of forest biomass and the slight increase of forested land occurred in the period following implementations of national forest policies in China in 1999. The pattern of changes in both forest biomass and biomass density was altered due to different management regimes adopted in light of those policies. This study reveals the usefulness of the remote sensing-based approach for detecting and monitoring quantitative changes in forest biomass at a landscape scale.

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Acknowledgments

This study was supported by the National Key Technologies R&D Program of China (2012BAD22B04) and Strategic Priority Research Program of Chinese Academy of Sciences (XDA05060200). Dr. Chen is supported in part by the National Science Foundation Grant (DBI-0821649). Dr. Wang is supported in part by the Doctoral Science Foundation of Henan Polytechnic University (B2012-071). The authors thank the Lushuihe Forestry Bureau for providing assistance in data collection. The authors also wish to thank Dengsheng Lu for his valuable suggestions regarding an earlier draft of this manuscript.

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Authors and Affiliations

  1. State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110164, People’s Republic of China

    Xinchuang Wang, Bernard J. Lewis, Guang Qi, Dapao Yu, Li Zhou & Limin Dai

  2. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, People’s Republic of China

    Xinchuang Wang

  3. Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, 47907, USA

    Guofan Shao

  4. Biology Department, University of Illinois, Springfield, IL, 62703, USA

    Hua Chen

  5. Pingdingshan University, Pingdingshan, 467000, People’s Republic of China

    Guang Qi

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  1. Xinchuang Wang
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  2. Guofan Shao
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  3. Hua Chen
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  4. Bernard J. Lewis
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  5. Guang Qi
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  6. Dapao Yu
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  7. Li Zhou
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  8. Limin Dai
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Correspondence to Limin Dai.

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Wang, X., Shao, G., Chen, H. et al. An Application of Remote Sensing Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectiveness of Forest Policies in Northeastern China. Environmental Management 52, 612–620 (2013). https://doi.org/10.1007/s00267-013-0089-6

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