Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data
Incremental improvement for remote sensing of forest height in tropical regions.
The authors extended a hybrid machine learning model for forest height estimation from TanDEM-X data by adding optical Landsat features, achieving a 13.5% reduction in RMSE and 16.6% reduction in MAE over the original model when validated against LiDAR in Gabon.
Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data. In this context, a ML model for estimating forest height from TanDEM-X interferometric coherence measurements has recently been proposed, that constrains the learning process through a PM. While the features used for training and inversion where selected to ensure the physical consistency of the solutions, they could not resolve all height / structure and baseline / terrain slope ambiguities in the data. To improve this, the extension of the feature space with optical Landsat data is proposed able to provide complementary information on forest type or structure. The extended model is applied and validated on several TanDEM-X acquisitions over the Gabonese Lopé national park site and assessed against airborne LiDAR measurements. Results show a 13.5% reduction in RMSE and a 16.6% reduction in MAE compared to the original hybrid model, confirming the added value of multispectral inputs.