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Machine Learning for Ecology and Sustainable Natural Resource Management pp 141–160Cite as

Mapping Aboveground Biomass of Trees Using Forest Inventory Data and Public Environmental Variables within the Alaskan Boreal Forest

Abstract

A method for mapping forest biomass was developed and tested on a study area in the boreal forest of interior Alaska. In order to understand above ground biomass values within this forest, we employed the Boruta algorithm, Random Forest (RF), and Classification and Regression Tree (CART) analysis (three different machine learning techniques) to predict above ground woody biomass for the entire region using a combination of forest inventory data and a suite of 32 predictors from public open-access data archives that included spectral reflectance, climatic, soil, distance from various features, and topographic variables. We also developed prediction maps at a 1km2 resolution for aboveground woody biomass. The method employed here yielded good accuracy for the huge Alaskan landscape despite the rather limited training set. The results indicate that the geographic patterns of biomass are strongly influenced by the tree size class diversity which contributed of a given stand.

Keywords

  • Predictive mapping
  • Aboveground forest biomass
  • Random forest
  • Regression tree analysis
  • Alaska
  • Boreal forest

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Acknowledgments

We thank Douglas Hanson for his valuable comments and insights. The authors also thank Thomas Malone and Dan Rees and their field assistants for collecting and compiling all the forest inventory data. Support for this work was provided by the National Science Foundation, through its Integrative Graduate Education and Research Traineeship (IGERT, NSF 0114423) to the Resilience and Adaptation Program (RAP) at the University of Alaska Fairbanks; and the State of Alaska Department of Natural Resources Division of Forestry.

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Authors and Affiliations

  1. Department of Natural Sciences, Landmark College, Putney, VT, USA

    Brian D. Young

  2. Department of Forest Sciences, University of Alaska Fairbanks, Fairbanks, AK, USA

    John Yarie & David Verbyla

  3. EWHALE Lab, Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska-Fairbanks, Fairbanks, AK, USA

    Falk Huettmann

  4. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA

    F. Stuart Chapin III

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  1. Brian D. Young
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  4. Falk Huettmann
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  5. F. Stuart Chapin III
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Correspondence to Brian D. Young .

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Editors and Affiliations

  1. Black Bawks Data Science Ltd, Fort Augustus, Scotland, New Zealand

    Grant Humphries

  2. U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, USA

    Dawn R. Magness

  3. Institute of Arctic Biology, University of Alaska - Fairbanks, FAIRBANKS, AK, USA

    Falk Huettmann

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Young, B.D., Yarie, J., Verbyla, D., Huettmann, F., Stuart Chapin, F. (2018). Mapping Aboveground Biomass of Trees Using Forest Inventory Data and Public Environmental Variables within the Alaskan Boreal Forest. In: Humphries, G., Magness, D., Huettmann, F. (eds) Machine Learning for Ecology and Sustainable Natural Resource Management. Springer, Cham. https://doi.org/10.1007/978-3-319-96978-7_7

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