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Remote Sens. 2017, 9(3), 213; doi:10.3390/rs9030213

Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas

1
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (Irstea), Unité Mixte de Recherche (UMR) Territoires, Environnement, Télédétection et Information Spatiale (TETIS), 500 rue Jean François Breton, 34093 Montpellier CEDEX 5, France
2
Centre de coopération internationale en recherche agronomique pour le développement (Cirad), Unité Propre de Recherche (UPR) Forêts et Sociétés (F&S), F-34398, Montpellier, France
3
Joint Research Center of the European Commission, Bio-economy unit, I-21027 Ispra, Italy
4
AgroParisTech, Unité Mixte de Recherche (UMR) Laboratoire d’étude des interactions Sol-Agrosystème- Hydrosystème (LISAH), 2 place Pierre Viala, 34060 Montpellier, France
*
Author to whom correspondence should be addressed.
Academic Editors: Lars T. Waser and Prasad Thenkabail
Received: 21 October 2016 / Revised: 8 February 2017 / Accepted: 22 February 2017 / Published: 25 February 2017
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Abstract

Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent’s AGB map) by using a map of correction factors generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data. The Vieilledent’s AGB map of Madagascar was established using optical images, with parameters calculated from the SRTM Digital Elevation Model, climatic variables, and field inventories. In the present study, first, GLAS LiDAR data were used to obtain a spatially distributed (GLAS footprints geolocation) estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density of 0.52 footprint/km2. Second, the difference between the AGB from the Vieilledent’s AGB map and GLAS AGB at each GLAS footprint location was calculated, and additional spatially distributed correction factors were obtained. Third, an ordinary kriging interpolation was thus performed by taking into account the spatial structure of these additional correction factors to provide a continuous correction factor map. Finally, the existing and the correction factor maps were summed to improve the Vieilledent’s AGB map. The results showed that the integration of GLAS data improves the precision of Vieilledent’s AGB map by approximately 7 t/ha. By integrating GLAS data, the RMSE on AGB estimates decreases from 81 t/ha (R2 = 0.62) to 74.1 t/ha (R2 = 0.71). Most importantly, we showed that this approach using LiDAR data avoids underestimating high biomass values (new maximum AGB of 650 t/ha compared to 550 t/ha with the first approach). View Full-Text
Keywords: aboveground biomass mapping; LiDAR; ICESat GALS; field inventories aboveground biomass mapping; LiDAR; ICESat GALS; field inventories
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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

Hajj, M.E.; Baghdadi, N.; Fayad, I.; Vieilledent, G.; Bailly, J.-S.; Minh, D.H.T. Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas. Remote Sens. 2017, 9, 213.

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