Forest Height Mapping With Multifrequency SAR in Mediterranean Forests

The importance of mapping the forest height (FH) is increasing due to the more frequent impacts of climate change in the society (wildfires, droughts, and extreme weather events). Remote Sensing is often used for mapping this variable; however, it usually relies in costly and extensive field or airborne campaigns. In addition, when using synthetic aperture radar (SAR), most approaches do not use freely available data. Considering this, in this work a model is proposed that resorts to Advanced Land Observing Satellite 2 (ALOS-2), Sentinel-1 (S1), and ancillary data. Airborne laser scanning (ALS) data are used for local calibration but, with the aim of developing a more scalable model, the latter is optimized to work with small calibration datasets (representative of just 25% of the study area to be mapped). With this purpose, the model combines a featuring generation and a features’ processing stage with a stacking regressor to produce estimates at the pixel level. Their impact was assessed, and an improvement of 8.11 and 2.01 pp in the relative root mean square error (rRMSE) was achieved by including the features’ generation and features’ processing stages, respectively. In addition, when the multifrequency dataset was used, the model achieved an rRMSE better than when using only a C-band dataset (S1) or only an L-band dataset (ALOS-2), respectively, by 4.21 and 3.05 pp. Finally, the model achieved an average ${R} ^{2}$ /rRMSE of 0.6240%/24.30% and 0.5901%/22.64% for the validation and test study areas, respectively. The proposed approach revealed to be effective on mapping the FH resorting to multifrequency SAR and small calibration datasets acquired by ALS.