Assessing airborne laser scanning and aerial photogrammetry for deep learning-based stand delineation

TL;DR

This study evaluates the effectiveness of deep learning for forest stand delineation using different remote sensing data sources, finding that digital photogrammetry can reliably replace airborne laser scanning in certain conditions.

Contribution

It demonstrates that deep learning models achieve comparable accuracy with DAP-derived CHMs and ALS data, and assesses the impact of including a DTM in the delineation process.

Findings

Deep learning models achieved 0.90-0.91 accuracy with all data types.

DAP-derived CHMs perform similarly to ALS-derived CHMs in delineation.

Including a DTM did not improve model performance.

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.