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Object-based classification of mixed forest types in Mongolia / E. Nyamjargal in Geocarto international, vol 35 n° 14 ([15/10/2020])  

Public [article]  inGeocarto international > vol 35 n° 14 [15/10/2020] . - pp 1615 - 1626 Titre : Object-based classification of mixed forest types in Mongolia Type de document : Article/Communication Auteurs : E. Nyamjargal, Auteur ; D. Amarsaikhan, Auteur ; A. Munkh-Erdene, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1615 - 1626 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] approche hiérarchique [Termes IGN] carte forestière [Termes IGN] classification bayesienne [Termes IGN] classification orientée objet [Termes IGN] classification pixellaire [Termes IGN] forêt [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image Sentinel-MSI [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] Mongolie [Termes IGN] peuplement mélangé Résumé : (auteur) The aim of this study is to produce updated forest map of the Bogdkhan Mountain, Mongolia using multitemporal Sentinel-2A images. The target area has highly mixed forest types and it is very difficult to differentiate the fuzzy boundaries among different forest types. To extract the forest class information, an object-based classification technique is applied and a rule-base to separate the mixed classes is developed. The rule-base uses a hierarchy of rules describing different conditions under which the actual classification has to be performed. To compare the result of the developed method with a result of a pixel-based approach, a Bayesian maximum likelihood classification is applied. The final result indicates overall accuracy of 90.87% for the object-based classification, while for the pixel-based approach it is 79.89%. Overall, the research indicates that the object-based method that uses a thoroughly defined segmentation and a well-constructed rule-base can significantly improve the classification of mixed forest types and produce of a reliable forest map. Numéro de notice : A2020-619 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1583775 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1583775 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95995 [article]

Applications of remote sensing and geographic information systems for urban land-cover change studies in Mongolia / D. Amarsaikhan in Geocarto international, vol 24 n° 4 (August - September 2009)  

Public [article]  inGeocarto international > vol 24 n° 4 (August - September 2009) . - pp 257 - 271 Titre : Applications of remote sensing and geographic information systems for urban land-cover change studies in Mongolia Type de document : Article/Communication Auteurs : D. Amarsaikhan, Auteur ; H. Blotevogel, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 257 - 271 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] changement d'occupation du sol [Termes IGN] classificateur paramétrique [Termes IGN] détection de changement [Termes IGN] fusion d'images [Termes IGN] image multitemporelle [Termes IGN] milieu urbain [Termes IGN] Mongolie [Termes IGN] occupation du sol [Termes IGN] système d'information géographique Résumé : (Auteur) The aim of this study is to compare the changes that occurred in the main urban land-cover classes of Ulaanbaatar city, Mongolia, during a centralized economy with those that occurred during a market economy and to describe the socio-economic reasons for the changes. For this purpose, multi-temporal remote sensing and geographical information system (GIS) data sets, as well as census data, are used. To extract the reliable urban land-cover information from the selected remotely sensed data sets, a refined parametric classification algorithm that uses spatial thresholds defined from local and contextual knowledge is constructed. Before applying the classification decision rule, some image fusion techniques are applied to the selected remotely sensed data sets to define the most efficient fusion method for training sample selection and for defining local and contextual knowledge. Overall, the study indicates that during the centralized economy significant changes occurred in a ger area of the city, whereas during the market economy the changes occurred in all areas. Copyright Taylor & Francis Numéro de notice : A2009-305 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802556173 Date de publication en ligne : 23/07/2009 En ligne : https://doi.org/10.1080/10106040802556173 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29935 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-09041	RAB	Revue	Centre de documentation	En réserve L003	Disponible