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, Fabre Frédéric, Perrin Jose. 2016. Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana. Remote Sensing, 8 (3):e240, 18 p.

Journal article ; Article de recherche ; Article de revue à facteur d'impact Revue en libre accès total
Published version - Anglais
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Fayad et al. - 2016 - Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data Applicati.pdf

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Quartile : Q1, Sujet : REMOTE SENSING

Abstract : LiDAR data has been successfully used to estimate forest parameters such as canopy heights and biomass. Major limitation of LiDAR systems (airborne and spaceborne) arises from their limited spatial coverage. In this study, we present a technique for canopy height mapping using airborne and spaceborne LiDAR data (from the Geoscience Laser Altimeter System (GLAS)). First, canopy heights extracted from both airborne and spaceborne LiDAR were extrapolated from available environmental data. The estimated canopy height maps using Random Forest (RF) regression from airborne or GLAS calibration datasets showed similar precisions (~6 m). To improve the precision of canopy height estimates, regression-kriging was used. Results indicated an improvement in terms of root mean square error (RMSE, from 6.5 to 4.2 m) using the GLAS dataset, and from 5.8 to 1.8 m using the airborne LiDAR dataset. Finally, in order to investigate the impact of the spatial sampling of future LiDAR missions on canopy height estimates precision, six subsets were derived from the initial airborne LiDAR dataset. Results indicated that using the regression-kriging approach a precision of 1.8 m on the canopy height map was achievable with a flight line spacing of 5 km. This precision decreased to 4.8 m for flight line spacing of 50 km. (Résumé d'auteur)

Mots-clés Agrovoc : forêt tropicale, Inventaire forestier, Biomasse, biomasse aérienne des arbres, Télédétection, Satellite radar, Imagerie par satellite, Cartographie, Analyse de données, Méthode statistique

Mots-clés géographiques Agrovoc : Guyane française

Mots-clés complémentaires : Régression (statistique)

Classification Agris : K01 - Forestry - General aspects
U30 - Research methods
U10 - Computer science, mathematics and statistics

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

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) ORCID: 0000-0002-0080-3140
  • Hérault Bruno, CIRAD-ES-UMR Ecofog (GUF) ORCID: 0000-0002-6950-7286
  • El Hajj Mahmoud, NOVELTIS (FRA)
  • Fabre Frédéric, Airbus Defense and Space (FRA)
  • Perrin Jose, BRGM (FRA)

Source : Cirad-Agritrop (

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