Discriminating the early stages of Sirex noctilio infestation using classification tree ensembles and shortwave infrared bands

International Journal of Remote Sensing

Volumn 32, Issue 15, 2011, Pages 4249-4266

  Ismail, R ^a   Mutanga, O ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; PHYSIOLOGY; RAIN;

CLASSIFICATION RESULTS; CLASSIFICATION TREES; LANDSCAPE LEVEL; MONITORING PURPOSE; RANDOM FORESTS; SHORT WAVE INFRARED; SUMMER RAINFALL; VARIABLE SELECTION METHODS;

INFRARED RADIATION;

ALGORITHM; CANOPY; FUNGUS; MUCUS; PARASITE INFESTATION; PHYSIOLOGY; REFLECTANCE; REMOTE SENSING; SHORTWAVE RADIATION; WASP;

SOUTH AFRICA;

FUNGI; PINUS PATULA; SIREX NOCTILIO;

EID: 80051776645     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2010.486413     Document Type: Article

References (63)

1. Archer, K.J. and Kimes, R.V., 2008, Empirical characterization of random forest variable importance measures. Computational Statistics and Data Analysis, 52, pp. 2249-2260.
2. ASD, 2006, FieldSpec 3_ User Manual (Boulder, CO: Analytical Spectral Devices Inc.).
3. Bajcsy, P. and Groves, P., 2004, Methodology for hyperspectral band selection. Photogrammetric Engineering and Remote Sensing, 70, pp. 793-802.
4. Breiman, L., 2001, Random forests. Machine Learning, 45, pp. 5-32.
5. Breiman, L., Friedman, J., Olshen, R. and Stone, C., 1984, Classification and Regression Trees, pp. 368 (California: Wadsworth & Brooks).
6. Bruzzone, L. and Serpico, S.B., 2000, Atechnique for feature selection in a multiclass problem. International Journal of Remote Sensing, 21, pp. 549-563.
7. Chan, J.C. and Paelinck x, 2008, Evaluation of random forest and adaboost tree-based ensembles classification and spectral band selection for ecotope mapping airborne hyperspectral imagery. Remote Sensing of the Environment, 112, pp. 2999-3011.
8. Collins, J.B. and Woodcock, C., 1996, An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Remote Sensing of Environment, 56, pp. 66-77.
9. Culp, M., Johnson, K. and Michailides, G., 2006, ada: an R package for stochastic boosting. Journal of Statistical Software, 17, pp. 1-27.
10. Defries, R.S. and Chan, J.C., 2000, Multiple criteria for evaluating machine learning algorithms for landcover classification from satellite data. Remote Sensing of the Environment, 74, pp. 503-515.
11. Diaz-Uriarte, R. and Alvarez De Andres, S., 2006, Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7, pp. 1-13.
12. Dietterich, T.G., 1999, An experimental comparison of three methods for constructing ensembles of decision tree: bagging, boosting and randomization. Machine Learning, 40, pp. 1-22.
13. Efron, B. and Tibshirani, R., 1993, An Introduction to Bootstrapping, p. 436 (New York: Chapman & Hall/CRC).
14. Efron, B. and Thibshirani, R.J., 1997, Improvements on cross-validation: the.632{thorn} bootstrap method. Journal American Statistical Association, 92, pp. 548-560.
15. Freund, Y. and Shapiro, R.E., 1996, Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the 13th International Conference, 3-6 July 1996, Bari, Italy, pp. 148-156 (San Francisco: Morgan Kaufmann).
16. GARZON, M.B., BLAZEK, R., NETELER, M., DE DIOS, R.S., OLLERO, H. S. and FURLANELLO, C., 2006, Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecological Modelling, 197, pp. 383-393.
17. Gislason, P.O., Benediktsson, J.A. and Sveinsson, J.R., 2006, Random forests for land cover classification. Pattern Recognition Letters, 27, pp. 294-300.
18. Granitto, P.M., Furlanello, C., Biasioli, F. and Gasperi, F., 2006, Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems, 83, pp. 83-90.
19. Guyon, I. and Elisseeff, A., 2003, An introduction to variable and feature selection. Journal of Machine Learning Research, 3, pp. 1157-1182.
20. Ham, J., Chen, Y., Crawford, M. and Ghosh, J., 2005, Investigation of the Random Forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43, pp. 492-501.
21. Hamza, M. and Larocque, D., 2005, An empirical comparison of ensemble methods based on classification trees. Journal of Computation and Simulation, 75, pp. 629-643.
22. Hurley, B.P., Slippers, B., Croft, P.K., Hatting, H.J., Van Der Linde, M., Morris, A.R., Dyer, C. and Wingfield, M.J., 2008, Factors influencing parasitism of Sirex noctilio (Hymenoptera: Siricidae) by the nematode Deladenus siricidicola (Nematoda: Neotylenchidae) in summer rainfall areas of South Africa. Biological Control, 45, pp. 450-459.
23. Ismail, R., Mutanga, O. and Ahmed, F., 2008a, Discriminating Sirex noctilio attack in pine forest plantations in South Africa using high spectral resolution data. In Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests, M. Kalacska and A. Sanchez-Azofeifa (Eds.), pp. 161-174 (London: Taylor & Francis/CRC Press).
24. Ismail, R., Mutanga, O. and Bob, U., 2007, Forest health and vitality: the detection and monitoring of Pinus patula trees infected by Sirex noctilio using digital multispectral imagery (DMSI). Southern Hemisphere Forestry Journal, 69, pp. 39-47.
25. Ismail, R., Mutanga, O., Kumar, L. and Bob, U., 2008b, Determining the optimal resolution of remotely sensed data for the detection of Sirex noctilio infestations in Pinus patula plantations in KwaZulu-Natal, South Africa. The South African Geographical Journal, 90, pp. 196-204.
26. Jin, S. and Sader, S., 2005, Comparison of time series tasseled cap wetness and the normalised moisture index in detecting forest disturbances. Remote Sensing of Environment, 94, pp. 364-372.
27. Kavzoglu, T. Andmather, P.M., 2002, The role of feature selection in artificial neural network applications. International Journal of Remote Sensing, 23, pp. 2919-2937.
28. Kohavi, R. and John, G.H., 1997, Wrappers for feature subset selection. Artificial Intelligence Journal, 97, pp. 273-324.
29. Kohavi, R., Sommerfield, D. and Dougherty, J., 1997, Data mining using MLC{thorn}{thorn}: a machine learning library in C{thorn}{thorn}. International Journal of Artificial Intelligence Tools, 6, pp. 537-566.
30. Kumar, L., Schmidt, K.S., Dury, S. and Skidmore, A., 2001, Review of hyperspectral remote sensing and vegetation science. In Imaging Spectrometry: Basic Principles and Prospective Applications, F. van der Meer and S. M. de Jong (Eds.), pp. 111-155 (Dordrecht: Kluwer Academic Press).
31. Langrebe, D., 2002, Hyperspectral image data analysis. IEEE Signal Processing Magazine, 19, pp. 17-28.
32. Lawrence, R., Bunn, A., Powell, S. and Zambon, M., 2004, Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sensing of the Environment, 90, pp. 331-336.
33. Lawrence, R.L. and Labus, M., 2003, Early detection of Douglas-fir beetle infestation with subcanopy resolution hyperspectral imagery. Western Journal of Applied Forestry, 18, pp. 1-5.
34. Lawrence, R.L., Wood, S.D. and Sheley, R.L., 2006, Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForests). Remote Sensing of Environment, 100, pp. 356-362.
35. Liaw, A. and Wiener, M., 2002, Classification and regression by randomForest. R News, 2, pp. 18-22.
36. Macfarlane, D., 2004, State of the Environment Report: Comrie-HCV Area, Important Rivers and Streams, p. 54 (Pietermaritzburg: Sappi).
37. Molinaro, A.M., Simon, R. and Pfeiffer, R.M., 2005, Prediction error estimation: a comparison of resampling methods. Bioinformatics, 21, pp. 3301-3307.
38. Mutanga, O. and Skidmore, A., 2004, Integrating imaging spectrometry and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sensing of Environment, 90, pp. 104-115.
39. Mutanga, O. and Skidmore, A.K., 2005, Discriminating tropical grass grown under different nitrogen treatments using hyperspectral data resampled to HYMAP. International Journal of Geoinformatics, 1, pp. 21-32.
40. Neumann, F.G. and Minko, G., 1981, The Sirex woodwasp in Australian radiata pine plantations. Australian Forestry, 44, pp. 46-63.
41. Pal, M., 2005, Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26, pp. 217-222.
42. Pal, M., 2007, Ensemble learning with decision tree for remote sensing classification. World Academy of Science, Engineering and Technology, 36, pp. 258-260.
43. Pal, M. Andmather, M., 2004, Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems, 20, pp. 1215-1225.
44. Peters, A., Hothorn, T. and Lausen, B., 2002, Ipred: improved predictors. RNews, 2, pp. 33-36.
45. Peters, J., De Baets, B., Verhoest, N., Samson, R., Degroeve, S., De Becker, P. and Huybrechts, W., 2007, Random forest as a tool for ecohydrological distribution modelling. Ecological Modelling, 207, pp. 304-318.
46. Pontius, J., Hallet, R. and Martin, M., 2005a, Assessing Hemlock decline using visible and near-infrared spectroscopy: indices comparison and algorithm development. Applied Spectroscopy, 59, pp. 836-843.
47. Pontius, J., Hallet, R. and Martin, M., 2005b, Using AVIRIS to assess hemlock abundance and early decline in Catskills, New York. Remote Sensing of Environment, 97, pp. 163-173.
48. Pontius, J., Martin, M., Plourde, L. and Hallett, R., 2008, Ash decline assessment in emerald ash borer-infested regions: a test of tree-level, hyperspectral technologies. Remote Sensing of the Environment, 112, pp. 2665-2676.
49. Prasad, A., Iverson, L. and Liaw, A., 2006, Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9, pp. 181-199.
50. RDEVELOPMENT CORE TEAM, 2008, R: A Language and Environment for Statistical Computing. (Vienna: R Foundation for Statistical Computing).
51. Schmidt, K.S. and Skidmore, A.K., 2001, Exploring spectral discrimination of grass species inAfrican rangelands. International Journal of Remote Sensing, 22, pp. 3421-3434.
52. Scholes, B. and Annamalai, L., 2006, CSIR imaging expertise propels SA to a science high. Aerospace Science Scope, 1, pp. 19-21.
53. Skakun, R.S., Wulder, M.A. and Franklin, S.E., 2003, Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sensing of the Environment, 86, pp. 433-443.
54. Soh, L.K., 1999, Segmentation of satellite imagery of natural scenes using data mining. IEEE Transactions on Geoscience and Remote Sensing, 37, pp. 1086-1099.
55. Stimson, H., Breshears, D., Ustin, S. and Kefauver, S., 2005, Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sensing of Environment, 96, pp. 108-118.
56. Strobl, C., Boulesteix, A., Zeileis, A. and Hothorn, T., 2007, Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8, pp.1-25.
57. Svetnik, V., Liaw, A., Tong, C., Culberson, J., Sheridan, R. and Feuston, B., 2003, Random Forest: A classification and regression tool for compound classification and QSAR modeling. Journal of Chemical Information and Computer Science, 43, pp. 1947-1958.
58. Svetnik, V., Liaw, A., Tong, C. and Wang, T., 2004, Application of Breiman's random forest to modeling structure-activity relationships of pharmaceutical molecules. In Multiple Classifier Systems, Fifth International Workshop, MCS 2004, 9-11 June 2004, Cagliari, Italy, pp. 334-343 (Berlin: Springer).
59. Tribe, G.D. and Cillie, J.J., 2004, The spread of Sirex noctilio Fabricius (Hymenoptera: Siricidae) in South African pine plantations and the introduction and establishment of its biological control agents. African Entomology, 12, pp. 9-17.
60. Vaiphasa, C., Ongsomwang, S., Vaiphasa, T. and Skidmore, A.K., 2005, Tropical mangrove species discrimination using hyperspectral data: a laboratory study. Estuarine, Coastal, and Shelf Science, 65, pp. 371-379.
61. Vaiphasa, C., Skidmore, A., De Boer, W. and Vaiphasa, T., 2007, A hyperspectral band selector for plant species discrimination. ISPRS Journal of Photogrammetry and Remote Sensing, 62, pp. 225-235.
62. Van Aardt, J. and Coppin, P., 2006, Current state and potential of the IS-HS project: Integration of insitu data and hyperspectral remote sensing for plant production modelling. In Precision Forestry in Plantations, Semi-Natural and Natural Forests, IUFRO International Precision Forestry Symposium, 5-10 March 2006, Stellenbosch, South Africa, pp. 33-41 (Stellenbosch: Stellenbosch University).
63. Zhu, X. and Wu, X., 2004, Class noise vs attribute noise: A quantitative study. Artificial Intelligence Review, 22, pp. 177-210.