With the challenges brought by global climate change,precise monitoring of forestry carbon sinks is crucial for developing effective carbon management policies.Using the Saihanba Mechanical Forest Farm as a case study,this research verifies the method of monitoring forest biomass based on microwave remote sensing.Freeman binary decomposition is used to process remote sensing images and combine the Random Forest Genetic Mixing(RFGM)algorithm with the Random Forest algorithm to estimate forest biomass.The results showed that the developed RFGM algorithm outperformed traditional stepwise regression models,with a determination coefficient of 0.767 and a relative root mean square error reduced to 20.37%.The research provides a new perspective for monitoring the biomass of the Saihanba Mechanical Forest Farm and contributes important data to global carbon cycling research.
维普
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