Abstract

The ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level. Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniques often fail to exploit the huge amount of three dimensional (3D) structural information in the high density LiDAR data to achieve accurate detection and delineation of the 3D crown in dense forests, and thus, the first contribution of the thesis is a technique that detects and delineates both dominant and subdominant trees in dense multilayered forests. The proposed method uses novel two dimensional (2D) and 3D features to achieve this goal. Species knowledge at individual tree level is relevant for accurate forest parameter estimation. Most state-of-the-art techniques use features that represent the distribution of data points within the crown to achieve species classification. However, the performance of such methods is low when the trees belong to the same taxonomic class (e.g., the conifer class). High density LiDAR data contain a huge amount of fine structural information of individual tree crowns. Thus, the second contribution of the thesis is on novel methods for classifying conifer species using both the branch level and the crown level geometric characteristics. Accurate localization of trees is fundamental to calibrate the individual tree level inventory data, as it allows to match reference to LiDAR data. An important biophysical parameter for precision forestry applications is the Diameter at Breast Height (DBH). SoA methods locate the stem directly below the tree top, and indirectly estimate DBH using species-specific allometric models. Both approaches tend to be inaccurate and depend on the forest type. Thus, in this thesis, a method for accurate stem localization and DBH measurement is proposed. This is the third contribution of the thesis. Qualitative and quantitative results of the experiments confirm the effectiveness of the proposed methods over the SoA ones.