An Investigation into the Location of the Crashed Aircraft Through the Use of Free Satellite Images

  • Azad RasulEmail author


Remote sensing data and techniques are being utilized for various purposes including natural disasters such as earthquake as well as flood mapping and detection. The research aims to consume liberates Landsat 8 images for investigating crashed airplanes such as MH370. Overall approximately 300 Landsat images with less than 10% clouds within a defined period were processed and utilized through the Google Earth Engine Platform. Due to the variation of materials as well as the colour of airplane body being different from the area in which the plane crash occurred, the characteristics of the template of a plane’s shape should be different in terms of albedo, temperature as well as a vegetation index value. The research demonstrates the potential of Landsat 8 data especially, the NDVI, the albedo and reflectance of band 4 are capable of distinguishing between the plane and its surrounding green area. Therefore, our result confirms that during the research period, there was no plane on the location and further adds that there is no evidence from the remote sensing to justify the presence of the crashed MH370 in the site as earlier reported.


Remote sensing Crashed aircraft NDVI Albedo MH370 Landsat 8 


Untersuchung zur Positionsbestimmung von abgestürzten Flugzeugen auf Grundlage von frei verfügbaren Satellitenszenen. Mit dieser Arbeit soll die Eignung von Landsat 8 Szenen für die Suche nach abgestürzten Flugzeugen wie der MH370 gezeigt werden. Verarbeitet wurden ca. 300 Szenen mit einem Bewölkungsgrad von weniger als 10% unter Nutzung der Google Earth Engine. Die Materialien des Flugzeugrumpfes und seine Farbe erlauben eine deutliche Unterscheidung von der Vegetation in der Umgebung. Es wurde gezeigt, dass bei Landsat 8 Szenen insbesondere der NDVI, die Albedo und die Reflektanz von Band 4 diese Unterscheidung gewährleisten können.



The author would like to thank the Google Earth Engine program. Furthermore, much gratitude goes to the USGS, for providing the research with liberates Landsat 8 images. Thanks to Dr. Saad Ibrahim for proofreading the paper.


This research received no external funding.

Compliance with Ethical Standards

Conflict of interest

The author declares no conflict of interest.


  1. Aytekin Ö, Zöngür U, Halici U (2013) Texture-based airport runway detection. IEEE Geosci Remote Sens Lett 10:471–475CrossRefGoogle Scholar
  2. Cheng G, Han J (2016) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 117:11–28CrossRefGoogle Scholar
  3. Gorelick N et al (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27CrossRefGoogle Scholar
  4. Hemalatha PC, Anitha M (2014) Aircraft recognition in high resolution satellite images. Int J Eng Sci IJES 3:21–25Google Scholar
  5. Jansen E, Coppini G, Pinardi N (2016) Drift simulation of MH370 debris using superensemble techniques. Nat Hazards Earth Syst Sci 16:1623–1628CrossRefGoogle Scholar
  6. Liu L, Shi Z (2014) Airplane detection based on rotation invariant and sparse coding in remote sensing images. Opt Int J Light Electron Opt 125:5327–5333CrossRefGoogle Scholar
  7. Nesterov O (2018) Consideration of various aspects in a drift study of MH370 debris. Ocean Sci 14:387–402CrossRefGoogle Scholar
  8. Xu Y et al (2018) Rapid airplane detection in remote sensing images based on multilayer feature fusion in fully convolutional neural networks. Sensors 18:2335CrossRefGoogle Scholar
  9. Yao X, Han J, Guo L, Bu S, Liu Z (2015) A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF. Neurocomputing 164:162–172CrossRefGoogle Scholar
  10. Zhang W, Sun X, Wang H, Fu K (2015) A generic discriminative part-based model for geospatial object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 99:30–44CrossRefGoogle Scholar

Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2019

Authors and Affiliations

  1. 1.Department of GeographySoran UniversitySoranIraq

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