Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

International Journal of Remote Sensing

Volumn 32, Issue 23, 2011, Pages 8207-8230

  Li, Guiying ^a   Lu, Dengsheng ^a   Moran, Emilio ^a   Hetrick, Scott ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; IMAGE CLASSIFICATION; MAXIMUM LIKELIHOOD ESTIMATION; NEURAL NETWORKS; REMOTE SENSING; TROPICAL ENGINEERING; VEGETATION;

CLASSIFICATION ALGORITHM; CLASSIFICATION PERFORMANCE; CLASSIFICATION-TREE ANALYSIS; LAND COVER CLASSIFICATION; LANDSAT THEMATIC MAPPER; MAXIMUM LIKELIHOOD CLASSIFICATIONS; OBJECT-BASED CLASSIFICATIONS; VEGETATION CLASSIFICATION;

IMAGE ENHANCEMENT;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LAND COVER; LANDSAT THEMATIC MAPPER; MAXIMUM LIKELIHOOD ANALYSIS; MULTISPECTRAL IMAGE; PATCH SIZE; SATELLITE IMAGERY; STAND STRUCTURE; TROPICAL ENVIRONMENT; VEGETATION INDEX;

ALGORITHMS; BRAZIL; FORESTRY; FORESTS; PLANTS; REMOTE SENSING; RESEARCH; TROPICAL ENGINEERING;

BRAZIL;

EID: 82055178195     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2010.532831     Document Type: Article

References (82)

1. ADAMS, J.B., SABOL, D.E., KAPOS, V., FILHO, R.A., ROBERTS, D.A., SMITH, M.O. and GILLESPIE, A.R., 1995, Classification of multispectral images based on fractions of endmembers: application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment, 52, pp. 137-154.
2. ALI, S.S., DARE, P.M. and JONES, S.D., 2009, A comparison of pixel- and object-level data fusion using Lidar and high-resolution imagery for enhanced classification. In Innovations in Remote Sensing and Photogrammetry, S. Jones and K. Reinke (Eds.), pp. 3-17 (Berlin: Springer-Verlag).
3. AUGUSTEIJN, M.F., CLEMENS, L.E. and SHAW, K.A., 1995, Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier. IEEE Transactions on Geoscience and Remote Sensing, 33, pp. 616-625.
4. BANNARI, A., MORIN, D. and BONN, E., 1995, A review of vegetation indices. Remote Sensing Reviews, 13, pp. 95-120.
5. BISCHOF, H., SCHNEIDER, W. and PINZ, A.J., 1992, Multispectral classification of Landsat images using neural networks. IEEE Transactions on Geoscience and Remote Sensing, 30, pp. 482-489.
6. BLASCHKE, T., 2010, Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, pp. 2-16.
7. BRONDÍZIO, E.S., MORAN, E.F., MAUSEL, P. and WU, Y., 1996, Land cover in the Amazon estuary: linking of the Thematic Mapper with botanical and historical data. Photogrammetric Engineering and Remote Sensing, 62, pp. 921-929.
8. BRUZZONE, L., CONESE, C., MASELLI, F. and ROLI, F., 1997, Multisource classification of complex rural areas by statistical and neural-network approaches. Photogrammetric Engineering and Remote Sensing, 63, pp. 523-533.
9. BUTUSOV, O.B., 2003, Textural classification of forest types from Landsat 7 imagery. Mapping Sciences and Remote Sensing, 40, pp. 91-104.
10. CASTRO, K.L., SANCHEZ-AZOFEIFA, G.A. and RIVARD, B., 2003, Monitoring secondary tropical forests using space-borne data: implications for Central America. International Journal of Remote Sensing, 24, pp. 1853-1894.
11. CHANDER, G., MARKHAM, B.L. and HELDER, D.L., 2009, Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113, pp. 893-903.
12. CHAVEZ, JR., P.S., 1996, Image-based atmospheric corrections - revisited and improved. Photogrammetric Engineering and Remote Sensing, 62, pp. 1025-1036.
13. CHEN, D., STOW, D.A. and GONG, P., 2004, Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case. International Journal of Remote Sensing, 25, pp. 2177-2192.
14. CONGALTON, R.G., 1991, A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment, 37, pp. 35-46.
15. CONGALTON, R.G. and GREEN, K., 2008, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed., p. 183 (Boca Raton, FL: CRC Press).
16. EHLERS, M., KLONUS, S., ASTRAND, P.J. and ROSSO, P., 2010, Multisensor image fusion for pansharpening in remote sensing. International Journal of Image and Data Fusion, 1, pp. 25-45.
17. FOODY, G.M., 2002, Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, pp. 185-201.
18. FOODY, G.M., 2004, Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering and Remote Sensing, 70, pp. 627-633.
19. FOODY, G.M., 2009, Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sensing of Environment, 113, pp. 1658-1663.
20. FOODY, G.M., PALUBINSKAS, G., LUCAS, R.M., CURRAN, P.J. and HONZÁK, M., 1996, Identifying terrestrial carbon sinks: classification of successional stages in regenerating tropical forest from Landsat TM data. Remote Sensing of Environment, 55, pp. 205-216.
21. FRANKLIN, S.E. and WULDER, M.A., 2002, Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26, pp. 173-205.
22. FRIEDL, M.A. and BRODLEY, C.E., 1997, Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61, pp. 399-409.
23. GONG, P., 1994, Integrated analysis of spatial data from multiple sources: an overview. Canadian Journal of Remote Sensing, 20, pp. 349-359.
24. GONG, P. and HOWARTH, P.J., 1992, Frequency-based contextual classification and gray-level vector reduction for land-use identification. Photogrammetric Engineering & Remote Sensing, 58, pp. 423-437.
25. HANSEN, M., DUBAYAH, R. and DEFRIES, R., 1996, Classification trees: an alternative to traditional land cover classifiers. International Journal of Remote Sensing, 17, pp. 1075-1081.
26. HARALICK, R.M., SHANMUGAM, K. and DINSTEIN, I., 1973, Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, pp. 610-620.
27. HARRIS, P.M. and VENTURA, S.J., 1995, The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogrammetric Engineering and Remote Sensing, 61, pp. 993-998.
28. HILL, R.A. and FOODY, G.M., 1994, Separability of tropical rain-forest types in the Tambopata-Candamo reserved zone, Peru. International Journal of Remote Sensing, 15, pp. 2687-2693.
29. HODGSON, M.E., JENSEN, J.R., TULLIS, J.A., RIORDAN, K.D. and ARCHER, C.M., 2003, Synergistic use Lidar and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering and Remote Sensing, 69, pp. 973-980.
30. JENSEN, J.R. 2004, Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd ed., p. 526 (Upper Saddle River, NJ: Pearson Prentice Hall).
31. KANELLOPOULOS, I. and WILKINSON, G.G., 1997, Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 18, pp. 711-725.
32. KASHYAP, R.L., CHELLAPPA, R. and KHOTANZAD, A., 1982, Texture classification using features derived from random field models. Pattern Recognition Letters, 1, pp. 43-50.
33. KUROSU, T., YOKOYAMA, S. and CHIBA, K., 2001, Land use classification with textural analysis and the aggregation technique using multi-temporal JERS-1 L-band SAR images. International Journal of Remote Sensing, 22, pp. 595-613.
34. LANDGREBE, D.A., 2003, Signal Theory Methods in Multispectral Remote Sensing, p. 508 (Hoboken, NJ: John Wiley & Sons).
35. LI, D., 2010, Remotely sensed images and GIS data fusion for automatic change detection. International Journal of Image and Data Fusion, 1, pp. 99-108.
36. LILLESAND, T.M. and KIEFER, R.W., 2000, Remote Sensing and Image Interpretation, 4th ed., p. 724 (New York: John Wiley & Sons).
37. LU, D., 2005a, Integration of vegetation inventory data and Landsat TM image for vegetation classification in the western Brazilian Amazon. Forest Ecology and Management, 213, pp. 369-383.
38. LU, D., 2005b, Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26, pp. 2509-2525.
39. LU, D., BATISTELLA, M. and MORAN, E., 2007, Land cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and RADARSAT data. International Journal of Remote Sensing, 28, pp. 5447-5459.
40. LU, D., BATISTELLA, M., MORAN, E. and DE MIRANDA, E.E., 2008, A comparative study of Landsat TM and SPOT HRG images for vegetation classification in the Brazilian Amazon. Photogrammetric Engineering and Remote Sensing, 70, pp. 311-321.
41. LU, D., HETRICK, S. and MORAN, E., 2010, Land cover classification in a complex urban-rural landscape with Quickbird imagery. Photogrammetric Engineering and Remote Sensing, 76, pp. 1159-1168.
42. LU, D., MAUSEL, P., BATISTELLA, M. and MORAN, E., 2004a, Comparison of land-cover classification methods in the Brazilian Amazon basin. Photogrammetric Engineering and Remote Sensing, 70, pp. 723-731.
43. LU, D., MAUSEL, P., BRONDÍZIO, E.S. and MORAN, E., 2002, Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research. International Journal of Remote Sensing, 23, pp. 2651-2671.
44. LU, D., MAUSEL, P., BRONDÍZIO, E.S. and MORAN, E., 2004b, Relationships between forest stand parameters and Landsat Thematic Mapper spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198, pp. 149-167.
45. LU, D., MORAN, E. and BATISTELLA, M., 2003, Linear mixture model applied to Amazonian vegetation classification. Remote Sensing of Environment, 87, pp. 456-469.
46. LU, D. and WENG, Q., 2007, A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, pp. 823-870.
47. LUCAS, R.M.,HONZÁK, M., CURRAN, P.J., FOODY, G.M., MILNE, R., BROWN, T. and AMARAL, S., 2000, The regeneration of tropical forests within the Legal Amazon. International Journal of Remote Sensing, 21, pp. 2855-2881.
48. LUCAS, R.M., HONZÁK, M., DO AMARAL, I., CURRAN, P.J. and FOODY, G.M., 2002, Forest regeneration on abandoned clearances in central Amazonia. International Journal of Remote Sensing, 23, pp. 965-988.
49. MARCEAU, D.J., HOWARTH, P.J., DUBOIS, J.M. and GRATTON, D.J., 1990, Evaluation of the grey-level co-occurrence matrix method for land-cover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing, 28, pp. 513-519.
50. MAUSEL, P., WU, Y., LI, Y., MORAN, E. and BRONDÍZIO, E., 1993, Spectral identification of succession stages following deforestation in Amazonia. Geocarto International, 8, pp. 11-20.
51. MAUSEL, P.W., KRAMBER, W.J. and LEE, J.K., 1990, Optimum band selection for supervised classification of multispectral data. Photogrammetric Engineering and Remote Sensing, 56, pp. 55-60.
52. MCDONALD, A.J., GEMMELL, F.M. and LEWIS, P.E., 1998, Investigation of the utility of spectral vegetation indices for determining information on coniferous forests. Remote Sensing of Environment, 66, pp. 250-272.
53. MORAN, E.F., 1981, Developing the Amazon (Bloomington, IN: Indiana University Press).
54. MORAN, E.F. and BRONDÍZIO, E.S., 1998, Land-use change after deforestation. In Amazônia, People and Pixels: Linking Remote Sensing and Social Science, D. Liverman, E.F. Moran, R.R. Rindfuss and P.C. Stern (Eds.), pp. 94-120 (Washington, DC: National Academy Press).
55. MORAN, E.F., BRONDÍZIO, E.S. and MAUSEL, P., 1994a, Secondary succession. Research and Exploration, 10, pp. 458-476.
56. MORAN, E.F., BRONDÍZIO, E.S., MAUSEL, P. and WU, Y., 1994b, Integrating Amazonian vegetation, land use, and satellite data. Bioscience, 44, pp. 329-338.
57. NARASIMHA RAO, P.V., SESHA SAI, M.V.R., SREENIVAS, K., KRISHNA RAO, M.V., RAO, B.R.M., DWIVEDI, R.S. and VENKATARATNAM, L., 2002, Textural analysis of IRS- 1D panchromatic data for land cover classification. International Journal of Remote Sensing, 23, pp. 3327-3345.
58. PAOLA, J.D. and SCHOWENGERDT, R.A., 1995, A review and analysis of back propagation neural networks for classification of remotely sensed multi-spectral imagery. International Journal of Remote Sensing, 16, pp. 3033-3058.
59. PODEST, E. and SAATCHI, S., 2002, Application of multiscale texture in classifying JERS- 1 radar data over tropical vegetation. International Journal of Remote Sensing, 23, pp. 1487-1506.
60. POHL, C. and VAN GENDEREN, J.L., 1998, Multisensor image fusion in remote sensing: concepts, methods, and applications. International Journal of Remote Sensing, 19, pp. 823-854.
61. RICHARDS, J.A. and JIA, X., 1999, Remote Sensing Digital Image Analysis: An Introduction, 3rd ed., p. 363 (Berlin: Springer-Verlag).
62. RIGNOT, E., SALAS, W.A. and SKOLE, D.L., 1997, Mapping deforestation and secondary growth in Rondônia, Brazil using imaging Radar and Thematic Mapper data. Remote Sensing of Environment, 59, pp. 167-176.
63. ROBERTS, D.A., BATISTA, G.T., PEREIRA, J.L.G., WALLER, E.K. and NELSON, B.W., 1998, Change identification using multitemporal spectral mixture analysis: applications in eastern Amazônia. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications, R.S. Lunetta and C.D. Elvidge (Eds.), pp. 137-161 (Ann Arbor, MI: Ann Arbor Press).
64. ROBERTS, D.A., NUMATA, I., HOLMES, K., BATISTA, G., KRUG, T., MONTEIRO, A., POWELL, B. and CHADWICK, O.A., 2002, Large area mapping of land-cover change in Rondônia using multitemporal spectral mixture analysis and decision tree classifiers. Journal of Geophysical Research, 107, 8073.
65. ROGAN, J., FRANKLIN, J., STOW, D., MILLER, J., WOODCOCK, C. and ROBERTS, D., 2008, Mapping land-cover modifications over large areas: a comparison of machine learning algorithms. Remote Sensing of Environment, 112, pp. 2272-2283.
66. SHABAN, M.A. and DIKSHIT, O., 2001, Improvement of classification in urban areas by the use of textural features: the case study of Lucknow city, Uttar Pradesh. International Journal of Remote Sensing, 22, pp. 565-593.
67. SHACKELFORD, A.K. and DAVIS, C.H., 2003, A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 1920-1932.
68. SKOLE, D.L., CHOMENTOWSKI, W.H., SALAS, W.A. and NOBRE, A.D., 1994, Physical and human dimension of deforestation in Amazonia. BioScience, 44, pp. 314-328.
69. SMITS, P.C., DELLEPIANE, S.G. and SCHOWENGERDT, R.A., 1999, Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a costbased approach. International Journal of Remote Sensing, 20, pp. 1461-1486.
70. SOLBERG, A.H.S., TAXT, T. and JAIN, A.K., 1996, A Markov random field model for classification of multisource satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 34, pp. 100-112.
71. STEFANOV, W.L., RAMSEY, M.S. and CHRISTENSEN, P.R., 2001, Monitoring urban land cover change: an expert system approach to land cover classification of semiarid to arid urban centers. Remote Sensing of Environment, 77, pp. 173-185.
72. SUN, W.X., HEIDT, V., GONG, P. and XU, G., 2003, Information fusion for rural land-use classification with high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 883-890.
73. TSO, B. and MATHER, P.M., 2001, Classification Methods for Remotely Sensed Data (London, UK: Taylor & Francis).
74. TUCKER, J.M., BRONDÍZIO, E.S. and MORAN, E.F., 1998, Rates of forest regrowth in Eastern Amazônia: a comparison of Altamira and Bragantina regions, Para State, Brazil. Interciencia, 23, pp. 64-73.
75. VIEIRA, I.C.G., DE ALMEIDA, A.S., DAVIDSON, E.A., STONE, T.A., DE CARVALHO, C.J.R. and GUERRERO, J.B., 2003, Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazonia. Remote Sensing of Environment, 87, pp. 470-481.
76. WILLIAMS, J., 2001, GIS Processing of Geocoded Satellite Data, p. 327 (Chichester, UK: Springer and Praxis Publishing).
77. WULDER, M.A., FRANKLIN, S.E., WHITE, J.C., LINKE, J. and MAGNUSSEN, S., 2006, An accuracy assessment framework for large-area land cover classification products derived from medium-resolution satellite data. International Journal of Remote Sensing, 27, pp. 663-683.
78. YANASSE, C.C.F., SANT'ANNA, S.J.S., FRERY, A.C., RENNÓ, C.D., SOARES, J.V. and LUCKMAN, A.J., 1997, Exploratory study of the relationship between tropical forest regeneration stages and SIR-C L and C data. Remote Sensing of Environment, 59, pp. 180-190.
79. YU, Q., GONG, P., CLINTON, N., BIGING, G., KELLY, M. and SCHIROKAUER, D., 2006, Objectbased detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72, pp. 799-811.
80. ZAMBON, M, LAWRENCE, R., BUNN, A. and POWELL, S., 2006, Effect of alternative splitting rules on image processing using classification tree analysis. Photogrammetric Engineering & Remote Sensing, 72, pp. 25-30.
81. ZHANG, J., 2010, Multisource remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1, pp. 5-24.
82. ZHANG, Y., LU, D., YANG, B., SUN, C. and SUN, M., 2011, Coastal wetland vegetation classification with a Landsat Thematic Mapper image. International Journal of Remote Sensing, 32, pp. 545-561.