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Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data / Ibrahim Fayad in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 52 (October 2016) . - pp 502 - 514 Titre : Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data Type de document : Article/Communication Auteurs : Ibrahim Fayad, Auteur ; Nicolas Baghdadi, Auteur ; Stéphane Guitet , Auteur ; Jean-Stéphane Bailly, Auteur ; Bruno Hérault, Auteur ; Valéry Gond, Auteur ; Mahmoud El-Hajj, Auteur ; Ho Tong Minh Dinh, Auteur Année de publication : 2016 Article en page(s) : pp 502 - 514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] données IceSat-Glas [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] Guyane (département français) [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] régression Résumé : (auteur) 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 (R2 = 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 ∼50 Mg/ha and R2 = 0.66 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. Numéro de notice : A2016--202 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2016.07.015 date de publication en ligne : 01/08/2016 En ligne : https://doi.org/10.1016/j.jag.2016.07.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96037 [article]

Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne Lidar data: application on French Guiana / Ibrahim Fayad in Remote sensing, vol 8 n° 3 (March 2016)  

Public [article]  inRemote sensing > vol 8 n° 3 (March 2016) Titre : Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne Lidar data: application on French Guiana Type de document : Article/Communication Auteurs : Ibrahim Fayad, Auteur ; Nicolas Baghdadi, Auteur ; Jean-Stéphane Bailly, Auteur ; Nicolas Barbier, Auteur ; Valéry Gond, Auteur ; Bruno Hérault, Auteur ; Mahmoud El-Hajj, Auteur ; Frédéric Fabre, Auteur ; José Perrin, Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données IceSat-Glas [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] Guyane (département français) [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] krigeage [Termes descripteurs IGN] régression Résumé : (auteur) 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. Numéro de notice : A2016--121 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article En ligne : http://doi.org/10.3390/rs8030240 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84818 [article]