Satellite Mapping of CO2 Emission from Forest Fires in Indonesia Using AIRS Measurements
Bibliographic record
Abstract
Results from the analysis of the retrieved carbon dioxide (CO2) columns in the free troposphere are presented for one year (2005) obtained by the Atmospheric Infrared Sounder (AIRS) included on the EOS Aqua satellite launched on May 4, 2002. Providing information for several greenhouse gases, CO2, CH4, CO and O3 is one goal of the AIRS instrument as well as to improve weather prediction and study the water and energy cycle. Carbon Dioxide (CO2) is the most prominent Greenhouse gas in Earth's atmosphere and plays a key role in earth's climate. It is emitted into the air as humans exhale, from burning fossil fuels for energy and deforestation of the planet. The aim of this study is to generate Monthly CO2 Distribution maps and to investigate the effects of Indonesia forest fires on CO2 distributions over Peninsular Malaysia, north Sumatra and Singapore for 2005. The CO2 concentration map of the study area was generated by using mole_fraction of CO2 in free troposphere, obtained from AIRS/Aqua Level 3 monthly CO2 retrieval product (AIRS+AMSU) V005 (AIRX3C2M) at GES DISC. Considerable variations were demonstrated in the annual changes of rainfall and drought patterns in various seasons (dry & wet season). Variations in the biomass burning and CO2 emissions where noted over the study area, while the highest CO2 occurred over industrial and congested urban zones and a greater draw down of CO2 occurred in the pristine marine environment over northeast coasts of Sumatra during 2005. In particular, we observe a quasi-biennial variation in CO2 emissions from study area with two peaks, the natural peak occurring at the end of each dry season (February to April), when biomass burning occurs, and the second peak at wet season (July to September), because of the influence of Indonesia forest fire. Examining satellite measurements, the results showed that the enhanced CO2 emission correlates with occasions of less rainfall during dry season.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".