Slope algorithm to map algal blooms in inland waters for Landsat 8/Operational Land Imager images

Igor Ogashawara; Lin Li; Max Jacobo Moreno-Madriñán

doi:10.1117/1.JRS.11.012005

28 December 2016 Slope algorithm to map algal blooms in inland waters for Landsat 8/Operational Land Imager images

Igor Ogashawara, Lin Li, Max Jacobo Moreno-Madriñán

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 012005 (December 2016). https://doi.org/10.1117/1.JRS.11.012005

Abstract

Monitoring algal blooms using traditional methods is expensive and labor intensive. The use of satellite technology can attenuate such limitations. A common problem associated with the application of such technology is the need to eliminate the effects of atmosphere, which can be, at least, a time-consuming task. Thus, a remote sensed algal bloom monitoring system needs a simple algorithm which is nonsensitive to atmospheric correction and that could be applied to small aquatic systems. A slope algorithm (SAred−NIR) was developed to detect and map the extension of algal blooms using the Landsat 8/Operational Land Imager. SAred−NIR was shown to have advantages over other commonly used indices to monitor algal blooms, such as normalized difference vegetation index (NDVI), normalized difference water index, and floating algae index. SAred−NIR was shown to be less sensitive to different atmospheric corrections, less sensitive to thin clouds, and less susceptible to confusion when classifying water and moderate bloom conditions. Based on ground truth data from Eagle Creek Reservoir, Indiana, SAred−NIR showed an accuracy of 88.46% while NDVI only showed a 46.15% accuracy. Finally, based on qualitative and quantitative results, SAred−NIR can be used as a tool to improve the governance of small size water resources.

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Igor Ogashawara, Lin Li, and Max Jacobo Moreno-Madriñán "Slope algorithm to map algal blooms in inland waters for Landsat 8/Operational Land Imager images," Journal of Applied Remote Sensing 11(1), 012005 (28 December 2016). https://doi.org/10.1117/1.JRS.11.012005

Received: 31 July 2016; Accepted: 6 December 2016; Published: 28 December 2016

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