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Local statistical spatial analysis [LoSSA]: Inventory and prospect / B. Boots in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 21 n° 3-4 (march - april 2007) . - pp 355 - 375 Titre : Local statistical spatial analysis [LoSSA]: Inventory and prospect Type de document : Article/Communication Auteurs : B. Boots, Auteur ; Atsuyuki Okabe, Auteur Année de publication : 2007 Article en page(s) : pp 355 - 375 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatiale [Termes IGN] exploration de données géographiques [Termes IGN] géostatistique [Termes IGN] jeu de données localisées Résumé : (Auteur) The past decade has witnessed extensive development of measures that examine characteristics of spatial subsets (local spaces) defined with respect to a complete data set (global space). Such procedures have evolved independently in fields such as geography, GIS, cartography, remote sensing, and landscape ecology. Collectively, we label these procedures as local spatial methods. We focus on those methods that share a common goal of identifying subsets whose characteristics are statistically 'significant' in some way. We propose the concept of local spatial statistical analysis (LoSSA) both as an integrative structure for existing methods and as a framework that facilitates the development of new local and global statistics. By formalizing what is involved when a particular local statistic is used, LoSSA helps to reveal the key features and limitations of the procedure. These include a consideration of the nature of the spatial subsets, their spatial relationship to the complete data set, and the relationship between a given global statistic and the corresponding local statistics computed for the data set. Copyright Taylor & Francis Numéro de notice : A2007-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810601034267 En ligne : https://doi.org/10.1080/13658810601034267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28479 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-07021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-07022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Predicting forest age classes from high spatial resolution remotely sensed imagery using Voronoi polygon aggregation / T. Nelson in Geoinformatica, vol 8 n° 2 (June - August 2004)  

Public [article]  inGeoinformatica > vol 8 n° 2 (June - August 2004) Titre : Predicting forest age classes from high spatial resolution remotely sensed imagery using Voronoi polygon aggregation Type de document : Article/Communication Auteurs : T. Nelson, Auteur ; B. Boots, Auteur ; Michael A. Wulder, Auteur ; R. Feick, Auteur Année de publication : 2004 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agrégation de données [Termes IGN] dendrochronologie [Termes IGN] diagramme de Voronoï [Termes IGN] forêt [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image panchromatique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] peuplement végétal [Termes IGN] polygone [Termes IGN] sylviculture Résumé : (Auteur) Efficient identification of forest age is useful for forest management and ecological applications. Here, we propose a user-assisted method for determining forest age using high spatial resolution remotely sensed imagery. This method requires individual trees to be extracted from imagery and represented as points. We use a local maximum filter to generate points that are converted to Voronoi polygons. Properties of the Voronoi polygons are correlated with forest age and used to aggregate points (trees) into areas (stands) based on three forest age classes. Accuracy of the aggregation ranges from approximately 68% to 78% and identification of the mature class is more consistent and accurate than the younger classes. Numéro de notice : A2004-168 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1023/B:GEIN.0000017745.92969.31 En ligne : https://doi.org/10.1023/B:GEIN.0000017745.92969.31 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26695 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-04021	RAB	Revue	Centre de documentation	En réserve L003	Disponible