Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data

Abstract

Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software’s to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.

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S. Arafat, M. Aboelghar and E. Ahmed, "Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data," Advances in Remote Sensing, Vol. 2 No. 2, 2013, pp. 63-70. doi: 10.4236/ars.2013.22009.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] J. R. Anderson, E. T. Hardy, J. T. Rocha and R. E. Witmer, “Land Use and Land Cover Classification System for Use with Remote Sensor Data,” US Geological Survey Professional Paper, Government Printing Office, Washington DC, 1976.
[2] R. J. Kauth and G. S. Thomas, “The Tasselled Cap—A Graphic Description of the Spectral Temporal Development of the Agricultural Crops as Seen by Landsat,” In: Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, 2004, pp. 4B-41-4B-51.
[3] S. G. Wheeler and P. N. Misra, “Linear Dimensionality of Landsat Agricultural Data with Implications for Classifications,” In: Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Laboratory for Applications of Remote Sensing, West Lafayette, 1976, pp. 2A-1-2A-9.
[4] E. P. Crist and R. J. Kauth, “The Tasseled Cap De-Mystified,” Photogrammetric Engineering and Remote Sensing, Vol. 52, No. 1, 1986, pp. 81-86.
[5] G. M. Foody, “Estimation of Land Coverage from Land Cover Classification Derived from Remotely Sensed Data,” GeoJournal, Vol. 36, No. 4, 1995, pp. 361-370. doi:10.1007/BF00807952
[6] M. L. Nirala and G. Venkatachalam, “Rotational Transformation of Remotely Sensed Data for Land Use Classification,” International Journal of Remote Sensing, Vol. 21, No. 11, 2000, pp. 2185-2202. doi:10.1080/01431160050029503
[7] H. N. S. Prakash, P. Nagabhushan and G. K. Chidanada, “Symbolic Agglomerative Clustering for Quantitative Analysis of Remotely Sensed Data,” International Journal of Remote Sensing, Vol. 21, No. 17, 2000, pp. 3239-3251. doi:10.1080/014311600750019868
[8] Z. Su, “Remote Sensing of Land Use and Vegetation for Mesoscale Hydrological Studies,” International Journal of Remote Sensing, Vol. 21, No. 2, 2000, pp. 213-233. doi:10.1080/014311600210803
[9] J. E. Vogelmann, T. Sohl and S. M. Howards, “Regional Characterization of Land Cover Using Multiple Sources of Data,” Photogrammetric Engineering and Remote Sensing, Vol. 61, No. 1, 1998, pp. 45-57.
[10] C. Homer, C. Yuang, Y. Limain, Y. B. Wylie and M. Coan, “Development of a 2001 National Land Cover Database for the United States,” Photogrammetric Engineering and Remote Sensing, Vol. 70, No. 7, 2004, pp. 829-840.
[11] T. M. Lillesand, R. W. Kiefer and J. W. Chipman, “Remote Sensing and Image Interpretation,” 5th Edition, John Wiley and Sons, New York, 2004.
[12] P. M. Mather, “Computer Processing of Remotely-Sensed Images: An Introduction,” 3rd Edition, John Wiley & Sons, Chichester, 2004.
[13] M. Kneubuehler, M. E. Schaepman and T. W. Kellenbergerm, “Comparison of Different Approaches of Selecting Endmembers to Classify Agricultural Land by Means of Hyperspectral Data (DAIS7915),” IEEE International Geoscience and Remote Sensing Symposium Proceedings, Seattle, 6-10 July 1998, pp. 888-890.
[14] P. S. Thenkabail, E. A. Enclona, M. S. Ashton and B. Van Der Meer, “Accuracy Assessments of Hyperspectral Waveband Performance for Vegetation Analysis Applications,” Remote Sensing of Environment, Vol. 91, No. 3-4, 2004, pp. 354-376.
[15] G. A. Blackburn, “Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches,” Remote Sensing of Environment, Vol. 66, No. 3, 1998, pp. 273-285. doi:10.1016/S0034-4257(98)00059-5
[16] G. A. Carter, “Reflectance Bands and Indices for Remote Estimation of Photosynthesis and Stomatal Conductance in Pine Canopies,” Remote Sensing of Environment, Vol. 63, No. 1, 1998, pp. 61-72. doi:10.1016/S0034-4257(97)00110-7
[17] M. Shibayama and T. Akiyama, “Estimating Grain Yield of Maturing Rice Canopies Using High Spectral Resolution Resenescence Flectance Measurements,” Remote Sensing of Environment, Vol. 36, No. 1, 1991, pp. 45-53. doi:10.1016/0034-4257(91)90029-6
[18] P. J. Curran, J. L. Dungan and H. L. Gholz, “Exploring the Relationship between Reflectance Red Edge and Chlorophyll Content in Slash Pine,” Tree Physiology, Vol. 7, No. 1-4, 1990, pp. 33-48. doi:10.1093/treephys/7.1-2-3-4.33
[19] USDA, “Keys to Soil Taxonomy,” 11th Edition, United States Department of Agriculture, Natural Resources Conservation Service (NRCS), New York, 2010.
[20] R. L. Mason, R. F. Gunst and J. L. Hess, “Statistical Design and Analysis of Experiments with Applications to Engineering and Science,” 2nd Edition, John Wiley & Sons, Hoboken, 2003.
[21] S. Axler, “Linear Algebra Done Right,” Springer-Verlag New York Inc., New York, 1995.
[22] M.A. Aboelghar and H. A. Abdel Wahab, “Spectral Footprint of Botrytis cinerea, a Novel Way for Fungal Characterization,” Advances in Bioscience and Biotechnology, Vol. 4, No. 3, 2013, pp. 374-382. doi:10.4236/abb.2013.43050

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