Forest Canopy Height Estimation Using Tandem-X Coherence Data

Author:

Chen HaoORCID,Cloude Shane R.,Goodenough David G.

Funder

Natural Resources Canada

Canadian Space Agency

German Aerospace Centre

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Cited by 56 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. Generation of country-scale canopy height maps over Gabon using deep learning and TanDEM-X InSAR data;Remote Sensing of Environment;2024-09

2. A Multi-Baseline Forest Height Estimation Method Combining Analytic and Geometric Expression of the RVoG Model;Forests;2024-08-27

3. Country-Scale Mapping of Forest Parameters Using Deep Learning and Tandem-X Insar Data;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

4. Advancing Forest Canopy Measurement: Deep Learning Approaches with Sentinel-2 Data;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

5. Forest Height Estimaton in Mountainous Terrain Using Ascending and Descending Tandem-X Data;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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