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Assessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja, Nigeria / Christopher Ifechukwude Chima in Geocarto international, vol 33 n° 9 (September 2018)  

Public [article]  inGeocarto international > vol 33 n° 9 (September 2018) . - pp 893 - 911 Titre : Assessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja, Nigeria Type de document : Article/Communication Auteurs : Christopher Ifechukwude Chima, Auteur ; Nigel Trodd, Auteur ; Matthew Blackett, Auteur Année de publication : 2018 Article en page(s) : pp 893 - 911 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse d'image orientée objet [Termes IGN] classification par maximum de vraisemblance [Termes IGN] image Landsat-ETM+ [Termes IGN] image NigeriaSat [Termes IGN] image SPOT 5 [Termes IGN] image SPOT-HRG [Termes IGN] occupation du sol Résumé : (Auteur) This study assesses the usefulness of Nigeriasat-1 satellite data for urban land cover analysis by comparing it with Landsat and SPOT data. The data-sets for Abuja were classified with pixel- and object-based methods. While the pixel-based method was classified with the spectral properties of the images, the object-based approach included an extra layer of land use cadastre data. The classification accuracy results for OBIA show that Landsat 7 ETM, Nigeriasat-1 SLIM and SPOT 5 HRG had overall accuracies of 92, 89 and 96%, respectively, while the classification accuracy for pixel-based classification were 88% for Landsat 7 ETM, 63% for Nigeriasat-1 SLIM and 89% for SPOT 5 HRG. The results indicate that given the right classification tools, the analysis of Nigeriasat-1 data can be compared with Landsat and SPOT data which are widely used for urban land use and land cover analysis. Numéro de notice : A2018-336 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1316778 Date de publication en ligne : 08/05/2017 En ligne : https://doi.org/10.1080/10106049.2017.1316778 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90550 [article]

Mapping malaria severity zones with Nigeriasat-1 incorporated into geographical information system / E. Ogunbadewa in Geocarto international, vol 27 n° 7 (November 2012)  

Public [article]  inGeocarto international > vol 27 n° 7 (November 2012) . - pp 593 - 610 Titre : Mapping malaria severity zones with Nigeriasat-1 incorporated into geographical information system Type de document : Article/Communication Auteurs : E. Ogunbadewa, Auteur Année de publication : 2012 Article en page(s) : pp 593 - 610 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse discriminante [Termes IGN] carte thématique [Termes IGN] données environnementales [Termes IGN] image NigeriaSat [Termes IGN] maladie parasitaire [Termes IGN] maladie tropicale [Termes IGN] Nigéria [Termes IGN] risque sanitaire [Termes IGN] surveillance sanitaire [Termes IGN] système d'information géographique [Termes IGN] zone à risque Résumé : (Auteur) The aim of this study is to derive environmental factors that are likely to influence malarial distribution from Nigeriasat-1 in a geographical information systems (GIS) environment and relate it to the empirical evidence of reported malarial cases in the hospitals using discriminant analysis (DA) to characterize, identify and map malarial risk zones. It is found that using a stepwise DA, Nigeriasat-1 and GIS it is possible to classify the accurately the low malarial risk zone (100%), medium and high risk zones (83.33%), with an overall accuracy of 88.9% being achieved for the study area. The results obtained were in agreement with the ground validation exercise that was carried out and the cross validation method of ‘‘leaving-one-out’ in DA function. These findings indicate that Nigeriasat-1 and GIS combined with statistical technique of DA can be utilized as a decision support tool for a precise identification of the areas warranting mitigation efforts. Numéro de notice : A2012-544 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.653407 Date de publication en ligne : 01/02/2012 En ligne : https://doi.org/10.1080/10106049.2011.653407 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31990 [article]

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059-2012071	RAB	Revue	Centre de documentation	En réserve L003	Disponible