Uncertainty estimation of the self-thinning process by Maximum-Entropy Principle

Shoufan Fang; George Z. Gertner

Treesearch

Uncertainty estimation of the self-thinning process by Maximum-Entropy Principle

Informally Refereed

Authors:

Shoufan Fang, George Z. Gertner

Year:

2000

Type:

General Technical Report

Station:

Northern Research Station

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. 199-206.

Download (PDF 403 KB)

Download (PDF 403 KB): https://research.fs.usda.gov/download/treesearch/15818.pdf

Abstract

When available information is scarce, the Maximum-Entropy Principle can estimate the distributions of parameters. In our case study, we estimated the distributions of the parameters of the forest self-thinning process based on literature information, and we derived the conditional distribution functions and estimated the 95 percent confidence interval (CI) of the self-thinning process for several tree species. The 95 percent CI indicated that the slope parameter of the so-called self-thinning law can be considered a random variable with a mean value of -3/2.

Parent Publication

Integrated tools for natural resources inventories in the 21st century

Citation

Fang, Shoufan; Gertner, George Z. 2000. Uncertainty estimation of the self-thinning process by Maximum-Entropy Principle. 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. 199-206.