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Remote Sens. 2015, 7(10), 13564-13585; doi:10.3390/rs71013564

Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data

1
School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Beijing 100081, China
2
Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA
3
Monterey Bay Aquarium Research Institute, 7700 Sandholdt Rd., Moss Landing, CA 95039, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Deepak R. Mishra, Eurico J. D’Sa, Sachidananda Mishra, Magaly Koch and Prasad S. Thenkabail
Received: 28 August 2015 / Revised: 1 October 2015 / Accepted: 13 October 2015 / Published: 19 October 2015
(This article belongs to the Special Issue Remote Sensing of Water Resources)
View Full-Text   |   Download PDF [904 KB, uploaded 19 October 2015]   |  

Abstract

This paper describes the use of machine learning methods to build a decision support system for predicting the distribution of coastal ocean algal blooms based on remote sensing data in Monterey Bay. This system can help scientists obtain prior information in a large ocean region and formulate strategies for deploying robots in the coastal ocean for more detailed in situ exploration. The difficulty is that there are insufficient in situ data to create a direct statistical machine learning model with satellite data inputs. To solve this problem, we built a Random Forest model using MODIS and MERIS satellite data and applied a threshold filter to balance the training inputs and labels. To build this model, several features of remote sensing satellites were tested to obtain the most suitable features for the system. After building the model, we compared our random forest model with previous trials based on a Support Vector Machine (SVM) using satellite data from 221 days, and our approach performed significantly better. Finally, we used the latest in situ data from a September 2014 field experiment to validate our model. View Full-Text
Keywords: remote sensing; machine learning; random forest; Monterey Bay remote sensing; machine learning; random forest; Monterey Bay
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Song, W.; Dolan, J.M.; Cline, D.; Xiong, G. Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data. Remote Sens. 2015, 7, 13564-13585.

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