Forest aboveground biomass estimation using deep learning data fusion of ALS, multispectral, and topographic data
Bibliographic record
Abstract
Estimating forest aboveground biomass (AGB) and its components (wood, branch, bark, foliage) is critical for forest inventories and provides important information for timber harvesting and carbon accounting. Current approaches for modelling forest AGB at the stand scale often employ airborne laser scanning (ALS) data which provide robust AGB estimates. However, in structurally complex forest ecosystems, ALS-based models may not estimate forest biomass and its components with sufficient accuracy. One method to improve ALS-based model performance is through data fusion. Deep neural networks (DNNs) are effective for data fusion because they can combine different data modalities without the need to modify the original data resolution. This study evaluated the effectiveness of a data fusion DNN that combines ALS, multispectral, and topographic data for forest biomass estimation (total and component). We implemented a DNN architecture consisting of three convolutional neural network (CNN) modules: Octree-CNN for ALS data; 1-D CNN for Landsat-8 multispectral data; and 2-D CNN for topographic data. Variants of the DNN architecture combining different input data modalities were trained and tested using sample plots from New Brunswick, Canada (n = 2,336). The model, including all three data modalities, performed best overall for total AGB estimation (R2 = 0.77; RMSE = 28.38 Mg/ha) and explained an additional 2−5% variation in wood, bark, and foliage biomass compared to the ALS-only model. This study demonstrates the effectiveness of a novel data fusion DNN architecture that extracts information directly from input data modalities for improving forest biomass estimates. However, relatively small performance gains should be weighed against computational resources and domain knowledge required to implement and interpret DNNs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".