The application of geospatial techniques in monitoring karst vegetation recovery in southwest China
Bibliographic record
Abstract
The karst region in southwestern China, one of the largest continuous karst areas in the world, is special for its high landscape heterogeneity, unique hydrology, high endemism among vegetation species and high intensity of human disturbance. The region had experienced severe degradation through karst rocky desertification (KRD) between the 1950s and 1990s. Starting in the late 1990s, various levels of the Chinese government conducted several ecological projects to recover degraded karst ecosystems. It was reported that the implementation of these projects had been successful in facilitating the recovery of karst vegetation in many areas. However, global climate changes may compromise the efficacy of recovery. Geospatial techniques had been employed to map and monitor karst ecosystem conditions during the recovery process. We examined the history and progress of the various geospatial techniques applied to monitor and evaluate karst vegetation conditions. In addition, we reviewed the techniques used to assess and monitor KRD, KRD influencing factors, vegetation community type, fractional vegetation cover, vegetation dynamics, vegetation productivity, ecosystem goods and services, vegetation biodiversity, ecosystem health and rural society changes. We also explored the potential to apply geospatial techniques for karst vegetation recovery in the future. It is projected that there will be more remotely sensed images for the vegetation dynamics monitoring at numerous scales. New techniques (e.g. image fusion and data assimilation) will be available to manage scale and heterogeneity issues in the karst landscape.
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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 | 0.000 |
| 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 | 0.000 |
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".