Fusing diameter distributions predicted by area-based approach and individual-tree detection in coniferous-dominated forests

An area-based approach (ABA) is the most common method to predict forest attributes using airborne laser scanning (ALS) data. Individual tree detection (ITD) offers an alternative to ABA, however, few studies have examined the selection of these two alternatives for the prediction of diameter distributions. We predicted diameter distributions by applying ABA and ITD in coniferous-dominated boreal forests using ALS data, and examined their predictive performance based on the shapes of the diameter distributions (Gaussian, Bi-modal, and Reverse J). We proposed an ABA-ITD fusion for diameter distribution prediction. Firstly, the fusion was optimized and its potential was evaluated using an error index. Secondly, we offer two alternatives to incorporate the fusion into ALS-based forest inventories. Our results indicate that ITD is more prone to errors than ABA, and that the predictive performance of ITD is more sensitive than ABA to the shape of the diameter distribution. The results show that ITD outperforms ABA with Gaussian diameter distributions. In contrast, ABA was seen as preferable to ITD with Bi-modal or Reverse J shape diameter distributions. The findings indicate that ABA-ITD fusion has potential for predicting diameter distributions, although the predictive capability of ITD is limited compared to ABA.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author