Land Cover Change Detection Using MSS and MODIS Data: A Case Study for Liangshan-Xiangling Region in Southwestern China
Bibliographic record
Abstract
As a result of rapid socioeconomic development and climate change, the land cover has been changing in the mountainous areas in southwestern China while the associated ecological environment has been seriously disturbed. This study is to quantify the land cover change in Liangshan-Xiangling Region in Sichuan Province in China from the 1970s to present and to compare the land cover change rates among different land cover types. Two groups of remote sensing data, including MSS data in 1974-1980 and MODIS data in 2002-2007, were utilized to investigate the land cover changes during different time periods in the study area. The NDVI differencing and unsupervised classification compassion methods were used to detect the land cover quality and quantity changes. The results showed that the vegetation cover in the study area decreased significantly in the 1970s, but increased in recent years due to the establishment of nature reserves and enhancement of environment protection.
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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 | 0.001 | 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".