The Google Earth Engine Mangrove Mapping Methodology (GEEMMM)
Bibliographic record
Abstract
Mangroves are found globally throughout tropical and sub-tropical inter-tidal coastlines. These highly biodiverse and carbon-dense ecosystems have multi-faceted value, providing critical goods and services to millions living in coastal communities and making significant contributions to global climate change mitigation through carbon sequestration and storage. Despite their many values, mangrove loss continues to be widespread in many regions due primarily to anthropogenic activities. Accessible, intuitive tools that enable coastal managers to map and monitor mangrove cover are needed to stem this loss. Remotely sensed data have a proven record for successfully mapping and monitoring mangroves, but conventional methods are limited by imagery availability, computing resources and accessibility. In addition, the variable tidal levels in mangroves presents a unique mapping challenge, particularly over geographically large extents. Here we present a new tool—the Google Earth Engine Mangrove Mapping Methodology (GEEMMM)—an intuitive, accessible and replicable approach which caters to a wide audience of non-specialist coastal managers and decision makers. The GEEMMM was developed based on a thorough review and incorporation of relevant mangrove remote sensing literature and harnesses the power of cloud computing including a simplified image-based tidal calibration approach. We demonstrate the tool for all of coastal Myanmar (Burma)—a global mangrove loss hotspot—including an assessment of multi-date mapping and dynamics outputs and a comparison of GEEMMM results to existing studies. Results—including both quantitative and qualitative accuracy assessments and comparisons to existing studies—indicate that the GEEMMM provides an accessible approach to map and monitor mangrove ecosystems anywhere within their global distribution.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".