Monitoring Coastal Blue Carbon Ecosystems by Combing Satellite and UAV Remote Sensing Data in Southern China
Bibliographic record
Abstract
Coastal blue carbon ecosystems are among the most productive, vulnerable and threatened ecosystems in the world. They play important roles in marine biodiversity maintenance, water purification, nutrient recycling, carbon sequestration and storage. Remote sensing methods have become complementary to conventional surveying methods due to their rapidity, large area coverage, and repeatability of observations. The persistent cloud cover, tidal inundation, variable water turbidity near the coastlines make it necessary to monitor coastal blue carbon ecosystems by combing satellite and Unmanned Aerial Vehicle (UAV) remote sensing data. Here, we presented two examples, which integrate satellite, UAV and field survey data to monitor mangroves, salt marshes, and seagrass beds in Southern China. With multi-source Chinese Earth observation satellite imagery as well as UAV data in 2019 and 2022, we monitored the distribution changes of mangroves and salt marshes in Guangdong province, Guangxi Zhuang Autonomous Region and Hainan province. The mangrove areas increased dramatically, mainly due to the Special Action Plan for Mangrove Protection and Restoration (2020–2025) which aims to increase China’s mangrove area by 70% by 2025. The salt marsh area in Guangxi decreased, partly due to the controlling measurements of the invasive species Spartina alterniflora by the local government. Based on high-spatial resolution satellite, UAV and field data, we showed the spatial distribution map of seagrass beds (Halophila beccarii) near an estuary in Shantou city, Guangdong province in 2020, demonstrating the potential of combing satellite and UAV imagery to monitor seagrass beds on the local scale. With more remote sensing data accumulated due to the frequent satellite overpasses and UAV surveys, further research is required for continued improvements in multi-source remote sensing data fusion techniques designed specifically for blue carbon ecosystem monitoring.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".