Analysis of Environmental Data 2
Course description
This distance course is aimed at those who want to learn more about how to handle and analyse big datasets. The course focuses on combining spatial data with machine learning to analyse forest land and environmental data from different aspects. The course consists of lectures and individual exercises where you get to apply the methods to real data from authorities and companies.
Analysis of environmental data 2 builds on the course analysis of environmental data 1 and aims to equip you with the tools required to handle different types of data generated from different parts of society. National laser scans, satellites, and forest machines are some examples of modern data sources that are used both in research and business. In the course, you will learn to combine different data sources with field inventories to implement leading machine learning methods.
The course is structured into different modules where each module contains a lecture, an introductory example, and an individual task where you independently apply the method to new data. Through practical exercises, you get the best possible opportunities for increased learning.
Entry Requirements
- 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.
Course facts
SV0020, Analysis of Environmental Data 2, 7.5 Hp
Print syllabus
Syllabus
Finalized by: Programnämnden för utbildning inom skog (PN-S), 2022-11-15
Valid from : Spring semester 2024 (2024-01-15)
Level
Second cycle (A1N)
Main field of study
Forestry Science, Biology
Sub-area forestry science
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Grading Scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Course language
English
Entry Requirements
- 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.
Examination Formats and Requirements for Passing the Course
Approved assignments
Responsible Department/Equivalent
Department of Forest ecology and Management
Cooperating departments
Dept. of Wildlife, Fish and Environmental Studies
Supplementary information
Included in program
- Forest Ecology and Sustainable Management (MSc)
- Forest Ecology and Sustainable Management - mastersprogramme
- Conservation and Management of Fish and Wildlife Populations - Master's Programme
- Conservation and Management of Fish and Wildlife (MSc)
- Forest Science - Master's Programme
Module set
| Title | Credits | Code |
|---|---|---|
| Single module | 7.5 | 0001 |
Other 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