贝叶斯网络模型优化下的山地植被覆盖度遥感监测与规划技术
Remote Sensing Monitoring and Planning for Mountain Vegetation Coverage under Bayesian Network Model Optimization
王旭阳 1张莹1
作者信息
- 1. 河南省地质矿产勘察开发局第四地质勘察院 郑州市 450000
- 折叠
摘要
为准确划分山地植被树木种类,该文首先构建贝叶斯网络模型,将后验分布转换为另一种形式,不断优化该模型;然后提取山地地形、植被指数、纹理特征,并将其组合成特征集输入至贝叶斯网络模型中;最终实现山地植被覆盖度遥感监测与规划技术研究.实验结果表明,在贝叶斯网络模型优化下,山地植被覆盖度明显增加,规划后山地植被物种均匀度指数接近于1,效果良好.为山地植被覆盖度监测与规划提供了一种思路,具有一定的参考价值.
Abstract
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.
关键词
贝叶斯网络模型/山地植被/覆盖度/遥感监测/规划技术Key words
Bayesian network model/mountain vegetation/coverage/remote sensing monitoring/plan-ning techniques引用本文复制引用
出版年
2024
NETL
NSTL
万方数据