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Aerial Remote Sensing: History
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Aerial remote sensing is the subfield of remote sensing concerned with collecting geospatial data about Earth's surface and atmosphere using airborne platforms—such as fixed-wing aircraft, helicopters, and unmanned aerial vehicles (UAVs)—equipped with non-contact sensors [1][2]. Operating beneath satellite orbital trajectories, these platforms carry passive instruments like multispectral cameras or active sensors such as Light Detection and Ranging (LiDAR) to measure reflected or emitted electromagnetic radiation [2][3][4]. The distinguishing feature of aerial remote sensing lies in its spatial scale and operational flexibility; compared to satellite platforms, airborne platforms provide significantly higher spatial resolution and flexible flight scheduling, though they trade off the automated, global-scale temporal coverage characteristic of orbital systems [1][3][4]. Structurally allied with photogrammetry and geographic information systems (GIS), aerial remote sensing converts raw sensor records into georeferenced orthomosaics, digital elevation models, and point clouds [2][4]. While historically dominated by piloted photography, modern interpretations encompass multi-sensor payload integration across diverse airborne systems [1][2][3][4].

  • airborne remote sensing
  • remote sensing
  • aerial imagery
  • unmanned aerial systems
  • photogrammetry

References

  1. Jensen, John R. 2006. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd ed. Upper Saddle River, NJ: Pearson Prentice Hall.
  2. Lillesand, Thomas M., Ralph W. Kiefer, and Jonathan W. Chipman. 2015. Remote Sensing and Image Interpretation. 7th ed. Hoboken, NJ: John Wiley & Sons. 768 pp. ISBN 978-1-118-34328-9.
  3. Charles Toth; Grzegorz Jóźków; Remote sensing platforms and sensors: A survey. ISPRS J. Photogramm. Remote. Sens. 2016, 115, 22-36, 10.1016/j.isprsjprs.2015.10.004.
  4. I. Colomina; P. Molina; Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79-97, 10.1016/j.isprsjprs.2014.02.013.
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