Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data

TL;DR

This paper presents an enhanced hybrid machine learning model that combines TanDEM-X and Landsat data to improve forest height estimation accuracy, validated against airborne LiDAR in Gabon.

Contribution

The extension of a hybrid ML model with Landsat data significantly improves forest height estimation accuracy over previous models.

Findings

13.5% reduction in RMSE

16.6% reduction in MAE

Validated on Gabonese forest data

Abstract

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 Lop\'e 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.