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Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach / Stéphane Momo Takoudjou in Methods in ecology and evolution, vol 9 n° 4 (April 2018)  

Public [article]  inMethods in ecology and evolution > vol 9 n° 4 (April 2018) Titre : Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach Type de document : Article/Communication Auteurs : Stéphane Momo Takoudjou, Auteur ; Pierre Ploton, Auteur ; Bonaventure Sonké, Auteur ; Jan Hackenberg , Auteur ; Sébastien Griffon, Auteur ; François de Coligny, Auteur ; Narcisse Guy Kamdem, Auteur ; Moses Libalah, Auteur ; Gislain 2 Mofack, Auteur ; Gilles Le Moguédec, Auteur ; Raphaël Pélissier, Auteur ; Nicolas Barbier, Auteur Année de publication : 2018 Note générale : bibliographie Funding Information : Global Environment Facility (Grant Number: TF010038), World Bank and French Government scholarship Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Afrique centrale [Termes descripteurs IGN] AMAPstudio - Scan [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] Cameroun [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] puits de carbone [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Végétation et changement climatique Mots-clés libres : Quantitative Structure Model Résumé : (auteur) Calibration of local, regional or global allometric equations to estimate biomass at the tree level constitutes a significant burden on projects aiming at reducing Carbon emissions from forest degradation and deforestation. The objective of this contribution is to assess the precision and accuracy of Terrestrial Laser Scanning (TLS) for estimating volumes and above‐ground biomass (AGB) of the woody parts of tropical trees, and for the calibration of allometric models. We used a destructive dataset of 61 trees, with diameters and AGB of up to 186.6 cm and 60 Mg respectively, which were scanned, felled and weighed in the semi‐deciduous forests of eastern Cameroon. We present an operational approach based on available software allowing the retrieving of TLS volume with low bias and high accuracy for large tropical trees. Edition of the obtained models proved necessary, mainly to account for the complexity of buttressed parts of tree trunks, which were separately modelled through a meshing approach, and to bring a few corrections in the topology and geometry of branches, thanks to the amapstudio‐scan software. Over the entire dataset, TLS‐derived volumes proved highly reliable for branches larger than 5 cm in diameter. The volumes of the remaining woody parts estimated for stumps, stems and crowns as well as for the whole tree proved very accurate (RMSE below 2.81% and R² above of .98) and unbiased. Once converted into AGB using mean local‐specific wood density values, TLS estimates allowed calibrating a biomass allometric model with coefficients statistically undistinguishable from those of a model based on destructive data. The Unedited Quantitative Structure Model (QSM) however leads to systematic overestimations of woody volumes and subsequently to significantly different allometric parameters. We can therefore conclude that a non‐destructive TLS approach can now be used as an operational alternative to traditional destructive sampling to build the allometric equations, although attention must be paid to the quality of QSM model adjustments to avoid systematic bias. Numéro de notice : A2018-205 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1111/2041-210X.12933 date de publication en ligne : 07/11/2017 En ligne : https://doi.org/10.1111/2041-210X.12933 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93819 [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 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]