Centre de documentation
scientifique

Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data / Gaia Vaglio Laurin in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 49 - 58 Titre : Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data Type de document : Article/Communication Auteurs : Gaia Vaglio Laurin, Auteur ; Qi Chen, Auteur ; Jeremy A. Lindsell, Auteur ; David A. Coomes, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 49 - 58 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Afrique tropicale [Termes IGN] bilan du carbone [Termes IGN] biomasse [Termes IGN] données lidar [Termes IGN] forêt tropicale [Termes IGN] image hyperspectrale [Termes IGN] modélisation de la forêt Résumé : (Auteur) The estimation of above ground biomass in forests is critical for carbon cycle modeling and climate change mitigation programs. Small footprint lidar provides accurate biomass estimates, but its application in tropical forests has been limited, particularly in Africa. Hyperspectral data record canopy spectral information that is potentially related to forest biomass. To assess lidar ability to retrieve biomass in an African forest and the usefulness of including hyperspectral information, we modeled biomass using small footprint lidar metrics as well as airborne hyperspectral bands and derived vegetation indexes. Partial Least Square Regression (PLSR) was adopted to cope with multiple inputs and multicollinearity issues; the Variable of Importance in the Projection was calculated to evaluate importance of individual predictors for biomass. Our findings showed that the integration of hyperspectral bands (R2 = 0.70) improved the model based on lidar alone (R2 = 0.64), this encouraging result call for additional research to clarify the possible role of hyperspectral data in tropical regions. Replacing the hyperspectral bands with vegetation indexes resulted in a smaller improvement (R2 = 0.67). Hyperspectral bands had limited predictive power (R2 = 0.36) when used alone. This analysis proves the efficiency of using PLSR with small-footprint lidar and high resolution hyperspectral data in tropical forests for biomass estimation. Results also suggest that high quality ground truth data is crucial for lidar-based AGB estimates in tropical African forests, especially if airborne lidar is used as an intermediate step of upscaling field-measured AGB to a larger area. Numéro de notice : A2014-124 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.01.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.01.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33029 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014031	RAB	Revue	Centre de documentation	En réserve L003	Disponible