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Remote Sens. 2016, 8(3), 240; doi:10.3390/rs8030240

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

1
IRSTEA, UMR TETIS, 500 rue Jean François Breton, 34093 Montpellier Cedex 5, France
2
AgroParisTech, UMR LISAH, 2 Place Pierre Viala, 34060 Montpellier, France
3
IRD, UMP AMAP, 2050 Boulevard de la Lironde, 34000 Montpellier, France
4
CIRAD, UPR B&SEF, Campus de Baillarguet, 34398 Montpellier Cedex 5, France
5
CIRAD, UMR EcoFoG (AgroParisTech, Cirad, CNRS, Inra, Université des Antilles, Université de la Guyane), Campus Agronomique, BP 709, 97310 Kourou, French Guiana
6
NOVELTIS, 153 rue du Lac, 31670 Labège, France
7
Airbus Defense and Space, 31 rue des Cosmonautes Z.I. du Palays, 31402 Toulouse, France
8
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]   |  

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

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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.

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