Share | 04/24/2020
Abstract: In the European Union, the Water Framework Directive (2000/60/EC) requires periodic monitoring of reservoirs and water bodies. Currently, most of the reservoir monitoring programmes are based on specific measures taken in the field, which make it difficult to capture the spatial and temporal variability of phenomena such as cyanobacterial blooms, with irregular spatio-temporal patterns. The overall objective of the project is to study the viability of combining three types of data: Satellite imagery (Landsat 8 and Sentinel 2), UAV multispectral imagery and high frequency in situ water quality data (HF data) through the entire water column, for detailed monitoring of the water quality status in reservoirs. The project aims at developing a methodology for spatially-explicit modelling of relevant parameters (namely transparency, Chla, phycocyanin, CDOM and temperature) as a tool for supporting reservoir management and decision making, focused on problems with high social and economic impact (eutrophication, harmful algal blooms). In this work we present the first results obtained in the spatial modelling of Chlorophyll-a with OLI sensor imagery (on board Landsat 8) and with the images obtained with the first testing UAV flights, that were taken with a commercial multispectral sensor with 5 bands of acquisition in the visible and near-infrared (Rededge. Micasense) on board an octocopter (Atyges. System FV8). We have tested the performance of 6 published algorithms and 1 normalized index with Landsat 8 imagery (years 2016 and 2017) in two reservoirs in the same catchment in Spain (Galicia. NW Iberian Peninsula), an area with a high frequency of cloud cover. All of them showed significative correlation with surface in situ Chl-a but not for both reservoirs. The first testing UAV flight was done in one reservoir during september 2017, when a state of alert for drought and cyanobacterial bloom was declared and no satellite images were available for a period of 24 consecutive days due to cloud cover. The previously validated relationships were tested with the UAV imagery data and chlorophyll in situ data obtained both with an YSI6026 Chlorophyll probe attached to a YSI6600 V2 sonde at different depths, and Chlorophyll samples taken at 0,6*Secchi Depth (SD). The best performances were obtained with two published indexes: SABI (4 bands) and 2BDA (2 bands); and the in situ chlorophyll data obtained with the probe at depth of 0,6*SD.
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Authors: Castro, Carmen & Delgado, Jordi & Díaz Varela, Ramón & Domínguez, Antonio & Hinojo, Boris & Arango, Jose Luis & Cheda, Federico & Rubinos, Marco.
Associations: 3edata. Environmental Engineering; University of A Coruña; University of Santiago de Compostela
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