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Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis / Tao Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 28 - 38 Titre : Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis Type de document : Article/Communication Auteurs : Tao Cheng, Auteur ; Benoit Rivard, Auteur ; Arturo G. Sanchez-Azofeifa, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 28 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biomasse (combustible) [Termes descripteurs IGN] carbone [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Leaf Mass per Area [Termes descripteurs IGN] modèle physique [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] réflectance végétale [Termes descripteurs IGN] réponse spectrale [Termes descripteurs IGN] teneur en matière sèche [Termes descripteurs IGN] transformation en ondelettes Résumé : (Auteur) Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA). Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51–0.82, p Numéro de notice : A2014-009 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32914 [article]

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