Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends
Bibliographic record
Abstract
Science and reporting information needs for monitoring dynamics in land cover over time have prompted research, and made operational, a wide variety of change detection methods utilizing multiple dates of remotely sensed data. Change detection procedures based upon spectral values are common; however, landscape pattern analysis approaches which utilize spatial information inherent within imagery present opportunities for the generation of unique and ecologically important information. While the use of two images may provide the means to identify change, the use of more than two images for long-term monitoring affords the ability to identify a greater range of processes of landscape change, including rates and dynamics. The main objective of this review is to investigate and summarize the methods and applications of land cover spatial pattern analysis using three or more image dates. The potential and the limitations of landscape pattern indices are identified and discussed to inform application recommendations. The second objective of this review is to make recommendations, including appropriate landscape pattern indices, for the application of landscape pattern analysis of a long time series of remotely sensed data to a case study involving the mountain pine beetle in British Columbia, Canada. The review concludes with recommendations for future research.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".