The thesis is composed of two parts. In the first part we developed a new method for lidar
DTM generation. In the second part we used vertical lidar profiles for model-based prediction
of the percentages of individual tree species in the forest and to predict different light
properties of the forest In steep forested relief, the existing algorithms for computing DTM
from the lidar data have problems to distinguish between the ground returns and the
vegetation returns, because on the steep slopes the local cloud neighborhood has properties
similar to the vegetation. In the first part of the thesis we introduced a new method of DTM
computation from the lidar data, called REIN, which is especially adapted to the steep
forested topography. The method makes use of the lidar point redundancy to mitigate errors.
It does not belong into any of the known algorithm groups, because it randomly samples the
point cloud. REIN has a greater ability to adapt to variations in the terrain and forest cover.
Because of ensuring that each part of the area of interest gets equal probability of being
sampled, REIN results in a homogeneous DTM even under non-homogeneous data input
conditions. REIN also takes care of the problem of negative outliers due to multi-path
reflections. In the second part of the thesis we used vertical vegetation profiles, computed
from the small-footprint discrete lidar data, to predict the percentages of individual tree
species in the forest and to predict different light properties of the forest. The ensemble
methods of machine learning were used together with different combinations of the
explanatory variables, derived both from the discrete lidar data and from the aerial infra-red
imagery. The correlations for the eight best modeled target variables are between 0,76 and
0,83. Relatively modest correlations are attributed to the heterogeneity of forests in the test
area, to the errors in the training set, and to the imprecise positioning of the field plots (due to
GPS errors under the forest canopy), resulting in a possible spatial shift between the field data
and lidar data. Infrared explanatory variables contribute the most to the predictions of target
variables referring to the tree composition. Lidar data are better suited to explain the forest
light properties, which in turn are linked to the spatial distribution of the above-ground forest
biomass. The machine-learned ensemble models are more accurate and more robust than the
linear regression models. The multi-target models are more suitable than the single-target
ensemble models, because the total time to set up a multi-target model is shorter than the time
needed to set up multiple single-target models. The multi-target models are also easier to
implement. The forest has been delimited into the forest stands by image segmentation based
on the model-based raster maps.
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