Skip to Main Content
Spatial interpolation of forest conditions using co-conditional geostatistical simulationAuthor(s): H. Todd Mowrer
Source: In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 214-220.
Publication Series: General Technical Report (GTR)
Station: North Central Research Station
PDF: View PDF (668.93 KB)
DescriptionIn recent work the author used the geostatistical Monte Carlo technique of sequential Gaussian simulation (s.G.s.) to investigate uncertainty in a GIS analysis of potential old-growth forest areas. The current study compares this earlier technique to that of co-conditional simulation, wherein the spatial cross-correlations between variables are included. As in the earlier study, uncertainties were assessed across 500 independent spatial Monte Carlo realizations for each of three variables of interest (quadratic mean stand diameter; age of dominant and co-dominant trees, and percent canopy cover). Potential old-growth for the study area was estimated for each set of these perturbed realizations using a simple GIS analysis. An uncertainty histogram was created by adding the 500 realizations on a cell-by-cell basis. Results were compared using empirical confidence legions from the upper percentiles of the histogram for each study. For uncertainty assessment using these particular co-located spatial data, co-conditional simulation creates intuitively less desirable results, and does not appear to provide any advantage over independent realizations using s.G.s.. For other data sets, where all variables are not measured at each location, improvements may result.
- 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.
- 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.
CitationMowrer, H. Todd. 2000. Spatial interpolation of forest conditions using co-conditional geostatistical simulation. In: Hansen, Mark; Burk, Tom, eds. Integrated tools for natural resources inventories in the 21st century. Gen. Tech. Rep. NC-212. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station. 214-220.
- Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation
- Approximating prediction uncertainty for random forest regression models
- Multivariate stochastic simulation with subjective multivariate normal distributions
XML: View XML