Detection of early plant stress responses in hyperspectral images

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 93, Issue , 2014, Pages 98-111

  Behmann, Jan ^a   Steinrücken, Jörg ^a   Plümer, Lutz ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Agriculture; Close range; Crop; Hyper spectral; Learning; Modelling

Indexed keywords

AGRICULTURE; CROPS; EXPERIMENTS; GRAIN (AGRICULTURAL PRODUCT); MODELS; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

CLOSE RANGE; DROUGHT STRESS CONDITIONS; HYPERSPECTRAL; HYPERSPECTRAL SIGNATURES; LEARNING; ORDINAL CLASSIFICATION; PLANT STRESS RESPONSE; STRESS RELATED PROCESS;

DROUGHT;

ADAPTATION; BARLEY; DETECTION METHOD; DROUGHT STRESS; MAIZE; NDVI; PHYSIOLOGICAL RESPONSE; PIXEL; REMOTE SENSING; SENESCENCE; SPECTRAL ANALYSIS;

HORDEUM; HORDEUM VULGARE; ZEA MAYS;

EID: 84899858887     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.03.016     Document Type: Article

References (82)

1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S., 2010. Slic Superpixels. Technical Report 149300, EPFL Technical Report No. 149300.
2. Agresti A. Categorical Data Analysis 2002, John Wiley & Sons, New Jersey, USA. second ed.
3. Blackburn G.A. Hyperspectral remote sensing of plant pigments. J. Exp. Bot. 2007, 58(4):855-867.
4. Boyer J.S. Plant productivity and environment. Science 1982, 218(4571):443-448.
5. Carlson T., Ripley D. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 1997, 62(3):241-252.
6. Chaerle L., Van Der Straeten D. Imaging techniques and the early detection of plant stress. Trends Plant Sci. 2000, 5(11):495-501.
7. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20(3):273-297.
8. Curran P., Windham W., Gholz H. Exploring the relationship between reflectance red edge and chlorophyll concentration in slash pine leaves. Tree Physiol. 1995, 15(3):203-206.
9. Curran P., Dungan J., Peterson D. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the kokaly and clark methodologies. Remote Sens. Environ. 2001, 76(3):349-359.
10. Datt B. A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using eucalyptus leaves. J. Plant Physiol. 1999, 154(1):30-36.
11. Davies W.J., Zhang J., Yang J., Dodd I.C. Novel crop science to improve yield and resource use efficiency in water-limited agriculture. J. Agric. Sci. 2011, 149(Supplement S1):123-131.
12. de Jong S.M., Addink E.A., Hoogenboom P., Nijland W. The spectral response of buxus sempervirens to different types of environmental stress - a laboratory experiment. ISPRS J. Photogramm. Remote Sens. 2012, 74:56-65.
13. Duan K., Keerthi S. Which is the best multiclass SVM method? An empirical study. Proceedings of the 6th International Workshop on Multiple Classifier Systems 2005, 278-285. Springer.
14. Elvidge C.D. Visible and near infrared reflectance characteristics of dry plant materials. Int. J. Remote Sens. 1990, 11(10):1775-1795.
15. Exelis Visual Information Solutions, 2012. ENVI 5.0 Help. Exelis Visual Information Solutions, Colorado.
16. FAO, 2003. More Crop Per Drop. Food and Agriculture Organisation (FAO) of the United Nations. <> (last accessed 12.11.13). http://www.fao.org/english/newsroom/focus/2003/water.htm.
17. FAO, 2009. Declaration of the World Summit on Food Security. Food and Agriculture Organisation (FAO) of the United Nations, 16-18 November, Rome. <> (last accessed 12.11.13). http://www.fao.org/wsfs/wsfs-list-documents/en/.
18. Filella I., Peñuelas J. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens. 1994, 15(7):1459-1470.
19. Fischer A.M. The complex regulation of senescence. Crit. Rev. Plant Sci. 2012, 31(2):124-147.
20. Foody G.M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 2002, 80(1):185-201.
21. Fröhlich B., Bach E., Walde I., Hese S., Schmullius C., Denzler J. Land cover classification of satellite images using contextual information. ISPRS Ann. Photogramm., Remote Sens. Spatial Inform. Sci. II-3/W1 2013, 1-6.
22. Gamon J., Surfus J. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 1999, 143(1):105-117.
23. Gamon J., Penuelas J., Field C. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41(1):35-44.
24. Gao Z., Gao W., Chang N.-B. Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images. Int. J. Appl. Earth Observ. Geoinform. 2011, 13(3):495-503.
25. Gaspar T., Franck T., Bisbis B., Kevers C., Jouve L., Hausman J.F., Dommes J. Concepts in plant stress physiology. application to plant tissue cultures. Plant Growth Regul. 2002, 37(3):263-285.
26. Genc H., Genc L., Turhan H., Smith S., Nation J. Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat. Afr. J. Biotechnol. 2010, 7(2):173-180.
27. Gitelson A., Merzlyak M. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol. 1994, 143(3):286-292.
28. Gitelson A.A., Buschmann C., Lichtenthaler H.K. The chlorophyll fluorescence ratio F735/F700 as an accurate measure of the chlorophyll content in plants. Remote Sens. Environ. 1999, 69(3):296-302.
29. Gitelson A., Merzlyak M., Chivkunova O. Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochem. Photobiol. 2001, 74(1):38-45.
30. Gitelson A., Zur Y., Chivkunova O., Merzlyak M. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol. 2002, 75(3):272-281.
31. Gonsamo A., Pellikka P. The sensitivity based estimation of leaf area index from spectral vegetation indices. ISPRS J. Photogramm. Remote Sens. 2012, 70:15-25.
32. Gornall J., Betts R., Burke E., Clark R., Camp J., Willett K., Wiltshire A. Implications of climate change for agricultural productivity in the early twenty-first century. Philos. Trans. Roy. Soc. B: Biol. Sci. 2010, 365(1554):2973-2989.
33. Govender M., Chetty K., Bulcock H. A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA 2009, 33(2):145-151.
34. Guiboileau A., Sormani R., Meyer C., Masclaux-Daubresse C. Senescence and death of plant organs: nutrient recycling and developmental regulation. CR Biol. 2010, 333(4):382-391.
35. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
36. Heiskanen J., Rautiainen M., Stenberg P., Mõttus M., Vesanto V.-H. Sensitivity of narrowband vegetation indices to boreal forest LAI, reflectance seasonality and species composition. ISPRS J. Photogramm. Remote Sens. 2013, 78:1-14.
37. Huete A., Liu H., Batchily K., Van Leeuwen W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 1997, 59(3):440-451.
38. Jackson R.D. Soil moisture inferences from thermal-infrared measurements of vegetation temperatures. IEEE Trans. Geosci. Remote Sens. 1982, GE-20(3):282-286.
39. Jafolla, J., Hendricks, L., Gargadennec, Y., 2001. SOC-700 and HS-Analysis 2 Users Manual. Surface Optics, San Diego.
40. Jensen J. Remote Sensing of the Environment: An Earth Resource Perspective 2009, Pearson Prentice Hall.
41. Jones H.G., Serraj R., Loveys B.R., Xiong L., Wheaton A., Price A.H. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant Biol. 2009, 36(11):978-989.
42. Karimi Y., Prasher S., Patel R., Kim S. Application of support vector machine technology for weed and nitrogen stress detection in corn. Comput. Electron. Agric. 2006, 51(1-2):99-109.
43. Kaufman Y., Tanré D. Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: from AVHRR to EOS-MODIS. Remote Sens. Environ. 1996, 55(1):65-79.
44. Kim Y., Glenn D.M., Park J., Ngugi H.K., Lehman B.L. Hyperspectral image analysis for water stress detection of apple trees. Comput. Electron. Agric. 2011, 77(2):155-160.
45. Kohavi R., John G. Wrappers for feature subset selection. Artif. Intell. 1997, 97(1-2):273-324.
46. Lee D.W. Anthocyanins in autumn leaf senescence. Adv. Bot. Res. 2002, 37:147-165.
47. Lichtenthaler H.K., Lang M., Sowinska M., Heisel F., Miehé J.A. Detection of vegetation stress via a new high resolution fluorescence imaging system. J. Plant Physiol. 1996, 148(5):599-612.
48. Li P., Burges C., Wu Q., Platt J., Koller D., Singer Y., Roweis S. Mcrank: learning to rank using multiple classification and gradient boosting. Adv. Neural Inform. Process. Syst. (NIPS) 2007, 897-904.
49. Lim P.O., Nam H.G. Aging and senescence of the leaf organ. J. Plant Biol. 2007, 50(3):291-300.
50. Liu Z.-Y., Wu H.-F., Huang J.-F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis. Comput. Electron. Agric. 2010, 72(2):99-106.
51. MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. California, USA, pp. 281-297.
52. Marris E. Water: more crop per drop. Nature 2008, 452(7185):273-277.
53. Maxwell K., Johnson G.N. Chlorophyll fluorescence - a practical guide. J. Exp. Bot. 2000, 51(345):659-668.
54. Merzlyak M.N., Gitelson A.A., Chivkunova O.B., Rakitin V.Y. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 1999, 106(1):135-141.
55. Munné-Bosch S., Alegre L. Die and let live: leaf senescence contributes to plant survival under drought stress. Funct. Plant Biol. 2004, 31(3):203-216.
56. Pennisi E. The blue revolution, drop by drop, gene by gene. Science 2008, 320(5873):171-173.
57. Peñuelas J., Filella I., Biel C., Serrano L., Save R. The reflectance at the 950-970 nm region as an indicator of plant water status. Int. J. Remote Sens. 1993, 14(10):1887-1905.
58. Peñuelas J., Baret F., Filella I. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 1995, 31:221-230.
59. Peñuelas J., Pinol J., Ogaya R., Filella I. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). Int. J. Remote Sens. 1997, 18(13):2869-2875.
60. Ranzanto M., Boureau Y.-l., LeCun Y. Sparse feature learning for deep belief networks. Adv. Neural Inform. Process. Syst. (NIPS) 2007, 20:1185-1192.
61. Reynolds, M.P. (Ed.), 2010. Climate Change and Crop Production. CABI Climate Change Series, vol. 1, CABI, Oxfordshire, UK.
62. Römer C., Wahabzada M., Ballvora A., Pinto F., Rossini M., Panigada C., Behmann J., Léon J., Thurau C., Bauckhage C., et al. Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Funct. Plant Biol. 2012, 39(11):878-890.
63. Rouse, J., Haas, R.H., Schell, J., D.W., Deering, 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Freden, S. (Ed.), Third Earth Resources Technology Satellite Symposium. NASA. Goddard Space Flight Center, pp. 309-317.
64. Rumpf, T., Mahlein, A., Dörschlag, D., Plümer, L., 2009. Identification of combined vegetation indices for the early detection of plant diseases. In: Neale, Christopher M.U./Maltese, A. (Ed.), Proceedings of SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, vol. 747217.
65. Rumpf T., Mahlein A., Steiner U., Oerke E., Dehne H., Plümer L. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 2010, 74(1):91-99.
66. Saeys Y., Inza I., Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23(19):2507-2517.
67. Sanches I., Souza Filho C., Magalhães L., Quitério G., Alves M., Oliveira W. Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy. ISPRS J. Photogramm. Remote Sens. 2013, 78:85-101.
68. Sims D., Gamon J. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 2002, 81(2-3):337-354.
69. Stagakis S., González-Dugo V., Cid P., Guillén-Climent M., Zarco-Tejada P. Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. ISPRS J. Photogramm. Remote Sens. 2012, 71:47-61.
70. Taiz L., Zeiger E. Plant Physiology 2010, Sinauer Associates, Sunderland and Mass. fifth ed.
71. Tarnavsky E., Garrigues S., Brown M.E. Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products. Remote Sens. Environ. 2008, 112(2):535-549.
72. Tester M., Langridge P. Breeding technologies to increase crop production in a changing world. Science 2010, 327(5967):818-822.
73. Thenkabail P., Smith R., De Pauw E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 2000, 71(2):158-182.
74. Tokarczyk P., Montoya J., Schindler K. An evaluation of feature learning methods for high resolution image classification. ISPRS Ann. Photogramm., Remote Sens. Spatial Inform. Sci. I-3 2012, 389-394.
75. Tuberosa R., Salvi S. Genomics-based approaches to improve drought tolerance of crops. Trends Plant Sci. 2006, 11(8):405-412.
76. Tuia D., Verrelst J., Alonso L., Pérez-Cruz F., Camps-Valls G. Multioutput support vector regression for remote sensing biophysical parameter estimation. Geosci. Remote Sens. Lett., IEEE 2011, 8(4):804-808.
77. Verrelst J., Alonso L., Caicedo J., Moreno J., Camps-Valls G. Gaussian process retrieval of chlorophyll content from imaging spectroscopy data. Sel. Top. Appl. Earth Observ. Remote Sens., IEEE 2013, 6(2):867-874.
78. Vogelmann J., Rock B., Moss D. Red edge spectral measurements from sugar maple leaves. Int. J. Remote Sens. 1993, 14(8):1563-1575.
79. West J.S., Bravo C., Oberti R., Lemaire D., Moshou D., McCartney H.A. The potential of optical canopy measurement for targeted control of field crop diseases. Ann. Rev. Phytopathol. 2003, 41(1):593-614.
80. Wu D., Feng L., Zhang C., He Y. Early detection of botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy. Trans. ASABE 2008, 51(3):1133-1139.
81. Yu K., Li F., Gnyp M.L., Miao Y., Bareth G., Chen X. Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain. ISPRS J. Photogramm. Remote Sens. 2013, 78:102-115.
82. Zygielbaum A.I., Gitelson A.A., Arkebauer T.J., Rundquist D.C. Non-destructive detection of water stress and estimation of relative water content in maize. Geophys. Res. Lett. 2009, 36(12):L12403.