Title: Mapping pre-European settlement vegetation at fine resolutions using a hierarchical Bayesian model and GIS
Author: He, Hong S.; Dey, Daniel C.; Fan, Xiuli; Hooten, Mevin B.; Kabrick, John M.; Wikle, Christopher K.; Fan, Zhaofei.
Source: Plant Ecology. 191: 85-94.
Description: In the Midwestern United States, the GeneralLandOffice (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 systemand 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.
Keywords: GLO, GIS, Hierarchical Bayesian models, Presettlement vegetation, Missouri
View or Print this Publication (340 KB)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
- This publication may be available in hard copy. Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat. During the capture process some typographical errors may occur. Please contact Sharon Hobrla, email@example.com if you notice any errors which make this publication unusable.
He, Hong S.; Dey, Daniel C.; Fan, Xiuli; Hooten, Mevin B.; Kabrick, John M.; Wikle, Christopher K.; Fan, Zhaofei. 2007. Mapping pre-European settlement vegetation at fine resolutions using a hierarchical Bayesian model and GIS. Plant Ecology. 191: 85-94.
Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility