基于多来源遥感数据和堆叠模型的澳大利亚木质植被生物质估计
Chenxi Liu1, Shuo Shi2,3,4, Zhanmang Liao5
1Electronic Information School, Wuhan University, Wuhan, 430072, China.
Scientific reports
|October 7, 2025
概括
准确估计澳大利亚的地面生物质 (AGB).
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 林业 林业 林业 林业 林业
背景情况:
- 陆地植被是一个关键的碳储存库,对于缓解气候变化至关重要.
- 澳大利亚多样化的景观和丰富的生物多样性为准确的地面生物质 (AGB) 估计带来了挑战.
- 空间异质性使区域AGB评估变得复杂.
研究的目的:
- 评估各种模型的准确性,以估计澳大利亚的地面生物质 (AGB).
- 将集体堆叠模型与单个机器学习模型的性能进行比较.
- 评估特征选择对AGB估计准确性的影响.
主要方法:
- 使用了一套数据集,将现场测量的生物质与多源遥感数据 (天花板高度,Landsat,地形,气候) 结合起来.
- 开发了一个集体堆叠模型,集成了七个基础学习者和一个元学习者.
- 使用K折交叉验证和递归特征消除 (RFE) 进行了比较实验.
- 使用蒙特卡洛模拟来评估生物质估计的不确定性.
主要成果:
- 堆叠模型表现出优于单个模型的性能,达到0.74的R平方值和49.79Mg/ha的RMSE.
- 梯度增强回归器 (GBR) 和随机森林 (RF) 模型也显示出强大的预测性能.
- 整合多源数据和集体学习增强了估计的稳定性.
结论:
- 集体学习,特别是堆叠模型,有效地提高了在异质的澳大利亚景观中的地面生物质 (AGB) 估计准确性.
- 多源数据集成克服了用于大规模AGB映射的单源数据集的局限性.
- 该研究为复杂生态系统中强大的AGB估计提供了宝贵的见解.
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