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

📝 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/km 2. 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 (R 2 = 0.62) to 74.1 t/ha (R 2 = 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).

💡 Analysis

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/km 2. 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 (R 2 = 0.62) to 74.1 t/ha (R 2 = 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).

📄 Content

Remote Sens. 2017, 9, 213; doi:10.3390/rs9030213 www.mdpi.com/journal/remotesensing Article Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas Mohammad El Hajj 1,*, Nicolas Baghdadi 1, Ibrahim Fayad 1, Ghislain Vieilledent 2,3,
Jean-Stéphane Bailly 4 and Dinh Ho Tong Minh 1 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; nicolas.baghdadi@teledetection.fr (N.B.); ibrahim.fayad@teledetection.fr (I.F.);
dinh.ho-tong-minh@irstea.fr (D.H.T.M) 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; ghislain.vieilledent@cirad.fr 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; bailly@agroparistech.fr

• Correspondence: mohammad.el-hajj@teledetection.fr; Tel.: +33-467-548-724 Academic Editors: Lars T. Waser and Prasad Thenkabail Received: 21 October 2016; Accepted: 22 February 2017; Published: 25 February 2017 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). Keywords: aboveground biomass mapping; LiDAR; ICESat GALS; field inventories

1. Introduction Monitoring the carbon cycle and carbon stocks is of high importance to understand climate change. Several studies have reported that more than 40% of the world’s vegetation carbon stocks is stored in tropical forests [1,2]. In tropical forests, the quantity of carbon represents 43% to 55% of Above Ground Biomass (AGB) [3–5]. Thus, mapping the AGB of tropical forests is of great Remote Sens. 2017, 9, 213 2 of 19

importance in monitoring carbon stocks. Field inventories for AGB estimates, either by destructive (cutting and then weighing the tree) or non-destructive methods (by means of allometric equations), provide good estimates. However, these methods are not operational because they involve a great deal of labor and time and allow AGB estimates only at a local scale. Thus, a forest cannot be mapped using field inventories, hence the importance of remote sensing technology that facilitates the mapping of AGB. Indeed, remote sensing technology provides data for AGB estimates that cover large areas with a high spatial resolution and high revisit time. Three main remote sensing data types are used for AGB estimates: optical, SAR (Synthetic Aperture Radar), and LiDAR. Optical images at low or medium resolutions and radar backscattering coefficient data are robust enough to estimate low to medium level AGB due to saturation of remote sensing data. Zhao et al. [6] and Lu et al. [7] have shown that optical data allow AGB estimates until AGB levels between 55 and 159 t/ha, depending on the forest species composition. In addition, SAR amplitude data, mainly in the L-band, were used to estimate the AGB. Luckman et al. [8] observed a saturation point of 60 t/h

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