%0 Articles
%T Prediction of diameter distributions in boreal forests using remotely sensed data
%A Räty, Janne
%D 2020
%J Dissertationes Forestales
%V 2020
%N 294
%R doi:10.14214/df.294
%U http://dissertationesforestales.fi/article/10364
%X <p>Diameter distributions are usually characterized in forest management inventories using probability density functions (PDF). Depending on the inventory method, the PDF parameters are derived using either predicted or assessed forest attributes. The application of PDF is not essential for forest inventories that rely on remotely sensed data, because the diameter distributions can be predicted using empirical tree lists via the nearest neighbor (NN) approach. This thesis comprises three objectives. The general aim is to investigate NN-based prediction of diameter distributions in Finnish forest inventories. Firstly, the response configurations of the NN approach were examined in the prediction of species-specific diameter distributions. Secondly, different remote sensing datasets were utilized in the prediction of diameter distribution for logwood-sized trees. For example, bitemporal and multispectral airborne laser scanning (ALS) datasets were compared to the Finnish forest inventory standard in which unispectral ALS and aerial images are used. Thirdly, two approaches that fuse an area-based approach (ABA) and individual-tree detection (ITD) in the prediction of diameter distributions were proposed. The results showed that the standard response configuration used in NN imputation is suboptimal if diameter distributions are of interest. The findings also indicate that the multispectral ALS dataset performs poorly in the prediction of logwood volumes by tree species. Instead, the use of bitemporal ALS (leaf-off and leaf-on) data provide almost comparable error rates with the use of ALS data and aerial images in the prediction of logwood volumes by tree species. The ABA-ITD fusion of diameter distributions provided slight improvements in the mean error index values associated with the predicted diameter distributions. It should be noted, however, that ITD is more sensitive to errors than ABA, for example, in forests with a bimodal or descending diameter distribution. Structural analysis of forests using ALS data is a possible indicator for the selection of prediction approach. The pulse density of the national ALS data will be increased in the 2020s, which opens up the possibility to apply the ABA-ITD fusion approach in practical applications.</p>