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Auteur Qi Chen |
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Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar / Qi Chen in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)
[article]
Titre : Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar Type de document : Article/Communication Auteurs : Qi Chen, Auteur Année de publication : 2015 Article en page(s) : pp 95 - 106 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] allométrie
[Termes IGN] biomasse forestière
[Termes IGN] changement climatique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] lasergrammétrie
[Termes IGN] modèle numérique
[Termes IGN] puits de carbone
[Termes IGN] tronc
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Estimating tree aboveground biomass (AGB) and carbon (C) stocks using remote sensing is a critical component for understanding the global C cycle and mitigating climate change. However, the importance of allometry for remote sensing of AGB has not been recognized until recently. The overarching goals of this study are to understand the differences and relationships among three national-scale allometric methods (CRM, Jenkins, and the regional models) of the Forest Inventory and Analysis (FIA) program in the U.S. and to examine the impacts of using alternative allometry on the fitting statistics of remote sensing-based woody AGB models. Airborne lidar data from three study sites in the Pacific Northwest, USA were used to predict woody AGB estimated from the different allometric methods. It was found that the CRM and Jenkins estimates of woody AGB are related via the CRM adjustment factor. In terms of lidar-biomass modeling, CRM had the smallest model errors, while the Jenkins method had the largest ones and the regional method was between. The best model fitting from CRM is attributed to its inclusion of tree height in calculating merchantable stem volume and the strong dependence of non-merchantable stem biomass on merchantable stem biomass. This study also argues that it is important to characterize the allometric model errors for gaining a complete understanding of the remotely-sensed AGB prediction errors. Numéro de notice : A2015-723 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.05.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78371
in ISPRS Journal of photogrammetry and remote sensing > vol 106 (August 2015) . - pp 95 - 106[article]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)
[article]
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 forêtRésumé : (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
in ISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 49 - 58[article]Réservation
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