During the course you will learn how to design, collect and process data in order to estimate amount of woody biomass biomass in forest stands - as a tool for forest inventory related mainly to carbon sequestration.

The purpose of the course is to familiarize the students with the basic concepts, terms, terminology and methods used by close-to-nature silviculture (CNS) as a central part of a modern, multifunctional forestry. Among others, after the course, the students should know and understand the implications of changing external conditions (human needs and requirements on forests, changes in the global environment) for how the forest management goals are (or should be) formulated and accomplished in a current-day practice. Further, students should be able to: 1) define the essence of the CNS, 2) identify and characterize its major components as well as 3) put into operation its principles while planning different types of silvicultural actions (relating to forest reproduction and forest tending treatments) in various categories of forest stands, with a special reference to Central European conditions.

Keywords: Silviculture, close-to-nature approach, computer-based analyses

"Internet Programming" - an elective course designed especially for Forest Information Technology students. It provides bases of creating web pages using HTML language and other tools using different platforms.

In this course (led in the seminar form) students present and discuss results of their Research Projects conducted during 3rd semester.

The course is the continuation of the Environmental Spatial Data Analysis I course offered within the frame of the Forest Information Technology program. It covers environmental data processing, including regression analysis and analysis of variance. In addition to that, basic statistical concepts, sampling, data processing, estimation procedures, significance level, confidence intervals, sample size determination, testing statistical hypotheses, simple and multiple linear regression, nonlinear regression and ANOVA will be discussed with the use R package.