Print Syllabus

Syllabus

SV0020 Analysis of Environmental Data 2, 7.5 Credits

Analys av miljödata 2

Subjects

Forestry Science Biology

Education cycle

Master’s level

Advanced study in the main field

Second cycle, has only first-cycle course/s as entry requirementsMaster’s level (A1N)

Grading scale

Language

English

Prior knowledge

Knowledge corresponding to 120 HP including

- 60 HP in Forestry Science or
- 60 HP in Biology or
- 60 HP in Environmental Science or
- 60 HP in Natural Resource Management or
- 60 HP in Forest Management or
- 60 HP in Soil Science or

including 7.5 hp in data analysis methods and English 6.

Objectives

The course will teach the student to handle large data sets specifically for forestry and related fields such as ecology, biology, soil science, and genetics, using programming and programs commonly used by researchers in the research fields and in practice. They learn to combine various spatial data sources with typical field data from forestry and ecology to implement some common machine-learning algorithms. The course has a focus on practical exercises supported by reading literature and lectures.

After completing the course, the student should be able to:

- **Collect** and **prepare** forest data from authorities such as the Swedish Forestry Agency and the Swedish Environmental Protection Agency for analysis.
- **Independently** perform spatial modeling of forest land based on remote sensing data.
- Identify and classify differences and similarities between statistics and machine learning for ecological data.
- **Compare** some traditional machine learning models based on accuracy and calculation speed.
- **Combine** machine learning with geographic data to **produce**

maps of biological conditions for sustainable forestry.

Content

This course uses environmental data from different ecosystems, with a focus on forest land. Some examples of data analyzed in the course are: laser data from LiDAR scans, hydrological data such as watercourses and surface groundwater, state-of-the-art data from harvesters, and field data from forest inventories.

The course focuses on applying statistical models and machine learning methods to spatial data. The course is divided into several sections: 1) processing of spatial data with open software. 2) Programming in, for example, Python to analyze spatial data; 3) combining data from field studies with spatial data and statistical methods and machine learning; 4) implementing machine learning models on spatial data.

*Implementation*

The course uses multiple teaching methods to promote student learning and discussion through interactive lectures and exercises.

Each section begins with online introductions and exercises, reading material and recorded lectures. Each section ends with a submission task where the students have to solve a problem with a new data set. The students will have the opportunity for consultations with the teacher thought the course.

The course focuses on the following general competencies: problem-solving, scientific methods, digital competence, and technology use.

Cooperation with the surrounding society takes place through course assignments based on real and complex data that students may encounter in their future jobs, for example, data from the Forestry Agency or the Geological Survey of Sweden.

Formats and requirements for examination

Approved assignments If a student fails a test, the examiner may give the student a supplementary assignment, provided this is possible and there is reason to do so.

If a student has been granted targeted study support because of a disability, the examiner has the right to offer the student an adapted test, or provide an alternative form of assessment.

If this course is discontinued, SLU will decide on transitional provisions for the examination of students admitted under this syllabus who have not yet been awarded a Pass grade.

For the assessment an independent project (degree project), the examiner may also allow a student to add supplemental information after  the deadline for submission.  For more information, please refer to the Education Planning and Administration Handbook.

• If the student fails a test, the examiner may give the student a supplementary assignment, provided this is possible and there is reason to do so.
• If the student has been granted special educational support because of a disability, the examiner has the right to offer the student an adapted test, or provide an alternative assessment.
• If changes are made to this course syllabus, or if the course is closed, SLU shall decide on transitional rules for examination of students admitted under this syllabus but who have not yet passed the course.
• For the examination of a degree project (independent project), the examiner may also allow the student to add supplemental information after the deadline. For more information on this, please refer to the regulations for education at Bachelor's and Master's level.

Other information

The right to take part in teaching and/or supervision only applies to the course instance which the student has been admitted to and registered on.

If there are special reasons, the student may take part in course components that require compulsory attendance at a later date. For more information, please refer to the Education Planning and Administration Handbook.

Additional information

This course prepares students for their master's theses and equips them with the skills to design their study, and collect and analyze their data. They also gain general competencies in several areas listed for the Master's program at the Faculty of Forestry

Responsible department

Department of Forest ecology and Management

Cooperating departments:

There are no Grading criteria posted for this course