Detection of standing dead trees using leaf-on and leaf-off UAV-borne laser scanning point cloud data in mixed forestsKrašovec, Nina (Avtor) Repe, Blaž (Mentor) Höfle, Bernhard (Komentor) LiDARdrone-basedtree mortalitybi-temporal datarandom forestThe assessment of forest health is gaining importance with the increasing frequency and severity of drought events. Forest ecosystems are becoming more vulnerable and susceptible to diseases and insect attacks, leading to increased tree mortality and risk of fire. Light Detection and Ranging (LiDAR) allows to obtain valuable 3-dimensional geometrical and spectral information, which is becoming more widely used in forest health assessments. The aim of this study was to examine the potential of bi-temporal UAV-borne laser scanning (ULS) data and voxel-based metrics for predicting the occurrence of standing dead trees without tree delineation. The position of each standing dead tree was measured during field inventory. ULS data was collected under leaf-on and leaf-off conditions. A 2D moving window approach was developed to extract feature sets based on cells, height bins, and columns from leaf-on and leaf-off datasets. The classification was carried out using a random forest classifier, resulting in accuracies of 0.87 and 0.86 for the two tested plots. These results were achieved using the bi-temporal dataset and using a threshold of 2.5 m for the distance of the 2D window position and the location of the field measurement. Among the two voxel-based groups of metrics (height bins and columns), height bins provided more valuable information from vertical vegetation strata, which improved the classification of live and dead trees. Metrics derived from the point cloud divided into columns proved to be prone to noisy points and missing data. The presented approach using a 2D moving window gave satisfactory results, but further analysis would be needed in different forest settings to determine the effects of vegetation structure on classification performance.20212021-07-13 16:00:44Magistrsko delo/naloga128432VisID: 472661sl