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Estimation of Forest Biomass in Beijing (China) Using Multisource Remote Sensing and Forest Inventory Data

https://doi.org/10.3390/f11020163

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/2016gb005465openalex-snapshot

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