混合深度学习 - - 地理统计绘制中国丽水的森林地表生物质
Rui Qian1, Qilin Zhang1, Yuying Gong1
1Co-Innovation Center for Sustainable Forestry in Southern China, College of Forestry, Nanjing Forestry University, Nanjing 210037, China.
Plants (Basel, Switzerland)
|February 27, 2026
概括
这项研究使用混合CNN-变压器-Kriging模型改进了森林地面生物质 (AGB) 地图绘制. 这种新方法提高了森林碳封存和生产率估计的准确性.
科学领域:
- 林业 林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 地质统计学 在地质统计学
背景情况:
- 地表生物质 (AGB) 对于森林生产力和碳捕获至关重要.
- 现有的遥感模型往往忽略了图形数据和残余的空间自相关性.
- 准确的AGB绘图对于有效的森林管理和保护至关重要.
研究的目的:
- 开发和验证一个混合框架,以改善区域AGB绘图.
- 将CNN-变压器模型与地球统计学Kriging的残留物集成.
- 为了解决遥感中空间自相对应的局限性,AGB估计.
主要方法:
- 提出了一个混合框架,结合了CNN转换器 (卷积神经网络转换器) 和剩余Kriging.
- 我们使用了来自Sentinel-2,ALOS-2 PALSAR-2和ALOS DEM的多源预测器.
- 使用随机森林评估了变量重要性,主要预测因素包括Sentinel-2波段,EVI,NDVI,HH/HV比率和海拔.
主要成果:
- 该CNN-变压器-Kriging模型实现了最高的预测准确度 (R2 = 0.72,RMSE = 12.18t/ha).
- 仅CNN-变压器模型就显示了强的性能 (R2=0.69,RMSE=12.22t/ha).
- 混合方法显著优于随机森林模型 (R2 = 0.59,RMSE = 14.31 t/ha).
结论:
- 拟议的混合框架有效地提高了森林AGB绘图的准确性.
- 整合地球统计学Kriging的残留物增强了CNN-变压器模型的性能.
- 该方法为支持森林管理和保护的墙壁到墙壁AGB绘图提供了一个强大的工具.
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