Remote sensing and deep learning-based detection of changes in aboveground carbon storage in young teak plantations (2019-2023): A case study in Pauk Khaung Township, West Bago Mountains, Myanmar
Bibliographic record
Abstract
Teak ( Tectona grandis Linn. f.) is an important tropical hardwood species with high ecological and economic value, and Myanmar—particularly the Bago Mountain region—is globally recognized as its native and prominent growing area. Monitoring aboveground carbon (AGC) storage in teak plantations is essential for forest-based climate mitigation and sustainable forest management, yet the traditional field-based methods used in Myanmar remain limited in their ability to do large scale and temporal monitoring. Therefore, this study aims to address this gap by integrating multi-source remote sensing data with machine learning and deep learning models to detect changes in AGC storage in young teak plantations. The study was conducted on data from young teak plantations in Pauk Kaung Township, West Bago Mountains, Myanmar, from 2019 to 2023. The integration of optical (Sentinel-2) and radar (ALOS PALSAR-2) datasets, including vegetation indices, with the ResNet-18 model yielded the suitable predictive performance (R² = 0.76), outperforming other model—scenario combinations. Results revealed that between 2019 and 2023, 89% of the study area showed an increase in AGC, while 11% of the area showed a decrease. The study demonstrates the effectiveness of combining remote sensing and deep learning techniques for detecting AGC changes in young teak plantations, providing valuable insights for REDD+ implementation, carbon accounting, and climate mitigation policy in Myanmar. • Integration of multi-source remote sensing data with machine learning and deep learning models for detecting aboveground carbon (AGC) changes in young teak plantations (2019-2023) in Myanmar. • Combinations of Sentinel-2, ALOS PALSAR-2, and vegetation indices with ResNet-18 model achieved the high predictive accuracy in AGC estimation. • Detected temporal changes in AGC from 2019 to 2023, with 89% of the area showing an AGC increase and 11% of the area showing a decrease in AGC storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".