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Biomass change in disturbed, secondary, and primary tropical forests from TanDEM-X., , , , and . IGARSS, page 329-331. IEEE, (2016)Tropical-Forest Biomass Estimation at X-Band From the Spaceborne TanDEM-X Interferometer., , , , , , , and . IEEE Geosci. Remote. Sens. Lett., 12 (2): 239-243 (2015)A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits., , , , , and . Remote. Sens., 14 (4): 933 (2022)Maximizing Value from Shared Research Data - The NICHD Data and Specimen Hub (DASH)., , , , , , and . AMIA, AMIA, (2019)Detecting leaf phenology of seasonally moist tropical forests in South America with multi-temporal \MODIS\ images, , , , and . Remote Sensing of Environment, 103 (4): 465 - 473 (2006)Simulation of nitrous oxide and nitric oxide emissions from tropical primary forests in the Costa Rican Atlantic Zone., , , and . Environ. Model. Softw., 15 (8): 727-743 (2000)Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR., , , , , and . Remote. Sens., 11 (6): 709 (2019)Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data., , , , , , and . Remote. Sens., 8 (1): 21 (2016)Post-drought decline of the Amazon carbon sink, , , , , , , , , and . Nature Communications, (Aug 9, 2018)Extending the reach of single-chamber inflatable soft robots using Magnetorheological Fluids., , , and . RoboSoft, page 119-125. IEEE, (2021)