Biomass Estimation and Uncertainty Quantification From Tree Height

Author:

Song Qian1,Albrecht Conrad M.2,Xiong Zhitong1ORCID,Zhu Xiao Xiang1ORCID

Affiliation:

1. Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich, Germany

2. Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Munich, Germany

Funder

German Federal Ministry of Education and Research

AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond

German Federal Ministry for Economic Affairs and Climate Action

National Center of Excellence ML4Earth

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Dominant Leaf Type Classification Using Sentinel-1 Time Series;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Optimization of the Vertical Wavenumber for PolInSAR Inversion Performance Based on Numerical CRLB Analysis;Remote Sensing;2023-11-10

3. A Dataset for Individual Tree Delineation from 3D Point Cloud data;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

4. 3d Point Cloud Simulation for Above-Ground Forest Biomass Estimation;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

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