Section (b.3) Modelling

Once data is collected from both remote sensing and ground-based methods, we feed it into various modelling processes. These models are essential for interpreting raw data, simulating ecosystem processes, and making predictions about future environmental conditions:

• AI/ML Models: Artificial intelligence and machine learning are employed to analyze large datasets, identify patterns, and continuously improve model predictions.

• Direct Mapping: Converts remote sensing data into maps that show specific ecosystem attributes, such as vegetation cover, species distribution, or land use.

• Process Models: These models simulate natural processes (e.g., carbon cycling, hydrology, and nutrient flows), providing insight into ecosystem functions and how they might change over time.

Models are either built internally by TLG or built externally by academic or commercial partners. Models are validated during the model building phase against test data to gauge their accuracy and robustness. Models built by external partners are trained and validated using data provided by TLG as well as data from other sources.