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Research Methods in Forest Sciences

Course number: 3513148; 3.5 ECTS credits; In Finnish: Metsätieteiden tutkimusmenetelmät

Blas MOLA-YUDEGO

 

Course description

Developing studies in forest sciences requires an understanding on statistical methods as well as to be familiar with the most common methodological approaches, in order to comprehend as well as to develop research projects. Research Methods in Forest Sciences aims to review several methodological tools, statistical and GIS based, related to forest studies. The course was originally conceived for students of the MSc of European Forestry in 2005, in order to prepare them for the final thesis, and shortly after it was then open to all students of the School of Forest Sciences of the University of Eastern Finland. The course was designed by Blas Mola, together with Celeste Lacuna, under the leadership of Paavo Pelkonen. Several instructors have been involved along years, among others: Janet Chaseling, José Ramon González-Olabarria, Alejandro Ruete and Olalla Díaz-Yáñez. The present form of the course dates back to 2015, with Blas Mola as main instructor and coordinator, and Timo Tokola and Sanna Härkönen as co-lecturers; the course includes background lectures, R and GIS exercises and a final exam.

 

 

 

Learning outcomes

This is the wiki of the course Research Methods in Forest Sciences. The course consists of lectures and exercises as well as independent and collaborative work on learning tasks at this Wiki learning environment. Upon successful completion of this course, you will be able to:

  • interpret statistical results

  • become familiarized with typical research methods in forest science, eg. GIS tools 

  • to present statistical information in an academic way

  • understand experiments, the reasons behind the set-ups, and the most common sampling techniques

  • build simple statistical models, for the most common analysis performed in forest science

Contents: Applied statistics, research methodology, biometric and ecosystem modeling concepts, R statistics, GIS and remote sensing techniques.

 

 

 

 

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