Global land cover classification at 1 km spatial resolution using a classification tree approach

International Journal of Remote Sensing

Volumn 21, Issue 6-7, 2000, Pages 1331-1364

  Hansen, M C ^a   Sohlberg, R ^a   Defries, R S ^a   Townshend, J R G ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AVHRR; CLASSIFICATION; LAND COVER; MODIS; VEGETATION COVER;

EID: 0034655867     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/014311600210209     Document Type: Article

References (49)

1. Agbu, P. A., and James, M. E., 1994, The NOAA/NASA Pathfinder AVHRR Land Dataset Users Manual (Greenbelt, MD, USA: Goddard Distributed Active Archive Center;Publications gCdG).
2. Belward, A. S., 1996, The IGBP-DIS Global 1km Land Cover Data Set ‘DISCOVER’: proposal and implementation plans. Report WP No. 13, IGBP-DIS, Stockholm,;Sweden.
3. Breiman, L., Friedman, J., Olshen, R., and Stone, C., 1984, Classification and Regression Trees (Monterey, California: Wadsworth).
4. Cihlar, J., Ly, Z. Q., Chen, J., Pokrant, H., and Huang, F. T., 1997, Multitemporal, multichannel AVHRR data sets for land biosphere studies—artifacts and corrections.Remote Sensing of Environment, 60, 35-57
5. Cihlar, J., Manak, D., and Voisin, N., 1994, AVHRR bidirectional reflectance eff'ects and compositing. Remote Sensing of Environment, 48, 77-88.
6. Cec, 1993, CORINE Land Cover Technical Guide. European Union. Directorate-General Environment, Nuclear Safety and Civil Protection, EUR, ISSN 1018-5593, ISBN;92-826-2578-8
7. Clark, L. A., and Pergibon, D., 1992, Tree-based models. In Statistical Models in S, edited by T. J. Hastie (Pacific Grove, CA: Wadsworth and Brooks).
8. Cushnie, J. L., 1987, The interactive eff'ect of spatial resolution and degree of internal variability within land-cover types on classification accuracies. International Journal of Remote;Sensing, 8, 15-29.
9. Danko, D. M., 1992, The Digital Chart of the World Project. Photogrammetric Engineering and Remote Sensing, 58, 1125-1128.
10. DeFries, R. S., Hansen, M. C., Townshend, J. R. G., and Sohlberg, R. S., 1998, Global land cover classificationss at 8 km spatial resolution: the use of training data derived from;Landsat imagery in decision tree classifiers. International Journal of Remote Sensing,;19, 3141-3168.
11. DeFries, R. S., and Townshend, J. R. G., 1994a, Global land cover: comparison of ground-based data sets to classifications with AVHRR data. In Environmental Remote Sensing from Regional to Global Scales, edited by G. Foody and P. Curran (Chichester: Wiley).
12. DeFries, R. S., and Townshend, J. R. G., 1994b, NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, 15, 3567-3586.
13. Dickinson, R. E., 1995, Land processes in climate models. Remote Sensing of Environment, 51, 27-38.
14. Eidenshink, J. C., and Faudeen, J. L., 1994, The 1km AVHRR global land data set: first stages in implementation. International Journal of Remote Sensing, 15, 3443-3462.
15. FAO, 1997, State of the World s Forests, Food and Agriculture Organization of the United Nations (Oxford: Words and Publications).
16. Freidl, M. A., and Brodley, C. E., 1997, Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61, 399-409.
17. Hammond, T. O., and Verbyla, D. L., 1996, Optimistic bias in classification accuracy assessment. International Journal of Remote Sensing, 17, 1261-1266.
18. Hansen, M., Dubayah, R., and DeFries, R., 1996, Classification trees: an alternative to traditional land cover classifiers. International Journal of Remote Sensing, 17,;1075-1081.
19. Holben, B. N., 1986, Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 12, 1147-1163.
20. Huete, A. R., and Tucker, C. J., 1991, Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. International Journal of Remote Sensing, 12,;1223-1242.
21. James, M. E., and Kalluri, S. N. V., 1994, The Pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. International Journal of Remote;Sensing, 15, 3347-3364.
22. Justice, C. O., Markham, B. L., Townshend, J. R. G., and Kennard, R. L., 1989, Spatial degradation of satellite data. International Journal of Remote Sensing, 10, 1539-1561.
23. Kloditz, C., van Boxtel, A., Carfagna, E., and van Deuresen, W., 1998, Estimating the accuracy of coarse scale classifications using high scale information. Photogrammetric;Engineering and Remote Sensing, 64, 127-133.
24. Lambin, E. F., and Ehlirch, D., 1996, The surface temperature-vegetation index space for land cover and land-cover change analysis. International Journal of Remote Sensing,;17, 463-487.
25. Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W., 2000, Development of a global land cover characteristics database and IGBP;DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21,;1303-1330.
26. Matthews, E., 1983, Global vegetation and land use: new high resolution data bases for climate studies. Journal of Climate and Applied Meteorology, 22, 474-487.
27. Mayaux, P., Achard, F., and Malingreau, J., 1998, Global tropical forest area measurements derived from coarse resolution satellite imagery: a comparison with other approaches.;Environmental Conservation, 25, 37-52.
28. Mayaux, P., Janodet, E., Blair-Myers, C. M., and Legeay-Janvier, P., 1997, Vegetation Map of Central Africa at 1:5,000,000, Publication Series D, EUR 17322, European;Commission, Tropical Ecosystem Environment Observation by Satellite (TREES);Project.
29. Moody, A., 1998, Using landscape spatial relationships to improve estimates of land-cover area from coarse resolution remote sensing. Remote Sensing of Environment, 64,;202-220.
30. Moody, A., and Woodcock, C. E., 1994, Scale-dependent errors in the estimation of landcover proportions: implications for global land-cover datasets. Photogrammetric Engineering and Remote Sensing, 60, 585-594.
31. Olson, J. S., Watts, J., and Allison, L., 1983, Carbon in live vegetation of major world ecosystems. Report W-7405-ENG-26, U.S. Departmentof Energy, Oak Ridge National;Laboratory, Oak Ridge, TN, USA.
32. Ouaidrari, H., Vermote, E., El Saleous, N., and Roy, D., 1997, AVHRR Pathfinder II data set: evaluation and improvements. Proceedings of the Seventh International Symposium;on Physical Measurements and Signatures in Remote Sensing, Courcheval, France,;17-11 April 1997 (Rotterdam: Balkema), pp. 131-137.
33. Quinlan, J. R., 1993, C4.5: Programs for Machine Learning, (San Mateo, California: Morgan Kaufmann).
34. Rasool, S. I., 1992, Requirements for terrestrial biospheric data for IGBP Core Projects. IGBP-DIS Working Paper #2, International Geosphere-Biosphere Programme - Data;and Information Systems, Paris, France.
35. Running, S. W., Loveland, T. R., and Pierce, L. L., 1994, A vegetation classification logic based on remote sensing for use in global biogeochemical models. Ambio, 23, 77-81.
36. Running, S. W., Loveland, T. W., Pierce, L. L., Nemani, R. R., and Hunt, E. R., Jr, 1995, A remote sensing based vegetation classification logic for global land cover analysis.;Remote Sensing of Environment, 51, 39-48.
37. Sellers, P. J., Dickinson, R. E., Randall, D. A., Betts, A. K., Hall, F. G., Mooney, H. A., Nobre, C. A., Sato, N., Field, C. B., and Henderson-Sellers, A., 1997, Modeling the;exchanges of energy, water, and carbon between continents and the atmosphere.;Science, 275, 502-509.
38. Stehman, S. V., and Czaplewski, R. L., 1998, Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment, 64, 331-344.
39. Stone, T. A., SchLesinger, P., Houghton, R. A., and WoodweLl, G. M., 1994, A map of the vegetation of South America based on satellite imagery. Photogrammetric Engineering;and Remote Sensing, 60, 541-551.
40. Townshend, J. R. G., (editor), 1992, Improved Global Data for Land Applications: A Proposal for a New High Resolution Data Set. Report No. 20, International Geosphere-Biosphere Program, Stockholm, Sweden.
41. Townshend, J. R. G., Bell, V., Desch, A., HavLicek, C., Justice, C., Lawrence, W. L., SkoLe, D., Chomentowski, W., MooRe, B. I., Salas, W., and TuckeR, C. J., 1995, The NASA;Landsat Pathfinder Humid Tropical Deforestation Project. Proceedings of the ASPRS;Conference Land Satellite Information in the Next Decade, Vienna, VA, USA,;25-28 September 1995 (Bethesda, MD: ASPRS), pp. 76-87.
42. Townshend, J. R. G., Justice, C. O., and Kalb, V. T., 1987, Characterization and classification of South American land cover types using satellite data. International Journal of Remote;Sensing, 8, 1189-1207.
43. Townshend, J. R. G., Justice, C. O., SkoLe, D., MaLingReau, J.-P., CihLAr, J., Teillet, P., SADowski, F., and Ruttenberg, S., 1994, The 1 km resolution global data set: needs;of the International Geosphere-Biosphere Programme. InternationalJournal of Remote;Sensing, 15, 3417-3441.
44. Tucker, C. J., Townshend, J. R. G., and Goff, T. E., 1985, African land-cover classification using satellite data. Science, 111, 369-375.
45. VenabLes, W. N., and Ripley, B. D., 1994, Modern Applied Statistics with S-Plus (New York: Springer-Verlag).
46. Verhoef, W., Meneti, M., and Azzali, S., 1996, A colour composite of NOAA-AVHRR NDVI based on time series analysis (1981-1992). International Journal of Remote;Sensing, 11, 231-235.
47. VogeLman, J. E., SohL, T., and Howard, S. M., 1998, Regional characterization of landcover using multiple sources of data. Photogrammetric Engineering and Remote Sensing,;64, 45-57.
48. WiLson, M. F., and HendeRson-SeLLERs, A., 1985, A global archive of land cover and soils data for use in general circulation models. Journal of Climatology, 5, 119-143.
49. Zhu, Z., and Evans, D. L., 1992, Mapping midsouth forest distributions. Journal of Forestry, 90, 27-30.