Hyperspectral remote sensing: Principles and applications

Hyperspectral Remote Sensing: Principles and Applications

Volumn , Issue , 2007, Pages 1-120

  Borengasser, Marcus ^a   Hungate, William S ^b   Watkins, Russell ^b  

^a FLORIDA INSTITUTE OF TECHNOLOGY   (United States)

^b NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85049961365     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (125)

1. Young, T. 1804. The Bakerian lecture: Experiments and calculations relative to physical optics, Philosophical Transactions of the Royal Society of London, 94, 1–16.
2. Hunt, Jr., E. R., A. E. Parker Williams. 2006. Detection of flowering leafy spurge with satellite multispectral imagery, Rangeland Ecology Management, 59(9), 494–499.
3. Ray, T. W. 1994. Vegetation Remote Sensing FAQs, Div. of Geological and Planetary Sciences, California Institute of Technology, www.yale.edu/ceo/Documentation/rsvegfaq.html.
4. Verhoef, W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model, Remote Sensing of Environment, 16, 125–141.
5. Weisstein, E. 2005. Solid angle, In: MathWorld, Wolfram Research, http://mathworld. wolfram.com/SolidAngle.html.
6. Mostafa, M., J. Hutton, and B. Reid. 2001. GPS/IMU products—the Applanix approach, In: Fritsch/Spiller (eds.), Photogrammetric Week 2001, Wichmann Verlag, Heidelberg, Germany, pp. 63–83.
7. Berk, A., L. S. Bernstein, and D. C. Robertson, 1989. “MODTRAN: A moderate resolution model for LOWTRAN 7,” Technical Report GL-TR-89-0122, Air Force Geophysics Laboratory, Bedford, Burlington, MA.
8. Chandrasekhar, S., 1960. Radiative Transfer. Dover, Mineola, NY.
9. Lord Rayleigh (J. W. Stratt), 1871. Philosophical Magazine, Vol. 41, pp. 107–120, 274–279.
10. Rees, W. G., 2001. Physical Principles of Remote Sensing, 2nd ed. Cambridge University Press.
11. Schott, J. R., 1997. Remote Sensing: The Image Chain Approach, Oxford Series in Optical and Imaging Sciences. Oxford University, June 1997.
12. Van de Hulst, H. C., 1981. Evaluating the light from the sun. Optical Spectra, 6(3), 32–35.
13. Adams, J. B., M. O. Smith, P. E. Johnson. 1986. Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research, 91(B8), 8090–8112.
14. Arya, S. P. 2001. Introduction to Micrometeorology, Academic Press, New York, 450 pp.
15. Berk, A., L.S. Bernstein, and D.C. Robertson. 1989. MODTRAN: A moderate resolution model for LOWTRAN 7, Final Report, GL-TR-0122, AGFL, Hanscom AFB, MA, 42 pp.
16. Clark, R. N. 1999. Chapter 1: Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy, In: Manual of Remote Sensing, Volume 3, Remote Sensing for the Earth Sciences, A. N. Rencz, ed., John Wiley and Sons, New York, 3–58.
17. Clark, R. N., and T. L. Roush. 1984. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications, Journal of Geophysical Research, 89, 6329–6340.
18. Clark, R. N., and G. A. Swayze. 1995. Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice, and snow, and other materials: The USGS Tricorder Algorithm. In: Summaries of the Fifth Annual JPL Airborne Earth Science Workshop, JPL Publication 95-1, 1, 39–40.
19. Clark, R. N., G. A. Swayze, K. E. Livo, R. F. Kokaly, S. J. Sutley, J. B. Dalton, R. R. McDougal, and C. A. Gent. 2003. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems: Journal of Geophysical Research, 108(12), 5-1–5-44.
20. Dwivedi, R. S., and B. R. M. Rao. 1992. The selection of the best possible TM band combination for delineating salt-affected soils. International Journal of Remote Sensing, 13, 2051–2058.
21. Huertas, A., R. Nevatia, and D. Landgrebe. 1999. Spectral Mapping Use of Hyperspectral Data with Intensity Images for Automatic Building Modeling, in: Proceedings of the Second International Conference on Information Fusion, Sunnyvale, CA, July, 680–687.
22. Kruse, F. A., A. B. Lefkoff, J. W. Boardman, K. B. Heidebrecht, A. T. Shapiro, J. P. Barloon, and A F. H. Goetz. 1993. The spectral image processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data, Remote Sensing of Environment, 44, 145–163.
23. Price, J. C. 1994. Band selection procedure for multispectral scanners, Applied Optics, 33(15), 3281–3288.
24. Brown, A. J., M. R. Walter, and T. J. Cudahy. 2005. Hyperspectral imaging spectroscopy of a Mars analogue environment at the North Pole Dome, Pilbara Craton, Western Australia, Australian Journal of Earth Sciences, 52, 353–364.
25. Huq, R. 2002. Temporal Annex. University of Maryland at College Park, http://temporal_ science.tripod.com.
26. Khoobehi, B., J. M. Beach, and H. Kawano. 2004. Hyperspectral imaging for measurement of oxygen saturation in the optic nerve head, Investigative Ophthalmology and Visual Science, 45, 1464–1472.
27. Maurer, J. 2002. Mapping ponderosa and Douglas-fir using AVIRIS: An exploratory analysis in the Sierra Nevadas, Department of Geology, University of Colorado at Boulder, May, p. 1.
28. Niemeyer, I, S. Nussbaum, and M. J. Canty. 2005. Automation of change detection procedures for nuclear safeguards-related monitoring purposes, Geoscience and Remote Sensing Symposium Proceedings, IEEE International, 3(25–29) 2133–2136.
29. Apan, A., A. Held, S. Phinn, and J. Markley. 2004. Detecting sugarcane “orange rust” disease using EO-1 Hyperion hyperspectral imagery, International Journal of Remote Sensing, 25(2), 489–498.
30. Bach, H., and W. Mauser. 1991. Extraction of agricultural parameters from imaging spectrometry data and simulated MERIS data through red edge analyses. European Imaging Spectroscopy Aircraft Campaign. ESA SP-360 (Noordwijk: ESA).
31. Bach, H., A. Demircan, and W. Mauser. 1995. The use of AVIRIS data for the determination of agricultural plant development and water content. Proceedings of MAC Europe ’91—Final Results, ESA WPP-88 (Paris: ESA), Lenggries, Germany.
32. Boardman, J. W. 1998. Post-ATREM polishing of AVIRIS apparent reflectance data using EFFORT: A lesson in accuracy versus precision. In: Summaries of the Seventh JPL Airborne Earth Science Workshop, JPL, Pasadena, CA, 1, 53.
33. Bonham-Carter, G. F. 1988. The numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data, Geoscience, 14, 339–356.
34. Brown, C. A., and M. G. Brown. 2001. Florida Citrus Outlook for 2003–2004 Season. Presented to the Florida Citrus Commission, Lakeland, Florida, March 20, 25 pp.
35. Clark, R. N., T. V. V. King, C. Ager, and G. A. Swayze. 1995. Initial vegetation species and senescence/stress mapping in the San Luis Valley, Colorado using imaging spectrometer data. Proceedings: Summitville Forum ‘95, H. H. Posey, J. A. Pendelton, and D. Van Zyl, Eds., Colorado Geological Survey Special Publication 38, 64–69.
36. Croft, B., R. Magarey, and P. Whitle. 2000. Disease management. In: Manual of Cane-growing, edited by M. Hogarth and P. Allsopp, Bureau of Sugar Experiment Stations, Brisbane, 263–289.
37. Datt, B., T. R. McVicar, T. G. Van Niel, D. L. B. Jupp, and J. S. Pearlman. 2003. Pre-processing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes, IEEE Transactions on Geoscience and Remote Sensing, 41, 1246–1259.
38. Dwivedi, R. S., and B. R. M. Rao. 1992. The selection of the best possible TM band combination for delineating salt-affected soils, International Journal of Remote Sensing, 13, 2051–2058.
39. Guyot, G., F. Baret, and S. Jacuemoud. 1992. Imaging spectroscopy for vegetation studies. In: Fundamentals and Prospective Applications, edited by F. Toselli and J. Bodechtel, Kluwer Academic, 145–165.
40. Kanemasu, E. T., C. L. Niblett, H. Manges, D. Lenhert, and M. A. Newman. 1974. Wheat: Its growth and disease severity as deduced from ERTS-1, Remote Sensing of Environment, 3, 255–260.
41. Miller, J. R., E. W. Hare, and J. Wu. 1990. Quantitative characterization of vegetation red edge reflectance: An inverted-Gaussian reflectance model, International Journal of Remote Sensing, 2, 1755–1773.
42. Miller, J. R., J. Wu, M. G. Boyer, M. Belanger, and E. W. Hare. 1991. Seasonal patterns in leaf reflectance red edge characteristics, International Journal of Remote Sensing, 12, 1509–1523.
43. Patel, N. K., C. Patnaik, S. Dutta, A. M. Shekh, and A. J. Dave. 2001. Study of crop growth parameters using airborne imaging spectrometer data, International Journal of Remote Sensing, 22(12), 2401–2411.
44. SPSS. 2001. SPSS for Windows (Release 11) (Chicago: SPSS).
45. Thenkabail, P., R. Smith, and E. Depauw. 2002. Evaluation of narrowband and broad-band vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization, Photogrammetric Engineering and Remote Sensing, 68, 607–621.
46. Antoine, D., and A. Morel. 1999. A multiple scattering algorithm for atmospheric correction of remotely sensed ocean colour (MERIS instrument): Principle and implementation for atmospheres carrying various aerosols including absorbing ones. International Journal of Remote Sensing, 20 (9), 1875–1916.
47. Asner, G. P. 1998. Biophysical and biochemical sources of variability in canopy reflectance, Remote Sens. Environ. 64, 234–253.
48. Clark, R. N. 1999. Spectroscopy of rocks and minerals, and principles of spectroscopy, In: The Manual of Remote Sensing, 3rd Edition (A Series): Volume 3: Remote Sensing for the Earth Sciences,. A. Rencz, (ed.), John Wiley and Sons, New York.
49. Clark, R. N., and T. L. Roush. 1984. Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications, Journal of Geophysical Research, 89(B7), 6329–6340.
50. Dekker, A. G. 1993. Detection of Optical Water Parameters for Eutrophic Lakes by High Resolution Remote Sensing. PhD Dissertation, Free University, Amsterdam.
51. Dekker, A. G., T. J. Malthus, and E. Seyhan. 1991. Quantitative modeling of inland water quality for high-resolution MSS systems, IEEE Transactions on Geoscience and Remote Sensing, 29 (1), 89–95.
52. ENVI. 1999. ENVI Users Guide, Research Systems, Boulder, Colorado. Gitelson, A. 1992. The peak near 700 nm on radiance spectra of algae and water: Relationships of its magnitude and position with chlorophyll concentration. International Journal of Remote Sensing, 13, 3367–3373.
53. Gitelson, A., G. Garbuzov, F. Szilagyi, K. H. Mittenwey, K. Karnieli, and A. Kaiser. 1993. Quantitative remote sensing methods for real-time monitoring of inland water quality. International Journal of Remote Sensing, 14, 1269–1295.
54. Kallio, K., T. Kutser, T. Hannonen, S. Koponen, J. Pulliainen, J. Vepsalainen, and T. Pyhalahti. 2001. Retrieval of water quality variables from airborne spectrometer in various lake types at different seasons. The Science of the Total Environment, 268(1–3), 59–78.
55. Koponen S., J. Pulliainen, K. Kallio, M. Hallikainen. 2002. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data, Remote Sensing of Environment, 79(1), 51–59.
56. Koponen, S., J. Pulliainen, H. Servomaa, Y. Zhang, M. Hallikainen, K. Kallio, K. Eloheimo, and T. Hannonen. 2001. Analysis on the feasibility of multi-source remote sensing observations for chl-a monitoring. The Science of the Total Environment, 268(1–3), 95–106.
57. Kruse, F. A., A. B. Lefkoff, and J. B. Dietze. 1993. Expert system-based mineral mapping in northern Death Valley, California/Nevada using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Remote Sens. Environ. 44, 309–336.
58. Makisara, K., M. Meinander, M. Rantasuo, J. Okkonen, M. Aikio, K. Sipola, P. Pylkko, and B. Braam. 1993. Airborne Imaging Spectrometer for Applications (AISA). Digest of IGARSS ‘93, Tokyo, Japan, August 18–21, 2, 479–481.
59. Moore, G., J. Aiken, and S. Lavender. 1999. The atmospheric correction of water colour and the quantitative retrieval of suspended particulate matter in Case II waters: Application to MERIS, International Journal of Remote Sensing, 20 (9), 1713–1733.
60. Morel, A., and L. Prieur. 1977. Analysis of variations in ocean color, Limnology and Oceanography, 22, 709–722.
61. Murphy, J., and J. P. Riley. 1962. A modified single solution method for the determination of phosphate in natural waters. Analytica Chimica Acta, 27, 31–36.
62. Orth, R. J., K. A. Moore, and J. van Montfrans. 1982. Submerged aquatic vegetation: Distribution and abundance in the lower Chesapeake Bay and interactive effects of light, epiphytes and grazers, Final Report, USEPA Chesapeake Program Grant No. X003246, 141 pp.
63. Preisendorfer, R. W. 1976. Hydrologic optics: Vol. 1. Introduction. Honolulu: U.S. Department of Commerce National Oceanic and Atmospheric Administration, Environment Research Laboratory, 218 pp.
64. Pulliainen, J., K. Kallio, K. Eloheimo, S. Koponen, H. Servomaa, T. Hannonen, S. Tauriainen, and M. Hallikainen. 2001. A semi-operative approach to water quality retrieval from remote sensing data, The Science of the Total Environment, 268(1–3), 79–94.
65. Rast, M., J. Bezy, and S. Bruzzi. 1999. The ESA Medium Resolution Imaging Spectrometer MERIS—A review of the instrument and its mission, International Journal of Remote Sensing, 20(9), 1681–1702.
66. Small, A. M., D. L. Roberts, W. H. Adey, S. M. Lutz, and E. G. Reese. 1996. A macrophyte-based rapid biosurvey of stream water quality: Restoration at the watershed scale, Restoration Eco. 4(2), 124–145.
67. Smith, R. C. and K. S. Baker. 1978. Optical classification of natural waters, Limnology and Oceanography, 23, 260–267.
68. Williams D. J., N. B. Rybicki, A. V. Lombana, T. M. O’Brien, and R. B. Gomez. 2003. Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing, Environmental Monitoring and Assessment, 81(1–3), 383–392.
69. Bernier, B., and M. Brazeau. 1988. Magnesium deficiency symptoms associated with sugar maple dieback in a Lower Laurentians site in southeastern Quebec, Canadian Journal of Forest Research, 18, 126–129.
70. Blackburn, G. A. 1999. Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves, Remote Sensing of Environment, 70, 224–237.
71. Boochs, F., G. Kupfer, K. Dockter, and W. Kuhbauch. 1990. Shape of the red edge as vitality indicator for plants, International Journal of Remote Sensing, 11(10), 1741–1753.
72. Carter, G. A. 1993. Responses of leaf spectral reflectance to plant stress, American Journal of Botany, 80, 239–243.
73. Chen, J. M. 1996. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands, Agricultural and Forest Meteorology, 80, 135–163.
74. Clark, R. N., T. V. V. King, C. Ager, and G. A. Swatze. 1995. Initial vegetation species and senescence/stress indicator mapping in the San Luis Valley, Colorado using imaging spectrometer data, In: Proceedings of Summitville Forum ’95, Posey, H. H., J. A. Pendelton, and D. Van Zyl (eds.). Colorado Geological Survey Special Publication, 38, 64–69.
75. Colombo, S. J., and W. C. Parker. 1999. Does Canadian forestry need physiology research? Forestry Chronicle, 75, 667–673.
76. Elvidge, C. D., and Z. Chen. 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indices, Remote Sensing Environment, 54, 38–48.
77. Ferritti, M. 1997. Forest health assessment and monitoring-issues for consideration, Environmental Monitoring and Assessment, 48, 45–72.
78. Gitelson, A. A., M. N. Merzlyak, and H. K. Lichtenthaler. 1996. Detection of red-edge position and chlorophyll content by reflectance measurements near 700 nm, Journal of Plant Physiology, 148, 501–508.
79. Guyot, G., D. Guyon, and J. Riom. 1989. Factors affecting the spectral response of forest canopies: A review, Geocarto International, 4(3), 3–18.
80. Holmgren, P., and T. Thuresson. 1998. Satellite remote sensing for forestry planning: A review, Scandinavian Journal of Forest Research, 13, 90–110.
81. Hoque, E., and P. J. S. Hutzler. 1992. Spectral blue-shift of red edge monitors damage class of beech trees, Remote Sensing Environment, 39, 81–84.
82. Horler, D. N., M. Dockray, and J. Barber. 1983. The red edge of plant leaf reflectance, International Journal of Remote Sensing, 4, 273–288.
83. Horsley, S. B., R. P. Long, S. W. Bailey, R. A. Hallet, and T. J. Hall. 2000. Factors associated with the decline disease of sugar maple on the Allegheny Plateau, Canadian Journal of Forest Research, 30, 1365–1378.
84. Kramer, P. J., and T. T. Kozlowski. 1979. Physiology of Woody Plants. Academic Press, New York, 811 pp.
85. Lawrence, R., and M. Labus. 2003. Early detection of Douglas-fir beetle infestation with subcanopy resolution hyperspectral imagery, Western Journal of Applied Forestry, 18(3), 202–206.
86. Leckie, D. G., D. P. Ostaff, P. M. Teillet, and G. Fedosejevs. 1989. Spectral characteristics of tree components of balsam fir and spruce damaged by spruce budworm, Forest Science, 35, 582–600.
87. Luther, J. E., and A. L. Carroll. 1999. Development of an index of balsam fir vigor by foliar spectral reflectance, Remote Sensing Environment, 69, 241–252.
88. Marceau, D. J., D. J. Gratton, R. A. Fournier, and I. P. Fortin. 1994. Remote sensing and the measurement of geographical entities in a forested environment, 2: The optimal spatial resolution, Remote Sensing Environment, 49, 105–117.
89. Miller, J. R, W. Jiyou, M. G. Boyer, M. Belanger, and E. W. Hare. 1991. Seasonal patterns in leaf reflectance red-edge characteristics, International Journal of Remote Sensing, 12(7), 1509–1523.
90. Mohammed, G. H, P. H. Sampson, S. J. Colombo, T. L. Noland, and J. R Miller. 1997. Bioindicators of forest sustainability: Development of a forest condition rating system for Ontario. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Sault Ste. Marie, ON. Forest Research Information Paper No.137. 22 pp.
91. Myneni, R. B., F. G. Hall, P. J. Sellers, and A. L. Marshak. 1995. The interpretation of spectral vegetation indexes, IEEE Transactions on Geoscience & Remote Sensing, 33, 481–486.
92. Radwan, M. A., and C. A. Harrington. 1986. Foliar chemical concentrations, growth, and site productivity relations in western red cedar, Canadian Journal of Forest Research, 16, 1069–1075.
93. Reich, R. W., and R. Price. 1999. Detection and classification of forest damage caused by tomentosus root rot using an airborne multispectral imager (CASI), In: Proceedings of International Forum: Automated interpretation of high spatial resolution digital imagery for forestry, Hill, D. A., and D. G. Leckie (eds.). Canadian Forest Service, Pacific Forestry Centre, Victoria, B.C., 179–185.
94. Rock, B. N., T. Hoshizaki, and J. R. Miller. 1988. Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline, Remote Sensing Environment, 24, 109–127.
95. Rosenthal S. I., and E. L. Camm. 1997. Photosynthetic decline and pigment loss during autumn foliar senescence in western larch (Larix occidentalis), Tree Physiology, 17, 767–775.
96. Sampson, P. H., G. H. Mohammed, P. J. Zarco-Tejada, J. R. Miller, T. L. Noland, D. Irving, P. M. Treitz, S. J. Colombo, and J. Freemantle. 2000. The bioindicators of forest condition project: A physiological, remote sensing approach, The Forestry Chronicle, 76(6), 941–952.
97. Sampson, P. H, P. J. Zarco-Tejada, G. H. Mohammed, J. R. Miller, and T. L. Noland. 2003. Hyperspectral remote sensing of forest condition: Estimating chlorophyll content in tolerant hardwoods, Forest Science, 49(3), 381–391.
98. Sampson, P. H., P. M. Treitz, and G. H. Mohammed. 2001. Forest condition assessment in tolerant hardwoods: An examination of spatial scale, structure and function, Canadian Journal of Remote Sensing, 27(3), 232–246.
99. Schmitz, R. F., and K. E. Gibson. 1996. Douglas-fir Beetle. USDA Forest Service Forest Insect & Disease Leaflet 5, R1-96-87, Washington, D.C.
100. Thomson, A. J., D. G. Goodenough, H. J. Barclay, Y. J. Lee, and R. N. Sturrock. 1996. Effects of laminated root rot (Phellinus weirii) on Douglas-fir foliar chemistry, Canadian Journal of Forest Research, 26, 1440–1445.
101. Treitz, P. M., and P. J. Howarth. 1999. Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems, Progress in Physical Geography, 23(3), 359–390.
102. Treitz, P. M., and P. J. Howarth. 2000. High spatial resolution remote sensing data for forest ecosystem classification: An examination of spatial scale, Remote Sensing Environment, 72(3), 268–290.
103. Treshow M., and M. R. Pack. 1970. Fluoride, In: Recognition of Air Pollution Injury to Vegetation, In: A Pictorial Atlas, Jacobson, J. S., and A. C. Hill (eds.). Air Pollution Control Assoc., Pittsburgh, PA, D1–D7.
104. Vogelmann, J. E., B. N. Rock, and D. M. Moss. 1993. Red edge spectral measurements from sugar maple leaves, International Journal of Remote Sensing, 14(8), 1563–1575.
105. Vogg G., R. Heim, J. Hansen, C. Schafer, and E. Beck. 1998. Frost hardening and photosynthetic performance of Scots pine (Pinus sylvestris L.) needles, I. Seasonal changes in the photosynthetic apparatus and its function, Planta, 204, 193–200.
106. Woodcock, C. E., and A. H. Strahler. 1987. The factor of scale in remote sensing, Remote Sensing Environment, 21, 311–322.
107. Yoder, B. J., and R. E. Pettigrew-Crosby. 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales, Remote Sensing Environment, 53, 199–211.
108. Zarco-Tejada, P. J., and J. R. Miller. 1999. Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery, Journal of Geophysical Research, 104 (D22), 27,921–27,948.
109. Zarco-Tejada, P. J., J. R. Miller, G. H. Mohammed, T. L. Noland, and P. H. Sampson. 1999. Canopy optical indices from infinite reflectance and canopy reflectance models for forest condition monitoring: Applications to hyperspectral CASI data, In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Hamburg, Germany, 1–4.
110. Zarco-Tejada, P. J, J. R. Miller, G. H. Mohammed, T. L. Noland, and P. H. Sampson. 2001. Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data, IEEE Transactions on Geoscience & Remote Sensing, 39(7), 1491–1507.
111. Arribas, A., Jr., J. J. Rytuba, R. O. Rye, C. G. Cunningham, M. H. Podwysocki, W. C. Kelly, A. Arribas, Sr., E. H. McKee, and J. G. Smith. 1989. Preliminary study of the ore deposits and hydrothermal alteration in the Rodalquilar Caldera Complex, southeastern Spain. U.S. Geological Survey Open-File Report 89-327, Reston, Virginia, 39 pp.
112. Clutis, E. A. 1989. Spectral reflectance properties of hydrocarbons: remote sensing implications, Science, 245, 165–168.
113. Cunningham, C. G., A. Arribas, Jr., J. R. Rytuda, and A. Arribas, Sr. 1989. Evolution of the Los Frailes caldera, Cabo de Gata volcanic field. U.S. Geological Survey Open-File Report 89-325, Reston, VA, 20 pp.
114. Ferrier G., and G. Wadge. 1996. The application of imaging spectrometry data to mapping alteration zones associated with gold mineralization in southern Spain, International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, 33(7), 308A–308A.
115. Heald, P., N. K. Foley, and D. O. Hayba. 1987. Comparative anatomy of volcanic-hosted epithermal deposits: Acid-sulphate and adularia-sericite types, Economic Geology, 82, 1–26.
116. Hernandez, P. A., P.A. Garcis-Estrada, and P. N. Cowley. 1989. Geologic setting, alteration, and lithogeochemistry of the Transaccion epithermal gold deposits, Rodalquilar mining district, southeastern Spain, Transactions Institute of Mining and Metallurgy, 98, B78–B82.
117. Hörig B., F. Kühn, F. Oschütz, and F. Lehmann. 2001. HyMap Hyperspectral Remote Sensing to Detect Hydrocarbons, International Journal of Remote Sensing, 22(8), 1413–1422.
118. Hunt, G. R. 1977. Spectral signatures of particulate minerals in the visible and near-infrared, Geophysics, 42, 501–503.
119. Krohn, M. D. 1986. Spectral properties (0.4–2.5 microns) of selected rocks associated with disseminated gold and silver deposits in Nevada and Idaho, Journal of Geophysical Research, 91, 767–783.
120. Rast, M. 1991. Imaging spectroscopy and its application in spaceborne systems. ESA SP-1144 (Paris: European Space Agency), 144 pp.
121. Roberts, D. A., Y. Yamagushi, and R. J. P. Lyon. 1986. Comparison of various techniques for calibration of AIS data. Proceedings of the 2nd Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, Pasadena, CA, 4–5 June 1990, edited by G. Vane and A. F. H. Goetz, JPL Publication 86-35 Pasadena, CA: Jet Propulsion Laboratory, 21–30.
122. Rytuba, J. J., A. Arribas, Jr., C. G. Cunningham, E. H. McKee, J. G. Smith, and A. Arribas, Sr. 1989. Evolution of the Rodalquilar caldera complex and associated gold-alunite deposits, Cabo de Gata volcanic field, Southeastern Spain. U.S. Geological Survey Open-File Report, 89-326, Reston, VA, 18 pp.
123. Scholten, S., S. Sujew, F. Wewel, J. Flohrer, R. Jaumann, F. Lehmann, P. Pischel, and G. Neukum. 1999. The high resolution stereo camera HRSC-A—digital 3-D image acquisition; photogrammetric processing and data evaluation, In: Proceedings, Joint Workshop Sensors and Mapping from Space 1999, Institut fur Photogrammetrie und Ingenieurvermessung, Universitat Hannover, Hannover, No. 18.
124. Vane, G., and A. F. H. Goetz. 1988. Terrestrial imaging spectroscopy, Remote Sensing of Environment, 24, 1–29.
125. Wadge, G., G. Ferrier, S. Mackin, J. McM. Moore, S. Manthripragada, J. G. Liu, and R. Murphy. 1993. Mapping hydrous pathfinder minerals associated with gold mineralization in southern Spain. Proceedings of the 25th International Symposium, Remote Sensing and Global Environmental Change, Graz, Austria, 4–89 April 1993 (Ann Arbor: ERIM), 1220–1230.