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2 results on '"Iasie Roumanie"'

1. APPLICATION OF DEEP LEARNING OF MULTI-TEMPORAL SENTINEL-1 IMAGES FOR THE CLASSIFICATION OF COASTAL VEGETATION ZONE OF THE DANUBE DELTA

Author

Dino Ienco, Jenica Hanganu, Simona Niculescu, Niculescu, Simona, Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), « Danube Delta » National Institute for Research and Development [Tulcea], Littoral, Environnement, Télédétection, Géomatique (LETG - Brest), Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-École pratique des hautes études (EPHE)-Université de Nantes (UN)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Normandie Université (NU)-Normandie Université (NU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Danube Delta, National Institute for Research and Development, Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Danube Delta National Institute for Research and Development, Iasie Roumanie, Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-Université de Nantes (UN)-École pratique des hautes études (EPHE), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
lcsh:Applied optics. Photonics, 010504 meteorology & atmospheric sciences, Computer science, [SHS.GEO] Humanities and Social Sciences/Geography, 0211 other engineering and technologies, Image processing, 02 engineering and technology, Land cover, computer.software_genre, 01 natural sciences, lcsh:Technology, [SPI]Engineering Sciences [physics], ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Contextual image classification, business.industry, lcsh:T, Deep learning, lcsh:TA1501-1820, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Random forest, Temporal database, Recurrent neural network, lcsh:TA1-2040, Data mining, Artificial intelligence, business, lcsh:Engineering (General). Civil engineering (General), computer
Abstract

Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.

Published
2018
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2. Viticultural Zoning: A Comparative Study Regarding the Accuracy of Different Approaches in Vineyards Climate Suitability Assessment

Author

Hervé Quénol, Liviu Irimia, Cristian Valeriu Patriche, Facultatea de Horticultura (USAMV), Iasie Roumanie, Romanian Academy, Department of Iasi, Geography Group, Romanian Academy of Sciences, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), and Quénol, Hervé

Subjects
2. Zero hunger, Wine, 010504 meteorology & atmospheric sciences, [SHS.GEO] Humanities and Social Sciences/Geography, [SHS.GEO]Humanities and Social Sciences/Geography, 04 agricultural and veterinary sciences, 15. Life on land, 01 natural sciences, Vineyard, 040501 horticulture, Plant science, Geography, Temperate climate, Physical geography, 0405 other agricultural sciences, Zoning, Scale (map), ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

The paper presents the results of a study regarding the mesoclimate suitability assessment of three Romania’s wine-growing centres (Urlaţi-Dealu Mare vineyard, Huşi-Huşi vineyard, Bucium-Iaşi vineyard), by the Huglin’s heliothermal index and by a GIS based multicriteria method. The results are compared between the two types of analysis and with the macroclimate suitability map of Romania’s wine regions, expressed by Huglin’s heliothermal index spatial distribution. The research show that the values of Huglin’s heliothermal index in Romania’s wine regions varies between 2341.48 on the Danube Terraces from the southern limit of the country to lower than 1500 on the intramountainous wine region Transylvania. The prevailing climate class over the Romania’s wine regions is IH-1 that define temperate climate. According to the assessment on Romania’s macroclimate scale by Huglin’s heliothermal index, the three wine-growing centers are characterized by temperate climate (IH-1), that indicate the existence of climate conditions for grapes maturation to Cabernet Sauvignon variety. The assessment on mesoclimate scale by the GIS based multicriteria method reveals a wider variability of local climate than that resulted from macroclimate and mesoclimate analysis by Huglin’s heliothermal index, as follows: the climate of Urlaţi wine-growing center is suitable for quality red wines production; in Huşi wine-growing centre only 16.95% from the area has climate suitable to produce red table wines; the climate of Bucium wine-growing centre is not suitable for red wine production. Comparison with the Romania’s vineyards wine production specialization confirms that the results of multicriteria GIS based evaluation reveal accurate the local climate suitability and demonstrate the need of the fine-scale assessment of vineyard climate in the viticultural zoning.

Published
2013
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