Study of landslides caused by the 1999 Chi-Chi earthquake, Taiwan, with multitemporal SPOT images
Bibliographic record
Abstract
AbstractThis paper presents the results of a study of landslides and other landscape changes caused by the 1999 Taiwan Chi-Chi earthquake using multitemporal Satellite pour l'observation de la terre (SPOT) images. An innovative method for landslide detection is proposed based on the normalized difference vegetation index (NDVI) using image differencing coupled with an automated change threshold calculation. A vegetation recovery index and a land-cover spatial distribution index are also developed to assess vegetation recovery. Landslide extraction by an autodetection technique was also undertaken, yielding Kappa values over 84.16% when compared with those from existing maps. Recovery since the earthquake, despite a typhoon event in 2002, was 66.25% for vegetation in the denuded areas but less on ridge and slope surfaces. The results were verified by field surveys. The methods presented in this paper can be used by government agencies to aid in landslide-area recovery and establish effective land-use policies.Cet article présente les résultats d'une étude sur les glissements de terrain et autres changements dans le paysage causés par le séisme de Chi-Chi, à Taiwan, en 1999, utilisant des images multidates de SPOT (Satellite pour l'observation de la terre). Une méthode innovatrice pour la détection des glissements de terrain est proposée basée sur l'indice NDVI (« normalized differenced vegetation index ») en utilisant la différenciation entre les images couplée au calcul automatisé du seuil de changement. Un indice de réhabilitation de la végétation et un indice de distribution spatiale du couvert sont également développés pour évaluer la réhabilitation de la végétation. L'extraction des glissements de terrain à l'aide d'une technique d'autodétection a aussi été réalisée donnant des valeurs Kappa de plus de 84,16 % comparativement aux cartes existantes. Le taux de réhabilitation de la végétation depuis le séisme, en dépit d'un typhon en 2002, a été de 66,25 % dans les régions dénudées, mais moins important sur les crêtes et les pentes. Les résultats ont été validés par relevé sur le terrain. Les méthodes présentées dans cet article peuvent être utilisées par les agences gouvernementales comme aide à la réhabilitation de la végétation dans les zones de glissements de terrain et pour la mise en place de politiques efficaces d'utilisation du sol.[Traduit par la Rédaction]
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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 | 0.000 | 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".