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A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index / Huanhuan Yuan in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 390 - 399 Titre : A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index Type de document : Article/Communication Auteurs : Huanhuan Yuan, Auteur ; Chaoyang Wu, Auteur ; Linlin Lu, Auteur ; Xiaoyue Wang, Auteur Année de publication : 2018 Article en page(s) : pp 390 - 399 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Canada [Termes IGN] croissance des arbres [Termes IGN] Enhanced vegetation index [Termes IGN] forêt [Termes IGN] modèle de simulation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] nuit [Termes IGN] Pinophyta [Termes IGN] production primaire brute [Termes IGN] simulation numérique [Termes IGN] température au sol Résumé : (Auteur) Accurate estimation of vegetation phenology (the start/end of growing season, SOS/EOS) is important to understand the feedbacks of vegetation to meteorological circumstances. Because the evergreen forests have limited change in greenness, there are relatively less study to predict evergreen conifer forests phenology, especially for EOS in autumn. Using 11-year (2000–2010) records of MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), together with gross primary production (GPP) and temperature data at five evergreen conifer forests flux sites in Canada, we comprehensively evaluated the performances of several variables in modeling flux-derived EOS. Results showed that neither NDVI nor EVI can be used to predict EOS as they had no significant correlation with ground observations. In comparison, temperature had a better predictive strength for EOS, and R2 between EOS and mean temperature (Tmean), the maximum temperature (Tmax, daytime temperature) and the minimum temperature (Tmin, nighttime temperature) were 0.45 (RMSE = 5.1 days), 0.32 (RMSE = 5.7 days) and 0.58 (RMSE = 4.6 days), respectively. These results suggest an unreported role of nighttime temperature in regulating EOS of evergreen forests, in comparison with previous study showing leaf-out in spring by daytime temperature. Furthermore, we demonstrated that it may be because nighttime temperature has a higher relationship with soil temperature (Ts) (R2 = 0.67, p  Numéro de notice : A2018-403 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.08.013 Date de publication en ligne : 17/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.08.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90855 [article]

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