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Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)
[article]
Titre : Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; R. Prasad, Auteur ; D. K. Gupta, Auteur ; V. N. Mishra, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 942 - 956 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance végétale
[Termes IGN] cultures
[Termes IGN] données polarimétriques
[Termes IGN] estimation statistique
[Termes IGN] hiver
[Termes IGN] image Sentinel-SAR
[Termes IGN] Leaf Area Index
[Termes IGN] régression
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste marge
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m2 area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R2 = 0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R2 = 0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms. Numéro de notice : A2018-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1316781 Date de publication en ligne : 18/04/2017 En ligne : https://doi.org/10.1080/10106049.2017.1316781 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90551
in Geocarto international > vol 33 n° 9 (September 2018) . - pp 942 - 956[article]Extraction of building roof planes with stratified random sample consensus / André C. Carrilho in Photogrammetric record, vol 33 n° 163 (September 2018)
[article]
Titre : Extraction of building roof planes with stratified random sample consensus Type de document : Article/Communication Auteurs : André C. Carrilho, Auteur ; Mauricio Galo, Auteur Année de publication : 2018 Article en page(s) : pp 363 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] morphologie mathématique
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] varianceRésumé : (Auteur) This paper describes a consensus‐set estimation for building roof‐plane detection using a stratified random sample consensus (sRANSAC) algorithm applied to point clouds acquired by laser scanning systems. The main idea is to use one initial classification to generate consensus‐set candidates to optimise the sampling mechanism compared to the original RANSAC. The initial classification is performed using mathematical morphology to filter ground returns and estimate local variance information to detect potential planar regions. Thus, the algorithm can prioritise points within planar segments and the number of iterations can be estimated dynamically from available data. The results based on experiments using five different lidar datasets indicate that the proposed method reduces the number of computations for building roof‐plane detection and also improves accuracy compared to RANSAC. Numéro de notice : A2018-620 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12254 Date de publication en ligne : 21/09/2018 En ligne : https://doi.org/10.1111/phor.12254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92864
in Photogrammetric record > vol 33 n° 163 (September 2018) . - pp 363 - 380[article]Fine-grained prediction of urban population using mobile phone location data / Jie Chen in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)
[article]
Titre : Fine-grained prediction of urban population using mobile phone location data Type de document : Article/Communication Auteurs : Jie Chen, Auteur ; Shih-Lung Shaw, Auteur ; Feng Lu, Auteur ; Mingxiao Li, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 1770 - 1786 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par réseau neuronal
[Termes IGN] données spatiotemporelles
[Termes IGN] modèle de simulation
[Termes IGN] population urbaine
[Termes IGN] Shanghai (Chine)
[Termes IGN] trace numériqueRésumé : (Auteur) Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events. Numéro de notice : A2018-304 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1460753 Date de publication en ligne : 26/04/2018 En ligne : https://doi.org/10.1080/13658816.2018.1460753 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90445
in International journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1770 - 1786[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018051 RAB Revue Centre de documentation En réserve L003 Disponible Improvement of countrywide vegetation mapping over Japan and comparison to existing maps / Ram C. Sharma in Advances in Remote Sensing, vol 7 n° 3 (September 2018)
[article]
Titre : Improvement of countrywide vegetation mapping over Japan and comparison to existing maps Type de document : Article/Communication Auteurs : Ram C. Sharma, Auteur ; Keitarou Hara, Auteur ; Hidetake Hirayama, Auteur Année de publication : 2018 Article en page(s) : pp 163 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Terra-MODIS
[Termes IGN] Japon
[Termes IGN] occupation du solRésumé : (Auteur) This paper presents an improved classification and mapping of vegetation types for all of Japan by utilizing the Moderate-resolution Imaging Spectroradiometer (MODIS) data. The Nadir BRDF-Adjusted Reflectance (MCD43A4 product) data were compared to the conventional Surface Reflectance (MOD09A1/MOY09A1 products) data for the classification of vegetation types: evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest, deciduous broadleaf forest, shrubs, herbaceous, arable; and non-vegetation. Very rich spectral and temporal features were prepared from MCD43A4 and MOD09A1/MOY09A1 products. Random Forests classifier was employed for the classification of vegetation types with the support of ground truth data prepared in the research. Accuracy metrics—confusion matrix, overall accuracy, and kappa coefficient calculated through 10-fold cross-validation approach—were used for quantitative comparison of MCD43A4 and MOD09A1/MOY09A1 products. The cross-validation results indicated better performance of the MCD43A4 (Overall accuracy = 0.73; Kappa coefficient = 0.69) product than conventional MOD09A1/MOY09A1 products (Overall accuracy = 0.70; Kappa coefficient = 0.66) for the classification. McNemar’s test was also used to confirm a significant difference (p-value = 0.0003) between MCD43A4 and MOD09A1/MOY09A1 products. Based on these results, by utilizing the MCD43A4 features, a new vegetation map was produced for all of Japan. The newly produced map showed better accuracy than the extant, MODIS Land Cover Type product (MCD12Q1) and Global Land Cover by National Mapping Organizations (GLCNMO) product in Japan. Numéro de notice : A2018-618 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.4236/ars.2018.73011 Date de publication en ligne : 05/09/2018 En ligne : https://doi.org/10.4236/ars.2018.73011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92849
in Advances in Remote Sensing > vol 7 n° 3 (September 2018) . - pp 163 - 170[article]Mise en oeuvre d’un SIG pour le projet FARMaine (Partie 2) / Adèle Debray in Géomatique expert, n° 124 (septembre - octobre 2018)
[article]
Titre : Mise en oeuvre d’un SIG pour le projet FARMaine (Partie 2) Type de document : Article/Communication Auteurs : Adèle Debray, Auteur ; Jean-Michel Follin, Auteur ; Benjamin Balland, Auteur Année de publication : 2018 Article en page(s) : pp 14 - 33 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] base de données localisées
[Termes IGN] bassin hydrographique
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] parcelle cadastrale
[Termes IGN] politique publique
[Termes IGN] Registre parcellaire graphique
[Termes IGN] Zone Naturelle d'Intérêt Ecologique Fantastique et Floristique
[Termes IGN] zone ruraleRésumé : (éditeur) Le projet FARMaine est un projet pluridisciplinaire fi nancé dans le cadre du
Programme de recherche PSDR 4 (Pour et Sur le Développement Régional) décliné à l’échelle du Grand Ouest. Il cherche à étudier les impacts fonciers, agricoles et paysagers des politiques publiques environnementales sur les fonds de vallée du bassin versant de la Maine, plus particulièrement sur les systèmes d’élevages extensifs. Ce projet s’appuie sur l’étude de quatre zones : les Basses Vallées Angevines (BVA), la vallée du Loir, la vallée de l’Oudon et la vallée du Sarthon-Alpes mancelles. Les outils mis en place dans le cadre de cette étude devront pouvoir être réutilisés dans des bassins versants similaires. Le présent travail vise à évaluer, grâce à un SIG, quel est l’impact des politiques publiques environnementales sur le paysage et le parcellaire des zones concernées.Numéro de notice : A2018-421 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90891
in Géomatique expert > n° 124 (septembre - octobre 2018) . - pp 14 - 33[article]Voir aussiExemplaires(1)
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