Multi-Temporal Monitoring Of Ecological Succession In Tropical Dry Forests Using Angular - Hyperspectral Data (Chris/Proba)
Bibliographic record
Abstract
The tropical dry forest is the largest and most threatened ecosystem in Latin America. Remote sensing can effectively contribute to the surveillance of conservation measurements and laws through the monitoring of natural protected areas, at the required temporal and spatial scales. CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral spectroscopy. These two characteristics permit the study of structural and compositional traces of successional stages within the tropical dry forest. The current study presents the results of mapping the succession of tropical dry forest in the Parque Estadual de la Mata-Seca, in Minas Gerais, Brazil, using a temporal analysis of CHRIS/PROBA images in a time frame of 7 years, between 2008 and 2014. For the purpose the -55° angle of observation has been used, which enhances spectral differences between successional stages. Spectral Angle Mapper has been used for mapping succession of tropical dry forest and afterwards Change Detection Analysis has been performed. Based on our observations, the tropical dry forest in the Parque Estadual de la Mataseca recovers at a fast rate, for the observed period (2008-2014). More than the 50% of the early and intermediate forests has been recovered to a mature forest. Significantly, around a 12% of old pastures have been converted into forest. The spatial analysis also reveals that the areas that recover most rapidly are located in the east of the Park, close to mature forests. The provision of seeds from these forests might be the cause for the fast recovery.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".