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A Hierarchical Binary Process Model to Assess Deviation from Desired Ecological Condition across a Broad Forested Landscape in Alabama

Author

Listed:
  • Pete Bettinger

    (Warnell School of Forestry and Natural Resources, University of Georgia, 180 E. Green Street, Athens, GA 30602, USA)

  • Krista Merry

    (Warnell School of Forestry and Natural Resources, University of Georgia, 180 E. Green Street, Athens, GA 30602, USA)

  • Jonathan Stober

    (U.S. Forest Service, Talladega National Forest, 45 Highway 281, Heflin, AL 36264, USA)

Abstract

This work describes the development and analysis of a spatially explicit environmental model to estimate the current, ecological, condition class of a managed forest landscape in the southern United States. The model could be extendable to other similar temperate forest landscapes, yet is characterized as a problem-specific, hierarchical, binary process model given the explicit relationships it recognizes between the management of southern United States pine-dominated natural forests and historical ecological conditions. The model is theoretical, based on informed proposals of the landscape processes that influence the ecological condition, and their relationship to perceived ecological condition. The modeling effort is based on spatial data that describe the historical forest community classes, forest plan provisions, fire history, silvicultural treatments, and current vegetation conditions, and six potential ecological condition classes (ECC) are assigned to lands. A case study was provided involving a large national forest, and validation of the outcomes of the modelling effort suggested that the overall accuracy when predicting the exact ecological condition class was about 46%, while the overall accuracy ±1 class was about 81%. For large, heterogeneous forest areas, issues remain in estimating the input variables relatively accurately, particularly the pine basal area.

Suggested Citation

  • Pete Bettinger & Krista Merry & Jonathan Stober, 2022. "A Hierarchical Binary Process Model to Assess Deviation from Desired Ecological Condition across a Broad Forested Landscape in Alabama," Land, MDPI, vol. 11(6), pages 1-14, May.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:6:p:775-:d:823427
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    References listed on IDEAS

    as
    1. David Cleland & Keith Reynolds & Robert Vaughan & Barbara Schrader & Harbin Li & Larry Laing, 2017. "Terrestrial Condition Assessment for National Forests of the USDA Forest Service in the Continental US," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    2. Bagstad, Kenneth J. & Semmens, Darius J. & Waage, Sissel & Winthrop, Robert, 2013. "A comparative assessment of decision-support tools for ecosystem services quantification and valuation," Ecosystem Services, Elsevier, vol. 5(C), pages 27-39.
    3. Michael C. Stambaugh & Richard P. Guyette & Esther D. Stroh & Matthew A. Struckhoff & Joanna B. Whittier, 2018. "Future southcentral US wildfire probability due to climate change," Climatic Change, Springer, vol. 147(3), pages 617-631, April.
    4. Paul F. Hessburg & Keith M. Reynolds & R. Brion Salter & James D. Dickinson & William L. Gaines & Richy J. Harrod, 2013. "Landscape Evaluation for Restoration Planning on the Okanogan-Wenatchee National Forest, USA," Sustainability, MDPI, vol. 5(3), pages 1-36, February.
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