Next Article in Journal
Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia
Next Article in Special Issue
Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements
Previous Article in Journal
Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary
Previous Article in Special Issue
Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2016, 8(3), 240;

Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana

IRSTEA, UMR TETIS, 500 rue Jean François Breton, 34093 Montpellier Cedex 5, France
AgroParisTech, UMR LISAH, 2 Place Pierre Viala, 34060 Montpellier, France
IRD, UMP AMAP, 2050 Boulevard de la Lironde, 34000 Montpellier, France
CIRAD, UPR B&SEF, Campus de Baillarguet, 34398 Montpellier Cedex 5, France
CIRAD, UMR EcoFoG (AgroParisTech, Cirad, CNRS, Inra, Université des Antilles, Université de la Guyane), Campus Agronomique, BP 709, 97310 Kourou, French Guiana
NOVELTIS, 153 rue du Lac, 31670 Labège, France
Airbus Defense and Space, 31 rue des Cosmonautes Z.I. du Palays, 31402 Toulouse, France
BRGM, 3 Avenue Claude Guillemin, Orléans 45060, France
Author to whom correspondence should be addressed.
Academic Editors: Lars T. Waser, Josef Kellndorfer and Prasad S. Thenkabail
Received: 3 November 2015 / Revised: 3 February 2016 / Accepted: 4 March 2016 / Published: 16 March 2016
(This article belongs to the Special Issue Remote Sensing of Vegetation Structure and Dynamics)
View Full-Text   |   Download PDF [7832 KB, uploaded 16 March 2016]   |  


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. View Full-Text
Keywords: canopy height mapping; airborne LiDAR; ICESat GLAS; forests; French Guiana canopy height mapping; airborne LiDAR; ICESat GLAS; forests; French Guiana

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Fayad, I.; Baghdadi, N.; Bailly, J.-S.; Barbier, N.; Gond, V.; Hérault, B.; El Hajj, M.; Fabre, F.; Perrin, J. Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana. Remote Sens. 2016, 8, 240.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top