Application of the MODIS global supervised classification model to vegetation and land cover mapping of central america

International Journal of Remote Sensing

Volumn 21, Issue 6-7, 2000, Pages 1115-1138

  Muchoney, D ^a   Borak, J ^a   Chi, H ^a   Friedl, M ^a   Gopal, S ^a   Hodges, J ^a   Morrow, N ^a   Strahler, A ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION; LAND COVER; MODIS; VEGETATION MAPPING;

CENTRAL AMERICA;

EID: 0034655487     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/014311600210100     Document Type: Article

References (60)

1. Achard, F., and EstreguiL, C., 1995, Forest classification of Southeast Asia using NOAA AVHRR data. Remote Sensing of Environment, 54, 198-208.
2. Bailey, R. G., 1980, Description of the Ecoregions of the United States. Misc. Publ. 1391, USDA, Washington DC, USA.
3. BelwaRD, A., and Loveland, T. R., 1995, The IGBP 1km Land Cover Project. Proceedings of the 21st Annual Conference of the Remote Sensing Society, Southampton, UK, 11-14September, Nottingham, Remote Sensing Society, 1099-1106.
4. Bonan, G. B., 1996, A land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: technical description and user’s guide. NCAR/TN-417 + STR, NCAR Technical Note, National Center for Atmospheric Research, Boulder, CO, USA.
5. BrodLey, C. E., 1995, Recursive automatic bias selection for classifier construction. Machine Learning, 20, 63-94.
6. BrodLey, C. E., and Utgoff, P. E., 1995, Multivariate decision trees. Machine Learning, 19, 45-77.
7. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., and Rosen, D. B., 1992, Fuzzy ART: a neural network architecture for incremental supervised learning ofanalog multidimensional maps. IEEE Transactions on Neural Networks, 3, 698-713.
8. Carpenter, G. A., Grossberg, S., and Reynolds, J. H., 1991a, ARTMAP: supervised realtime learning and classification of nonstationary data by a self-organizing neural network. Neural Networks, 4, 566-588.
9. Carpenter, G. A., Grossberg, S., and Rosen, D. B., 1991b, Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. NeuralNetworks, 4, 759-771.
10. Cihlar, J., 1996, Identification of contaminated pixels in AVHRR composite images for studies of land biosphere. Remote Sensing of Environment, 56, 149-163.
11. CongaLton, R. G., 1991, A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 7, 33-46.
12. CongaLton, R. G., ODeRwaLD, R. G., and Mead, R. A., 1983, Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. PhotogrammetricEngineering and Remote Sensing, 49, 1671-1678.
13. DeFries, R. S., and Townshend, J. R. G., 1994, NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing, 15, 3567-3586.
14. Dickinson, R. E., Henderson-SeLLers, A., and Kennedy, P. J., 1993, Biosphere Atmosphere Transfer Scheme (BATS) Version l as coupled to the NCAR Community Climate Model.NCAR Technical Note 387, Boulder: NCAR, 80 pp.
15. Diego Gomez, L., 1985, Mesoamerica Vegetation Map 1. Provisional vegetation types map of Mexico and Central America (Washington, D. C.: The Nature Conservancy).
16. Dinerstein, E., Olson, D. M., GRaHAm, D. J., Webster, A. L., Primm, S. A., BookbiNDer, M. P., and Ledec, G., 1995, A Conservation Assessment of the Terrestrial Ecoregionsof Latin America and the Caribbean (Washington, D.C.: The World Bank).
17. Duggin, M. J., Piwinski, D., WHiteHeAd, V., and RyLANd, G., 1982, Scan-angle dependence of radiance recorded by the NOAA-AVHRR. Proceedings SPIE, 363, 98-101.
18. EidensHink, J. C., and Faundeen, J. L., 1994, The 1-km AVHRR global land data set: first stages in implementation. International Journal of Remote Sensing, 15, 3443-3462.
19. FriedL, M. A., and BrodLey, C. E., 1997, Decision tree classification of landcover from remotely sensed data. Remote Sensing of Environment, 61, 399-409.
20. FriedL, M. A., BrodLey, C. E., and Strader, A. H., 1999, Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEETransactions on Geoscience and Remote Sensing, 37, 969-977
21. Gopal, S., SkLArew, D. M., and Lambin, E., 1994, Fuzzy-neural networks in multitemporal classification of landcover change in the Sahel. New Tools for Spatial Analysis, Proceedings of the Workshop, Lisbon, 18-20 November 1993 (Luxembourg: Office forOfficial Publ. Eur. Communities), pp. 69-81.
22. Gopal, S., Woodcock, C., and StrAhLer, A. H., 1999, Fuzzy ARTMAP classification of global land cover from the 1 degree AVHRR data set. Remote Sensing of Environment,67, 230-243.
23. Henderson-SeLLers, A., 1987, Effects of change in land use on climate in the humid tropics. In The Geophysiology of Amazonia: Vegetation and Climate Interactions, edited byR. E. Dickinson (New York: Wiley), pp. 463-493
24. HoLben, B., 1986, Characteristics of maximum-value composite images from multitemporal AVHRR data. International Journal of Remote Sensing, 7, 1417-1434.
25. HoLdridge, L. R., 1962a, Mapa ecologico de Honduras (Washington, D. C.: Organization of American States)
26. HoLdridge, L. R., 1962b, Mapa ecologico de Nicaragua (Managua, Nicaragua: United States Agency for International Development).
27. HoLdridge, L. R., 1967, Life Zone Ecology (San Jose, Costa Rica: Tropical Science Center).
28. HoLdridge, L. R., 1975a, Zonas de vidas ecologicas de El Salvador; Proyecto Desarrollo, Forestal y Ordenacion de Cuencas Hidrograficas (New York: Food and AgricultureOrganization).
29. HoLdridge, L. R., 1975b, Mapa ecologico de El Salvador (San Salvador: Ministerio de Agricultura y Ganaderia).
30. Justice, C., Vermote, E., Townshend, J., et al, 1998, The Moderate Resolution Imaging Spectroradiometer(MODIS): land remote sensing for global change research. IEEETransactions on Geoscience and Remote Sensing, 36, 1228-1249.
31. Lippmann, R. P., 1989, Pattern classification using neural networks. IEEE Communications Magazine, 27, 47-55.
32. LoveLAnd, T. R., Merchant, J. W., Brown, J. F., OhLen, D. O., Reed, B. C., Olson, P., and Hutchinson, J., 1995, Seasonal land-cover of the United States. Annals Association ofAmerican Geographers, 85, 339-355.
33. LoveLANd, T. R., MerchANt, J. W., OhLen, D. O., and Brown, J. F., 1991, Development of a land-cover characteristics database for the conterminous U. S. PhotogrammetricEngineering and Remote Sensing, 57, 1453-1463.
34. MALingreAu, J.-P., 1986, Global vegetation dynamics: satellite observation over Asia. International Journal of Remote Sensing, 7, 1121-46.
35. 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.
36. Muchoney, D. M., Grossman, D. H., and SoLomon, R., 1991, Rapid ecological assessment for conservation planning. ASPRS/ACSM Technical Papers (Bethesda, MD: ASPRS), pp. 141-145.
37. Muchoney, D. M., StrahLer, A., Hodges, J., and Locastro, J., 1999. The IGBP DISCover confidence sites and the system for terrestrial ecosystem parameterization: tools forvalidating global land cover data. Photogrammetric Engineering and Remote Sensing,65, 1061-1067.
38. Myneni, R. B., Nemani, R., and Running, S. W., 1997, Estimation of global leaf area index and absorbed PAR using Radiative Transfer Models IEEE Transactions on Geoscienceand Remote Sensing, 35, 1380-1393.
39. Nemani, R., and Running, S. W., 1996, Implementing a hierarchical global vegetation classification in ecosystem function models. Journal of Vegetation Science, 7, 337-346.
40. Nemani, R., and Running, S. W., 1997, Land cover characterization using multitemporal red, near-IR, and thermal-IR data from NOAA/AVHRR. Ecological Applications, 7, 79-90.
41. Olson, J. S., and Watts, J., 1982, Major world ecosystem complexes. Earth’s Vegetation and Atmospheric Carbon Dioxide. In Carbon Dioxide Review, edited by D. B. Jones(Oxford: Oxford University Press), pp. 388-99.
42. Olson, J. S., Watts, J., and ALLison, L., 1983, Carbon in Live Vegetation of Major World Ecosystems. Report W-7405-ENG-26U.S.Dept. of Energy, Oak Ridge National Lab., Oak Ridge, TN, USA.
43. Omemik, J. M., 1987, Ecoregions of the conterminous United States. Annals Association of American Geographers, 77, 118-125.
44. Qurman, J. R., 1993, C4.5: Programs for Machine Learning (San Mateo, CA: Morgan Kaufmann).
45. QumLan, J. R., 1996, Bagging, boosting, and C4.5. Proceedings of the 13th National Conference on Artificial Intelligence, (Portland, OR: AAAI Press), pp. 725-730.
46. 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.
47. Running, S. W., LoveLand, T. R., PieRce, L. L., Nemani, 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.
48. SeLLeRs, P. J., Mintz, Y., Sud, Y. C., and DaLcheR, A., 1986, A simple biosphere model (SiB) for use within general circulation models. Journal of Atmospheric Science, 43, 503-531.
49. SeLLeRs, P. J., RandaLl, D. A., CoLLatz, G. J., et al, 1996, A revised land surface parameterization (SiB2) for atmospheric GCMs. Part 1: model formulation. Journal of Climate, 9, 676-705.
50. SeLLeRs, P. J., ShuttLewoRth, W. J., DoRman, J. L., DaLcher, A., and RoBeRts, J. M., 1989, Calibrating the Simple Biosphere Model for Amazonia tropical forest using field andremote sensing data. Part I: Average calibration with field data. Journal of AppliedMeteorology, 28, 727-759.
51. SHAPiRe, R. E., 1990, The strength of weak learnability. Machine Learning, 5, 197-227.
52. Stone, T. A., SchLesmger, P., Houghton, R. A., and WoodweLL, G. M., 1994, A map of the vegetation of South America Based on satellite imagery. Photogrammetric Engineeringand Remote Sensing, 60, 541-551.
53. StRahLeR, A. H., 1980, The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10, 135-163.
54. StRahLer, A., Townshend, J., Muchoney, D., BoRak, J., FRiedl, M., Gopal, S., Hyman, A., Moody, A., and LamBin, E., 1996, MODIS Land Cover and Land-Cover ChangeAlgorithm Theoretical Basis Document (ATBD), Version 4.1 (Boston: BostonUniversity).
55. 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 RemoteSensing, 8, 1189-1207.
56. TuckeR, C. J., Townshend, J. R. G., and Goff, T. E., 1985, African land-cover classification using satellite data. Science, 227, (4685), 369-375.
57. Van DiJk, A., Calhs, S. I., Sakamoto, C. M., and Decker, W. I., 1987, Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance inNOAA/AVHRR Data. Photogrammetric Engineering and Remote Sensing, 53,1059-1067.
58. Williamson, J. R., 1995, Gaussian ARTMAP: A neural network for fast incremental learning of noisy multidimensional maps. Technical Report CAS/CNS-95-003, BostonUniversity, USA
59. Wilson, M., and Henderson-Sellers, A., 1985, A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, 5, 119-143.
60. Zhu, Z.-L., and Yang, L., 1996, Characteristics of the 1-km AVHRR data set for North America. International Journal of Remote Sensing, 17, 1915-1924.