对撒哈拉以南非洲生物质估计模型预测准确度和错误的元分析
Dan Abudu1, Katie Chong2, Lucy Bastin3
1Energy and Bioproducts Research Institute (EBRI), College of Engineering and Physical Science, Aston University, B4 7ET, Birmingham, UK.
The Science of the total environment
|September 17, 2025
概括
在撒哈拉以南非洲,准确的森林生物质估计依赖于生物质估计模型 (BEMs). 这一元分析表明,使用多传感器遥感数据进行本地校准的混合模型为森林监测和碳核算提供了最佳的准确性.
科学领域:
- 林业科学 林业科学
- 遥感 遥感 遥感 遥感
- 生态建模 生态建模
背景情况:
- 准确的生物质估计对于森林监测,能源规划和撒哈拉以南非洲 (SSA) 的碳核算至关重要.
- 在SSA中,破坏性采样往往是不切实际的,需要使用可扩展的生物质估计模型 (BEMs).
- 现有的BEM在不同的森林类型,物种和数据源中显示出可变的预测准确性.
研究的目的:
- 通过元分析系统地评估SSA中的22项同行评审研究中的39个BEM的性能.
- 使用标准化指标 (R2,RMSE) 评估模型准确性,并确定影响性能的因素.
- 为改善SSA生物质估计提供建议.
主要方法:
- 根据PRISMA指南,对来自22项SSA研究的39个BEM进行了系统的元分析.
- 包括使用现场和遥感 (RS) 数据的破坏性和非破坏性模型.
- 采用费舍尔的Z转换和随机效应建模来分析来自全球全米树数据库,Scopus和Web of Science的数据.
主要成果:
- 总体而言,预测准确度很高 (平均R2 = 0.82),但由于生态和方法的多样性 (I2 = 99.87%),错误差异很大 (平均RMSE = 108.9 Mg/ha).
- 局部校准的全米模型表现最好;只有光学数据的RS模型的错误率更高.
- 将LiDAR,雷达和光学数据与机器学习集成的混合模型显示出卓越的性能.
结论:
- 建议根据当地条件量身定制的混合BEM并结合多传感器RS数据,以改善生物质监测.
- 关键预测因素,如胸高对直径,树高和木材密度,显著提高了模型的准确性.
- 调查结果支持加强森林保护,REDD+监测,报告和验证 (MRV) 系统,以及SSA的可持续能源规划.
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