Descripteur
Documents disponibles dans cette catégorie (100)
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
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 Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model / Hasan Aksoy in Geocarto international, vol 37 n° 4 ([15/02/2022])
[article]
Titre : Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model Type de document : Article/Communication Auteurs : Hasan Aksoy, Auteur ; Sinan Kaptan, Auteur Année de publication : 2022 Article en page(s) : pp 1183 - 1202 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] gestion forestière
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TM
[Termes IGN] modèle de Markov
[Termes IGN] occupation du sol
[Termes IGN] surface cultivée
[Termes IGN] surface forestière
[Termes IGN] Turquie
[Termes IGN] utilisation du solRésumé : (auteur) This study aimed to simulate and assess forest cover and land use/land cover (LULC) changes between 2019 and 2039 using the cellular automata-Markov model. The performance of the model was evaluated by comparing the 2019 simulation map with the 2019 supervised classified map, and it was found to be reliable, with a similarity rate of 85.43%. The LULC analysis and estimates were carried out for a total of six classes: coniferous, broad-leaf, mixed forest, settlement, water and agriculture. Between 1999 and 2019, the areas of total forest increased by 17.4%, settlement by 84.6% and water by 20.1%, whereas the agriculture area decreased by 33.2%. According to 2019‒2039 land use/cover simulation results, there were decreases of 2.4% in total forest area and 3.7% in residential and water surface areas, but a 6.9% decrease in the agriculture class. Tracking these changes will contribute to decision making and strategy development efforts of forest planners and managers. Numéro de notice : A2022-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1778102 Date de publication en ligne : 22/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1778102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100691
in Geocarto international > vol 37 n° 4 [15/02/2022] . - pp 1183 - 1202[article]Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network / Ekrem Saralioglu in Geocarto international, vol 37 n° 2 ([15/01/2022])
[article]
Titre : Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network Type de document : Article/Communication Auteurs : Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur Année de publication : 2022 Article en page(s) : pp 657 - 677 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image Ikonos
[Termes IGN] image multibande
[Termes IGN] image Pléiades-HR
[Termes IGN] image Worldview
[Termes IGN] occupation du sol
[Termes IGN] segmentation sémantique
[Termes IGN] TurquieRésumé : (auteur) Research to improve the accuracy of very high-resolution satellite image classification algorithms is still one of the hot topics in the field of remote sensing. Successful results of deep learning methods in areas such as image classification and object detection have led to the application of these methods to remote sensing problems. Recently, Convolutional Neural Networks (CNNs) are among the most common deep learning methods used in image classification, however, the use of CNN’s in satellite image classification is relatively new. Due to the high computational complexity of 3D CNNs, which aim to extract both spatial and spectral information, 2D CNNs focussing on the extraction of spatial information are often preferred. High-resolution satellite images, however, contain crucial spectral information as well as spatial information. In this study, a 3D-2D CNN model using both spectral and spatial information was applied to extract more accurate land cover information from very high-resolution satellite images. The model was applied on a Worldview-2 satellite image including agricultural product areas such as tea, hazelnut groves and land use classes such as buildings and roads. The results of the CNN based model were also compared against those of the Support Vector Machine (SVM) and Random Forest (RF) algorithms. The post-classification accuracies were obtained using 800 control points generated by a web interface created for crowdsourcing purposes. The classification accuracy was 95.6% for the 3D-2D CNN model, 89.2% for the RF and 86.4% for the SVM. Numéro de notice : A2022-305 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2020.1734871 Date de publication en ligne : 04/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1734871 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100379
in Geocarto international > vol 37 n° 2 [15/01/2022] . - pp 657 - 677[article]An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web / Abdulkadir Memduhoglu in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)
[article]
Titre : An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web Type de document : Article/Communication Auteurs : Abdulkadir Memduhoglu, Auteur ; Melih Basaraner, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] appariement géométrique
[Termes IGN] approche participative
[Termes IGN] bâtiment
[Termes IGN] données localisées des bénévoles
[Termes IGN] données multiéchelles
[Termes IGN] Istanbul (Turquie)
[Termes IGN] ontologie
[Termes IGN] OpenStreetMap
[Termes IGN] SWRL
[Termes IGN] web des données
[Termes IGN] web sémantique
[Termes IGN] zone urbaineRésumé : (auteur) The advent of Web 2.0 has emerged abundant but often unstructured user-generated georeferenced data, such as those from Volunteered Geographic Information (VGI) initiatives. In many cases, these data can be considered as complementary to the authoritative geospatial data. With the increasing availability of multi-source geospatial data, the efforts for geospatial data integration have gained momentum, aiming at gathering maximum information to answer sophisticated questions that cannot be answered using a single data source. Although there are various approaches employed for this purpose with different degrees of success, semantic web methods and tools have not been tested sufficiently in this scope, particularly for multi-scale urban building data integration and enrichment. Attempting to fill this gap, in this study, multi-source and multi-scale urban building data were integrated with a geometric matching method based on the overlapping area, then a geospatial ontology was developed to define multi-scale representations and detailed cardinality relations of the building features. Finally, some features from the geospatial ontology were then linked to popular knowledge bases such as DBpedia and YAGO. For the exploitation on the web, query and visualization processes were demonstrated using sample questions. The semantic web enabled to model complex cardinality of relations between the features from three different building data sets using inferencing and Semantic Web Rule Language (SWRL). The study showed that integrating different geospatial data sets as a knowledge base can facilitate answering sophisticated questions from different users. Numéro de notice : A2022-016 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1952108 Date de publication en ligne : 24/08/2021 En ligne : https://doi.org/10.1080/15230406.2021.1952108 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99147
in Cartography and Geographic Information Science > vol 49 n° 1 (January 2022) . - pp 1 - 17[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Investigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)
[article]
Titre : Investigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations Type de document : Article/Communication Auteurs : Caglar Bayik, Auteur ; Saygin Abdikan, Auteur ; Alpay Ozdemir, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1201 - 1220 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse diachronique
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] données géologiques
[Termes IGN] données GNSS
[Termes IGN] effondrement de terrain
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Istanbul (Turquie)
[Termes IGN] surveillance géologique
[Termes IGN] urbanisationRésumé : (auteur) This study aims to detect recent landslide displacements caused by geological structure of the region where there is intense urbanization using advanced Interferometric Synthetic Aperture Radar (InSAR) techniques and with Global Navigation Satellite Systems (GNSS) observations in the Beylikdüzü and Esenyurt districts in Istanbul megacity, Turkey. In this study, multiple satellites with different frequencies (C-band, L-band) and periodic GNSS observations were employed. For the entire peninsula, we processed 149 images from the ascending orbit, 144 images from the descending orbit of Sentinel-1 (C-Band) and 24 ALOS-2 (L-band) images from the ascending orbit. The evaluations were carried out in the period between 2015 and 2020 for Sentinel-1 imagery and 2015–2020 for ALOS-2 imagery respectively. Since the study area is covered by dense settlements, the Persistent Scatterer InSAR (PSI) technique was utilized to determine the landslide behaviors. According to the results, for both orbits of the Sentinel-1, the horizontal displacement and the vertical displacement were observed in the range of − 10 to 6 mm. Compared to the magnitude of displacement signal measured by Sentinel-1, ALOS-2 data has higher values due to the high surface penetration of the L-band. The results showed that most of the old landslide regions are reactivated. Horizontal movement derived through Sentinel-1 showed that the highest movement overlaps with old landslides. L-band ALOS-2 provided better spatial coverage of landslide movement than C-band Sentinel-1 data, especially at the rural Numéro de notice : A2021-752 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT/URBANISME Nature : Article DOI : 10.1007/s11069-021-04875-7 Date de publication en ligne : 20/06/2021 En ligne : https://doi.org/10.1007/s11069-021-04875-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98737
in Natural Hazards > vol 109 n° 1 (October 2021) . - pp 1201 - 1220[article]Quantifying coherence between TDM90, SRTM90 and ASTER90 / Umut Gunes Sefercik in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkVariation in morphological and wood cell traits in coppice stems of Populus nigra L. and Salix alba L. / Seray Özden in Journal of forest science, vol 67 n° 8 (August 2021)PermalinkAnomalous variations of air temperature prior to earthquakes / Irfan Mahmood in Geocarto international, vol 36 n° 12 ([01/07/2021])PermalinkDesign and development 3D RRR model for Turkish cadastral system using international standards / Mehmet Alkan in Survey review, Vol 53 n° 379 (July 2021)PermalinkImplementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)PermalinkImpact of different sampling rates on precise point positioning performance using online processing service / Serdar Erol in Geo-spatial Information Science, vol 24 n° 2 (June 2021)PermalinkThe use of land cover indices for rapid surface urban heat island detection from multi-temporal Landsat imageries / Nagihan Aslan in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkThe delineation of tea gardens from high resolution digital orthoimages using mean-shift and supervised machine learning methods / Akhtar Jamil in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkIntegration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)Permalink