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

2026-04-08

AI Summary

This study addresses the limitation of existing remote sensing–based tree height estimation methods, which predominantly rely on point predictions and lack quantification of uncertainty, thereby hindering their utility in risk-sensitive ecological decision-making. To overcome this, the work proposes the first integration of quantile regression into a lightweight deep learning framework for tree height estimation, enabling the generation of statistically calibrated uncertainty intervals directly from satellite imagery. The approach not only achieves efficient uncertainty modeling but also reveals meaningful relationships between model confidence and key remote sensing challenges—such as terrain complexity and vegetation heterogeneity—automatically yielding lower confidence in more complex environments. Experimental results demonstrate that the proposed method significantly enhances the reliability and applicability of remotely sensed tree height products.

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 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.

Problem

• tree height estimation
• uncertainty quantification
• remote sensing
• quantile regression
• ecological monitoring

Innovation

• quantile regression
• uncertainty quantification
• tree height estimation
• remote sensing
• statistical calibration