DEA Fractional Cover Percentiles (Landsat) Version 4.0.0
Notice bibliographique
Résumé
Fractional Cover Percentiles (Landsat) estimate the 10th, 50th, and 90th percentiles independently for the green vegetation, non-green vegetation, and bare soil fractions observed in each calendar year from 1987.<br>The spatial extent is all Australia and the spatial resolution is 30 m x 30 m.<br>Percentiles provide an indicator of where an observation sits, relative to the rest of the observations for the pixel. For example, the 90th percentile is the value below which 90% of the observations fall. The 10th, 50th, and 90th percentiles represent low, median and high values in a distribution that are robust against outliers. These values can be used separately or combined to understand the land cover dynamics. For example, the three percentiles for the green cover fraction can serve as proxies for the minimum, typical and maximum green cover for a given year. Difference between the 10th and 90th percentiles provides an estimate of the magnitude of change within a year. A large range of values may be observed in the agricultural land for all cover types while high green cover and a small difference between 10th and 90th percentiles are expected for forest cover.A representative view of the landscape in a year can be obtained by combining the 50th percentiles, or the median values, for the three cover types.<br><strong>The statistics are calculated using the following satellites for the following periods of time:</strong>- 1987-1998 : Landsat 5 only- 1999 : Landsat 5 and Landsat 7- 2000-2002 : Landsat 7 only- 2003 : Landsat 5 and Landsat 7- 2004-2010 : Landsat 5 only- 2011-2012 : Landsat 7 only- 2013-2021 : Landsat 8 only- 2022 onwards: Landsat 8 and Landsat 9<br><strong>The values for this product are as follows:</strong>For the fractional cover bands (PV, NPV, BS)0-100 = fractional cover values that range between 0 and 100%<br><strong>Quality Assurance:</strong>This layer provides a breakdown of each FCP pixel between:- sufficient observations- insufficient observations dry- insufficient observations wetFor insufficient observations, these are pixels that have been masked out of the percentiles results e.g. NODATA, and provides an explanation as to why they have been masked out.<br><strong>Each product’s datasets is:</strong>- divided into tiles of 3200 x 3200 pixels, with a pixel size of 30 m x 30 m- presented in EPSG:3577 coordinate reference system<br><strong>Fractional Cover Masking</strong>DEA Water Observations are used to identify clear pixels from DEA Fractional Cover to be included in percentile calculation. A Fractional Cover observation is included if:<br>- it has corresponding DEA Water Observation information. If an observation within DEA Fractional Cover has no corresponding Water Observation, it is discarded. This can happen for ARD scenes that have a geometric quality assessment of greater than one, which occurs when there is poor geometric quality.<br>- the DEA Water Observation has the following characteristics: -- it is contiguous (data for all bands is present and valid), -- it is not saturated, -- it is not cloud, -- it is not cloud shadow, -- it is not terrain shadow, -- it is not low solar angle, -- it can be high slope, -- it is not wet, -- there are at least 3 clear and dry observations for the time period.<br>- No land/sea masking is applied.<br>- Observation dates for given percentiles are not captured.<br>Link to data: https://data.dea.ga.gov.au/?prefix=derivative/ga_ls_fc_pc_cyear_3/4-0-0/Link to DEA Knowledge Hub: https://knowledge.dea.ga.gov.au/data/product/dea-fractional-cover-percentiles-landsat/?tab=description<br><br>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».