A comparison of classification algorithms for the identification of smoke plumes from satellite images.
Obtaining accurate measures of exposure to forest fire smoke is important for the assessment of health risk. Estimating exposure from air quality monitors is challenging because of the sparseness of the monitoring networks in remote areas. However, satellite imagery offers a novel and data-rich tool to provide visual information on smoke plumes. We will discuss statistical techniques for obtaining estimates of forest fire smoke plumes using classification algorithms on data from satellite imagery in order to develop automated processes for identifying exposure. The aim is to identify whether such methods may offer a high-resolution approach that provides a reliable estimate of smoke and a more thorough caption of the spatial distribution of smoke from fires than is currently available.
Wan
V
V
Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Canada.
Braun
Wj
W
Dean
Cb
C
Henderson
S
S
D000465:Algorithms; D001955:British Columbia; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D004781:Environmental Exposure; D005390:Fires; D006801:Humans; D015233:Models, Statistical; D052638:Particulate Matter / Q000009:adverse effects*; D012906:Smoke; D018531:Spacecraft; D014197:Trees
D052638:Particulate Matter; D012906:Smoke