Macrophytes Monitoring System and Solution

China Three Gorges Brasil

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The Macrophyte Monitoring System (SMM) is a computational system based on geotechnology that brought together extensive environmental and physical field data as well imagery and real-time data collected from upstream hydropower plants and meteorological buoys installed at the hydropower plant reservoirs.

The solution is innovative and unique because it combines floristics, bathymetry, meteorology, limnology, hydro sedimentology, open-source satellite imaging combined with UAVs and real-time hydrology data into a single platform to predict the plants’ behavior.

The system’s goal is to support the hydropower operators who have operating problems with macrophytes, aiming for satellite monitoring the displacement of macrophyte banks and supporting the development of decision making and action plan operations ahead of time to avoid accidents with aquatic plants, ensuring the continuity of power generation.

The system is adopted from a geographic information module that maps the plants in the reservoir and the average travel time to the water intake, an alert module and a detachment trend module both designed to serve as a functional tool for everyday use and to indicate risk situations to operators, and finally a growth indicators module that allows the investigation of environmental parameters that are strongly related to the growth and displacement of aquatic plants.

  • The solution resulting from the R&D undertaken by CTG Brasil and Lactec Institute at Jupia Hydropower Plant in Brazil (a case study), which has been having operational issues with macrophyte plants, demonstrated advances to the current bibliography through the implementation of a fully automated and integrated system.
  • The solution is equipped with an alert mechanism whose conceptual model was designed to trigger alerts about events that represent risks to the reservoir through the modeling of flow rate parameters, water quality, meteorology and areas occupied by aquatic macrohytes from the automatic classification of periodic orbital images.
  • A processing cycle was developed using machine learning techniques, which provided the best quality classification combined with automation, without requiring user interaction with the machine.
  • Its main functionality consists of a modern graphic interface that displays the percentages of occupation by emergent and submerged macrophytes in the reservoir, presented as a function of the passage time of the satellite.
  • The system also associates the modeling of bio-optical, hydrological and hydrodynamic parameters, allowing the monitoring of physical and biological conditions for the possible detachment and displacement of these plants, as well as obtaining the reading of the estimated times it takes for their displacement to reach the dam.

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