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All issues Volume 120 (2017) MATEC Web Conf., 120 (2017) 09004 References
Open Access
Issue
MATEC Web Conf.
Volume 120, 2017
International Conference on Advances in Sustainable Construction Materials & Civil Engineering Systems (ASCMCES-17)
Article Number 09004
Number of page(s) 21
Section Geographic Information Systems & Remote Sensing
DOI https://doi.org/10.1051/matecconf/201712009004
Published online 09 August 2017
  1. B. L. Markham, et al., The Landsat data continuity mission operational land imager (OLI) radiometric calibration. In IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), pp. 2283–2286 (2010). [Google Scholar]
  2. U.S. Geological Survey, “Landsat Data Continuity Mission”, U.S. Geological Survey, Washington, DC (2012). [Google Scholar]
  3. D. Gerace, Demonstrating Landsat’s new potential to monitor coastal and inland Water. Ph.D dissertation, Dept. Imaging Science, Rochester Inst. Technol., Rochester, NY (2010). [Google Scholar]
  4. A. Gerace, and J. Schott, The increased potential of the Landsat data continuity mission to contribute to case 2 water quality studies. In Proc. SPIE, Earth Observing Systems XIV, 7452, 74520U, San Diego, CA (Aug 2009). [Google Scholar]
  5. N. Pehlevan, and J. R. Schott, Investigating the potential of the operational land imager (OLI) for monitoring case II waters using a look-up-table approach. In Pecora 18: Forty Years of Earth Observation-Understanding a changing World, Herndon, VA (2011). [Google Scholar]
  6. S. N. Kloiber, P. L. Berezonik, L. G. Olmanson, and M. E. Bauer, A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sens. Environ. 82, 38–47, (2002). [CrossRef] [Google Scholar]
  7. W. Pervez, Valiuddin, S. A., Khan, J. A. Khan, 2016. Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery. J. Appl. Remote Sens. 10 (2), 026004 (2016); doi: 10.1117/1.JRS.10.026004 [CrossRef] [Google Scholar]
  8. D. Roy, M. Wulder, T. Loveland, C. Woodcock, R. Allen,, M. Anderson, D. Helder, J. Irons, D. Johnson, R. Kennedy, et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172 (2014). [Google Scholar]
  9. N. Flood, Continuity of Reflectance Data between Landsat-7 ETM+ and Landsat-8 OLI, for both top-of-atmospheric and surface reflectance. A Study in the Australian landscape, Remote Sens. 6, 7952–7970 (2014). [Google Scholar]
  10. B. Markham, J. Barsi, G. Kvaran, L. Ong, E. Kaita, S. Biggar, J. Czapla-Myers, N. Mishra, D. Helder, Landsat-8 operational land imager radiometric calibrationand stability. Remote Sens. 6, 12275–12308 (2014). [CrossRef] [Google Scholar]
  11. J. Czapla-Myers, N. McCorkel, Anderson, K. Thome, S. Biggar, D. Helder, D. Aaron, L. Leigh, N. Mishra, The ground-based absolute radiometric calibration of Landsat 8 OLI. Remote Sens. 7, 600–626 (2015). [CrossRef] [Google Scholar]
  12. R. Morfitt, J. Barsi, R. Levy, B. Markham, E. Micijevic, L. Ong, P. Scaramuzza, K. Vanderwerff, Landsat-8 Operational land imager (OLI) radiometric performance on orbit. Remote Sens. 7, 2208–2237 (2015). [CrossRef] [Google Scholar]
  13. E. Knight, G. Kvaran, Landsat-8 operational land imager design, characterization and performance. Remote Sens. 6, 10286–10305, (2014). [CrossRef] [Google Scholar]
  14. P. Li, Jiag, Z. Feng, Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper (ETM_) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sens. 6, 310–329 (2014). [CrossRef] [Google Scholar]
  15. Y. Ke, J. Im, J. Lee, H. Gong, Y. Ryu, Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sens. Environ. 164, 298–313 (2015). [CrossRef] [Google Scholar]
  16. J. Rogan, D. Chen, Remote sensing technology for mapping and monitoring land cover and land use change. Progr. Plan. 61(4), 301–325 (2004). [Google Scholar]
  17. P. Sinha, L. Kumar, Indedpendent t wo step thresholding of binary images in inter-annual land cover change/no cange identification. ISPRS J. Photogram. Remote Sensing 81, 31–43 (2013). [CrossRef] [Google Scholar]
  18. C. Benedek, M. Shadaydeh, Z. Kato, J. Sziranyi, Multilayer Markov Random Field models for change detection in optical remote sensing images. ISPRS J. Photogram. Remote Sensing (2015). [Google Scholar]
  19. T. Blasche, Object based image analysis for remote sensing. ISPRS J. Photogram. Remote Sensing 65(1), 2–16 (2010). [Google Scholar]
  20. J. Chen, M. Lu, X. Chen, J. Chen, L. Chen, A spectral gradient difference based approach for land cover change detection ISPRS J. Photogram. Remote Sensing 85, 1–12 (2013). [CrossRef] [Google Scholar]
  21. X. Chen, J. Chen, Y. Shi, Y. Yamaguchi, An automated approach for updating land cover maps based on integrated change detection and classification methods ISPRS J. Photogram. Remote Sensing 71, 86–95 (2012). [CrossRef] [Google Scholar]
  22. G. Metternicht, Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision. ISPRS J. Photogram. Remote Sensing 54(4), 221–233 (1999). [CrossRef] [Google Scholar]
  23. I. Bruzzone, D. F. Prieto, Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sensing 38(3), 1171–1182 (2000). [CrossRef] [Google Scholar]
  24. J. L. Silvan-Cardenas, I. Wang, On quantifying post- classification subpixel land cover changes. ISPRS J. Photogram. Remote Sensing 98, 94–105 (2014). [CrossRef] [Google Scholar]
  25. F. Bovolo, L. Bruzzone, M. Marconcini, A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Trans. Geosci. Remote Sensing 45(7), 2070–2080 (2008). [CrossRef] [Google Scholar]
  26. C. He, A. Wei, P. Shi, Q. Zhang, Y. Zhao, Detecing land use/land cover change in rural-urban fringe areas using extended chabge-vector analysis. Int. J. Appl. Easth Observ. Geoinform. 13(4), (2011). [Google Scholar]
  27. L. Bruzzzone, R. Cossu, G. Vernazza, Detection of land cover transitions by combining multidate classifiers, Pattern Recogn. Lett. 25(13) 1491–1500 (2004). [CrossRef] [Google Scholar]
  28. M.J. Canty, Image Analysis, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, 2nd ed.; CRC Press: Boca Raton, FL, USA, (2010). [Google Scholar]
  29. A. Almutairi, T.A. Warner, Change Detection Accuracy and Image Properties: A Study Using Simuated Data. Remote Sens. 2, 1508–1529, 2010. [CrossRef] [Google Scholar]
  30. A. Hecheltjen, F. Thonfeld, G. Menz, Recent advances in remote sensing change detection- A review. In Land Use and Land Cover Mapping in Europe, Manakos, I., Braun, M., Eds.; Springer Netherlands: Dordecht, The Neterlands, pp. 145–178, (2014). [CrossRef] [Google Scholar]
  31. D. Lu, P. Mausel, E. Brondizio, E. Moran, Change detection techniques. Int. J. Remote Sens. 25, 2365–2401 (2004). [CrossRef] [Google Scholar]
  32. P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, E. lambin, Review Article Digital Image change detection methods in ecosystem monitoring: A review. Int. J. Remote Sens. 25,1565–1596, (2004). [CrossRef] [Google Scholar]
  33. M. Hussain, D. Chen, A. Cheng, H. Wie, and Stanley, D., Change detection from Remotely Sensed Images: From Pixel-Based to Object –Based Approches. ISPRS J. Photogramm and remote Sensing 80: 91–106. Doi:10.1016/j.isprs.2013.03.006. (2013). [CrossRef] [Google Scholar]
  34. J. Unger, et al., Modelling of the Urban Heat Island Pattern Based on the Relationship between Surface and Air temperature. Quarterly Journal of the Hungarian Meterlogical Service 114, 287–302 (2010). [Google Scholar]
  35. L. Musci, L. Muladi, A. Henits, Farsang, and V. Albrecht, Large scale UHI Mapping Based on Spatial Information Provided by Young Volunteers. Carpathian Journal of Earth and Environmental Sciences 9 (2): 31–43 (2014). [Google Scholar]
  36. D. Lu, E. Moran, and S. Hetrick, Detection of Impervious Surface Change with Multitemporal Landsat Images in an Urban-Rural Frontier. ISPRS J. Photogramm and remote Sensing, 66: 298–306. doi: 10.1016/j.isprsjprs.2010.10.010. (2011). [CrossRef] [Google Scholar]
  37. A. Schneider, Monitoring Land Cover Change in Urban and Peri-Urbanareas Using Dense Time Stacks pf Landsat Satellite Data a data Mining Approach. Remote Sens. Environ, 124, 689–704. Doi:10.1016/j.rse.2012.06.006. (2012). [CrossRef] [Google Scholar]
  38. F. Yuan, K.E. Swaya, B. C. Loefffelholz, and M.E. Bauer, Land Cover Classification and Change Analysis of the Twin Cities (Minnesota) Metropolitan Area by Multitemporal Landsat Remote Sensing. Remote Sens. Environ, 98, (2–3): 317–328. Doi:10.1016/j.rse.2005.08.006. (2005). [Google Scholar]
  39. B.E. Hubbard, J.K. Crowley, Mineral mapping on the Chilean-Bolivian Altiplano using co-orbital ALI, ASTER and Hyperion imagery: data dimensionality issues and solutions, Remote Sens, 99, 173–186, 2005. [Google Scholar]
  40. National Aeronautics and space administration, Earth Observing-1 Advanced Land Imager, 2002 http://eo1.gsfc.nasa.gov/Technology/ALIhome1.htm [Google Scholar]
  41. National Aeronautics and Space Administaration, Earth Observating-1 Eo1 General Mission, 2004 http://eo1.gsfc.nasa.gov/new/general/ [Google Scholar]
  42. M.A. Wulder, J.C. White, S.N. Goward, G.M. Jeffrey, J.R. Irons, M. Herold, W.B. Cohen, T.R. Loveland, C.E. Woodcock, Landsat continuity: issues and opportunities for land cover monitoring. Remote Sens. Environ. 112, 955–969, (2008). [CrossRef] [Google Scholar]
  43. M. Folkman, J. Pearlman, L. Liao, and P. Jarecke, EO-1/ Hyperion hyperspectral imager design, development, characterization, and calibration in W. L. Smith and Y. Yasuoka, (Eds.). Hyperspectral Remote Sensing of the Land and Atmosphere 4151, 40–51, (2001). [CrossRef] [Google Scholar]
  44. L. C. Sanders, J. R. Schott, and R. Raqueno, A VNIR/SWIR atmospheric correction algorithm for hyperspectral imagery with adjacency effect. Remote Sens. Environ. 78, 252–263, (2001). [CrossRef] [Google Scholar]
  45. E. J. Hochberg, S. Andrefouet, and M. R. Tyler, Sea surface correction of high surface resolution Ikonos images to improve bottom mapping in near shore environment. IEEE Trans. Geosci. Remote Sens. 41, 1724–1729, (2003). [CrossRef] [Google Scholar]
  46. D. Mishra, S. Narumalani, D. Rundquist, and M. Lawson. Benthic habit mapping in tropical marine environments using Quick bird multispectral data. Photogramm. Eng. Remote Sens. 72, 1037–1048, (2006). [CrossRef] [Google Scholar]
  47. S. Andrefouet et al., Multi-site evaluation of IKONOS data for classification of tropical coral reef environments. Remote Sens. Environ. 88, 128–143, (2003). [CrossRef] [Google Scholar]
  48. M. Pal., Ensemble of support vector machines from land cover classification. Int. J. Remote Sens. 26(5), 1007–1011, (2008). [CrossRef] [Google Scholar]
  49. Y. Kavzoglu, and I. Colkesen., A kernel function analysis for support vector machines for land cover classification. Int. J. Appl. Earth Obs. Geoinf. 11, 352–359, (2009). [CrossRef] [Google Scholar]
  50. A. Mathur, and G.M. Foody, Crop classification by support vector machine with intelligently selected training data for nonoperational application. Int. J. Remote Sens. 29(8), 2227–2240, (2008). [CrossRef] [Google Scholar]
  51. H. Hakvoort, et al., Towards airborne remote sensing of water quality in the Netherlands-Validation and error analysis. ISPRS J. Photogramm. Remote Sens. 57, 171–183, (2002). [CrossRef] [EDP Sciences] [Google Scholar]
  52. S. Thiemann, and H. Kaufmann. Lake water quality monitoring using hyperspectral airborne data-A semiempirical multispectral and multitemporal approach for the Mecklenburg Lake District, Germany. Remote Sens. Environ. 81, 228–237, (2002). [CrossRef] [Google Scholar]
  53. M. J. Canty, Boosting a fast neural network for supervised land cover classification. Comput. Geosci. 35(6), 1280–1295, (2009). [CrossRef] [Google Scholar]
  54. B. Dixon, and N. Candade, Multispectral land use classification using neural networks and support vector machines, one or the other, or both? Int. J. Remote Sens. 29(4), 1185–1206, (2008). [CrossRef] [Google Scholar]
  55. P. Barry,, EO-1/Hyperion Science Data User’s Guide, Level 1_B. May 2001, http://www.eoc.csiro.au//hswww/oz_pi/doc/EO1HSDataUsersGuide.pdf (18 March 2016). [Google Scholar]
  56. R. Beck,, EO-1 User Guide-Version 2.3, Satellite Systems Branch. USGS Earth Resources Observation Systems Data Center, Cincinnati, (2003). [Google Scholar]
  57. B. W. Pengra, C. A. Johnston, and T. R. Loveland, Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sens. Environ. 108(1), 74–81 (2007). [CrossRef] [Google Scholar]
  58. D. D. Xu, G.-Q. Ni, L.-L. Jiang, Y.-T. Shen, T.-L. S. Li, Ge, and X.-B. Shu, Exploring for natural gas using reflectance spectra of surface soils. Adv. Space Res. 41, 1800–1817 (2008). [CrossRef] [Google Scholar]
  59. R. Zhang, and J. Ma, Feature selection for hyperspectral data based on recursive support vector machines. Int. J. Remote Sens. 30(14), 3669–3677 (2009). [CrossRef] [Google Scholar]
  60. P. Du, K. Tan, and X. Xing, Wavelet SVM in reproducing kernel Hilbert space for hyperspectral remote sensing image classification. Opt. Commun. 283, 4978–4984 (2010). [CrossRef] [Google Scholar]
  61. S. Pignatti, R. M. Cavalli, V. Cuomo, V. Fusilli, S. Pascucci, M. Poscolieri>, et al., Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy. Remote Sens. Environ. 113, 622–634, (2009). [CrossRef] [Google Scholar]
  62. J. Wang, Y. Chen, T. He, C. Lv, A. Liu, Application of geographic image cognition approach in land type classification using Hyperion image: A case study in China, Int. J. Appl. Earth Obs. Geoinf. 12S, S212–S222, (2010). [CrossRef] [Google Scholar]
  63. S. J Walsh, A. L. McCleary, C. F. Mena, Y. Shao, J. P. Tuttle, A. González, et., Quick Bird and Hyperion data analysis of an invasive plant species in the Galapagos Islands of Ecuador: Implications for control and land use management. Remote Sens. Environ. 112, 1927–1941, (2008). [CrossRef] [Google Scholar]
  64. C. Huang, L. S. Davis, and G. Jr. Townshend, An assessment of support vector machines for land cover classification. Int. J. Remote Sens. 23, 725–749, (2002). [CrossRef] [Google Scholar]
  65. H. Nemmour, and Y. Chibani, Multiple support vector machines for land cover change detection: An application for mapping urban extension. ISPRS J. Photogramm. 61, 125–133, (2006). [CrossRef] [Google Scholar]
  66. M. L. Clark, D. A. Roberts, and D. B. Clark, Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens. Environ. 96, 375–398, (2005). [CrossRef] [Google Scholar]
  67. D. Vyas, N. S. R. Krishnayya, K. R. Manjunat, S. S. Ray, and S. Painigraphy, Evaluation of classifiers for processing Hyperion (E-O1) data of tropical vegetation. Int. J. Appl. Earth Obs. Geoinf. 13 228–235, (2011). [CrossRef] [Google Scholar]
  68. D.G. Googenough et al., Processing Hyperion and ALI for forest classification. IEEE Trans. Geosci. Remote Sens 41, 1321–1331, (2003). [CrossRef] [Google Scholar]
  69. M. Dalponte, L. Bruzzone, L. Vescovo, and D. Gianelle, The role of spectral resolution and classifier complexity in the hyperspectral images of forest areas. Remote Sens. Environ. 113, 2345–2355, (2009). [CrossRef] [Google Scholar]
  70. A. Plaza>, et al., Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113, S110–S122, (2009). [CrossRef] [Google Scholar]
  71. F. Melgani, and L. Bruzzone, Classification of hyperspectral remote sensing image with support vector machines. IEEE T. Geosci. Remote 42, 1778–1790, (2004). [Google Scholar]
  72. M. Pal, and P. M. Mather, Some issues in the classification of DAIS hyperspectral data. Int. J. Remote Sens. 27, 2895–2916, (2006). [CrossRef] [Google Scholar]
  73. S. G. Ungar, J. S. Pearlman, J. A. Mendenhall, Reuter, D., Overview of the earth observing one (E-O1) mission. IEEE Trans. Geosci. Remote. 41, 1149–1159, (2003). [CrossRef] [Google Scholar]
  74. D. Roy, M. Wulder, T. Loveland, R. Allen, M. Anderson, D. Helder, J. Irons, D. Johnson, R. Kennedy, T. Scambos, Landsat-8: Science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154–172, (2014). [Google Scholar]
  75. D. R. Hearn, C. J. Digenis, D. E. Lencioni, Mendenhall, J. A., Evans, J. B., Walesh, R. D., E-O1 advanced land imager overview and spatial performance. IEEE Trans. Geosci. Remote Sens. 2, 897–899, (2001). [Google Scholar]
  76. Landsat 8 (L8) Data Users Handbook (2015), LSDA-1574 Version 1.0, Department of the Interior U.S. Geological Survey, EROS Sioux Falls, South Dokota. [Google Scholar]
  77. 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 Sens. Environ. 91(3–4), 354–376, (2004a). [CrossRef] [Google Scholar]
  78. P. S. Thenkabail, E. A. Enclona, M. S. Ashton, C. Legg, and M. J. D. Dieu, Hyperion, IKONOS, ALI and ETM+ sensors in the study of African rainforests. Remote Sens. Environ. 90, 23–43, (2004b). [CrossRef] [Google Scholar]
  79. B. Datt, et al., Preprocessing EO-1 Hyperion hyperspectral data support the application of agriculture indexes. IEEE Trans. Geosci. Remote Sens. 41, 1246–1259, (2003). [CrossRef] [Google Scholar]
  80. M. G. Allan, D. Hamiton, B. J. PHicks, and L. Brabyn, Landsat remote sensing of chlorophyll a concentration in central North Island lakes of New Zealand. Int. J. Remote. Sens., 32, 2037–2055, (2011). [CrossRef] [Google Scholar]
  81. Y. H. Ahn, P. Shanmugam, and J. E. Moon, Retrieval of ocean colour from high resolution multi-spectral imagery for monitoring highly dynamic ocean features. Int. J. Remote. Sens., 27, 367–392, (2006). [CrossRef] [Google Scholar]
  82. B. L. Markham, M. O. Haque, J. A. Barsi, E. Micijevic, D. L. Helder, K. J. Thome, D. Aaron, and J. S. Czapla-Myers, Landsat-7 ETM+: 12 years on-orbit reflective-band radiometric performance. IEEE Trans. Geosci. Remote Sens. 50(5), 2056–2062, (2012). [CrossRef] [Google Scholar]
  83. D. A. Palandro, et al., Qualification of two decades of shallow-water coral reef habitat decline in the Florida Keys National Marine Sanctuary using Landsat data (1984–2002). Remote Sens. Environ. 112, 3388–3399, (2008). [CrossRef] [Google Scholar]
  84. V. E. Brando, and A. G. Dekker, Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Trans. Geosci. Remote Sens. 41, 1378–1387, (2003). [Google Scholar]
  85. C. D. Mobley, and L. K. Sundman, Hydrolight 5, Ecolight User Guide, Bellevue, WA: Sequoia Scientific, Inc., (2008). [Google Scholar]
  86. Z. Lee, Y. H. Ahn, C. Mobley, and R. Arnone, Removal of surface reflected light for the measurement of remote-sensing reflectance from an above-surface platform. Opt. Express, 18, 26313–26324, (2010). [CrossRef] [Google Scholar]
  87. K. G. Ruddick, F. Ovidio, and M. Rijkeboer, Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters. Appl. Opt. 39, 897–912, (2000). [CrossRef] [Google Scholar]

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