Scholarlink

<p>Forest resources constitute a vital component of the Earth’s ecosystem, playing a pivotal role in providing material resources, regulating climate, and safeguarding ecological security. To establish a dynamic and precise forest resource asset valuation system, this study integrates multi-source monitoring data from wireless sensor networks by combining an improved particle swarm optimization algorithm with a back propagation neural network. The accuracy of data fusion is enhanced by introducing a node residual energy factor and a spatial migration step size optimization algorithm, thereby constructing a three-dimensional “ecological-economic-social” assessment model. Taking the temperate broadleaf forests of Qingyuan Manchu Autonomous County, Liaoning Province as the study subject, this research compared multiple data fusion models and evaluation methods. The results indicated that the data fusion model achieved a root mean square error of 0.291 and a mean absolute relative error of 0.265, representing a significant improvement over the pre-optimized model. Additionally, the remaining network energy exhibited a more gradual decline. The three-dimensional evaluation model demonstrated an R² value exceeding 0.93. Verified through 12 representative forest ecosystems and four-dimensional scenarios, including geographical distribution, forest type, climate zone, and season, the model maintains a mean absolute error of less than 25,000 yuan across different scenarios. The model’s coefficient of determination (R²) exceeds 0.91 on average and reaches 0.96 in some scenarios. The evaluation cycle is only 2.0-2.2 days. The study establishes a scientific and systematic framework for assessing the value of forest resource assets, effectively enhancing the precision and timeliness of evaluations. The proposed data fusion model reduces redundant monitoring data and extends the lifecycle of wireless sensor networks, providing reliable technical support for the long-term dynamic assessment of forest resource assets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Forest resource asset valuation based on a data fusion model for environmental monitoring in wireless sensor networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月30日

查看全文
  • Linwei Wan,
  • Wentao Xing

摘要

Forest resources constitute a vital component of the Earth’s ecosystem, playing a pivotal role in providing material resources, regulating climate, and safeguarding ecological security. To establish a dynamic and precise forest resource asset valuation system, this study integrates multi-source monitoring data from wireless sensor networks by combining an improved particle swarm optimization algorithm with a back propagation neural network. The accuracy of data fusion is enhanced by introducing a node residual energy factor and a spatial migration step size optimization algorithm, thereby constructing a three-dimensional “ecological-economic-social” assessment model. Taking the temperate broadleaf forests of Qingyuan Manchu Autonomous County, Liaoning Province as the study subject, this research compared multiple data fusion models and evaluation methods. The results indicated that the data fusion model achieved a root mean square error of 0.291 and a mean absolute relative error of 0.265, representing a significant improvement over the pre-optimized model. Additionally, the remaining network energy exhibited a more gradual decline. The three-dimensional evaluation model demonstrated an R² value exceeding 0.93. Verified through 12 representative forest ecosystems and four-dimensional scenarios, including geographical distribution, forest type, climate zone, and season, the model maintains a mean absolute error of less than 25,000 yuan across different scenarios. The model’s coefficient of determination (R²) exceeds 0.91 on average and reaches 0.96 in some scenarios. The evaluation cycle is only 2.0-2.2 days. The study establishes a scientific and systematic framework for assessing the value of forest resource assets, effectively enhancing the precision and timeliness of evaluations. The proposed data fusion model reduces redundant monitoring data and extends the lifecycle of wireless sensor networks, providing reliable technical support for the long-term dynamic assessment of forest resource assets.