在混合温带森林中使用多式遥感观测和机器学习进行地面生物质估计
Shashika Himandi Gardeye Lamahewage1, Chandi Witharana2,3, Rachel Riemann4
1Department of Natural Resources and the Environment, College of Agriculture, Health and Natural Resources, University of Connecticut, Storrs, CT, 06269, USA. shashika_himandi.lamahewa@uconn.edu.
Scientific reports
|August 24, 2025
概括
准确估计森林地表树木生物量 (AGB) 对于碳储存的评估至关重要. 这项研究使用遥感数据和随机森林算法来改进AGB预测模型,提高森林碳监测效率.
科学领域:
- 森林管理
- 遥感技术
- 生态学
背景情况:
- 在森林碳储存评估中,地面树木生物质 (AGB) 是至关重要的.
- 传统的森林库存和分析 (FIA) 方法缺乏精细分辨率AGB估计的采样强度.
- 远程探测 (RS) 提供了一种更有效的森林碳监测方法.
研究的目的:
- 使用随机森林 (RF) 算法开发地面树木生物质 (AGB) 的准确预测模型.
- 评估各种遥感数据源在AGB估计中的有效性.
- 提高森林碳监测的效率和准确性
主要方法:
- 使用来自三种遥感数据源 (LiDAR,航空图像,卫星图像) 的67个解释变量.
- 为AGB预测开发了九个随机森林 (RF) 模型,每个模型都经过了变量选择和超参数调整.
- 使用诸如根平均平方误差 (RMSE) 和R平方 (R2) 等指标评估模型性能.
主要成果:
- 最佳射频模型包含了28个解释变量,实现了27.19Mgha-1的RMSE和0.41的R2.
- 将LiDAR数据与空中和卫星图像度量相结合,显著提高了AGB预测的准确性.
- 这项研究表明了综合RS数据对大面积生物质绘制的潜力.
结论:
- 远程传感数据,特别是当结合起来时 (例如,LiDAR与图像),可显著提高地面树木生物质 (AGB) 估计的准确性.
- 随机森林算法为开发预测性AGB模型提供了一个强大的框架.
- 使用RS改进的AGB估计对于有效的碳库存评估和明智的气候变化决策至关重要.
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