Descripteur
Documents disponibles dans cette catégorie (1011)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Assessment of land suitability potentials for winter wheat cultivation by using a multi criteria decision Support-Geographic information system (MCDS-GIS) approach in Al-Yarmouk Basin (Syria) / Safwan Mohammed in Geocarto international, vol 37 n° 6 ([01/04/2022])
[article]
Titre : Assessment of land suitability potentials for winter wheat cultivation by using a multi criteria decision Support-Geographic information system (MCDS-GIS) approach in Al-Yarmouk Basin (Syria) Type de document : Article/Communication Auteurs : Safwan Mohammed, Auteur ; Karam Alsafadi, Auteur ; Haidar Ali, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1645 - 1663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse multicritère
[Termes IGN] blé (céréale)
[Termes IGN] cultures
[Termes IGN] données météorologiques
[Termes IGN] état du sol
[Termes IGN] MNS SRTM
[Termes IGN] outil d'aide à la décision
[Termes IGN] qualité du sol
[Termes IGN] Syrie
[Termes IGN] système d'information géographiqueRésumé : (auteur) In the last few years, the agricultural sector in Syria has suffered from major problems related to land degradation. To cope with this problem, a land suitability assessment has become an essential tool for sustainable land use management. The present research qualitatively evaluated the suitability of land in the Al-Yarmouk Basin (S-Syria) for rainfed winter wheat (Triticum aestivum) cultivation. In this study, a regional spatial approach involving three steps was developed, based on the method proposed by Sys et al. In the first step, a soil survey was carried out and 107 soil profiles were described, sampled and analyzed. In the second step, climatic gridded datasets from 1984–2014 MRm at a high spatial resolution (30 meters) and the Digital Elevation Model (DEM) were clipped from NASA's Shuttle Radar Topography Mission (SRTM) and prepared for the study area. In the third step, a land suitability assessment was performed using the geographical information system (GIS) and multi criteria decision support (MCDS). Soil survey outcomes showed that the study area was dominated by five soil orders: Mollisols, Inceptisols, Vertisols, Entisols and Aridisols. Also, results from the Sys model illustrated that more than 23.8% of the study area is highly suitable (S1–0) for wheat production without any limitations, whereas 38.7% and 37.5% are highly suitable (S1–1) and moderately suitable (S2), respectively. Also, the study emphasizes the important role of topographical factors in the study area for wheat cultivation. All in all, this research suggests W-Syria as a potential region for wheat cultivation, instead of the eastern area which is subject to climate change and a shortage of water. Integrating the Sys-approach and the GIS framework offers a good tool for policy-makers to apply in Syria for land suitability assessments. Numéro de notice : A2022-474 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1790674 Date de publication en ligne : 15/07/2020 En ligne : https://doi.org/10.1080/10106049.2020.1790674 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100821
in Geocarto international > vol 37 n° 6 [01/04/2022] . - pp 1645 - 1663[article]Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data / Yanyan Wang in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)
[article]
Titre : Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data Type de document : Article/Communication Auteurs : Yanyan Wang, Auteur ; Shenghui Fang, Auteur ; Lingli Zhao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102720 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] carte de la végétation
[Termes IGN] Chine
[Termes IGN] croissance végétale
[Termes IGN] données spatiotemporelles
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] maïs (céréale)
[Termes IGN] mesure de similitude
[Termes IGN] phénologie
[Termes IGN] saison
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) This study aims to map the planting area of summer maize and estimate the spatiotemporal phenology information with parcel-based classification method through integration of Sentinel-1/2 data in Jiaozuo located in North China Plain. For the maize mapping, the combination of Sentinel-1/2 data with the parcel-based method has the highest classification accuracy, suggesting that the integration of Sentinel-1/2 data with parcel-based method has great potential for regional maize mapping. For the estimation of maize phenology, the dynamic threshold method is used to extract the tasseling and milk ripening date through the time series σ0VH. In order to reduce the influence of precipitation or irrigation on SAR data, a Local Minimum Value Composite (LMVC) method is proposed to filter the original time series SAR data. The systematic phenology estimation method mainly includes LMVC, S-G filtering, Fourier curve fitting and dynamic threshold points extracting. Compared with the actual phenology date by field investigation, the errors of estimated tasseling and milk ripening date are 4.3 days and 5.5 days respectively, indicating that the time series σ0VH derived from the SAR data has great potential in spatiotemporal phenology estimation of field maize. Finally, the scattering mechanism of the maize field to C-band microwave in different growth periods was analyzed. It was also found that the phenology of maize was delayed in the coal mining subsidence areas and the areas with insufficient field management. Numéro de notice : A2022-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102720 Date de publication en ligne : 24/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102720 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100121
in International journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102720[article]Projections of climate change impacts on flowering-veraison water deficits for Riesling and Müller-Thurgau in Germany / Chenyao Yang in Remote sensing, vol 14 n° 6 (March-2 2022)
[article]
Titre : Projections of climate change impacts on flowering-veraison water deficits for Riesling and Müller-Thurgau in Germany Type de document : Article/Communication Auteurs : Chenyao Yang, Auteur ; Christoph Menz, Auteur ; Maxim Simões De Abreu Jaffe, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1519 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Allemagne
[Termes IGN] changement climatique
[Termes IGN] données météorologiques
[Termes IGN] phénologie
[Termes IGN] sécheresse
[Termes IGN] stress hydrique
[Termes IGN] viticulture
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) With global warming, grapevine is expected to be increasingly exposed to water deficits occurring at various development stages. In this study, we aimed to investigate the potential impacts of projected climate change on water deficits from the flowering to veraison period for two main white wine cultivars (Riesling and Müller-Thurgau) in Germany. A process-based soil-crop model adapted for grapevine was utilized to simulate the flowering-veraison crop water stress indicator (CWSI) of these two varieties between 1976–2005 (baseline) and 2041–2070 (future period) based on a suite of bias-adjusted regional climate model (RCM) simulations under RCP4.5 and RCP8.5. Our evaluation indicates that the model can capture the early-ripening (Müller-Thurgau) and late-ripening (Riesling) traits, with a mean bias of prediction of ≤2 days and a well-reproduced inter-annual variability for more than 60 years. Under climate projections, the flowering stage is advanced by 10–20 days (higher in RCP8.5) between the two varieties, whereas a slightly stronger advancement is found for Müller-Thurgau than for Riesling for the veraison stage. As a result, the flowering-veraison phenophase is mostly shortened for Müller-Thurgau, whereas it is extended by up to two weeks for Riesling in cool and high-elevation areas. The length of phenophase plays an important role in projected changes of flowering-veraison mean temperature and precipitation. The late-ripening trait of Riesling makes it more exposed to increased summer temperature (mainly in August), resulting in a higher mean temperature increase for Riesling (1.5–2.5 °C) than for Müller-Thurgau (1–2 °C). As a result, an overall increased CWSI by up to 15% (ensemble median) is obtained for both varieties, whereas the upper (95th) percentile of simulations shows a strong signal of increased water deficit by up to 30%, mostly in the current winegrowing regions. Intensified water deficit stress can represent a major threat for high-quality white wine production, as only mild water deficits are acceptable. Nevertheless, considerable variabilities of CWSI were discovered among RCMs, highlighting the importance of efforts towards reducing uncertainties in climate change impact assessment. Numéro de notice : A2022-252 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/rs14061519 Date de publication en ligne : 21/03/2022 En ligne : https://doi.org/10.3390/rs14061519 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100208
in Remote sensing > vol 14 n° 6 (March-2 2022) . - n° 1519[article]Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping / Jiangsan Zhao in Remote sensing, vol 14 n° 5 (March-1 2022)
[article]
Titre : Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping Type de document : Article/Communication Auteurs : Jiangsan Zhao, Auteur ; Ajay Kumar, Auteur ; Balaji Naik Banoth, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1272; Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture de précision
[Termes IGN] apprentissage profond
[Termes IGN] erreur absolue
[Termes IGN] image multibande
[Termes IGN] image RVB
[Termes IGN] Inde
[Termes IGN] phénologie
[Termes IGN] reconstruction d'imageRésumé : (auteur) Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture. Numéro de notice : A2022-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14051272 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.3390/rs14051272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100033
in Remote sensing > vol 14 n° 5 (March-1 2022) . - n° 1272;[article]Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images / Omer Gokberk Narin in Geocarto international, vol 37 n° 5 ([01/03/2022])
[article]
Titre : Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images Type de document : Article/Communication Auteurs : Omer Gokberk Narin, Auteur ; Saygin Abdikan, Auteur Année de publication : 2022 Article en page(s) : pp 1378 - 1392 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image multitemporelle
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] phénologie
[Termes IGN] rendement agricole
[Termes IGN] tournesol
[Termes IGN] TurquieRésumé : (Auteur) With the increase of the world’s population, while urbanization is increasing, agricultural lands are decreasing. Therefore, monitoring of up-to-date agricultural lands is important for agricultural product estimation. The study investigates suitability of Sentinel-2 data for the phenological stage analysis and yield estimation of sunflower plant. To this aim, fieldworks was conducted and sunflower parcels were identified in Zile district of Tokat province, Turkey which has dense sunflower production. In this study, ten Vegetation Indices (VIs) were performed by using multi-temporal Sentinel-2 data obtained during the growth stages of sunflower plant and yield estimation was obtained. As a result, the indices obtained on 30 June, at the stage of inflorescence emergence, provided coefficient of determination (R2) higher than 0.67 and The Root Mean Square Error (RMSE) lower than 13 kg/da. Among the VIs, the best forecast obtained by NDVI (R2 = 0.74 and RMSE = 10.80 kg/da) approximately three months before the harvest of sunflower. Numéro de notice : A2022-276 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1765886 Date de publication en ligne : 25/05/2020 En ligne : https://doi.org/10.1080/10106049.2020.1765886 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100784
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1378 - 1392[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Comparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)PermalinkConservation zones increase habitat heterogeneity of certified Mediterranean oak woodlands / Teresa Mexia in Forest ecology and management, vol 504 (January-15 2022)PermalinkAdaptation of the standardized vegetation optical depth index for satellite-based soil moisture / Juliette Raabe (2022)PermalinkAttributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision / Anis Amziane (2022)PermalinkIn situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region / Nadia Ouaadi (2022)PermalinkLatent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck / Flavia Tauro in Remote sensing of environment, vol 268 (January 2022)PermalinkPermalinkMonitoring 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 (2022)PermalinkPotentialité de la télédétection thermique pour la modélisation climatique en milieu viticole / Gwenaël Morin (2022)PermalinkParticle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])Permalink