An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA

GIScience and Remote Sensing

Volumn 49, Issue 5, 2012, Pages 623-643

  Ghimire, Bardan ^a   Rogan, John ^a   Galiano, Víctor ^b   Panday, Prajjwal ^a   Neeti, Neeti ^a  

^a CLARK UNIVERSITY   (United States)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY ASSESSMENT; ALGORITHM; CALIBRATION; FOREST COVER; IMAGE CLASSIFICATION; LAND COVER; PERFORMANCE ASSESSMENT; REMOTE SENSING;

CAPE COD; MASSACHUSETTS; UNITED STATES;

EID: 84866847148     PISSN: 15481603     EISSN: None     Source Type: Journal    
DOI: 10.2747/1548-1603.49.5.623     Document Type: Article

References (69)

1. Atkinson, P. M. and A. R. L. Tatnall, 1997, Introduction: Neural Networks in Remote Sensing, International Journal of Remote Sensing, 18(4):699-709.
2. Breiman, L., 1996, Bagging Predictors, Machine Learning, 24(2):123-140.
3. Breiman, L., 2001, Random Forests, Machine Learning, 45(1):5-32.
4. Breiman, L., Freedman, J., Olshen, R., and C. Stone, 1984, Classification and Regression Trees, Belmont, CA: Wadsworth.
5. Chan, J. C. W., Huang, C. and R. DeFries, 2001, Enhanced Algorithm Performance for Land Cover Classification from Remotely Sensed Data Using Bagging and Boosting, IEEE Transactions on Geoscience and Remote Sensing, 39(3):693-695.
6. Chan, J. C. W. and D. Paelinckx, 2008, Evaluation of Random Forest and Adaboost Tree-Based Ensemble Classification and Spectral Band Selection for Ecotope Mapping Using Airborne Hyperspectral Imagery, Remote Sensing of Environment, 112(6):2999-3011.
7. Chandra, A. and X. Yao, 2006, Evolving hybrid ensembles of learning machines for better generalisation, Neurocomputing, 69(7-9):686-700.
8. Chavez, P. S., 1996, Image-Based Atmospheric Corrections-Revisited and Improved, Photogrammetric Engineering and Remote Sensing, 62(9):1025-1035.
9. Cherrill, A. and C. McClean, 1995, An Investigation of Uncertainty in Field Habitat Mapping and the Implications for Detecting Land Cover Change, Landscape Ecology, 10(1):5-21.
10. Cihlar, J., 2000, Land Cover Mapping of Large Areas from Satellites: Status and Research Priorities, International Journal of Remote Sensing, 21(6):1093-1114.
11. Congalton, R. G., 1991, A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, Remote Sensing of Environment, 37(1):35-46.
12. Congalton, R. G., Oderwald, R. G. and R. A. Mead, 1983, Assessing Landsat Classification Accuracy Using Discrete Multivariate Analysis Statistical Techniques, Photogrammetric Engineering and Remote Sensing, 49(12):1671-1678.
13. DeFries, R. S. and J. C. W. Chan, 2000, Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data, Remote Sensing of Environment, 74(3):503-515.
14. DeFries, R. S. and J. R. G. Townshend, 1994, NDVI-Derived Land Cover Classifications at a Global Scale, International Journal of Remote Sensing, 15(17):3567-3586.
15. DeNormandie, J. and C. Corcoran, 2009, Losing Ground: Beyond the Footprint, Lincoln, MA: Mass Audubon.
16. Fayyad, U. M. and K. B. Irani, 1992, On the Handling of Continuous-Valued Attributes in Decision Tree Generation, Machine Learning, 8(1):87-102.
17. Fletcher, P. C., 1993, Soil Survey of Barnstable County, Massachusetts, Washington, DC: United States Department of Agriculture, Soil Conservation Service.
18. Foody, G. M., 1995, Land Cover Classification by an Artificial Neural Network with Ancillary Information, International Journal of Geographical Information Science, 9(5):527-542.
19. Foody, G. M. and A. Mathur, 2004, A Relative Evaluation of Multiclass Image Classification by Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, 42(6):1335-1343.
20. Franklin, S. E. and M. A. Wulder, 2002, Remote Sensing Methods in Medium Spatial Resolution Satellite Data Land Cover Classification of Large Areas, Progress in Physical Geography, 26(2):173-205.
21. Freund, Y. and R. E. Schapire, 1996, Experiments with a New Boosting Algorithm, Machine Learning: Proceedings of the Thirteenth International Conference, San Francisco, CA: Morgan-Kaufman, 148-156.
22. Friedl, M. A. and C. E. Brodley, 1997, Decision Tree Classification of Land Cover from Remotely Sensed Data, Remote Sensing of Environment, 61(3):399-409.
23. Friedl, M. A., Brodley, C. E., and A. H. Strahler, 1999, Maximizing Land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales, IEEE Transactions on Geoscience and Remote Sensing, 37(2):969-977.
24. Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and C. Schaaf, 2002, Global Land Cover Mapping from MODIS: Algorithms and Early Results, Remote Sensing of Environment, 83(1-2):287-302.
25. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., and F. Herrera, 2011, An Overview of Ensemble Methods for Binary Classifiers in Multi-class Problems: Experimental Study on One-vs-One and One-vs-All Schemes, Pattern Recognition, 44(8):1761-1776.
26. Gao, J., 2002, Integration of GPS with Remote Sensing and GIS: Reality and Prospect, Photogrammetric Engineering and Remote Sensing, 68(5):447-454.
27. Ghimire, B., Rogan, J., and J. Miller, 2010, Contextual Land-Cover Classification: Incorporating Spatial Dependence in Land-Cover Classification Models Using Random Forests and the Getis Statistic, Remote Sensing Letters, 1(1):45-54.
28. Gislason, P. O., Benediktsson, J. A., and J. R. Sveinsson, 2006, Random Forests for Land Cover Classification, Pattern Recognition Letters, 27(4):294-300.
29. Griffin, S., Rogan, J., and D. M. Runfola, 2011, Application of Spectral and Environmental Variables to Map the Kissimmee Prairie Ecosystem Using Classification Trees, GIScience & Remote Sensing, 48(3):299-323.
30. Hall, B., Motzkin, G., Foster, D. R., Syfert, M., and J. Burk, 2002, Three Hundred Years of Forest and Land Use Change in Massachusetts, USA, Journal of Biogeography, 29(10-11):1319-1335.
31. Hansen, M., Dubayah, R., and R. DeFries, 1996, Classification Trees: An Alternative to Traditional Land Cover Classifiers, International Journal of Remote Sensing, 17(5):1075-1081.
32. Hansen, M. C., Egorov, A., Roy, D. P., Potapov, P., Ju, J., Turubanova, S., Kommareddy, I., and T. R. Loveland, 2011, Continuous Fields of Land Cover for the Conterminous United States Using Landsat Data: First Results from the Web-Enabled Landsat Data (WELD) Project, Remote Sensing Letters, 2(4):279-288.
33. Hill, J. and B. Sturm, 1991, Radiometric Correction of Multitemporal Thematic Mapper Data for Use in Agricultural Land-Cover Classification and Vegetation Monitoring, International Journal of Remote Sensing, 12(7):1471-1491.
34. Huang, C., Davis, L. S. and J. R. G. Townshend, 2002, An Assessment of Support Vector Machines for Land Cover Classification, International Journal of Remote Sensing, 23(4):725-749.
35. Kampichler, C., Wieland, R., Calmé, S., Weissenberger, H., and S. Arriaga-Weiss, 2010, Classification in Conservation Biology: A Comparison of Five Machine-Learning Methods, Ecological Informatics, 5(6):441-450.
36. Kotsiantis, S. and P. Pintelas, 2004, Combining Bagging and Boosting, International Journal of Computational Intelligence, 1(4):324-333.
37. Lawrence, R., Bunn, A., Powell, S., and M. Zambon, 2004, Classification of Remotely Sensed Imagery Using Stochastic Gradient Boosting as a Refinement of Classification Tree Analysis, Remote Sensing of Environment, 90(3):331-336.
38. Lippitt, C. D., Rogan, J., Li, Z., Eastman, J. R., and T. G. Jones, 2008, Mapping selective logging in mixed deciduous forest: A comparison of machine learning algorithms, Photogrammetric Engineering and Remote Sensing, 74(10):1201-1211.
39. Lu, D., Li, G., Moran, E., Dutra, L., and M. Batistella, 2011, A Comparison of Multisensor Integration Methods for Land Cover Classification in the Brazilian Amazon, GIScience & Remote Sensing, 48(3):345-370.
40. Lunetta, R. S., Congalton, R. G., Fenstermaker, L. K., Jensen, J. R., McGwire, K. C., and L. R. Tinney, 1991, Remote Sensing and Geographic Information System Data Integration: Error Sources and Research Issues, Photogrammetric Engineering and Remote Sensing, 57(6):677-687.
41. McIver, D. K. and M. A. Friedl, 2002, Using Prior Probabilities in Decision-Tree Classification of Remotely Sensed Data, Remote Sensing of Environment, 81(2-3):253-261.
42. McRoberts, R. E., 2010, The Effects of Rectification and Global Positioning System Errors on Satellite Image-Based Estimates of Forest Area, Remote Sensing of Environment, 114(8):1710-1717.
43. Motzkin, G., Eberhardt, R., Hall, B., Foster, D. R., Harrod, J., and D. MacDonald, 2002, Vegetation Variation across Cape Cod, Massachusetts: Environmental and Historical Determinants, Journal of Biogeography, 29(10-11):1439-1454.
44. Mountrakis, G., Im, J., and C. Ogole, 2010, Support Vector Machines in Remote Sensing: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, 66(3):247-259.
45. Muchoney, D., Borak, J., Chi, H., Friedl, M., Gopal, S., Hodges, J., Morrow, N., and A. Strahler, 2000, Application of the MODIS Global Supervised Classification Model to Vegetation and Land Cover Mapping of Central America, International Journal of Remote Sensing, 21(6):1115-1138.
46. Na, X., Zhang, S., Li, X., Yu, H., and C. Liu, 2010, Improved Land Cover Mapping Using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data, Photogrammetric Engineering and Remote Sensing, 76(7):833-840.
47. Oldale, R. N., Barlow, R. A. and Geological Survey, 1995, Geologic Map of Cape Cod and the Islands, Massachusetts, Reston, VA: U. S. Geological Survey.
48. Pal, M., 2005, Random Forest Classifier for Remote Sensing Classification, International Journal of Remote Sensing, 26(1):217-222.
49. Pal, M. and P. M. Mather, 2003, An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification, Remote Sensing of Environment, 86(4):554-565.
50. Parshall, T., Foster, D. R., Faison, E., MacDonald, D., and B. C. S. Hansen, 2003, Long-Term History of Vegetation and Fire in Pitch Pine-Oak Forests on Cape Cod, Massachusetts, Ecology, 84(3):736-748.
51. Pino-Mejías, R., Cubiles-de-la-Vega, M. D., Anaya-Romero, M., Pascual-Acosta, A., Jordán-López, A., and N. Bellinfante-Crocci, 2010, Predicting the Potential Habitat of Oaks with Data Mining Models and the R System, Environmental Modelling and Software, 25(7):826-836.
52. Prasad, A. M., Iverson, L. R., and A. Liaw, 2006, Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction, Ecosystems, 9(2):181-199.
53. R Development Core Team, 2011, R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
54. Rodríguez-Galiano, V. F., Abarca-Hernández, F., Ghimire, B., Chica-Olmo, M., Atkinson, P. M., and C. Jeganathan, 2011, Incorporating Spatial Variability Measures in Land-Cover Classification Using Random Forest, Procedia Environmental Sciences, 3:44-49.
55. Rodriguez-Galiano, V. F., Ghimire, B., Pardo-Iguzquiza, E., Chica-Olmo, M., and R. G. Congalton, 2012a, Incorporating the Downscaled Landsat TM Thermal Band in Land-Cover Classification Using Random Forest, Photogrammetric Engineering and Remote Sensing, 78(2):129-138.
56. Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and J. P. Rigol-Sanchez, 2012b, An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification, ISPRS Journal of Photogrammetry and Remote Sensing, 67:93-104.
57. Rogan, J., Bumbarger, N., Kulakowski, D., Christman, Z. J., Runfola, D. M., and S. D. Blanchard, 2010, Improving Forest Type Discrimination with Mixed Lifeform Classes Using Fuzzy Classification Thresholds Informed by Field Observations, Canadian Journal of Remote Sensing, 36(6):699-708.
58. Rogan, J. and D. M. Chen, 2004, Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change, Progress in Planning, 61(4):301-326.
59. Rogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C., and D. Roberts, 2008, Mapping Land-Cover Modifications over Large Areas: A Comparison of Machine Learning Algorithms, Remote Sensing of Environment, 112(5):2272-2283.
60. Rogan, J. and J. Miller, 2007, Integrating GIS and Remotely Sensed Data for Mapping Forest Disturbance and Change, in Understanding Forest Disturbance and Spatial Pattern: Remote Sensing and GIS Approaches, Wulder, M. A. and S. E. Franklin (Eds.), Boca Raton, FL: CRC Taylor & Francis, 133-172.
61. Rogan, J., Miller, J., Stow, D., Franklin, J., Levien, L., and C. Fischer, 2003, Land-Cover Change Monitoring with Classification Trees Using Landsat TM and Ancillary Data, Photogrammetric Engineering and Remote Sensing, 69(7):793-804.
62. Sesnie, S. E., Finegan, B., Gessler, P. E., Thessler, S., Bendana, Z. R., and A. M. S. Smith, 2010, The Multispectral Separability of Costa Rican Rainforest Types with Support Vector Machines and Random Forest Decision Trees, International Journal of Remote Sensing, 31(11):2885-2909.
63. Strahler, A. H., 1980, The Use of Prior Probabilities in Maximum Likelihood Classification of Remotely Sensed Data, Remote Sensing of Environment, 10(2):135-163.
64. Townshend, J. R. G., Justice, C. O., and V. Kalb, 1987, Characterization and Classification of South American Land Cover Types Using Satellite Data, International Journal of Remote Sensing, 8(8):1189-1207.
65. Versluis, A. and J. Rogan, 2010, Mapping Land-Cover Change in a Haitian Watershed Using a Combined Spectral Mixture Analysis and Classification Tree Procedure, Geocarto International, 25(2):85-103.
66. Warner, T. A., Nellis, M. D. and G. M. Foody, 2009, The SAGE Handbook of Remote Sensing, London, UK: Sage Publications Ltd.
67. Woodcock, C. E. and S. Gopal, 2000, Fuzzy Set Theory and Thematic Maps: Accuracy Assessment and Area Estimation, International Journal of Geographical Information Science, 14(2):153-172.
68. Zambon, M., Lawrence, R., Bunn, A., and S. Powell, 2006, Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis, Photogrammetric Engineering and Remote Sensing, 72(1):25-30.
69. Zhang, P., Zhu, X., Shi, Y., Guo, L., and X. Wu, 2010, Robust Ensemble Learning for Mining Noisy Data Streams, Decision Support Systems, 50(2):469-479.