Tree species proportion prediction using airborne laser scanning and Sentinel-2 data within a deep learning based dual-stream data fusion approach
Bibliographic record
Abstract
The integration of airborne laser scanning (ALS) technology into forest inventory practices has significantly improved forest management by providing accurate predictions of forest structural attributes. However, ALS offers limited insight into the spectral properties of tree crowns, hindering the accurate prediction of various physiological attributes and the identification of tree species. The fusion of multitemporal spectral information with ALS data has been proposed as an important step towards addressing this limitation. While previous studies have explored combining ALS with optical data for forest species mapping, the fusion process often requires feature generation and selection, which restrict the scalability and effectiveness of these approaches. There remains a need for an approach that effectively leverages both the structural information of ALS and spectral dynamics of optical imagery in a fully data-driven manner. We propose a novel dual-stream deep learning approach that fuses ALS point-cloud data with multitemporal Sentinel-2 (S2) imagery to predict the proportions of seven species and two genera across a 630,000 ha Canadian boreal forest, capturing both structural and spectral features within 20 m grid cells. The results showed an R2 of 0.58 and an RMSE of 0.14 for all proportional values, with an 8% increase in accuracy for the detection of broadleaf species when using seasonal multispectral images, compared to using ALS data alone. Additionally, lower R2 values (0.49–0.57) were observed only when the S2 imagery was used. When identifying the leading species from the model predictions, a weighted F1 score of 0.62 and an overall accuracy of 0.65 were achieved for the seven species and two genera. This research highlights the potential of deep learning and data fusion to advance forest inventory practices by offering a scalable and reproducible method for detailed mapping of species proportions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".