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Comparing remote sensing-based forest biomass mapping approaches using new forest inventory plots in contrasting forests in northeastern and southwestern China
We then applied LightGBM and random forest regression to generate wall-to-wall AGB maps at 25 m resolution, using extensive GEDI footprints as well as Sentinel-1 data, ALOS-2 PALSAR-2 and Sentinel-2 optical data.
Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data
Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data.
Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning
AU-FC achieved intermediate R2 of 0. 64, RMSE of 44. 92 Mgha-1, and bias of -0. 56 Mg ha-1, outperforming RF but underperforming AU model using spatial information.
