Enhancing tree species composition mapping using Sentinel-2 and multi-seasonal deep learning fusion
Bibliographic record
Abstract
Accurate wall-to-wall mapping of tree species composition (TSC) is essential for effective forest management. However, distinguishing species-level information from satellite imagery remains a challenge due to the coarse spatial resolution of open-access satellite imagery. In this study, we present the first systematic evaluation of spatial resolution enhancement and multi-seasonal data fusion for deep learning (DL)-based TSC mapping using Sentinel-2 imagery. Specifically, we assessed: (1) the impact of different spatial resolutions and enhancement methods, comparing native 20 m Sentinel-2 imagery against bilinear resampled imagery at 10 m and 5 m, super-resolution (SR)-enhanced imagery at 10 m and their combined use; (2) the contributions of multi-seasonal imagery and auxiliary environmental data (climate, topography); and (3) the effectiveness of a novel multi-source multi-seasonal fusion (MSMSF) method for integrating seasonal and environmental datasets. Our results demonstrated substantial improvements (7% higher Radj2) when increasing spatial resolution from 20 m to 10 m and achieved the best result (RMSE = 0.120, Radj2 = 0.731) by combining bilinear resampled 5 m and SR-enhanced 10 m datasets. Additionally, our proposed MSMSF module and multi-seasonal data outperformed the best single-season model by >5% in terms of Radj2. These findings establish a new benchmark for DL-based TSC mapping and highlight the novelty of combining resolution enhancement with a detail-preserving fusion strategy to enable scalable, high-precision forest inventories using freely available satellite data.
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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 | 0.001 |
| 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 | 0.001 |
| 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".