Computational approaches for satellite data analysis for chlorophyll concentration
Pasumarthi, Reshma Chowdary
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Both environmental monitoring and the assessment of risks to the ecosystem play a significant role in maintaining environmental sustainability. Among several impacts, toxins produced by cyanobacteria in water affect aquatic plants, animals and human beings, they can grow faster with high availability of nutrients and warm temperatures. Based on public reports, the National Wildlife Federation noted in a report that cyanobacterial harmful algal blooms are common, with 21 states of the U.S reporting blooms at 147 locations between May and September 2013 . In our research, we have studied the process to extract “chlorophyll a” concentration from lakes in Georgia using scenes captured by MERIS satellite; also studied the process for extracting physical parameters Land Usage Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Palmer Drought Severity Index (PDSI) data for lakes in Georgia. We have also studied lakes, which have similar trend with respect to “chlorophyll a” concentration from 2002 to 2012, and impact of physical parameters for change in concentration by performing machine learning analysis. Our research seeks to explore the challenges and approaches in the extraction of data from satellite scenes and to apply data analytics to environmental monitoring.