Canopy Tree Height Estimation Using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensing
This work addresses uncertainty estimation for ecological monitoring and biomass assessment, but it is incremental as it modifies existing models rather than introducing a new approach.
The paper tackles the problem of tree height estimation from satellite data by adapting existing models with quantile regression to provide uncertainty quantification, showing that the model's uncertainty correlates with challenging conditions like terrain complexity and vegetation heterogeneity.
Accurate tree height estimation is vital for ecological monitoring and biomass assessment. We apply quantile regression to existing tree height estimation models based on satellite data to incorporate uncertainty quantification. Most current approaches for tree height estimation rely on point predictions, which limits their applicability in risk-sensitive scenarios. In this work, we show that, with minor modifications of a given prediction head, existing models can be adapted to provide statistically calibrated uncertainty estimates via quantile regression. Furthermore, we demonstrate how our results correlate with known challenges in remote sensing (e.g., terrain complexity, vegetation heterogeneity), indicating that the model is less confident in more challenging conditions.