Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne lidar data: application on French Guiana

Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne lidar data: application on French Guiana. Fayad Ibrahim, Baghdadi Nicolas, Bailly Jean Stéphane, Barbier Nicolas, Gond Valéry, Hérault Bruno, El Hajj Mahmoud, Lochard Jeremie, Perrin Jose. 2015. In : Proceedings IGARSS 2015 Remote sensing: understanding the earth for a safer world. IEEE. Piscataway : IEEE, 4109-4112. ISBN 978-1-4799-7929-5

International Geoscience and Remote Sensing Symposium, Milan, Italie, Juillet 2015/Juillet 2015.
Version post-print - Anglais
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Résumé : LiDAR remote sensing has been shown to be a good technique for the estimation of forest parameters such as canopy heights and aboveground biomass. Whilst airborne LiDAR data are in general very dense but only available over small areas due to the cost of their acquisition, spaceborne LiDAR data acquired from the Geoscience Laser Altimeter System (GLAS) have a coarser acquisition density associated with a global cover. It is therefore valuable to analyze the integration relevance of canopy heights estimated from LiDAR sensors with ancillary data such as geological, meteorological, and phenological variables in order to propose a forest canopy height map with good precision and high spatial resolution. In this study, canopy heights extracted from both airborne and spaceborne LiDAR, were first extrapolated from available environmental data. The estimated canopy height maps using random forest (RF) regression from the airborne or GLAS calibration datasets showed similar precisions (RMSE better than 6.5 m). In order to improve the precision of the canopy height estimates regression-kriging (kriging of RF regression residuals) was used. Results indicated an improvement in the RMSE (decrease from 6.5 to 4.2 m) for the regression-kriging maps from the GLAS dataset, and from 5.8 to 1.8 m for the regression-kriging map from the airborne LiDAR dataset. (Résumé d'auteur)

Classification Agris : K01 - Foresterie - Considérations générales
U40 - Méthodes de relevé
U10 - Méthodes mathématiques et statistiques
F62 - Physiologie végétale : croissance et développement

Auteurs et affiliations

  • Fayad Ibrahim, IRSTEA (FRA)
  • Baghdadi Nicolas, IRSTEA (FRA)
  • Bailly Jean Stéphane, AgroParisTech (FRA)
  • Barbier Nicolas, IRD (FRA)
  • Gond Valéry, CIRAD-ES-UPR BSef (FRA)
  • Hérault Bruno, CIRAD-ES-UMR Ecofog (GUF)
  • El Hajj Mahmoud, NOVELTIS (FRA)
  • Lochard Jeremie, Airbus Defense and Space (FRA)
  • Perrin Jose, BRGM (FRA)

Source : Cirad-Agritrop

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