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Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)  

Public [article]  inGeocarto international > vol 33 n° 10 (October 2018) . - pp 1017 - 1035 Titre : Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier Type de document : Article/Communication Auteurs : Huanxue Zhang, Auteur ; Qiangzi Li, Auteur ; Jiangui Liu, Auteur ; Taifeng Dong, Auteur ; Heather McNairn, Auteur Année de publication : 2018 Article en page(s) : pp 1017 - 1035 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] bande spectrale [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] corrélation par régions de niveaux de gris [Termes IGN] extraction de traits caractéristiques [Termes IGN] image SPOT 5 [Termes IGN] indice de végétation [Termes IGN] limite de terrain [Termes IGN] Ontario (Canada) [Termes IGN] réflectance spectrale [Termes IGN] segmentation d'image [Termes IGN] surface cultivée [Termes IGN] surveillance agricole [Termes IGN] texture d'image [Termes IGN] variogramme Résumé : (auteur) In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification. Numéro de notice : A2019-049 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1333533 Date de publication en ligne : 23/06/2017 En ligne : https://doi.org/10.1080/10106049.2017.1333533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92063 [article]

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059-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Effects of water and heat on growth of winter wheat in the North China Plain / Hongyan Wang in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)  

Public [article]  inGeocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 210 - 224 Titre : Effects of water and heat on growth of winter wheat in the North China Plain Type de document : Article/Communication Auteurs : Hongyan Wang, Auteur ; Qiangzi Li, Auteur ; Xin Du, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 210 - 224 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] appariement d'images [Termes IGN] blé (céréale) [Termes IGN] chaleur terrestre [Termes IGN] Chine [Termes IGN] humidité du sol [Termes IGN] image satellite [Termes IGN] indice foliaire [Termes IGN] teneur en eau de la végétation Résumé : (Auteur) The North China Plain (NCP) was selected as the study area and the effects of water and heat were analysed to determine the dominant factor affecting winter wheat growth. The mean, minimum and maximum temperatures, precipitation and soil moisture data were selected to analyse the correlations between the leaf area index (the growth indicator) and these factors using long time series half-monthly data (2–5 months) (from 1982 to 2010). The results showed that temperature was the main factor affecting the growth of winter wheat in the NCP. The growth of winter wheat had weak correlations with precipitation and soil moisture and the influence of water on winter wheat growth was smaller than the influence of heat. In the northern part of the NCP, mainly including the north-west region of Shandong Province and the southern region of Hebei Province, irrigation was necessary in late February and early March. Numéro de notice : A2016-108 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1043357 Date de publication en ligne : 03/08/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1043357 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80004 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016011	RAB	Revue	Centre de documentation	En réserve L003	Disponible