Remote sensing of fire regimes in semi-arid Nusa Tenggara Timur, eastern Indonesia: current patterns, future prospects
Bibliographic record
Abstract
Substantial areas of eastern Indonesia are semi-arid (with a pronounced dry season extending from April to November) with extensive areas of uncultivated vegetation dominated by savanna grasslands and woodlands. These are highly fire-prone, despite high population densities reliant on intensive subsistence agriculture and an official national fire policy that prohibits all burning. To date, no regional studies have been undertaken that reliably assess the seasonal extent and patterning of prescribed burning and wildfire. Focusing on two case studies in east Sumba (7000 km2) and central Flores (3000 km2) in the eastern Indonesian province of Nusa Tenggara Timur, the present paper addresses: (1) the efficacy of applying standard remote sensing and geographic information system tools as developed for monitoring fire patterns in savanna landscapes of adjacent northern Australia, for (2) describing the seasonal patterning of burning at village and broader regional scales in 2003 and 2004. Despite recurring cloudiness, which significantly affected daily fire detection of ‘hotspots’ from Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer sensors, fire mapping from Landsat imagery was undertaken successfully to reveal: (1) fires burnt an annual average of 29% of eastern Sumba (comprising mostly grassland savanna), and 11% of central Flores (with large forested areas); (2) most fire extent occurred in savanna grassland areas, and significantly also in cultivated lands and small remnant patches of forest; (3) most fire activity occurred under harsh, late dry season conditions; and (4) while the great majority of individual fires were less than 5 ha, some late dry season fires were hundreds of hectares in extent. The potential routine application of different image sensors for fire mapping and hotspot detection is considered in discussion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".