Plant Ecology

, Volume 191, Issue 1, pp 85–94 | Cite as

Mapping pre-European settlement vegetation at fine resolutions using a hierarchical Bayesian model and GIS

  • Hong S. HeEmail author
  • Daniel C. Dey
  • Xiuli Fan
  • Mevin B. Hooten
  • John M. Kabrick
  • Christopher K. Wikle
  • Zhaofei Fan
Original Paper


In the Midwestern United States, the General Land Office (GLO) survey records provide the only reasonably accurate data source of forest composition and tree species distribution at the time of pre-European settlement (circa late 1800 to early 1850). However, GLO data have two fundamental limitations: coarse spatial resolutions (the square mile section and half mile distance between quarter corner and section corner) and point data format, which are insufficient to describe vegetation that is continuously distributed over the landscape. Thus, geographic information system and statistical inference methods to map GLO data and reconstruct historical vegetation are needed. In this study, we applied a hierarchical Bayesian approach that combines species and environment relationships and explicit spatial dependence to map GLO data. We showed that the hierarchical Bayesian approach (1) is effective in predicting historical vegetation distribution, (2) is robust at multiple classification levels (species, genus, and functional groups), (3) can be used to derive vegetation patterns at fine resolutions (e.g., in this study, 120 m) when the corresponding environmental data exist, and (4) is applicable to relatively moderate map sizes (e.g., 792 × 763 pixels) due to the limitation of computational capacity. Our predictions of historical vegetation from this study provide a quantitative and spatial basis for restoration of natural floodplain vegetation. An important assumption in this approach is that the current environmental covariates can be used as surrogates for the historical environmental covariates, which are often not available. Our study showed that terrain and soil covariates least affected by past natural and anthropogenic alternations can be used as covariates for GLO vegetation mapping.


GLO GIS Hierarchical Bayesian models Presettlement vegetation Missouri 



Funding support is from US Forest Service North Central Research Station, RWU 4154 Ecology and Management of Central Hardwood Ecosystem, and University of Missouri GIS Mission Enhancement Program.


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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Hong S. He
    • 1
    Email author
  • Daniel C. Dey
    • 2
  • Xiuli Fan
    • 1
  • Mevin B. Hooten
    • 3
  • John M. Kabrick
    • 2
  • Christopher K. Wikle
    • 4
  • Zhaofei Fan
    • 1
  1. 1.School of Natural ResourcesUniversity of Missouri-ColumbiaColumbiaUSA
  2. 2.US Forest Service, North Central Research StationColumbiaUSA
  3. 3.Department of Mathematics and StatisticsUtah State UniversityLoganUSA
  4. 4.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA

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