- 7 core geomatics courses taught by experts in the Faculty of Forestry:
- GEM 500 (3), GEM 510 (3), GEM 511 (3), GEM 520 (3), GEM 521 (3), GEM 530 (3), GEM 540 (3)
- 5 management & leadership and policy analysis courses taught by experts in the Faculty of Forestry, the Sauder School of Business and the UBC School of Public Policy and Global Affairs:
- FCOR 500 (1.5), FCOR 501 (1.5), FCOR 502 (1.5), FCOR 503 (1.5), FCOR 599 (3)
Alternate core credits may be allowed if approved by the Program Director.
Summer Term (July-August): MGEM registration and tuition assessment begins at the start of UBC’s Summer Term Two (July 1st). However, MGEM students are not required to arrive on campus until mid-August for program orientation and the first course (GEM 500 – Landscape Ecology and Management). Prior to mid-August, students are sent a pre-arrival reading package and are expected to be familiar with this material in advance of orientation. FCOR 599 registration in the summer reflects pre-reading work only for the program, classes start in September for this course.
|WINTER TERM 1
|WINTER TERM 2
Landscape Ecology and Management (3cr)
|FORESTRY CORE MODULES
FCOR 500 – Leadership and Sustainability (1.5cr)
FCOR 501 – Project Management (1.5 cr)
|FORESTRY CORE MODULES
FCOR 502 – Fundamentals in Entrepreneurship (1.5 cr)
FCOR 503 – Policy Analysis and Evaluation (1.5 cr)
Geographic Information Systems for Forestry and Conservation (3cr)
Advanced Geographic Information Systems for Environmental Management (3cr)
Remote Sensing for Ecosystem Management (3cr)
Advanced Earth Observation and Image Processing (3cr)
Geospatial Data Analysis (3cr)
Linear Regression Models and Introduction to Spatial Statistics (3cr)
Project Proposal Development and Proof of Concept (3cr)
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
Personal and professional skills in leading change to influence the triple-bottom-line; sustainability, change agency, systems thinking; personal awareness and perspective taking for effective engagement and communication; human change dynamics; case studies in social change.
By the end of the course students will be able to:
- Establish connections between one’s own discipline and sustainable development dimensions such as triple bottom line approaches
- Demonstrate the ability to integrate knowledge of social and ecological systems to predict or forecast, assess, analyze and integrate the effects of human activities
- Create a personal vision for the changes one intends through understanding one’s leadership purpose
- Engage in self-assessment, self-reflection, and analysis and have a strong awareness of one’s own values and how they inform one’s perspectives
- Develop leadership skills, including communication, collaboration, mediation and consensus building strategies, to advocate for positive changes, and demonstrate empathy for others and the ability to weigh multiple perspectives
This course provides students with a solid foundation in project management concepts, processes and tools. Learners will acquire fundamental knowledge and skills that can be applied to projects in a multitude of industries and of varying complexity, but with specific focus on the environmental and forestry sectors.
By the end of the course students will be able to:
- Define project management and compare common project frameworks and standards
- Evaluate project management processes for projects within a specified company
- Describe and apply stakeholder analysis and management
- Explain the importance of scope definition to project planning
- Demonstrate use of project planning processes by developing core project plans including schedules, budgets, risk matrices and communication plans
- Demonstrate use of project managing processes especially change control
- Examine factors that contribute to project success and failure
- Assess impact of team leadership on success of projects
Winter Term 2
This course will expose students to the fundamentals of innovation and entrepreneurship. It is a standalone course that provides useful concepts for all students, regardless of their eventual specialization.
Students will be made aware of some of the fundamental challenges facing existing companies that wish to innovate. There will also be coverage of the main elements of starting a venture, from idea generation to customer discovery and business model design, through prototyping and research, to funding, company building and commercialization.
By the end of the course students will be able to:
- manage decision-making with incomplete and ambiguous information
- develop hypotheses regarding customer problems and design tests to inform decision-making and specify design criteria
- approach early stage financing of pre-revenue ventures;
- make decisions in case, simulation and live discussion, when new information is revealed;
- connect and apply entrepreneurial thinking in corporate innovation roles, as well in start-ups;
- link course learning to personal career planning.
This course teaches the fundamentals of policy analysis and project evaluation, with an emphasis on the practical decision making and behavioral considerations that underlie policy making, stakeholder engagement, and implementation processes. Both quantitative and qualitative assessment methods will be used and real-world cases in Canada, the U.S., Europe, Asia, and Africa will be relied on to illustrate key points. There are no pre-requisites.
Students will learn to apply different evaluation frameworks as decision aids to help construct, understand, and evaluate public policies. Course learning will emphasize the pros and cons of different policy analysis concepts and methods. Techniques drawn from decision analysis, economics, ecology, psychology, anthropology, sociology, political science, and negotiation analysis will be applied to help structure the key elements and tough trade-offs that characterize policy development and analysis. Students are expected to be curious, form coherent and compelling arguments, and be constructively critical of their own and others’ knowledge.
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
Winter Term 1 & Winter Term 2
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 student’s current program, 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
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”