The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Open accessAbstract
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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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Global Consequences of Land Use
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., et al. (2005) · Science · doi:10.1126/science.1111772 · Cited by 13,272
Terrestrial ecoregions of the world: a new map of life on Earth
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., et al. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2 · Cited by 8,488
Classification of wetlands and deepwater habitats of the United States
Cowardin, L. M., Service, U. S. F. A. W., & (U.S.), B. S. P. (1979) · doi:10.5962/bhl.title.4108 · Cited by 2,153
Estimating historical changes in global land cover: Croplands from 1700 to 1992
Ramankutty, N., & Foley, J. A. (1999) · Global Biogeochemical Cycles · doi:10.1029/1999gb900046 · Cited by 2,074
Effects of Land Cover Conversion on Surface Climate
Bounoua, L., DeFries, R., Collatz, G. J., Sellers, P., & Khan, H. (2002) · Climatic Change · doi:10.1023/a:1013051420309 · Cited by 353
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Soil carbon storage informed by particulate and mineral-associated organic matter
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Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015
Keenan, R. J., Reams, G. A., Achard, F., de Freitas, J. V., Grainger, A., & Lindquist, E. (2015) · Forest Ecology and Management · doi:10.1016/j.foreco.2015.06.014 · Cited by 1,656
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Joppa, L. N., O'Connor, B., Visconti, P., Smith, C., Geldmann, J., Hoffmann, M., Watson, J. E. M., Butchart, S. H. M., Virah-Sawmy, M., Halpern, B. S., et al. (2016) · Science · doi:10.1126/science.aaf3565 · Cited by 288
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Metadata
- Authors
- Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng, Y., Ji, L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu, X., Shi, T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N., Zhu, Z., Chen, J., & Chen, J.
- Venue
- International Journal of Remote Sensing
- Publisher
- Informa UK Limited
- Published
- 2012-12-21
- Volume
- 34
- Issue
- 7
- Pages
- 2607-2654
- ISSN
- 0143-1161 (ISSN-L)
- 1366-5901
- OpenAlex
- W2001510610
- Cited by
- 1,687
- References
- 7
- FWCI
- 48.14
- Type
- journal article
- Language
- en
- Open access
- bronze
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