Developing an UAV method of monitoring of cyanobacterial blooms in freshwater ecosystems
Start Date
24-5-2022 5:45 PM
End Date
24-5-2022 7:00 PM
Abstract
Cyanobacterial blooms are widespread phenomenon naturally occurring in different types of water bodies. Characteristic indicator of the bloom is a change of water color to blue-green, due to pigments contained by growing cyanobacterial biomass. Recently observed climatic changes and increase of Earth’s surface temperature may lead to intensification and proliferation of cyanobacterial blooms, severe reduction of water quality and changes in functioning of freshwater ecosystems. Monitoring of cyanobacterial blooms appears to be crucial in order to undertake remediation policy, such as biomass harvesting. The aim of our study was to develop an index for cyanobacterial blooms detection with use of unmanned aerial vehicles (UAV). The data was collected from 30 frames floating on the surface of blooming waterbody. First, the reflectances inside the frames were measured by UAV equipped with multispectral camera, then immediately the and phycocyanin concentrations inside frames were measured in situ with use of multiparameter probe. Further statistical analyses allowed to create an index based on Red Edge 740 nm and 717 nm and NIR 842 nm spectral bands. The correlation of the index with phycocyanine concentration measured in situ was estimated as +70%.
Developing an UAV method of monitoring of cyanobacterial blooms in freshwater ecosystems
Cyanobacterial blooms are widespread phenomenon naturally occurring in different types of water bodies. Characteristic indicator of the bloom is a change of water color to blue-green, due to pigments contained by growing cyanobacterial biomass. Recently observed climatic changes and increase of Earth’s surface temperature may lead to intensification and proliferation of cyanobacterial blooms, severe reduction of water quality and changes in functioning of freshwater ecosystems. Monitoring of cyanobacterial blooms appears to be crucial in order to undertake remediation policy, such as biomass harvesting. The aim of our study was to develop an index for cyanobacterial blooms detection with use of unmanned aerial vehicles (UAV). The data was collected from 30 frames floating on the surface of blooming waterbody. First, the reflectances inside the frames were measured by UAV equipped with multispectral camera, then immediately the and phycocyanin concentrations inside frames were measured in situ with use of multiparameter probe. Further statistical analyses allowed to create an index based on Red Edge 740 nm and 717 nm and NIR 842 nm spectral bands. The correlation of the index with phycocyanine concentration measured in situ was estimated as +70%.