Forest inventory predictions from individual tree crowns: regression modeling within a sample framework

Abstract

Remotely sensed data can be used to make digital maps showing individual tree crowns (ITC) for entire forests. Attributes of the ITCs may include area, shape, height, and color. The crown map is sampled in a way that provides an unbiased linkage between ITCs and identifiable trees measured on the ground. Methods of avoiding edge bias are given. In an example from a forest of young southern pine, the forest is delineated into several thousand stands. Forty stands are sampled, each with two 0.12 acre plots. The resultant estimator of a volume surrogate, tree basal area times height summed over all trees, is unbiased and has a 90-percent confidence interval of ± 4.1 percent. The root mean square errors for basal area and the volume surrogate at the stand level are estimated at 9.7 percent and 12.8 percent, respectively. That precision in basal area for individual stands is approximately the same as would have been achieved by ground sampling with ten 0.12 acre plots in each stand, making no use of the remotely sensed data.

Parent Publication

• Proceedings of the eighth annual forest inventory and analysis symposium

Citation

Flewelling, James W. 2009. Forest inventory predictions from individual tree crowns: regression modeling within a sample framework. In: McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.; McWilliams, William H., eds. Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16-19; Monterey, CA. Gen. Tech. Report WO-79. Washington, DC: U.S. Department of Agriculture, Forest Service. 203-210.