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Author (up) Fayad, I.; Baghdadi, N.; Bailly, J.-S.; Barbier, N.; Gond, V.; Herault, B.; El Hajj, M.; Fabre, F.; Perrin, J. pdf  url
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  Title Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana Type Journal Article
  Year 2016 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 8 Issue 3 Pages 240  
  Keywords Airborne LiDAR; Canopy height mapping; Forests; French Guiana; ICESat GLAS  
  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. © 2016 by the authors.  
  Address BRGM, 3 Avenue Claude Guillemin, Orléans, France  
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  Area Expedition Conference  
  Notes Export Date: 22 April 2016 Approved no  
  Call Number EcoFoG @ webmaster @ Serial 675  
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