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Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data / Marta Chiesi in European journal of remote sensing, vol 55 n° 1 (January 2022)  

Public [article]  inEuropean journal of remote sensing > vol 55 n° 1 (January 2022) . - pp 23 - 36 Titre : Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data Type de document : Article/Communication Auteurs : Marta Chiesi, Auteur ; Luca Angeli, Auteur ; Piero Battista, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 23 - 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] bilan hydrique [Termes IGN] carte agricole [Termes IGN] cultures irriguées [Termes IGN] image Aqua-MODIS [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] irrigation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Toscane (Italie) Résumé : (auteur) A recent study has proposed and tested a semi-empirical method to estimate crop irrigation based on a water balance logic and Sentinel-2 Multi Spectral Instrument (MSI) NDVI imagery. The current paper aims at extending the same approach to the analysis of the main irrigation patterns occurred in Tuscany (Central Italy) during the 2000–2019 period. This operation was made possible by feeding the irrigation water (IW) estimation method with 250-m spatial resolution Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The results of this operation were first assessed versus various reference datasets available for the region; next, the annual maps of IW estimated for the 20 study years were analyzed at province scale in conjunction with relevant agricultural statistics. The use of MODIS in place of MSI images reduces the IW estimation accuracy irregularly at local scale, depending on the size and spatial arrangement of irrigated and non-irrigated fields; the reduction in accuracy is, however, marginal over relatively large areas. Irrigated crops are decreasing throughout most Tuscany provinces, while they are increasing in the most southern and driest province. The possible reasons and implications of these findings are finally discussed in relation to the main environmental issues affecting the region. Numéro de notice : A2022-099 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1080/22797254.2021.2013735 Date de publication en ligne : 05/01/2022 En ligne : https://doi.org/10.1080/22797254.2021.2013735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99549 [article]

Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset / Marta Chiesi in Annals of Forest Science [en ligne], vol 73 n° 3 (September 2016)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 73 n° 3 (September 2016) . - pp 713 – 727 Titre : Testing the applicability of BIOME-BGC to simulate beech gross primary production in Europe using a new continental weather dataset Type de document : Article/Communication Auteurs : Marta Chiesi, Auteur ; Gherardo Chirici, Auteur ; Marco Marchetti, Auteur Année de publication : 2016 Article en page(s) : pp 713 – 727 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biome [Termes IGN] données météorologiques [Termes IGN] Fagus (genre) [Termes IGN] production primaire brute [Termes IGN] teneur en carbone [Termes IGN] teneur en eau liquide [Termes IGN] variation saisonnière [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message : A daily 1-km Pan-European weather dataset can drive the BIOME-BGC model for the estimation of current and future beech gross primary production (GPP). Annual beech GPP is affected primarily by spring temperature and more irregularly by summer water stress. Context : The spread of beech forests in Europe enhances the importance of modelling and monitoring their growth in view of ongoing climate changes. Aims : The current paper assesses the capability of a biogeochemical model to simulate beech gross primary production (GPP) using a Pan-European 1-km weather dataset. Methods : The model BIOME-BGC is applied in four European forest ecosystems having different climatic conditions where the eddy covariance technique is used to measure water and carbon fluxes. The experiment is in three main steps. First, the accuracy of BIOME-BGC GPP simulations is assessed through comparison with flux observations. Second, the influence of two major meteorological drivers (spring minimum temperature and growing season dryness) on observed and simulated inter-annual GPP variations is analysed. Lastly, the impacts of two climate change scenarios on beech GPP are evaluated through statistical analyses of the ground data and model simulations. Results : The weather dataset can drive BIOME-BGC to simulate most of the beech GPP evolution in all four test areas. Both observed and simulated inter-annual GPP variations are mainly dependent on minimum temperature around the beginning of the growing season, while spring/summer dryness exerts a secondary role. BIOME-BGC can also reasonably predict the impacts of the examined climate change scenarios. Conclusion : The proposed modelling approach is capable of approximately reproducing spatial and temporal beech GPP variations and impacts of expected climate changes in the examined European sites. Numéro de notice : A2016-713 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-016-0560-7 Date de publication en ligne : 07/06/2016 En ligne : https://doi.org/10.1007/s13595-016-0560-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82091 [article]