Fully Funded MSc in Remote Sensing and Wildfire Science at the University of Victoria, Canada, 2027: Eligibility, Research Details and How to Apply

Introduction Are you interested in environmental science, wildfire research, remote sensing, geographic information systems (GIS), forest ecology, or artificial intelligence? The University of Victoria in Canada is offering a two-year funded Master of Science (MSc) research opportunity focused on mapping and quantifying wildfire severity using satellite time series and three-dimensional forest structure data. The research position is advertised through the College of the Environment at the University of Washington and is based in the Remote Sensing and AI Lab within the Department of Geography at the University of Victoria, Canada. The selected student is expected to begin the programme in Fall 2027. The research project will investigate how wildfires affect forest structure by combining satellite observations, airborne and spaceborne Light Detection and Ranging (LiDAR) data, and geospatial analysis. The work will use dense Landsat satellite time series alongside three-dimensional forest structure information to examine relationships between spectral burn severity and structural changes in forests after fires. The position is described as a two-year funded MSc opportunity, supported through research funding and/or teaching assistantships. Applicants interested in the opportunity are invited to email Dr. Ji Won Suh with a CV, unofficial academic transcripts, and a brief statement explaining their research interests and relevant experience. The deadline to express interest is November 15, 2026. This opportunity may appeal to graduates in geography, environmental science, forestry, geomatics, earth science, and related disciplines who want to develop advanced research skills while investigating wildfire impacts and the application of geospatial technologies to environmental challenges. Overview of the Funded MSc Opportunity The position combines postgraduate education with a specific research project in remote sensing and wildfire science. Rather than following only a classroom-based programme, the selected student will undertake research under the supervision of a faculty member at the University of Victoria. The following are the main details provided in the project announcement. • Opportunity: MSc research position in Remote Sensing and Wildfire Science • Host university: University of Victoria • Department: Department of Geography • Research laboratory: Remote Sensing and AI Lab • Advertised through: University of Washington College of the Environment • Research supervisor and contact: Dr. Ji Won Suh • Degree: Master of Science (MSc) • Research duration: Two years • Expected start: Fall 2027 • Funding: Two-year funded position through research funding and/or teaching assistantships • Research focus: Mapping and quantifying wildfire severity using satellite time series and LiDAR-derived three-dimensional forest structure • Preferred research interests: Remote sensing, GIS, forest ecology, wildfire science, environmental science, or related fields • Desirable technical skills: Remote sensing, LiDAR, GIS, Python, R, Google Earth Engine, and geospatial data analysis • Technical experience: Desirable but not required, according to the project announcement • Expression-of-interest deadline: November 15, 2026 • Initial application method: Email Dr. Ji Won Suh • Required initial documents: CV, unofficial transcripts, and a brief statement of research interests and relevant experience • Location: Victoria, British Columbia, Canada The advertisement describes the position as funded for two years, but it does not specify the exact funding amount, monthly stipend, tuition coverage, or whether funding is guaranteed entirely through research funding, teaching assistantships, or a combination of the two. Applicants should seek clarification from the supervisor before making financial plans. About the University of Victoria The University of Victoria (UVic) is a Canadian university located in Victoria, British Columbia. Its Geography MSc programme provides graduate-level research training in the study of environmental systems, landscapes, human-environment relationships, and other geographical questions. According to the programme information supplied, students develop their independent research abilities and complete a thesis focused on a specific area of geography under the supervision of a faculty member. The university identifies several research strengths within Geography, including: • Coastal systems • Geomatics • Human geography • Physical geography • Environment and sustainability These areas demonstrate the breadth of research conducted within the department. The advertised wildfire project is particularly connected to geomatics, physical geography, environmental research, and sustainability. The Geography MSc programme also offers opportunities for students to engage in fieldwork, apply for lab-instructor work, and attend departmental colloquia and other academic events. For a student interested in environmental research, these activities can complement the work undertaken for a thesis by providing opportunities to communicate research, learn from other researchers, and engage with the wider academic community. The university's general programme information describes the MSc as a full-time, on-campus master's degree with a typical duration of two years. It is a thesis-based or project/thesis programme rather than a course-based-only option. Applicants should distinguish these general programme details from the specific wildfire research advertisement. The advertised position identifies its own two-year funding arrangement and Fall 2027 start, while the final admission requirements and funding conditions must be confirmed with the supervisor and the university. About the Remote Sensing and AI Lab The advertised position is based in the Remote Sensing and AI Lab within the University of Victoria's Department of Geography. Remote sensing involves collecting information about the Earth's surface without directly contacting it. Satellite imagery, airborne sensors, and other observation systems can provide information about vegetation, land cover, environmental changes, and the effects of natural disturbances. Artificial intelligence and computational techniques can help researchers process large datasets, identify patterns, and analyze environmental observations. When combined with geographic information systems and ecological knowledge, these tools can support investigations into changes in forests and other landscapes. The advertised project brings together several of these methods to study wildfire severity and changes in forest structure. The supplied announcement does not provide a detailed description of every activity undertaken by the laboratory, the specific AI algorithms that will be used, or the full composition of the research team. Applicants interested in the laboratory's research direction should contact Dr. Ji Won Suh for additional information. Research Project: Mapping and Quantifying Wildfire Severity The central research topic is the use of satellite time series and LiDAR-derived three-dimensional forest structure to map and quantify wildfire severity. Wildfires can alter forests in several ways. Depending on the intensity of the fire and the characteristics of the affected ecosystem, a wildfire may damage vegetation, change the structure of the forest canopy, reduce vegetation cover, and modify the physical arrangement of trees and other forest components. Understanding these changes is important for studying wildfire impacts, assessing ecosystem disturbance, and improving knowledge of how forest landscapes respond to fire. The advertised project will combine different forms of environmental observation to investigate both the spectral and structural effects of wildfire. 1. Mapping wildfire severity Wildfire severity describes the degree of ecological change caused by a fire. It is related to the effects of burning on vegetation, forest structure, and other ecosystem components. Researchers can use remotely sensed observations to identify differences between burned and unburned areas and examine variations in the apparent severity of a fire. Satellite imagery can provide information about changes in vegetation reflectance before and after a wildfire. These observations can be analyzed to characterize the extent and intensity of visible environmental change. The project aims to use satellite time series and LiDAR observations to map and quantify wildfire severity. This means the research will examine how different data sources can contribute to a more detailed understanding of fire-induced forest changes. 2. Examining changes in forest structure The project will also investigate the structural effects of wildfire on forests. Forest structure refers to the physical arrangement and characteristics of vegetation, including aspects of tree height, canopy arrangement, vertical distribution, and the three-dimensional organization of forest components. Wildfires can affect these characteristics differently depending on local conditions and the nature of the fire. Three-dimensional observations can provide information about forest structure that may not be fully captured by examining satellite spectral data alone. The advertised project will use LiDAR observations to characterize fire-induced changes in forest structure and investigate how these changes relate to spectral measures of burn severity. 3. Connecting spectral burn severity with structural forest change A central part of the research is examining relationships between spectral burn severity and structural changes in forests. Spectral information describes how surfaces reflect or emit electromagnetic energy at different wavelengths. Satellite observations can be used to identify differences in vegetation and surface characteristics associated with wildfire disturbance. Structural information, by contrast, concerns the physical form of the forest, including its three-dimensional arrangement. By integrating these sources, the research can examine whether and how patterns detected in satellite imagery correspond to changes in forest structure. This relationship is important because environmental changes can be complex. Two areas may show differences in their spectral responses, but understanding the corresponding structural changes may provide additional information about the effects of a wildfire. The announcement does not specify the exact statistical models, severity indices, or validation methods to be used. Those details may be discussed during the research planning process with the supervisor. Technologies and Research Methods The advertised project will integrate several geospatial and environmental research technologies. Applicants do not need to have experience with every tool listed in the announcement, but an interest in these methods may help them assess whether the project aligns with their research goals. Landsat satellite time series Landsat is a satellite observation programme that provides repeated observations of the Earth's surface. Satellite time series are collections of observations acquired over multiple dates. The project will use dense Landsat time series to investigate wildfire-related changes in forests. Repeated satellite observations can help researchers compare environmental conditions before and after a fire, observe changes over time, and examine the evolution of vegetation responses. Using a time series rather than relying on a single image can help capture changes that occur at different stages of a disturbance and recovery process. The announcement specifically identifies dense Landsat time series as one of the main data sources for the research. Airborne LiDAR LiDAR stands for Light Detection and Ranging. It is a remote sensing technology that uses laser pulses to measure distances and generate information

• Bachelor's degree in geography, environmental science, forestry, geomatics, earth science, or related discipline • CV required for initial application • Unofficial academic transcripts required for initial application • Brief statement explaining research interests and relevant experience required for initial application