Remote Sensing Monitoring and Planning for Mountain Vegetation Coverage under Bayesian Network Model Optimization
To accurately classify the mountain vegetation and tree species,this paper firstly develops a Bayesian network model and transformes the posterior distribution into an alternative form to continully op-timize it.Then,the features of mountain topography,mountain vegetation index and texture characteristic are extracted into a feature set which are input into Bayesian network model.Finally,This approach real-ize the research on remote sensing monitoring and planning technology of mountain vegetation coverage.Experimental results show that under the optimization of Bayesian network model,the mountain vegetation coverage obviously increases,with a evenness index of post-planning species approaching 1,demonstra-ting effective outcomes.This research provides a novel approach for monitoring and planning mountain vegetation coverage,offering valuable insights for similar applications.
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