Forest Height Estimation using Machine Learning Regressors with SAR Data

One of the most important forests' characteristics is its height, which can give valuable knowledge for different purposes, such as its management, wildfire prevention, carbon stock estimation, or even helping in obtaining other indicators. Since forests are normally spread over extensive areas, it can be expensive and time-consuming to map all areas with precise measurements taken directly on the ground or airborne. A solution that has been studied over the last years is using satellite imagery to help map forests' height. The purpose of this paper is to present how Synthetic Aperture Radar (SAR) data, particularly from Sentinel-1, can improve the forest height estimation with Machine Learning (ML) regressors. First, an analysis of SAR's Single Look Complex (SLC) data was performed to test how this data can provide similar results to Ground Range Detected (GRD) data. Then an analysis of how the results with GRD data can be improved with the use of Land Cover Land Use (LCLU) maps, specifically on the speckle filters usually applied. Finally, a combination of SLC and GRD data was tested, showing an improvement in the overall results of ML regressors, with a Stacking Regressor averaging a R2 of 70.39% and a relative RMSE of 21.44%. All the tests were performed on six regions of Portugal (and a few tests on ten more similar regions, from Spain and California), with data from six months in 2020.