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Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach., , , , , , , , , and 15 other author(s). Remote. Sens., 12 (17): 2685 (2020)Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning., , , , , , , , , and 7 other author(s). Comput. Geosci., (2022)Non-Parametric Retrieval of Aboveground Biomass in Siberian Boreal Forests with ALOS PALSAR Interferometric Coherence and Backscatter Intensity., , , , , and . J. Imaging, 2 (1): 1 (2016)Editorial for Special Issue: "Remote Sensing of Forest Cover Change"., and . Remote. Sens., 11 (1): 38 (2019)Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion., , , , , , , , , and . Remote. Sens., 12 (15): 2380 (2020)Investigating spatial error structures in continuous raster data., , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2019)The Use of Multifrequency SAR Data for Assessing Levels of Forest Disturbance in Bajo Calima Colombia., , , and . IGARSS, page 7015-7018. IEEE, (2018)Past decade above-ground biomass change comparisons from four multi-temporal global maps., , , , , , , , , and 20 other author(s). Int. J. Appl. Earth Obs. Geoinformation, (April 2023)Aboveground Woody Biomass Estimation of the Brazilian Cerrado Biome Using Data Integration., , , , , , , , , and 11 other author(s). IGARSS, page 1402-1405. IEEE, (2021)Airborne S-Band SAR for Forest Biophysical Retrieval in Temperate Mixed Forests of the UK., , , , , , , , , and 13 other author(s). Remote. Sens., 8 (7): 609 (2016)