Remote Sensing of Algal Bloom Dynamics

The Use of Remote Sensing and Gis Technology in Harmful Algal Bloom Research

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

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 diffic...

Retinoblastoma protein: Combating algal bloom

The discovery of a homolog of the retinoblastoma protein (Rb) in a single-celled eukaryote--the alga Chlamydomonas--promises new and surprising insights into Rb's function in cell-cycle regulation.

Prediction of Algal Bloom Using Genetic Programming

In this study an attempt is made to mathematically model and predict algal blooms in Tolo Harbour (Hong Kong) using Genetic Programming (GP). Chlorophyll plays a vital role and is taken as a measure of algal bloom biomass and 8 other variables are taken as input for its prediction. It is observed that GP evolves multiple models with almost same values of errors – of – measure. Previous studies ...

SeaWiFS discrimination of harmful algal bloom evolution

The discrimination of harmful algal blooms (HABs) from space would benefit both the capability of early warning systems and the study of environmental factors affecting the initiation of blooms. Unfortunately, there are no published techniques using global monitoring satellite sensors to distinguish the resulting subtle changes in ocean colour, so in situ sampling is needed to identify the spec...