Canopy Tree Height Estimation using Quantile Regression: Modeling and Evaluating Uncertainty in Remote Sensing

Karsten Schrödter, Jan Pauls, Fabian Gieseke

TL;DR: We enhance tree height estimation models with quantile regression to provide statistically calibrated uncertainty estimates, improving their applicability in risk-sensitive scenarios and showing reduced confidence in challenging conditions.

Abstract:

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 on tree height estimation rely on point predictions, which limits their applicability in risk-sensitive scenarios. In this work, we show that with minor modifications to the 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.

Submission Number: 1483