评估空中激光扫描和航空摄影测量在基于深度学习的林分 delineation 中的应用
Assessing airborne laser scanning and aerial photogrammetry for deep learning-based stand delineation
AI总结：
本文评估了空中激光扫描和航空摄影测量在基于深度学习的林分 delineation 中的应用，发现不同数据组合在性能上表现相似，且框架对输入数据变化具有鲁棒性。
摘要：
准确的林分 delineation 对森林调查和管理至关重要，但仍然是一个主要依赖人工和主观的过程。最近的一项研究显示，当结合航空影像和空中激光扫描（ALS）数据时，深度学习可以产生与专家解释者相当的林分 delineation。然而，数据源之间的时间错位限制了操作扩展性。通过数字摄影测量（DAP）获得的冠层高度模型（CHMs）提供了更好的时间对齐，但可能会平滑冠层表面和冠层空隙，从而引发问题：它们是否能够可靠地替代基于 ALS 的 CHMs？同样，加入数字地形模型（DTM）被认为可以提高 delineation 性能，但在已发表的文献中尚未得到验证。使用专家 delineated 的森林林分作为参考数据，我们评估了一个基于 U-Net 的语义分割框架，在挪威东南部六个市政厅的市一级交叉验证中进行了测试。我们比较了多光谱航空影像结合（i）基于 ALS 的 CHM、（ii）基于 DAP 的 CHM、以及（iii）基于 DAP 的 CHM 与 DTM 的组合。结果表明，所有数据组合的性能相似，总体准确率在 0.90-0.91 之间。模型预测之间的一致性显著大于与参考数据的一致性，突显了模型的一致性和林分 delineation 的固有主观性。尽管结构细节减少，DAP-CHMs 的相似表现以及 DTM 没有改进，表明该框架对输入数据的变化具有鲁棒性。这些发现表明，可以通过包括时间对齐的 ALS 数据和 DAP 点云的项目来构建大规模数据集用于基于深度学习的林分 delineation。
英文摘要：
Accurate forest stand delineation is essential for forest inventory and management but remains a largely manual and subjective process. A recent study has shown that deep learning can produce stand delineations comparable to expert interpreters when combining aerial imagery and airborne laser scanning (ALS) data. However, temporal misalignment between data sources limits operational scalability. Canopy height models (CHMs) derived from digital photogrammetry (DAP) offer better temporal alignment but may smoothen canopy surface and canopy gaps, raising the question of whether they can reliably replace ALS-derived CHMs. Similarly, the inclusion of a digital terrain model (DTM) has been suggested to improve delineation performance, but has remained untested in published literature. Using expert-delineated forest stands as reference data, we assessed a U-Net-based semantic segmentation framework with municipality-level cross-validation across six municipalities in southeastern Norway. We compared multispectral aerial imagery combined with (i) an ALS-derived CHM, (ii) a DAP-derived CHM, and (iii) a DAP-derived CHM in combination with a DTM. Results showed comparable performance across all data combinations, reaching overall accuracy values between 0.90-0.91. Agreement between model predictions was substantially larger than agreement with the reference data, highlighting both model consistency and the inherent subjectivity of stand delineation. The similar performance of DAP-CHMs, despite the reduced structural detail, and the lack of improvements of the DTM indicate that the framework is resilient to variations in input data. These findings indicate that large datasets for deep learning-based stand delineations can be assembled using projects including temporally aligned ALS data and DAP point clouds.
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