Visualization of Post-Fire Remote Sensing Using CiteSpace: A Bibliometric Analysis
Bibliographic record
Abstract
At present, remote sensing serves as a key approach to track ecological recovery after fires. However, systematic and quantitative research on the research progress of post-fire remote sensing remains insufficient. This study presents the first global bibliometric analysis of post-fire remote sensing research (1994–2024), analyzing 1155 Web of Science publications and using CiteSpace to reveal critical trends and gaps. The key findings include the following: As multi-sensor remote sensing and big data technologies evolve, the research focus is increasingly pivoting toward interdisciplinary, multi-scale, and intelligent methodologies. Since 2020, AI-driven technologies such as machine learning have become research hotspots and continue to grow. In the future, more extensive time-series monitoring, holistic evaluations under compound disturbances, and enhanced fire management strategies will be required to addressing the global climate change challenge and sustainability. The USA, Canada, China, and multiple European nations work jointly on fire ecology research and technology development, but Africa, as a high wildfire-incidence area, currently lacks appropriate local research. Remote sensing of the environment and remote sensing and forests maintain a pivotal role in scholarly impact and information exchange. This work redefines post-fire remote sensing as a nexus of ecological urgency and social justice, demanding inclusive innovation to address climate-driven post-fire recovery regimes.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.095 | 0.098 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".