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Serious errors impair an assessment of forest carbon projects: A rebuttal of West et al. (2023)
Independent retrospective analyses of the effectiveness of reducing deforestation and forest degradation (REDD) projects are vital to ensure climate change benefits are being delivered.
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.
Detecting Deforestation from Sentinel-1 Data in the Absence of Reliable Reference Data
Large and small scale deforestation across the globe is threatening the stability of our climate, forest biodiversity, and therefore the preservation of fragile ecosystems and our natural habitat as a whole.
