Event Title
Applying Atmospheric Correction Methods to Landsat Imagery to Accurately Monitor Algal Blooms in Western Michigan Lakes
Presentation Type
Oral and/or Visual Presentation
Presenter Major(s)
Natural Resource Management
Mentor Information
James McNair, Wanxiao Sun
Department
Annis Water Resource Institute (AWRI), Geography and Planning
Location
Kirkhof Center 2263
Start Date
11-4-2012 12:00 PM
Keywords
Environment, Physical Science, Technology
Abstract
Nuisance algal blooms are a common problem in Michigan lakes, causing both aesthetic and ecological degradation. Cyanobacterial blooms are of special concern, since they are sometimes toxic to humans. Accurate methods of detecting and monitoring cyanobacterial blooms are therefore needed. Traditional field monitoring of Michigan's numerous inland lakes is not feasible, but advancements in remote sensing technology have provided a low-cost alternative based on multispectral Landsat imagery. The purpose of this study is to analyze different methods of atmospheric correction so as to more accurately identify and monitor cyanobacterial blooms in inland lakes of western Michigan via remotely sensed images. IDRISI and ERDAS Imagine software are used to perform atmospheric correction of Landsat imagery, which is then processed using published algorithms to predict cyanobacterial abundance as the concentration of phycocyanin, a distinctive photosynthetic pigment of cyanobacteria.
Applying Atmospheric Correction Methods to Landsat Imagery to Accurately Monitor Algal Blooms in Western Michigan Lakes
Kirkhof Center 2263
Nuisance algal blooms are a common problem in Michigan lakes, causing both aesthetic and ecological degradation. Cyanobacterial blooms are of special concern, since they are sometimes toxic to humans. Accurate methods of detecting and monitoring cyanobacterial blooms are therefore needed. Traditional field monitoring of Michigan's numerous inland lakes is not feasible, but advancements in remote sensing technology have provided a low-cost alternative based on multispectral Landsat imagery. The purpose of this study is to analyze different methods of atmospheric correction so as to more accurately identify and monitor cyanobacterial blooms in inland lakes of western Michigan via remotely sensed images. IDRISI and ERDAS Imagine software are used to perform atmospheric correction of Landsat imagery, which is then processed using published algorithms to predict cyanobacterial abundance as the concentration of phycocyanin, a distinctive photosynthetic pigment of cyanobacteria.