The MGEM is a three-term, course-based degree program consisting of 30 credits:
- 27 required credits (GEM 500, GEM 510, GEM 511, GEM 520, GEM 521, GEM 530, GEM 540, GEM 580, GEM 599) and 3 elective credits
- In addition, a series of non-credit Project Management Principals Workshops must also be completed
Summer Term 2017 (July-August): MGEM registration and tuition assessment officially begins at the start of UBC’s Summer Term Two (July 1). Note that students are not required to arrive on campus until mid-August for a brief orientation followed by the first course (GEM500 – Landscape Ecology and Management). Prior to GEM500, students are sent a pre-arrival reading package and are expected to be familiar with this material by their arrival.
|WINTER TERM 1
|WINTER TERM 2
Geographic Information Systems for Forestry and Conservation
Advanced Geographic Information Systems for Environmental Management
Remote Sensing for Ecosystem Management
Advanced Earth Observation and Image Processing
Geospatial Data Analysis
Linear Regression Models and Introduction to Spatial Statistics
|Elective (Winter Term 1 or Winter Term 2)|
Project Proposal Development and Proof of Concept
Project Management Principals Workshops
Landscape ecology has grown tremendously over the past few decades. The field emphasizes spatial patterning and spatial heterogeneity and often focuses on dynamics over large regions. Over the course of 3 weeks, students will delve into the current concepts, methods, and applications of landscape ecology. The main goals of the course are to provide students with a base of concepts and skills to facilitate problem-solving approaches to natural resource issues through the application of landscape ecology principles and tools within the realm of geospatial analysis. Students of will also be introduced to the following core themes of the Master of Geomatics for Environmental Management program: resilience, carbon and biomass, ecological goods and services, landscape pattern, heterogeneity and change, and social-ecological perspectives for environmental management.
Upon successful completion of the course requirements, students will be able to:
- Understand and explain concepts of spatial heterogeneity and scale
- Explain why spatial heterogeneity is important to ecological processes
- Quantify spatial patterns using standard software packages (e.g. Fragstats) and understand the strengths and limitations of metrics used to characterize pattern
- Appropriately calculate, interpret and apply metrics of connectivity in landscape assessments
- Create and build an simple spatial model to answer a self-designed question
- Understand the concept of spatial resilience and how it relates landscape dynamics in a range of landscapes (forests, urban areas, agricultural landscapes and aquatic systems)
- Begin to appreciate how patch-level decisions relate to landscape-level (cross-scale) dynamics
Winter Term 1
GEM 510 is an introductory graduate level course, which covers the use and application of GIS. It combines an overview of general principles of GIS, analytical use of spatial information, and practical experience in map production. The lectures provide a broad overview of the structure, processing and communication of geographic information. The assignment component involves "hands-on" use of an analytical software package to complete GIS exercises. Each student will also be required to apply and integrate various GIS operations on their course project involving spatial analysis, requiring some time outside of class hours.
The goal of the course is to provide students with experiences in the design, development, analysis, and visualization of geographic data. Upon completion of the course, students should be able to:
- Demonstrate an understanding of concepts used in GIS
- Develop conceptual designs for GIS databases
- Develop informed field data collection and management techniques
- Conduct spatial and logical queries on geospatial data
- Describe and communicate analytical findings to a non-technical audience
- Demonstrate a working knowledge of GIS software capabilities
- Understand the conceptual and practical limitations and advantages of GIS
- Meet the prerequisite skill requirements of advanced GIS courses
This course is an introduction to the methods used to gather spatial information about the Earth’s surface by remote sensing and how it can be used to map, monitor and better manage forest, vegetation and ecosystem resources. The course begins with coverage of the fundamentals of spatial data capture, the theory of electromagnetic radiation, the spectral properties of both natural and manufactured materials and the characteristics of airborne and satellite sensor systems used in earth observations. This is followed by consideration of the principles of photographic analysis and aerial photointerpretation, basic digital image processing (image rectification and classification) and new advances such as LIDAR and RADAR image analysis.
It is expected that at the completion of this course students should be able to:
- Understand the variety and capabilities of current and future airborne and space borne sensors.
- Describe the spectral properties of the Earth surface and how they can be used to interpret vegetation and other aspects of the environment.
- Understand the principles and techniques of LiDAR and RADAR and how these techniques can be used in the geosciences.
- Demonstrate an understanding of how to acquire ground truth data and practical aspects of the interpretation of remotely sensed data.
- Conduct basic image analysis procedures and understand how they work
- Understand characteristics and statistical properties of spatial data.
- Meet the prerequisite skill requirements of the advanced Remote sensing course
Understanding geo-spatial data structures, geo databases and learning approaches to developing, maintaining and utilizing geo-spatial data is a critical component for geomatics specialists. In addition, designing and implementing geo-spatial workflows requires an understanding of scripting, and developing analytical tools to process geo-spatial data in an efficient framework is paramount. In addition to actual problem solving learning geo-spatial data analysis tools enables an interactive approach to learning through the development of workflows and new ideas which can be immediately explored and tested. This course through the development of these skills will train students in scientific thinking, as well as provide exposure to the era of “big data” in ecology such as biodiversity (e-Bird), landcover change and other global datasets.
By the end of the course, students will be able to:
- Understand how to use database technology to increase GIS software functionality, including the creation of new analytical tools
- Design, implement, and evaluate geodatabase models for complex spatial datasets
- Understand the availability and strengths of “big data” datasets such as biodiversity (web of life, e-bird) and geographic data (Google earth engine, land cover change) and how to interface to these effectively
- Show proficiency in setting up complex spatial queries
- Establish working environments where spatial data can be simultaneously managed, accessed, and queried
- Understand how scripts can be used to interface with geo-spatial packages, in particular ARCGIS including calling external functions and data manipulation
- Understand how statistical scripting languages such as R can be used to perform basic tasks and process geo-spatial data
- Show competency at map automation and production
Winter Term 2
This course is designed to build upon a basic knowledge of Geographic Information Systems (GEM 510) to more advanced topics in GIS applications. The goal of this course is to provide a comprehensive introduction to the techniques and functions used in the analysis of spatial data. Students will be encouraged to think deeply about the underlying complexity of environmental management and the difficulties in accurately modeling environmental process in a GIS.
Upon completion of the course, students should be able to:
- Demonstrate an understanding of advanced GIS concepts and theory, as well as practical skills
- Develop practical data editing skills and critical thinking required for quality control
- Demonstrate an understanding of the concept of autocorrelation and how to analyze point patterns
- Understand the patterns and processes that lie beneath the features represented in the spatial database
- Describe and communicate analytical findings via the web
This course is designed to build upon a basic knowledge of remote sensing to more advanced topics in digital remote sensing applications. The aim is to encourage students to think and apply remote sensing more deeply, and understand its applicability to a wide range of environmental issues. The course is a combination of lectures covering theoretical and conceptual underpinnings of the science, as well as emphasizes a hands-on learning environment. Primary focus will be placed on advanced active and passive sensors characteristics in particular Light Detection and Ranging (LiDAR), hyperspectral, digital image analysis, assessing forest cover change and processing for a broad range of sensors and applications.
It is expected that at the completion of this subject and lab program students should have learnt the following:
- Understand LiDAR remote sensing technology, its underlying principles and its application
- Understand the role of LiDAR in Enhanced Forest Inventory
- Conduct basic LiDAR data processing
- Assess the capacity of hyperspectral imagery to address environmental issues
- Conduct and demonstrate how to acquire ground truth data and practical aspects of the interpretation of remotely sensed data
- Understand the role of image processing for assessing forest cover change
This course is an introduction to linear regression models and spatial statistics. The course begins with a brief review of basic statistics, matrix algebra, and simple linear regression models. The first half of the course then covers multiple linear regression models in detail: inferences, diagnostic statistics and remedial measures, as well as model selection and validation. The second half of the course focuses on spatial statistics: concept and measures of autocorrelation, spatial interpolation procedures and spatial regression models, and sampling in space.
It is expected that at the completion of this course students will:
- Understand the theory behind and how to apply simple and multiple linear regression to fit models using sample data
- Describe regression results and make inferences
- Understand assumptions underlying linear regression models
- Understand the concept of spatial autocorrelation
- Demonstrate an understanding of measures of spatial autocorrelation
- Conduct a basic stratification for sampling in space
- Apply spatial interpolation procedures and fit regression models for spatially autocorrelated data
- Demonstrate competency in “R”
Winter Term 1 & Winter Term 2
This course is intended to familiarize students with current events in and applications of geomatics in environmental management. The course content will be taught through a series of lecture-and-discussion seminars led by current practitioners of GIS, remote sensing and geoinformatics applications in commercial, consulting, non-government and governmental institutions in and around the Vancouver area. Students will be involved in structuring the trajectory of the seminars by having input on speaker selection and developing in-depth questions to be answered by each speaker. This will allow the students to self-direct their studies based on their interests, learn about the possibilities for future employment and network with seminar speakers.
By the end of this course students will be able to:
- Understand key issues and opportunities associated with utilizing geomatics skills in environmental management issues
- Analyze key issues surrounding the uptake of geomatics skills by the environmental management issues and propose solutions
- Participate in discussions and debates about mechanisms and approaches to improve the use of geomatics skills in environmental management activities
- Develop a comprehensive understanding of the applications of geomatics software and tools by various user groups focusing on different aspects of ecosystem management
- Become comfortable with discussing and debating issues with specialists in the field and improve networking skills
- Conduct a critical analysis of key issues associated with the use of geomatics tools in a number of user sectors and discuss these in a presentation setting
The objective of this course is to have students understand how to develop, write and deliver an effective project proposal. Students will form a project proposal around a topic of interest from either their recent employer, home country or one of their own choosing. With the assistance of an assigned Faculty mentor, students will develop key questions about a topic that can be addressed using geospatial information. They will then develop a research proposal which will include: a literature review, hypothesis generation, a data acquisition plan, a suitable research approach, the appropriate modelling and analysis strategies and an initial assessment of the likely results and implications from the work. Students will present both a written proposal and oral presentation for assessment. While the research itself is not undertaken within the MGEM, it is anticipated the developed proposal could form the basis of ongoing research in the future.
By the end of the course students will be able to:
- Identify a topic of interest and problem
- Pose ecological questions and underlying model
- Develop a data acquisition plan
- Propose a data model and/or method of analysis
- Present effective visualization approaches
- Propose potential outcomes in a final report
- Conduct academic research and writing at a graduate-level