Morgan A. Crowley
PhD Candidate (Department of Natural Resource Sciences)
Research Interests: fire mapping, disturbance detection, Remote sensing, multi-source data synthesis, Landsat, Sentinel-2, MODIS, Google Earth Engine, Bayesian Updating of Land Cover algorithm
Extreme fire seasons are becoming increasingly common across Canada, and annual burned area is predicted to escalate. Widespread and large fires impact ecosystem services such as timber supply, wildlife habitat, and carbon storage, and diminished air quality has a detrimental effect on human health and well-being. Accurate maps of active fire progression can help stakeholders fight fires, better understand how they burn, and model future landscape disturbance and subsequent impacts on ecosystem services. The primary objective of my thesis is to develop data fusion methods to produce and analyse decades of fire progressions at the provincial level. By using novel classification and data-fusion methods to refine burned-area mapping, this dataset and subsequent analyses hope to offer insights to the fields of fire ecology and disturbance monitoring.
Twitter
ResearchGate
OrcID
Google Scholar
Research Interests: fire mapping, disturbance detection, Remote sensing, multi-source data synthesis, Landsat, Sentinel-2, MODIS, Google Earth Engine, Bayesian Updating of Land Cover algorithm
Extreme fire seasons are becoming increasingly common across Canada, and annual burned area is predicted to escalate. Widespread and large fires impact ecosystem services such as timber supply, wildlife habitat, and carbon storage, and diminished air quality has a detrimental effect on human health and well-being. Accurate maps of active fire progression can help stakeholders fight fires, better understand how they burn, and model future landscape disturbance and subsequent impacts on ecosystem services. The primary objective of my thesis is to develop data fusion methods to produce and analyse decades of fire progressions at the provincial level. By using novel classification and data-fusion methods to refine burned-area mapping, this dataset and subsequent analyses hope to offer insights to the fields of fire ecology and disturbance monitoring.
ResearchGate
OrcID
Google Scholar
|
|
|
|
