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Aerial and Satellite Imagery and Big Data: Blending Old Technologies with New Trends

Abstract

Over the past decades, the successful employment of aerial and satellite imagery and remote sensing (RS) data has been very diverse and important in many scientific fields. Firstly, a brief review of RS history is presented in section one. Then, basic properties, which are also challenges, of RS big data are concisely discussed. Volume, variety and velocity are mainly described as characteristics of RS big data while variety, value and visualization are primarily denoted as new challenges. The third section is concentrated on justifying the relevance of RS big data in today’s society and the needs to integrate it with other kind of data sources to develop useful services. In this sense, a special section is dedicated to Copernicus initiative and some case studies of specific applications are also shown. Finally, some general conclusions are presented paying attention to the spatial nature of RS big data, which gives it a special added value in the new digital era.

Keywords

  • Aerial and satellite imagery
  • Remote sensing
  • Spatial big data
  • Integration

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Fig. 2.1
Fig. 2.2

Source Imagine the Universe!—NASA

Fig. 2.3
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Fig. 2.8
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Fig. 2.11

Source http://copernicus.eu/data-access

References

  1. Davenport A (2000) The history of photography: an overview, 2nd edn. The University of New Mexico Press, Albuquerque

    Google Scholar 

  2. Barber M, Wickstead H (2010) One immense black spot’: aerial views of London 1784–1918. Lond J 35:236–254

    CrossRef  Google Scholar 

  3. Campbell JB, Wynne RH (2011) Introduction to remote sensing, 5th edn. The Guilford Press, New York

    Google Scholar 

  4. Butler MJA, Mouchot MC, Barale V, LeBlanc C (1988) The application of remote sensing technology to marine fisheries: an introductory manual. Food and Agriculture Organization of United Nations, Rome

    Google Scholar 

  5. Gosh S (1981) History of Photogrammetry. Laval University, Québec

    Google Scholar 

  6. Schenk T (2005) Introduction to photogrammetry, 1st edn. The Ohio State University, Columbus

    Google Scholar 

  7. Stichelbaut B (2006) The application of First World War aerial photography to archaeology: the Belgian images. Antiquity 80:161–172

    CrossRef  Google Scholar 

  8. The Professional Aerial Photographers Association (2017) History of aerial photography

    Google Scholar 

  9. Monmonier M (2002) Aerial photography at the agricultural adjustment administration: acreage controls, conservation benefits, and overhead surveillance in the 1930s. Photogramm Eng Remote Sens 68:1257–1262

    Google Scholar 

  10. Rango A, Havstad K, Estell R (2011) The utilization of historical data and geospatial technology advances at the Jornada experimental range to support Western America ranching culture. Remote Sens 3:2089–2109

    CrossRef  Google Scholar 

  11. Cracknell A, Haynes L (1991) Introduction to remote sensing, 2nd edn. Taylor & Francis Ltd., London

    Google Scholar 

  12. Ruffner K (2017) Corona: America’s first satellite program. Central Intelligence Agency, Washington, DC

    Google Scholar 

  13. NASA Science Website (2016) TIROS: The television infrared observation satellite program. In: NASA Science Website

    Google Scholar 

  14. Graham S (1999) Remote sensing: introduction and history. In: NASA Earth Observatory. https://earthobservatory.nasa.gov/Features/RemoteSensing/

  15. Mack P (1990) Viewing the earth: The social construction of the landsat satellite sytem. The MIT Press, London

    Google Scholar 

  16. NASA Landsat Science (2017) History: from the beginning. In: NASA Landsat Science

    Google Scholar 

  17. Van Wie P, Stein M (1976) A landsat digital image rectification system. Greenbelt

    Google Scholar 

  18. Patra P (2010) Remote sensing and geographical information system (gis). Assoc Geogr Stud

    Google Scholar 

  19. Antenucci JC, Brown K, Croswell PL, Kevany MJ, Archer H (1991) Geographic information systems. A guide to the technology. New York

    Google Scholar 

  20. Foresman T (2010) GIS, History of geographic information systems. Encycl Geogr 1281–1284

    Google Scholar 

  21. NASA Jet Propulsion Laboratory (2010) AVIRIS—airborne visible/infrared imaging spectrometer—general overview. https://aviris.jpl.nasa.gov/aviris/

  22. NASA Terra—The EOS Flagship (2017) Terra Instruments|Terra. https://terra.nasa.gov/about/terra-instruments

  23. Mohamed B, Werner K (2007) Geospatial information bottom-up: a matter of trust and semantics. In: Fabrikant SI, Wachowicz M (eds) The European information society. Springer, pp 365–387

    Google Scholar 

  24. Farman J (2010) Mapping the digital empire: Google earth and the process of postmodern cartography. New Media Soc 12:869–888

    CrossRef  Google Scholar 

  25. Ma Y, Wu H, Wang L, Huang B, Ranjan R, Zomaya A, Jie W (2015) Remote sensing big data computing: challenges and opportunities. Futur Gener Comput Syst 51:47–60. https://doi.org/10.1016/j.future.2014.10.029

    CrossRef  Google Scholar 

  26. Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class Hadoop and streaming data, 1st edn. McGraw-Hill Osborne Media (IBM)

    Google Scholar 

  27. NASA (2010) On-orbit satellite servicing study

    Google Scholar 

  28. Kambatla K, Kollias G, Kumar V, Grama A (2014) Trends in big data analytics. J Parallel Distrib Comput 74:2561–2573

    CrossRef  Google Scholar 

  29. NASA Earth Data (2017) Getting petabytes to people: how the EOSDIS facilitates earth observing data discovery and use. https://earthdata.nasa.gov/getting-petabytes-to-people-how-the-eosdis-facilitates-earth-observing-data-discovery-and-use

  30. ITC (2017) ITC-ITC’s database of satellites and sensors—all sensors. https://www.itc.nl/Pub/sensordb/AllSensors.aspx

  31. Villars RL, Olofson CW, Eastwood M (2011) Big data: what it is and why you should care. White Pap. https://doi.org/10.1080/01616846.2017.1313045

    CrossRef  Google Scholar 

  32. Justice CO, Vermote E, Townshend JRG, Defries R, Roy DP, Hall DK, Salomonson VV, Privette JL, Riggs G, Strahler A, Lucht W, Myneni RB, Knyazikhin Y, Running SW, Nemani RR, Zhengming Wan Z, Huete A, van Leeuwen W, Wolfe RE, Giglio L, Muller J, Lewis P, Barnsley MJ (1998) The moderate resolution imaging spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans Geosci Remote Sens 36:1228–1249. https://doi.org/10.1109/36.701075

    CrossRef  Google Scholar 

  33. Datameer (2017) Getting more value from your data lake. https://www.datameer.com/. Accessed 12 Sep 2017

  34. Heger D, Ogunleye J (2015) Big data, the cloud and challenges of operationalising big data analytics. Curr Stud Comp Educ Sci Technol 2:427–435

    Google Scholar 

  35. Mazhar M, Rathore U, Paul A, Ahmad A, Chen B-W, Huang B, Ji W (2015) Real-time big data analytical architecture for remote sensing application. IEEE J Sel Top Appl Earth Obs, Remote Sens, p 8

    Google Scholar 

  36. Datameer (2017) Best practice for a successful Big Data jouney

    Google Scholar 

  37. Freitas RM (2011) Virtual laboratory of remote sensing time series: visualization of MODIS EVI2 data set over South America. J Comput Interdiscip Sci 2:57–68. https://doi.org/10.6062/jcis.2011.02.01.0032

    CrossRef  Google Scholar 

  38. Vatsavay R, Chandola V (2016) Guest editorial: big spatial data. Geoinformatica. https://doi.org/10.1007/s10707-016-0269-7

    CrossRef  Google Scholar 

  39. Zicari RV, Rosselli M, Ivanov T, Korfiatis N, Tolle K, Niemann R, Reichenbach C (2016) Setting up a big data project: challenges, opportunities, technologies and optimization. In: Big data optimization: recent developments and challenges. Studies in big data. https://doi.org/10.1007/978-3-319-30265-2_2

    Google Scholar 

  40. González SM, Berbel T dos RL (2014) Considering unstructure data for OLAP: a feasability study using a systematic review. Rev Sist Informação da FSMA 14:26–35

    Google Scholar 

  41. Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19:171–209. https://doi.org/10.1007/s11036-013-0489-0

    CrossRef  Google Scholar 

  42. Khan N, Yaqoob I, Abaker I, Hashem T (2014) Big data: survey, technologies, opportunities, and challenges. Sci World J 18

    Google Scholar 

  43. Lang S (2008) Object-based image analysis for remote sensing applications: modeling reality—dealing with complexity. In: Blaschke T, Lang S, Hay GJ (eds) Object based image anal. Springer, pp 3–27

    Google Scholar 

  44. Hay GJ, Castilla G (2006) Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT). OBIA, Int Arch Photogramm Remote Sens Spat Inf Sci 3

    Google Scholar 

  45. Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16

    CrossRef  Google Scholar 

  46. Audubon, Cornell Lab of Orithnology (2017) About eBird|eBird. http://ebird.org/content/ebird/about/

  47. Wood C, Sullivan B, Iliff M, Fink D, Kelling S (2011) eBird: engaging birders in science and conservation. PLoS Biol 9

    Google Scholar 

  48. Fink D, Hochachka WM, Zuckerberg B, Winkler DW, Shaby B, Munson MA, Hooker G, Riedewald M, Sheldon D, Kelling S (2010) Spatiotemporal exploratory models for broad-scale survey data. Ecol Appl 20:2131–2147. https://doi.org/10.1890/09-1340.1

    CrossRef  Google Scholar 

  49. Beddington JR, Agnew DJ, Clark CW (2007) Current problems in the management of marine fisheries. Science 80(316):1713–1716

    CrossRef  Google Scholar 

  50. Gorospe KD, Michaels W, Pomeroy R, Elvidge C, Lynch P, Wongbusarakum S, Brainard RE (2016) The mobilization of science and technology fisheries innovations towards an ecosystem approach to fisheries management in the Coral Triangle and Southeast Asia. Mar Policy 74:143–152. https://doi.org/10.1016/j.marpol.2016.09.014

    CrossRef  Google Scholar 

  51. Yamaguchi T, Asanuma I, Park JG, Mackin KJ, Mittleman J (2016) Estimation of vessel traffic density from Suomi NPP VIIRS day/night band. Ocean 2016 MTS/IEEE Monterey. OCE 2016:5–9. https://doi.org/10.1109/OCEANS.2016.7761309

    CrossRef  Google Scholar 

  52. Straka WC, Seaman CJ, Baugh K, Cole K, Stevens E, Miller SD (2015) Utilization of the suomi national polar-orbiting partnership (npp) visible infrared imaging radiometer suite (viirs) day/night band for arctic ship tracking and fisheries management. Remote Sens 7:971–989. https://doi.org/10.3390/rs70100971

    CrossRef  Google Scholar 

  53. Addo KA (2010) Urban and peri-urban agriculture in developing countries studied using remote sensing and in situ methods. Remote Sens 2:497–513. https://doi.org/10.3390/rs2020497

    CrossRef  Google Scholar 

  54. Stefanov WL (2001) Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to urban centers. Remote Sens Environ 77:173–185. https://doi.org/10.1016/S0034-4257(01)00204-8

    CrossRef  Google Scholar 

  55. Yuliang Q, Buzhou M, Jiuliang F (2000) Study on monitoring farmland by using remote sensing and GIS in Shanxi China. Adv Space Res 26:1059–1064. https://doi.org/10.1016/S0273-1177(99)01118-7

    CrossRef  Google Scholar 

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Correspondence to P. Fdez-Arroyabe .

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Salazar Loor, J., Fdez-Arroyabe, P. (2019). Aerial and Satellite Imagery and Big Data: Blending Old Technologies with New Trends. In: Dey, N., Bhatt, C., Ashour, A. (eds) Big Data for Remote Sensing: Visualization, Analysis and Interpretation. Springer, Cham. https://doi.org/10.1007/978-3-319-89923-7_2

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