Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 88, Issue , 2014, Pages 48-59

  Abdel Rahman, Elfatih M ^a,b   Mutanga, Onisimo ^a   Adam, Elhadi ^a   Ismail, Riyad ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

^b UNIVERSITY OF KHARTOUM   (Sudan)

Author keywords

Hyperspectral data; Lighting damage; Pinus spp.; Random forest; Sirex grey stage; Support vector machines

Indexed keywords

DECISION TREES; FORESTRY; LIGHTNING;

AIRBORNE HYPERSPECTRAL DATA; DEAD STANDING TREES; GREY STAGE; HYPERSPECTRAL DATA; LEARNING CLASSIFIERS; OVERALL ACCURACIES; PINUS SPP; RANDOM FORESTS;

SUPPORT VECTOR MACHINES;

AIRBORNE SENSING; ALGORITHM; CONIFEROUS TREE; DAMAGE; DEFOLIATION; FORESTRY PRACTICE; IMAGE CLASSIFICATION; INSECT; LIGHTNING; PEST SPECIES; PLANTATION FORESTRY; SYMPTOM; VISUAL ANALYSIS;

DAMAGE; DECISION MAKING; FORESTRY; ILLUMINATION; TREES;

SIREX; SIREX NOCTILIO;

EID: 84890837785     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2013.11.013     Document Type: Article

References (88)

1. Adam E.M., Mutanga O., Rugege D., Ismail R. Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP. Int. J. Remote Sens. 2012, 33:552-569.
2. Agrawal G., Sarup J. Comparison of QUAC and FLAASH atmospheric correction modules on EO-1 hyperion data of Sanchi. Int. J. Adv. Eng. Sci. Technol. 2011, 4:178-186.
3. Agresti A. An Introduction to Categorical Data Analysis 1996, John Wiley, New York.
4. Bandos T.V., Bruzzone L., Camps-Valls G. Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans. Geosci. Remote Sens. 2009, 47:862-873.
5. Belousov A.I., Verzakov S.A., vonFrese J. A flexible classification approach with optimal generalisation performance: support vector machines. Chermometrics Intell. Lab. Syst. 2002, 64:15-25.
6. Bernstein, L.S., Adler-Golden, S.M., Sundberg, R.L., Levine, R.Y., Perkins, T.C., Berk, A., 2005. Validation of the QUick atmospheric correction (QUAC) algorithm for VNIR SWIR multi- and hyperspectral imagery. In: Shen., S.S., Lewis, P.E. (Eds.), Proc SPIE 5806 Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XI, Orlando, Florida, 13 July, pp. 668-678.
7. Biau G. Analysis of a random forests model. J. Mach. Learn. Res. 2012, 13:1063-1095.
8. Breiman L. Random forests. Machine Learning 2001, 45:5-32.
9. Brown M., Gunn S.R., Lewis H.G. Support vector machines for optimal classification and spectral unmixing. Ecol. Model. 1999, 120:167-179.
10. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 1998, 2:121-167.
11. Camps-Valls G., Bruzzone L. Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2005, 43:1351-1362.
12. Carnegie A.J., Eldridge R.H., Waterson D.G. History and management of sirex wood wasp in pine plantations in New South Wales, Australia. NZ J. Forest. Sci. 2005, 35:3-24.
13. Carrão H., Gonçalves P., Caetano M. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sens. Environ. 2008, 112:986-997.
14. Chave J., Coomes D., Jansen S., Lewis S.L., Swenson N.G., Zanne A.E. Towards a worldwide wood economics spectrum. Ecol. Lett. 2009, 12:351-366.
15. Chi M., Bruzzone L. Semisupervised classification of hyperspectral images by SVMs optimized in the primal. IEEE Trans. Geosci. Remote Sens. Lett. 2007, 45:1870-1880.
16. Chi M., Feng R., Bruzzone L. Classification of hyperspectral remote sensing data with primal SVM for small-sized training dataset problem. Adv. Space Res. 2008, 41:1793-1799.
17. Ciesla W.M. European woodwasp: a potential threat to North Americas conifer forests. J. Forest. 2003, 101:18-23.
18. Coops N.C., Goodwin N., Stone C. Predicting Sphaeropsis sapinea damage in Pinus radiata canopies using spectral indices and spectral mixture analysis. Photogramm. Eng. Remote Sens. 2006, 72:405-416.
19. Corley J.C., Villacide J.M., Bruzzone O.A. Spatial dynamics of a Sirex noctilio woodwasp population within a pine plantation in Patagonia, Argentina. Entomol. Exp. Appl. 2007, 125:231-236.
20. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20:273-297.
21. Coutts M.P. The mechanism of pathogenicity of Sirex noctilio on Pinus radiata I. Effects of the symbiotic fungus Amylostereum sp. (Thelophoraceae). Aust. J. Biol. Sci. 1969, 22:915-924.
22. Díaz-Uriarte R., De Andres S.A. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006, 7. 10.1186/1471-2105-7-3 (Accessed 17 February, 2013.
23. Dobyn, N., 2009. Environmental Management Plan: Sappi Forests, Hodgsons. Unpublished Report.
24. Dye M., Mutanga O., Ismail R. Detecting the severity of woodwasp, Sirex noctilio, infestation in a pine plantation in KwaZulu-Natal, South Africa, using texture measures calculated from high spatial resolution imagery. Afr. Entomol. 2008, 16:263-275.
25. Dye M., Mutanga O., Ismail R. Examining the utility of random forest and AISA Eagle hyperspectral image data to predict Pinus patula age in KwaZulu-Natal, South Africa. Geocarto Int. 2011, 26:275-289.
26. ENVI Environment for Visualising Images 2006, ITT industries Inc., Boulder, USA.
27. Everingham Y.L., Lowe K.H., Donald D.A., Coomans D.H., Markley J. Advanced satellite imagery to classify sugarcane crop characteristics. Agron. Sustain. Dev. 2007, 27:111-117.
28. Ferencz C., Bognár P., Lichtenberger J., Hamar D., Tarcsai G., Timár G., Molnár G., Pásztor S., Steinbach P., Székely B., Ferencz O.E., Ferencz-Árkos I. Crop yield estimation by satellite remote sensing. Int. J. Remote Sens. 2004, 25:4113-4149.
29. Foody G.M. Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogram. Eng. Remote Sens. 2004, 70:627-633.
30. Goetz A.F.H. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sens. Environ. 2009, 113:S5-S16.
31. Goldstein B.A., Hubbard A.E., Cutler A., Barcellos L.F. An application of random forests to a genome-wide association dataset: methodological considerations & new findings. BMC Genet. 2010, 11:1-19.
32. Held, M. et al., 2012. EnMAP-Box Manual, Version 1.4, Humboldt-Universität zu Berlin, Germany.
33. Hsu C.-W., Lin C.-J. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 2002, 13:415-425.
34. Hsu, C., Chang, C., Lin, C., 2010. A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan.
35. Hsu P.-H. Feature extraction of hyperspectral images using wavelet and matching pursuit. ISPRS J. Photogram. Remote Sens. 2007, 62:78-92.
36. Huang C., Davis L.S., Townshend J.R.G. An assessment of support vector machines for land cover classication. Int. J. Remote Sens. 2002, 23:725-749.
37. Huang C., Song K., Kim S., Townshend J.R.G., Davis P., Masek J.G., Goward S.N. Use of dark object concept and support vector machine to automate forest cover change analysis. Remote Sens. Environ. 2008, 112:970-985.
38. Hughes G.F. On the mean accuracy of statistical pattern recognition. IEEE Trans. Inf. Theory 1968, 14:55-63.
39. Hurley B.P., Slippers B., Wingfield M.J. A comparison of control results for the alien invasive woodwasp, Sirex noctilio, in the southern hemisphere. Agric. For. Entomol. 2007, 9:159-171.
40. Hurley B.P., Slippers B., Croft P.K., Hatting H.J., van der Linde M., Morris A.R., Dyer C., Wingfield M.J. Factors influencing parasitism of Sirex noctilio (Hymenoptera:Siricidae) by the nematode Deladenus siricidicola (Nematoda:Neotylenchidae) in summer rainfall areas of South Africa. Biol. Control 2008, 45:450-459.
41. Ismail, R., Mutanga, O., Bob, U., 2006. The use of high resolution airborne imagery for the detection of forest canopy damage by Sirex noctilio. In: Langin, PA, Antonides MC (Eds.), Proc. The International Precision Forestry Symposium, University of Stellenbosch, Stellenbosch, 5-10 March, pp. 119-134.
42. Ismail R., Mutanga O., Bob U. Forest health and vitality: The detection and monitoring of Pinus patula trees infected by Sirex noctilio using digital multispectral imagery (DMSI). Southern Hemisphere For. J 2007, 69:39-47.
43. Ismail, R., Mutanga, O., Ahmed, F., 2008. Discriminating Sirex noctilio attack in pine forest plantations in South Africa using high spectral resolution data. In: Kalacska Margaret, and Sanchez-Azofeifa G. Arturo (Eds.), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests. Taylor and Francis, London, pp. 161-74.
44. Karatzoglou, A., Meyer, D., Hornik, K., 2006. Support Vector Machines in R. Journal of Statistical Software 15, 1-28.
45. Kavzoglu T., Colkesen I. A kernel functions analysis for support vector machines for land cover classification. Int. J. Appl. Earth Obs. Geoinf. 2009, 11:352-359.
46. Kohavi R., John G.H. Wrappers for feature subset selection. Artif. Intell. 1997, 97:273-324.
47. Kumar L., Dury S.J., Schmidt K., Skidmore A. Imaging spectrometry and vegetation science. Image Spectrometry 2003, 111-156. Kluwer Academic Publishers, London. F.D.V.D. Meer, S.M.D. Jong (Eds.).
48. Leeuw J.D., Jia H., Yang L., Liu X., Schmidt K., Skidmore A.K. Comparing accuracy assessments to infer superiority of image classification methods. Int. J. Remote Sens. 2006, 27:223-323.
49. Liaw A., Wiener M. Classification and regression by randomForest. R News 2002, 2:18-22.
50. Lillesand T.M., Kiefer R.W. Remote Sensing and Image Interpretation 2001, John Wiley & Sons Inc., New York. fourth ed.
51. Marcus W.A., Marston R.A., Colvard C.R., Gray R.D. Mapping the spatial and temporal distributions of woody debris in streams of the Greater Yellowstone Ecosystem, USA. Geomorphology 2002, 44:323-335.
52. Mathur A., Foody G.M. Multiclass and binary SVM classification: Implications for training and classification users. IEEE Geosci. Remote Sens. Lett. 2008, 5:241-245.
53. Mazzoni D., Garay M.J., Davies R., Nelson D. An operational MISR pixel classifier using support vector machines. Remote Sens. Environ. 2007, 107:149-158.
54. Meddens A.J.H., Hicke J.A., Vierling L.A. Evaluating the potential of multispectral imagery to map multiple stages of tree mortality. Remote Sens. Environ. 2011, 115:1632-1642.
55. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., 2012. Package 'e1071'. (accessed 15.02.13). http://cran.rproject.org/web/packages/e1071/index.html.
56. Meyer D. Support vector machines. R News 2001, 1/3:23-26.
57. Mountrakis G., Im J., Ogole C. Support vector machines in remote sensing: a review. ISPRS J. Photogram. Remote Sens. 2011, 66:247-259.
58. Neumann F.G., Minko G. The sirex wood wasp in Australian radiata pine plantations. Aust. Forestory 1981, 44:46-63.
59. Ogutu, J.O., Piepho, H.-P., Schulz-Streeck, T., 2011. A comparison of random forests, boosting and support vector machines for genomic selection, BMC Proc. 5, doi:10.1186/1753-6561-5-S3-S11 (accessed 15.01.13).
60. Oommen T., Misra D., Twarakavi N.K.C., Prakash A., Sahoo B., Bandopadhyay S. An objective analysis of support vector machine based classification for remote sensing. Math. Geosci. 2008, 40:409-424.
61. Pal M. Random forest classifier for remote sensing classification. Int. J. Remote Sens. 2005, 26:217-222.
62. Pasher J., King D.J. Mapping dead wood distribution in a temperate hardwood forest using high resolution airborne imagery. For. Ecol. Manage. 2009, 258:1536-1548.
63. Petropoulos G.P., Kalaitzidis C., Vadrevu K.P. Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Comput. Geosci. 2012, 41:99-107.
64. Petropoulos G.P., Kontoes C., Keramitsoglou I. Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using support vector machines. Int. J. Appl. Earth Observ. Geoinform. 2011, 13:70-80.
65. Plaza A., Benediktsson J.A., Boardman J.W., Brazile J., Bruzzone L., Camps-Valls G., Chanussot J., Fauvel M., Gamba P., Gualtieri A. Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 2009, 113:S110-S122.
66. Pontius R.G., Millones M. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32:4407-4429.
67. Popescu C.-M., Popescu M.-C., Singurel G., Vasile C., Argyropoulos D.S., Willfor S. Spectral characterization of Eucalyptus wood. Appl. Spectrosc. 2007, 61:1168-1177.
68. Prasad A.M., Iverson L.R., Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 2006, 9:181-199.
69. R Development Core Team. 2012. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna. (accessed 5.01.13). http://www.R-project.or.
70. Rabe, A., van der Linden, S., Hostert, P., 2010. imageSVM, Version 2.1, Software. (accessed 18.11.13). http://www.hu-geomatics.de.
71. Rakotomamonjy A. Variable selection using SVM-based criteria. J. Mach. Learn. Res. 2003, 3:1357-1370.
72. Rutherford, M.C., Mucina, L., Powrie, L.W., 2006. Biomes and bioregions of southern Africa. In: Mucina, L., Rutherford, M.C. (Eds.), The vegetation of South Africa, Lesotho and Swaziland. Strelitzia 19. South African National Biodiversity Institute, Pretoria, pp. 30-51.
73. Sappi, 1993. Forest Land Types of the Natal Region Sappi Forests Research, Howick, KwaZulu Natal, South Africa.
74. Schulze, R.E., Maharaj, M., 1997. South African Atlas of Agrohydrology and-Climatology, Report TT82/96. Water Research Commission.
75. Schulze, R.E., Maharaj, M., Lynch, S.D., Howe, B.J., Melvil-Thomson, B., 1997. South African atlas of agrohydrology and climatology. Water Research Commission, Report, TT82/96.
76. Sesnie S.E., Finegan B., Gessler P.E., Thessler S., Bendana Z.R., Alistair M.S.S. The multispectral separability of Costa Rican rainforest types with support vector machines and random forest decision trees. Int. J. Remote Sens. 2010, 31:2885-2909.
77. So, C.-L., Groom, L.H., Rials, T.G., Snell, R., Kelley, S.S., Meglen, R., 2002. Rapid assessment of the fundamental property variation of wood. In: Outcalt, K.W. (Ed.), Proc the Eleventh Biennial Southern Silvicultural Research Conference. General Technical Report SRS-48. Asheville, NC. US Department of Agriculture, Forest Service. Southern Research Station. pp. 176-180.
78. Spradbery J.P. A comparative study of the phytotoxic effects of siricid woodwasps on conifers. Ann. Appl. Biol. 1973, 75:309-320.
79. Statnikov A., Wang L., Aliferis C.F. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008, 9:1-10.
80. Touw, W.G., Bayjanov, J.R., Overmars, L., Backus, L., Boekhorst, J., Wels, M., Hijum, S.A.F.T.V., 2012. Data mining in the life sciences with random forest: a walk in the park or lost in the jungle? Briefings in Bioinformatics, doi:10.1093/bib/bbs034 (accessed 15.01.13).
81. Tribe G.D., Cillié J.J. The spread of Sirex noctilio Fabricius (Hymenoptera: Siricidae) in South African pine plantations and the introduction and establishment of its biological control agents. Afr. Entomol. 2004, 12:9-17.
82. Tso B., Mather P.M. Classification Methods for Remotely Sensed Data 2009, CRC Press/ Taylor & Francis Group, London. second ed.
83. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
84. Verikas A., Gelzinis A., Bacauskiene M. Mining data with random forests: a survey and results of new tests. Pattern Recogn. 2011, 44:330-349.
85. Waske B., Benediktsson J.A. Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2010, 48:2880-2889.
86. Waske B., Benediktsson J.A., Ãrnason K., Sveinsson J.R. Mapping of hyperspectral AVIRIS data using machine-learning algorithms. Can. J. Remote Sens. 2009, 35:106-116.
87. Waske B., et al. ImageRF - a user-oriented implementation for remote sensing image analysis with Random Forests. Environ. Modell. Softw. 2012, 35:192-193.
88. Yu L., Porwal A., Holden E.-J., Dentith M.C. Towards automatic lithological classification from remote sensing data using support vector machines. Comput. Geosci. 2012, 45:229-239.