Species diversity assessment and aboveground biomass estimation of mangroves using remote sensing and field observations
Bibliographic record
Abstract
Mangrove ecosystems are recognized for their significant role in carbon storage and fixation on global coastlines, and above-ground biomass (AGB) inventory is crucial for various applications. However, detailed species-specific AGB estimation and comprehensive assessment of mangrove diversity using high-resolution remote sensing in Bali remain limited.This study aimed to conduct a species diversity assessment and estimate the AGB of mangrove ecosystems in Bali. A total of 12 different remote sensing indices derived from the spectral band combinations were used. Furthermore, Random Forest Regression model showed that both L8 and S2 derived models provide better prediction results with high accuracy ( R2 > 0.7). Approximately 8384 trees were collected from 77 field quadrats with 10 × 10 m plots, which consisted of 12 tree species with DBHs ranging from 0.95 to 95.49 cm. The average density of mangroves in Benoa Bay was 10 906 trees ha−1, and the basal cover was 93.29 m2 ha−1. The highest AGB was contributed by S. alba, with 2.22 Mg trees−1. Moreover, an average of 78.99 Mg ha−1 biomass ranging from 13.3 to 254 Mg ha−1 was recorded in the mangrove forest. This study provides a significant contribution to the scientific community by revealing the intricate connections among mangrove ecosystems, blue carbon dynamics, and climate change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".