Liquid Trees develops models using satellite imagery, field data, LiDAR imaging, and other remote sensing data to identify key parameters used to estimate carbon fixation and sequestration.
We develop machine learning models for our river bioremediaton and carbon removal projects. Using satellite images, field measurements, and simulation, we track microalgae biomass concentration, nutrients load, and other parameters along the river. Liquid Trees use a dynamic baseline updated quarterly according to seasonal changes on the river to compare the current status of the river without the project.
Microalgae possess remarkable capabilities in converting carbon dioxide into oxygen through photosynthesis while fixing carbon in their biomass, making them invaluable in combating climate change. We use models to estimate the contribution of diatoms to organic carbon in the riverbed soil. We use field data to calibrate the models according to the specific conditions at each target river.
Liquid Trees develops a specific river monitoring strategy for each project to assess the spatial and temporal evolution of water and sediment parameters during the algal bioremediation.
Liquid Trees is open to use these tools for the development of new river bioremediation projects, restoring water quality and sequestering carbon in tropical and subtropical regions around the world.