Estimating Stand Characteristics by Combining Single Tree Pattern Recognition of Digital Video Imagery and a Theoretical Diameter Distribution Model

Abstract

Abstract This article presents a new method combining pattern recognition of single trees and a theoretical diameter distribution to determine stand characteristics. The applied remote sensing material was digital video imagery. A super-resolution technique was used in order to improve the quality of the video imagery. Tree crowns were identified and crown areas segmented from the super-resolution image. After that, tree diameters were predicted using detected crown areas. However, only large trees (dbh >17 cm) could be recognized from digital video image. Therefore, the theoretical Weibull distribution was predicted to be able to also calculate the number of small trees (dbh <17 cm). The mean characteristics information needed for predicting the parameters of Weibull distribution was obtained from the resulting truncated distribution of large trees. The final estimate of the diameter distribution is a combination of these two parts. The reliability of prediction of stand characteristics considered, i.e., number of stems, stand basal area, and volume was improved with the use of the theoretical diameter distribution model. However, these results should be considered preliminary, because they are based on a small validation data set. According to these results, especially the accuracy of the estimate of the number of stems was increased considerably. This improvement is important when simulating future stand development in forest management planning software packages. FOR. SCI. 49(1):98–109.