Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data

International Journal of Remote Sensing

Volumn 34, Issue 2, 2013, Pages 712-728

  Abdel Rahman, Elfatih M ^a,b   Ahmed, Fethi B ^a   Ismail, Riyad ^a  

^a UNIVERSITY OF KWAZULU NATAL   (South Africa)

^b UNIVERSITY OF KHARTOUM   (Sudan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85010540243     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2012.713142     Document Type: Article

References (66)

1. Abdel-Rahman, E. M., F. B. Ahmed, and M. van den Berg. 2008. “Imaging Spectroscopy for Estimating Sugarcane Leaf Nitrogen Concentration.” In Proceedings of Remote Sensing for Agriculture, Ecosystems, and Hydrology X, edited by C. M. U. Neale, M. Owe, and G. D’Urso, Cardiff, UK, September 16-18, 2008, V-1-12. Cardiff: SPIE.
2. Abdel-Rahman, E. M., F. B. Ahmed, and M. van den Berg. 2010. “Estimation of Sugarcane Leaf Nitrogen Concentration Using In Situ Spectroscopy.” International Journal of Applied Earth Observation and Geoinformation, Remote Sensing for Africa: 12S: S52-S57.
3. Abdel-Rahman, E. M., M. van den Berg, M. J. Way, F. B. Ahmed, and C. Sewpersad. 2009. “Using Spectroscopic Data Sets to Predict Numbers of Thrips (Fulmekiola Serrata) in Sugarcane.” In Proceedings of South African Sugar Technologists’ Association, Durban, South Africa, August 26-28, 2009, 82, 441-445. Durban: SASTA.
4. Archer, K. J., and R. V. Kimes. 2008. “Empirical Characterisation of Random Forest Variable Importance Measures.”Computational Statistics and Data Analysis52: 2249-2260.
5. Atkinson, P. R., and K. J. Nuss. 1989. “Associations Between Host-Plant Nitrogen and Infestation of Sugarcane Borer, Eldana Saccharina Walker (Lepidoptera: Pyralidae).”Bulletin ofEntomological Research79: 489-506.
6. Baret, F., and Th. Fourty. 1997. “Radiometric Estimates ofNitrogen Status of Leaves and Canopies.” In Diagnosis of the Nitrogen Status in Crops, edited by G. Lemaire, 201-27. Berlin: SpringerVerlag Publisher.
7. Blumenthal, J. M., D. D. Baltensperger, K. G. Cassman, S. C. Mason, and A. D. Pavlista. 2001. “Importance and Effect ofNitrogen on Crop Quality and Health.” In Nitrogen in the Environment: Sources, Problems, and Management, edited by R. F. Follet and J. L. Hatfield, 45-63. London: Elsevier.
8. Borengasser, M., W. S. Hungate, and R. Watkins. 2008. Hyperspectral Remote Sensing: Principles and Applications, 119 p. New York: Taylor Francis.
9. Bortolot, Z. J., and R. H. Wynne. 2003. “A Method for Predicting Fresh Green Leaf Nitrogen Concentration from Shortwave Infrared Reflectance Spectra Acquired at the Canopy Level That Requires No In Situ Nitrogen Data.”International Journal of Remote Sensing24: 619-624.
10. Breiman, L. 2001. “Random Forests.”Machine Learning45: 5-32.
11. Chan, C.-W J., and D. Paelinckx. 2008. “Evaluation of Random Forest and Adaboost Tree-Based Ensemble Classification and Spectral Band Selection for Ecotype Mapping Using Airborne Hyperspectral Imagery.”Remote Sensing of Environment112: 2999-3011.
12. Cho, M. A., and A. K. Skidmore. 2006. “A New Technique for Extracting the Red Edge Position from Hyperspectral Data: The Linear Extrapolation Method.”Remote Sensing of Environment101: 181-193.
13. Curran, P. J. 1994. “Imaging Spectrometry - Its Present and Future Role in Environmental Research.” In Imaging Spectrometry -A Tool for Environmental Observations, edited by J. Hill and J. Mégier, 1-23. London: Kluwer Academic Publishers.
14. Curran, P. J., J. L. Dungan, and H. L. Gholz. 1990. “Exploring the Relationship Between Reflectance Red Edge and Chlorophyll Content in Slash Pine.”Tree Physiology7: 33-48.
15. de Jong, S. M. 1998. “Imaging Spectrometry for Monitoring Tree Damage Caused by Volcanic Activity in the Long Valley Caldera, California.”ITC Journal1: 1-10.
16. Demir, B., and S. Ertürk. 2008. “Phase Correlation Based Redundancy Removal in Feature Weighting Band Selection for Hyperspectral Images.”International Journal of Remote Sensing29: 1801-1807.
17. Díaz-Uriarte, R., and S. A. de Andrés. 2006. “Gene Selection and Classification of Microarray Data Using Random Forest.” BMC Bioinformatics. Accessed April 24, 2009. http://www.biomedcentral.com/content/pdf/1471-2105-7-3.pdf.
18. ENVI. 2006. Environment for Visualising Images. Release 4.3.Boulder, CO: ITT Industries.
19. Everingham, Y. L., K. H. Lowe, D. A. Donald, D. H. Coomans, and J. Markley. 2007. “Advanced Satellite Imagery to Classify Sugarcane Crop Characteristics.”Agronomy for Sustainable Development27: 111-117.
20. Fageria, N. K. 2009. The Use of Nutrients in Crop Plants, 430 p. New York: Taylor Francis.
21. Ferencz, Cs., P. Bognár, J. Lichtenberger, D. Hamar, G. Tarcsai, G. Timár, G. Molnár, SZ. Pásztor, P. Steinbach, B. Székely, O. E. Ferencz, and I. Ferencz-Árkos. 2004. “Crop Yield Estimation by Satellite Remote Sensing.”International Journal of Remote Sensing20: 4113-4149.
22. Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forest for Land Cover Classification.”Pattern Recognition Letters27: 294-300.
23. Goldstein, B. A., A. E. Hubbard, A. Cutler, and L. F. Barcellos. 2010. “An Application of Random Forests to a Genome-Wide Association Dataset: Methodological Consideration New Findings.”BMC Genetics11: 1-13.
24. Grimm, R., T. Behrens, M. Märker, and H. Elsenbeer. 2008. “Soil Organic Carbon Concentrations and Stocks on Barro Colorado Island - Digital Soil Mapping Using Random Forests Analysis.”Geoderma146: 102-113.
25. Guyon, I., and A. Elisseeff. 2003. “An Introduction to Variable and Feature Selection.”Journal of Machine Learning Research3: 1157-1182.
26. Hansen, P. M., and J. K. Schjoerring. 2003. “Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalised Difference Vegetation Indices and Partial Least Squares Regression.”Remote Sensing of Environment86: 542-553.
27. Ismail, R. 2009. “Remote Sensing of Forest Health: The Detection and Mapping of Pinus patula Trees Infested by Sirex noctilio.” PhD thesis, University of KwaZulu-Natal, South Africa.
28. Jain, N., S. S. Ray, J. P. Singh, and S. PanigrAhy. 2007. “Use of Hyperspectral Data to Assess the Effects of Different Nitrogen Applications on a Potato Crop.”Precision Agriculture8: 225-239.
29. Jiang, X., L. Tang, C. Wang, and C. Wang. 2004. “Spectral Characteristics and Feature Selection of Hyperspectral Remote Sensing Data.”International Journal of Remote Sensing25: 51-59.
30. Kempeneers, P., P. J. Zarco-Tejada, P. R. J. North, S. De Backer, S. Delalieux, G. Sepulcre-Canto, F. Morales, J. A. N. van Aardt, R. Sagardoy, P. Coppin, and P. Scheunders. 2008. “Model Inversion for Chlorophyll Estimation in Open Canopies from Hyperspectral Imagery.”International Journal of Remote Sensing29: 5093-5111.
31. Kohavi, R. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Québec, Cananda, August 20-25, 1995, 1137-1143. Québec: Morgan Kaufmann.
32. Kohavi, R., and G. H. John. 1997. “Wrappers for Feature Subset Selection.”Artificial Intelligence97: 273-324.
33. Kumar, L., K. Schmidt, S. Dury, and A. Skidmore. 2003. “Imaging Spectrometry and Vegetation Science.” In Image Spectrometry, edited by F. D. van der Meer, and S. M. de Jong, vol. 3,111-156. London: Kluwer Academic Publishers.
34. Lawrence, R. L., S. D. Wood, and R. L. Sheley. 2006. “Mapping Invasive Plants Using Hyperspectral Imagery and Breiman Cutler Classifications (RANDomFOREST).”Remote Sensing of Environment100: 356-362.
35. LECO. 2006. TruSPEC N® Analyser, FP-2000.St Joseph, MO: Leco Corporation.
36. Liaw, A., and M. Wiener. 2002. “Classification and Regression by Random Forest.”R News2/3: 18-22.
37. Lillesand, T. M., and R. W. Kiefer. 2001. Remote Sensing and Image Interpretation. 4th ed. 724 p. New York: John Wiley Sons.
38. Ma, L., L. R. Ahuja, and T. W Bruulsema. 2009. “Current Status and Future Needs in Modelling Plant Nitrogen Uptake: A Preface.” In Quantifying and Understanding Plant Nitrogen Uptake for Systems Modelling, edited by L. Ma, L. R. Ahuja, and T. Bruulsema, 1-11. New York: Taylor Francis.
39. Maindonald, J., and J. Braun. 2006. Data Analysis and Graphics Using R: An Example-Based Approach. 2nd ed., edited by R. Gill, B. D. Ripley, S. Ross, B. W Silverman, and M. Stein, 540 p. Cambridge: Cambridge University Press.
40. Munden, R., P. J. Curran, and J. A. Catt. 1994. “The Relationship Between Red Edge and Chlorophyll Concentration in the Broadbalk Winter Wheat Experiment at Rothamsted.”International Journal of Remote Sensing15: 705-709.
41. Mutanga, O., A. K. Skidmore, and H. H. T. Prins. 2004. “Predicting In Situ Pasture Quality in Kruger National Park, South Africa, Using Continuum-Removed Absorption Features.”Remote Sensing ofEnvironment89: 393-408.
42. Nansen, C., T. Macedo, R. Swanson, and D. K. Weaver. 2009. “Use of Spatial Structure Analysis of Hyperspectral Data Cubes for Detection of Insect-Induced Stress in Wheat Plants.”International Journal of Remote Sensing30: 2447-2464.
43. Oppelt, N., and W Mauser. 2004. “Hyperspectral Monitoring of Physiological Parameters of Wheat During a Vegetation Period Using AVIS Data.”International Journal of Remote Sensing25: 145-159.
44. Osborne, S. L., J. S. Schepers, D. D. Francis, and M. R. Schlemmer. 2002. “Detection of Phosphorus and Nitrogen Deficiencies in Corn Using Spectral Radiance Measurements.”Agronomy Journal94: 1215-1221.
45. Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.”International Journal of Remote Sensing26: 217-222.
46. Palmer, D. S., N. M. O’Boyle, R. C. Glen, and J. B. O. Mitchell. 2007. “Random Forest Models to Predict Aqueous Solubility.”Journal of Chemical Information and Modelling47: 150-158.
47. Prasad, A. M., L. R. Iverson, and A. Liaw. 2006. “Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction.”Ecosystems9: 181-199.
48. R Development Core Team. 2008. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0. Accessed April 29, 2010. http://www.R-project.org.
49. Read, J. J., L. Tarpley, J. M. MCKinion, and K. R. Reddy. 2002. “Narrow-Waveband Reflectance Ratios for Remote Estimation of Nitrogen Status in Cotton.”Journal of Environmental Quality31: 1442-1452.
50. Rezaei, Y., M. R. Mobasheri, and M. J. Valadan Zoej. 2008. “Unsupervised Information Extraction Using Absorption Line in Hyperion Images.”The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences37: 383-388.
51. Rice, R. W., R. A. Gibert, and R. S. Lentini. 2006. Nutritional Requirements for Florida Sugarcane. Institute of Food and Agricultural Sciences, University of Florida Document No. SS-AGR-228. Gainesville, FL: IFAS, UF.
52. SASRI. 2003. Leaf Sampling, Information sheet. Mount Edgecombe: South African Sugarcane Research Institute.
53. Shafri, H. Z. M., M. A. M. Salleh, and A. Ghiyamat. 2006. “Hyperspectral Remote Sensing of Vegetation Using Red Edge Position Techniques.”American Journal of Applied Sciences3: 1864-1871.
54. Shen, S. S. 2007. “Optimal Band Selection and Utility Evaluation for Spectral Systems.” In Hyperspectral Data Exploitation: Theory and Applications, edited by C.-I. Chang, 227-243. New York: John Wiley Sons.
55. Smith, M. -L., M. E. Martin, L. Plourde, and S. V. Ollinger. 2003. “Analysis of Hyperspectral Data for Estimation of Temperate Forest Canopy Nitrogen Concentration: Comparison between an Airborne (AVIRIS) and Spaceborne (Hyperion) Sensor.”IEEE Transactions on Geoscience and Remote Sensing41: 1332-1337.
56. Tarpley, L., K. R. Reddy, and G. F. Sassenrath-Cole. 2000. “Reflectance Indices with Precision and Accuracy in Predicting Cotton Leaf Nitrogen Concentration.”Crop Science40: 1814-1819.
57. Thenkabail, P. S., E. A. Enclona, M. S. Ashton, C. Legg, and M. J. De Dieu. 2004. “Hyperion, IKNOS, ALI and ETM+ Sensors in the Study of African Rainforests.”Remote Sensing of Environment90: 23-43.
58. Tilling, A. K., G. J. O’Leary, J. G. Ferwerda, S. D. Jones, G. J. Fitzgerald, D. Rodriguez, and R. Belford. 2007. “Remote Sensing of Nitrogen and Water Stress in Wheat.”Field Crops Research104: 77-85.
59. Verikas, A., A. Gelzinis, and M. Bacauskiene. 2011. “Mining Data with Random Forests: A Survey and Results of New Tests.”Pattern Recognition44: 330-349.
60. Wenjiang, H., W. Jihua, W. Zhijie, Z. Jiang, L. Liangyun, and W. Jindi. 2004. “Inversion of Foliar Biochemical Parameters at Various Physiological Stages and Grain Quality Indicators of Winter Wheat with Canopy Reflectance.”International Journal of Remote Sensing 25: 2409-2419.
61. Wiedenfeld, R. P. 2000. “Water Stress during Different Sugarcane Growth Periods on Yield and Response to N Fertilization.” Agricultural Water Management43: 173-182.
62. Xue, L., W. Cao, W. Luo, T. Dai, and Y. Zhu. 2004. “Monitoring Leaf Nitrogen Status in Rice with Canopy Spectral Reflectance.”Agronomy Journal96: 135-142.
63. Yi, Q., J. Huang, F. Wang, and X. Wang. 2010. “Evaluating the Performance of PC-ANN for the Estimating of Rice Nitrogen Concentration from Canopy Hyperspectral Reflectance.”International Journal of Remote Sensing31: 931-940.
64. Yoder, B. J., and R. E. Pettigrew-Crosby. 1995. “Predicting Nitrogen and Chlorophyll Content and Concentration from Reflectance Spectra (400-2500 Nm) at Leaf and Canopy Scales.”Remote Sensing of Environment53: 199-211.
65. Zhao, D., K. R. Reddy, V G. Kakani, J. J. Read, and G. A. Carter. 2003. “Corn (Zea Mays L.) Growth, Leaf Pigment Concentration, Photosynthesis and Leaf Hyperspectral Reflectance Properties as Affected by Nitrogen Supply.”Plant and Soil257: 205-217.
66. Zhu, Y., X. Yao, Y. C. Tian, X. Liu, and W. Cao. 2008. “Analysis of Common Canopy Vegetation Indices for Indicating Leaf Nitrogen Accumulations in Wheat and Rice.”International Journal of Applied Earth Observation and Geoinformation10: 1-10.