Abstract
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.
Abstract (translated)
准确的森林地块划分对于林业调查和管理至关重要，但这一过程仍然主要依赖手工操作且具有主观性。最近的一项研究表明，深度学习技术在结合航空影像与机载激光扫描（ALS）数据时，能够产生与专家解释结果相当的地块划分效果。然而，由于不同数据源之间的时间错配问题限制了其实际应用中的可扩展性。基于数字摄影测量（DAP）生成的冠层高度模型（CHM）提供了更好的时间对齐度，但可能会使树冠表面和空隙变得平滑，从而引发了它们是否能够可靠地替代ALS生成的冠层高度模型的问题。同样，虽然有人建议引入数字地形模型（DTM）可以提高地块划分性能，但在已发表文献中尚未对其进行测试。 我们使用专家划定的森林地块作为参考数据，在挪威东南部六个市镇范围内采用基于U-Net的语义分割框架进行跨市镇验证。我们比较了多光谱航空影像与以下三种组合：(i) ALS生成的冠层高度模型，(ii) DAP生成的冠层高度模型以及(iii) 在DAP生成的冠层高度模型基础上结合数字地形模型（DTM）。结果显示，在所有数据组合中，模型性能相当，总体准确率在0.90-0.91之间。模型预测之间的吻合度显著高于与参考数据的吻合程度，这既体现了模型的一致性也揭示了地块划分过程中的内在主观性。 尽管DAP生成的冠层高度模型减少了结构细节，其性能仍然类似，并且DTM未能带来改进的效果表明该框架具有较强的输入数据变异性抗性。这些发现暗示通过包括时间对齐的ALS数据和DAP点云项目可以构建用于基于深度学习的地块划分的大规模数据集。
URL
https://arxiv.org/abs/2602.21709
