IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2023)

Validation of the DART Model for Airborne Laser Scanner Simulations on Complex Forest Environments

  • Florian de Boissieu,
  • Florence Heuschmidt,
  • Nicolas Lauret,
  • Dav M. Ebengo,
  • Gregoire Vincent,
  • Jean-Baptiste Feret,
  • Tiangang Yin,
  • Jean-Philippe Gastellu-Etchegorry,
  • Josiane Costeraste,
  • Marie-Jose Lefevre-Fonollosa,
  • Sylvie Durrieu

DOI
https://doi.org/10.1109/JSTARS.2023.3302030
Journal volume & issue
Vol. 16
pp. 8379 – 8394

Abstract

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With the recent progresses in lidar technology for Earth remote sensing, the development of a reliable lidar simulator is becoming central in order to define specifications for new sensors, perform intercomparisons, train machine learning algorithms, and help transferring information from one scale to another. The discrete anisotropic radiative transfer (DART) model includes such a lidar simulator. Although already tested on several virtual scenes, the DART outputs still need to be rigorously evaluated against actual sensor acquisitions, especially on real complex scenes of various forest types, such as dense tropical forests. That is the purpose of the present study. A real airborne laser scanner (ALS) with full-waveform capacity was first radiometrically calibrated on targets of measured reflectance. The properties of the ALS system were then introduced in the DART model, along with a 3-D virtual scene built from terrestrial laser scans and spectroscopic measurements acquired on a forest plot near the calibration site. Finally, an ALS acquisition was simulated and the shape and magnitude of the waveforms were compared with real acquisitions. The comparison between measured and simulated data was performed at different scales by aggregating waveform samples into a 3-D grid with a vertical resolution of 1 m and a horizontal resolution ranging from 2 to 80 m. Results showed a high similarity between simulated and measured waveforms at all scales with R2>0.9 and NRMSE<10%. These promising results open up numerous perspectives for improved spaceborne and airborne lidar data processing and for the development of new systems.

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