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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 1, pp. 322-333

Distributed data management of hyperspectral remote sensing data for scientific purposes and applications

I.A. Uvarov1 , A.M. Matveev1 , M.A. Bourtsev1 , E.A. Loupian1 , A.A. Mazurov1 , A.A. Proshin1 , V.P. Savorskiy2 , O.A. Sydneva1 
1 Space Research Institute, Russian Academy of Sciences, Moscow, Russia
2 V.A. Kotelnikov Institute of Radioengineering and Electronics, Fryazino Department, Russian Academy of Sciences, Fryazino, Moscow Region 141190, Russia
The article discusses the issues of management of hyperspectral (HS) remotely sensed data for using in various scientific projects. The main scientific problems traditionally solved with the use of HS data are overviewed. A particular interest to systems and HS data analysis tools providing joint processing with other Earth remote sensing data is emphasized. The paper describes a technology developed to build HS data processing modules to be included in various information systems aimed at distributed remotely sensed data analysis in the framework of scientific projects. The developed technology enables automated HS data acquisition from a number of data centers, HS data archiving with fast access capability, creation of specialized user interfaces providing distributed scientific teams with HS data lookup and analysis functionality. The technology is implemented in HS data processing subsystems in the framework of several specialized scientific information systems, including the “See the Sea” system aimed at the study of the world ocean, the “VolSatView” system created to provide the remote sensing toolkit for volcanology scientists, the “VEGA Science” system which realizes information support of the biosphere state and dynamics research.
Keywords: remote sensing, hyperspectral satellite date, information systems , distributed data processing.
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References:

  1. Balashov I.V., Khalikova O.A., Bourtsev M.A., Loupian E.A., Matveev A.M., Organizatsiya avto maticheskogo polucheniya naborov informatsionnykh produktov iz tsentrov arkhivatsii i rasprostraneniya sputnikovykh i meteodannykh (Organization of automatic data acquisition from satellite and meteorological data archiving and distribution centers), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol.10, No. 3, pp. 9–20.
  2. Bartalev S.A., Ershov D.V., Loupian E.A., Tolpin V.F., Vozmozhnosti ispol'zovaniya sputnikovogo servisa VEGA dlya resheniya razlichnykh zadach monitoringa nazemnykh ekosistem (Possibilities of satellite service “VEGA” using for different tasks of land ecosystems monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 1, pp. 49–56.
  3. Gordeev E.I., Girina O.A., Loupian E.A., Efremov V.Yu., Sorokin A.A., Mel’nikov D.V., Manevich A.G., Romanova I.M., Korolev S.P., Kramareva L.S., Vozmozhnosti ispol’zovaniya dannykh giperspektral’nykh sputnikovykh nabludenij dlya izucheniya aktivnosti vulkanov Kamchatki s pomosch’yu geoportala VolSatView (Using satellite hyperspectral data to study the activity of Kamchatka volcanoes on the basis of the VolSatView geoportal), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol.11, No. 1, pp. 267–284.
  4. Efremov V.Yu., Loupian E.A., Mazurov A.A., Proshin A.A., Flitman E.V., Tekhnologiya postroeniya avtomatizirovannykh sistem khraneniya sputnikovykh dannykh (The technology for creation of satellite data automated storage systems), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2004, No 1, pp. 437–443.
  5. Loupian E.A., Balashov I.V., Bourtsev M.A., Efremov V.Yu., Mazurov A.A., Mal’zev D.V., Matveev A.M., Proshin A.A., Tolpin V.A., Khalikova O.A., Krasheninnikova Yu.S., Vozmozhnosti raboty s dolgovremennym arkhivom dannykh sputnikov LANDSAT po territorii Rossii i prigranichnykh stran (Opportunities to work with long-term archive of LANDSAT satellite data on the territory of Russia and neighboring countries), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 3, pp. 307–315.
  6. Loupian E.A., Matveev A.M., Uvarov I.A., Bocharova T.Yu., Lavrova O.Yu., Sputnikovyi servis See the Sea - instrument dlya izucheniya protsessov i yavlenii na poverkhnosti okeana (The satellite service “See the Sea“ - a tool for the study of oceanic phenomena and processes), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No 2, pp. 251–262.
  7. Loupian E.A., Savin I.Yu., Bartalev S.A., Tolpin V.A., Balashov I.V., Plotnikov D.E. Sputnikovyi servis monitoringa sostoyaniya rastitel'nosti “VEGA” (Satellite service for vegetation monitoring “VEGA”) Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No.1, pp. 190–198.
  8. Tolpin V.A., Balashov I.V., Efremov V.Yu., Loupian E.A., Proshin A.A., Uvarov I.A., Flitman E.V. Sozdanie interfeisov dlya raboty s dannymi sovremennykh sistem distantsionnogo monitoringa (sistema GEOSMIS) (The GEOSMIS system: Developing interfaces to operate data in modern remote monitoring systems), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 3, pp. 93–108.
  9. Almond, Samuel The Remote Sensing Of Oil Slicks From Satellite Platforms. 2000. http://www.geog.ucl.ac.uk/~salmond/essay.html.
  10. Bannehr L., Luhmann Th., Piechel J., Roelfs T., Schmidt A., Extracting roof parameters and head bridges over the city of Oldenburg from hyperspectral, thermal, and airborne laser scanning data, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011, Vol. XXXVIII-4/W19, pp. 17–22.
  11. Bian M., Skidmore A., Schlerf M., Liu Y., Wang T., Estimating biochemical parameters of tea (camellia sinensis (L.)) using hyperspecttral techniques, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B8, pp. 237–241.
  12. Shibayama M. and Akiyama T., Seasonal visible, nearinfrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass, Remote Sensing of Environment, 1989, Vol. 27, pp. 119–127
  13. Chisense C., Classification of roof materials using hyperspectral data, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B7, pp. 103–107.
  14. Chisense C., Classification of Roofs using Hyperspectral Data, Master thesis, 2011, University of Applied Sciences Stuttgart, Germany
  15. Cloutis E.A., Spectral reflectance properties of hydrocarbons, remote-sensing implications, Science, 1989, Vol. 245, pp. 165–168.
  16. Dennison P., Fire detection in imaging spectrometer data using atmospheric carbon dioxide absorption, International Journal of Remote Sensing, 2006, Vol. 27, No. 4, pp. 3049–3055.
  17. Dennison P., Roberts D., Daytime fire detection using airborne hyperspectral data, Remote Sensing of Environment, 2009, Vol. 113, No. 8, pp. 1646–1657.
  18. De Souza E., Vicens R., Rosa A., Cruz C., Spectral analysis of different vegetation cover using the Hyperion sensor – a case study in the state of Rio de Janeiro – Brasil, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B7, pp.109–111.
  19. EO-1 User Guide, v. 2.3., University of Cicinatty, 2003.
  20. Ellis J.M., Davis H.H., Zamudio J.A., Exploring for onshore oil seeps with hyperspectral imaging, Oil and Gas Journal, 2001, No.10, pp. 49–56.
  21. Foudan M.F. Salem., Hyperspectral remote sensing: A new approach for oil spill detection and analysis, Doctor Dissertation, 2003, George Mason University (USA).
  22. Fung T., Ma F.Y. and Siu W.L., Hyperspectral data analysis for subtropical tree species recognition, Geoscience and Remote Sensing Symposium Proceedings, IGARSS '98. IEEE International, 1998, Vol. 3, pp. 1298–1300.
  23. Goetz A. F. H., G. Vane, J. E. Solomon, and B. N. Rock., Imaging Spectrometry for Earth Remote Sensing, Science, 1985, Vol. 228, pp. 1147–1153.
  24. Heather Freeman., Evaluation of the use of hyperspectral imagery for identification of microseeps near Santa Barbara, California, Sep. 26, 2003.
  25. Hestir E. L., Khanna S., Andrew M. E., Santos M. J., Viers J. H., Greenberg J. A., Rajapakse S. S. and Ustin S. L., Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem, Remote Sensing of Environment, 2008, Vol. 112, pp. 4034–4047.
  26. Horig B., Kuhn F., Oschutz F., Lehmann F., HyMap hyperspectral remote sensing to detect hydrocarbons, Int. J. Remote Sensing, 2001, Vol.22, No. 8, pp. 1213–1422.
  27. Inoue Y., Peñuelas P., Miyata A., and Mano M., Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice, Remote Sensing of Environment, 2008, Vol. 1112, pp. 156–172.
  28. Kurz T., Buckley S., Howell J., Close range hyperspectral imaging integrated with terrestrial lidar scanning applied to rock characterisation at centimetre scale, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B5, pp. 417–422.
  29. Lee J.-D., Dewitt B., Lee S.-S., Bhang K.-J., Sim J.-B., Analysis of concrete reflectance characteristics using spectrometer and VNIR hyperspectral camera, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B7, pp. 127–130.
  30. Mohammadi M., Road Classification and Condition Determination using Hyperspectral Imagery, Master thesis, 2011, University of Applied Sciences Stuttgart, Germany.
  31. Nakanishi T., Imai Y., Morita T., Akamatsu Y., Odagawa S., Takeda T., Kashimura O., Evaluation of wheat growth monitoring methods based on hyperspectral data of later grain filling and heading stages in western Australia, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B8, pp. 295–300.
  32. Newnham G., Lazaridis D., Sims N., Robinson A., Culvenor D., Assessing the significance of Hyperion spectral bands in forest classification, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B7, pp. 147–149.
  33. Phil P. R. P., Crisp P. T., Surface geochemical prospecting methods used for oi1 and gas prospecting, Journal of Geochemical Exploration, 1982, Vol. 17, No. 1, pp. l–34.
  34. Ricco A., Giunta G., Landi T., Migliaccio M., Remote optical observation of biomass burning: A feasibility and experimental case study with the SIM.GA hyperspectral system, International Journal of Remote Sensing, 2011, Vol. 32, No. 21, pp. 6241–6259
  35. Roberts D., Dennison P., Gardner M., Hetzel Y., Ustin S., Lee C., Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible Infrared Imaging Spectrometer, IEEE Transactions on Geoscience and Remote Sensing, 2003, Vol. 41, No. 6, pp. 1297–1310.
  36. Tian Q., Study on oil-gas reservoir detecting methods using hyperspectral remote sensing, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012. Vol. XXXIX-B7, pp. 157–162.
  37. Torbick N. and Becker B., Evaluating Principal Components Analysis for identifying optimal bands using wetland hyperspectral measurements from the Great Lakes, USA, Remote Sensing, 2009, Vol. 1, No. 3, pp. 208–417.
  38. White D., Lewis M., Mapping the wetland vegetation communities of the Australian Great Artesian Basin springs using SAM, MTMF and spectrally segmented PCA hyperspectral analysis, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012, Vol. XXXIX-B7, pp. 163–165.
  39. Williams A. and Lawrence G., The role of satellite seep detection in exploring the South Atlantic's ultra-deep water, Surface Exploration case Histories, etc, AAPG Studies in Geology, 2002, Vol. 48.
  40. Zomer R. J., Trabucco A., Ustin S. L., Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing, Journal of Environmental Management, 2009, Vol. 90, pp. 2170–2177.