Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Bibliographic record
Abstract
land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system.Using the four classification algorithms, we obtained the initial set of global land-cover maps.The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%),J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth.We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m.Using this subset, we found the OCA for the SVM to be 71.5%.As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production.The total area of cropland is 11.51% if unplanted croplands are included.The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively.The impervious surface covers only 0.66% of the world.Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.• The 1 km International Geosphere-Biosphere Programme Data and Information System Cover (IGBP-DISCover) map was produced from monthly normalized difference vegetation index (NDVI) composites derived from 1992 to 1993 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data (Loveland et al. 2000).An unsupervised classification method was used to produce this map.• The 1 km University of Maryland (UMD) land-cover map was produced with the same data set as mentioned above (Hansen et al. 2000).A supervised classification tree method was used to produce this map.• The 1 km Global Land Cover 2000 (GLC2000) map was produced from monthly NDVI data derived from 1999 to 2000 Satellite Pour l'Observation de la Terre (SPOT) vegetation data (Bartholome and Belward 2005).It was produced by people working separately, in parallel, on 19 different regions of the world using various types of algorithms.• 500 m Moderate Resolution Imaging Spectrometer (MODIS) land-cover maps are now being generated annually with MODIS data (Friedl et al. 2002(Friedl et al. , 2010)).Recently, a supervised classification tree algorithm has been used to produce these maps.• 300 m GlobCover land-cover maps were produced with bimonthly Medium Revolution Imaging Spectrometer (MERIS) data mosaics derived from the Environmental Satellite (ENVISAT) for 2005 and 2009(Arino et al. 2008;;Bontemps
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".