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<title lang=en>Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data</title>
<creator>Fayad, Ibrahim</creator>
<creator>Baghdadi, Nicolas</creator>
<creator>Guitet, Stéphane</creator>
<creator>Bailly, Jean-Stéphane</creator>
<creator>Hérault, Bruno</creator>
<creator>Gond, Valéry</creator>
<creator>Hajj, Mahmoud, </creator>
<creator>Ho Tong Minh, Dinh</creator>
<contributor>Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - AgroParisTech - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)</contributor>
<contributor>Institut National de la Recherche Agronomique (INRA)</contributor>
<contributor>Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut national de la recherche agronomique [Montpellier] (INRA Montpellier) - Université de Montpellier (UM) - Centre National de la Recherche Scientifique (CNRS) - Institut de Recherche pour le Développement (IRD [France-Sud])</contributor>
<contributor>Laboratoire d'étude des interactions entre sols, agrosystèmes et hydrosystèmes (LISAH) ; Institut National de la Recherche Agronomique (INRA)</contributor>
<contributor>AgroParisTech</contributor>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Université de Guyane (UG) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>CIRAD, UPR Bsef, F-34398 Montpellier, France ; </contributor>
<contributor>NOVELTIS [Sté]</contributor>
<description>International audience</description>
<source>ISSN: 0303-2434</source>
<source>International Journal of Applied Earth Observation and Geoinformation</source>
<publisher>Elsevier</publisher>
<identifier>hal-01381116</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01381116</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01381116/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01381116/file/Fayad2016a.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01381116</source>
<source>International Journal of Applied Earth Observation and Geoinformation, Elsevier, 2016, 52, pp.502 - 514. 〈10.1016/j.jag.2016.07.015〉</source>
<identifier>DOI : 10.1016/j.jag.2016.07.015</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jag.2016.07.015</relation>
<identifier>ARXIV : 1610.04371</identifier>
<relation>info:eu-repo/semantics/altIdentifier/arxiv/1610.04371</relation>
<language>en</language>
<subject lang=en>Aboveground biomass mapping</subject>
<subject lang=en>LiDAR</subject>
<subject lang=en>ICESat GLAS</subject>
<subject lang=en>forests</subject>
<subject lang=en>French Guiana</subject>
<subject>[SDE.MCG] Environmental Sciences/Global Changes</subject>
<subject>[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]</subject>
<subject>[STAT.AP] Statistics [stat]/Applications [stat.AP]</subject>
<subject>[SPI.OPTI] Engineering Sciences [physics]/Optics / Photonic</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter-and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R 2 =0.54, RMSE=48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain "wall-to-wall" AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ~51 Mg/ha and R²=0.48 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values.</description>
<rights>http://creativecommons.org/licenses/by/</rights>
<date>2016-07-21</date>
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