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A Landsat 8 OLI Satellite Data-Based Assessment of Spatio-Temporal Variations of Lake Sevan Phytoplankton Biomass
- Source :
- Annals of Valahia University of Targoviste, Geographical Series. 17:83-89
- Publication Year :
- 2017
- Publisher :
- Walter de Gruyter GmbH, 2017.
-
Abstract
- The Sevan is one of the world’s largest highland lakes and the largest drinking water reservoir to the South Caucasus. An intensive drop in the level of the lake that occurred over the last decades of the 20th century has brought to eutrophication. The 2000s were marked by an increase in the level of the lake and development of fish farming. To assess possible effect of these processes on water quality, creating a state-ofthe- art water quality monitoring system is required. Traditional approaches to monitoring aquatic systems are often time-consuming, expensive and non-continuous. Thus, remote sensing technologies are crucial in quantitatively monitoring the status of water quality due to the rapidity, cyclicity, large-scale and low-cost. The aim of this work was to evaluate potential applications of the Landsat 8 Operational Land Imager (OLI) to study the spatio-temporal phytoplankton biomass changes. In this study phytoplankton biomasses are used as a water quality indicator, because phytoplankton communities are sensitive to changes in their environment and directly correlated with eutrophication. We used Landsat 8 OLI (30 m spatial resolution, May, Aug, Sep 2016) images converted to the bottom of atmosphere (BOA) reflectance by performing standard preprocessing steps (radiometric and atmospheric correction, sun glint removal etc.). The nonlinear regression model was developed using Landsat 8 (May 2016) coastal blue, blue, green, red, NIR bands, their ratios (blue/red, red/green, red/blue etc.) and in situ measurements (R2=0.7, p3. In August vs. May a sharp increase in the quantity of phytoplankton around 1-5 g/m3 is observable. In September, very high contents of phytoplankton are observed for almost entire surface of the lake. Preliminary collation between data generated with help of the model and in-situ measurements allows to conclude that the RS model for phytoplankton biomass estimation showed reasonable results, but further validation is necessary.
- Subjects :
- 010504 meteorology & atmospheric sciences
Polymers and Plastics
Remote sensing (archaeology)
Ecology (disciplines)
Satellite data
Physical geography
010501 environmental sciences
Business and International Management
01 natural sciences
Industrial and Manufacturing Engineering
0105 earth and related environmental sciences
Phytoplankton biomass
Subjects
Details
- ISSN :
- 23931493
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- Annals of Valahia University of Targoviste, Geographical Series
- Accession number :
- edsair.doi...........e9264b9d351ded9ba55facf01436dba2
- Full Text :
- https://doi.org/10.1515/avutgs-2017-0008