Forest Height Estimation Using Sentinel-1/2 and ALOS-2

Forest monitoring is gaining new importance with the increasing number of events related to climate change (as wildfires). Therefore, mapping the Forest Height (FH) becomes an important activity in the forest management when preparing for the fire seasons and for an improved understanding of climate change. The FH can be used directly, or as a proxy of other variables, as the aboveground biomass. The most accurate way to measure this variable is through field campaigns or airborne laser scanning, however both approaches are expensive and have limitations in terms of spatial and temporal scalability. As an alternative, other Remote Sensing sensors can be used, such as Synthetic Aperture Radar (SAR) or Multispectral scanner. When using SAR data, the commonest approach is to estimate the FH through SAR interferometry, a technique that usually relies in data that is not freely available, making it less suitable for operational scenarios. Also, most of the approaches based on SAR or Multispectral data need large datasets for calibrating the algorithms. In this paper, a Regression Methodology (RM) that resorts to multifrequency SAR, from Sentinel-1 and ALOS-2, and Multispectral data, from Sentinel-2, is proposed for the generation of FH maps of Mediterranean forests. The RM uses a Stacking Regressor, that can generate FH maps, calibrated with data covering only 25% of the study area being mapped. A R2 between 50.79-78.01% and a RMSE between 0.76-3.68m were achieved on a total of 17 study areas across Portugal, Spain, and USA.