Centre de documentation
scientifique

Dépouillements

Geospatial database organization and spatial decision analysis for biodiversity databases in web GIS environment / Harish Chandra Karnatak in Geocarto international, vol 25 n° 1 (February 2010)  

Public [article]  inGeocarto international > vol 25 n° 1 (February 2010) . - pp 3 - 23 Titre : Geospatial database organization and spatial decision analysis for biodiversity databases in web GIS environment Type de document : Article/Communication Auteurs : Harish Chandra Karnatak, Auteur ; S. Saran, Auteur ; K. Bhatia, Auteur ; P. Roy, Auteur Année de publication : 2010 Article en page(s) : pp 3 - 23 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse multicritère [Termes IGN] analyse spatiale [Termes IGN] base de données localisées [Termes IGN] biodiversité [Termes IGN] distribution spatiale [Termes IGN] outil d'aide à la décision [Termes IGN] ressources naturelles [Termes IGN] système d'information géographique [Termes IGN] vecteur propre [Termes IGN] WebSIG Résumé : (Auteur) The spatial decision-making process in multi-user environment is a quite challenging and complex task in group decision-making environment. The effective database design and GIS analysis techniques are very important at the host organization level. The biodiversity conservation prioritization is one of the complex issues for the conservation authorities. Various ecological and socio-economic drivers govern the spatial distribution of biologically rich communities. These drivers are important inputs to the modelling process with different rank criteria and probabilistic weight in order to arrive at a decision-making process. In the present article, a new database design and decision analysis technique is proposed for the natural resource management and planning in Web geographic information systems environment. The study is demonstrated for the geospatial decision analysis with an output validation utility where the analytic hierarchy process is used to derive the eigen vectors with given multiple constraints and conflicting criteria and aims at selecting an optimal site for the biodiversity conservations. Copyright Taylor & Francis Numéro de notice : A2010-052 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802677045 Date de publication en ligne : 01/04/2009 En ligne : https://doi.org/10.1080/10106040802677045 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30248 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2010011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Mapping an annual weed with colour-infared aerial photography and image analysis / James H. Everitt in Geocarto international, vol 25 n° 1 (February 2010)  

Public [article]  inGeocarto international > vol 25 n° 1 (February 2010) . - pp 45 - 52 Titre : Mapping an annual weed with colour-infared aerial photography and image analysis Type de document : Article/Communication Auteurs : James H. Everitt, Auteur ; C. Yang, Auteur ; M.R. Davis, Auteur Année de publication : 2010 Article en page(s) : pp 45 - 52 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] analyse d'image numérique [Termes IGN] carte de la végétation [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] image numérisée [Termes IGN] parcours [Termes IGN] photographie aérienne [Termes IGN] photographie en couleur [Termes IGN] photographie infrarouge [Termes IGN] surveillance de la végétation [Termes IGN] Texas (Etats-Unis) Résumé : (Auteur) Silverleaf sunflower (Helianthus argophyllus, Torr and Gray) is an annual weed found on rangelands in south and southeast Texas. Colour-infrared aerial photography and computer image analysis techniques were evaluated for detecting and mapping silverleaf sunflower infestations on a south Texas rangeland area. Supervised and unsupervised image analysis classification techniques were used to classify photographs from two study sites. Supervised classification of the two photographs showed that silverleaf sunflower had mean producer's and user's accuracies of 95.2% and 91.3%, respectively. Unsupervised classification of the two photographs had mean producer's and user's accuracies for silverleaf sunflower of 65.7% and 80.1%, respectively. These results indicate that the supervised technique is superior to the unsupervised technique for mapping silverleaf sunflower infestations using colour-infrared aerial photos. Numéro de notice : A2010-053 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802677037 Date de publication en ligne : 31/03/2009 En ligne : https://doi.org/10.1080/10106040802677037 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30249 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2010011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm / T. Oyana in Geocarto international, vol 25 n° 1 (February 2010)  

Public [article]  inGeocarto international > vol 25 n° 1 (February 2010) . - pp 53 - 69 Titre : Automatic cluster identification for environnemental applications using the self-organizing maps and a new genetic algorithm Type de document : Article/Communication Auteurs : T. Oyana, Auteur ; D. Dai, Auteur Année de publication : 2010 Article en page(s) : pp 53 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] analyse de groupement [Termes IGN] carte de Kohonen [Termes IGN] découverte de connaissances [Termes IGN] environnement [Termes IGN] exploration de données géographiques [Termes IGN] photo-interprétation assistée par ordinateur [Termes IGN] système d'information géographique Résumé : (Auteur) A rapid increase of environmental data dimensionality emphasizes the importance of developing data-driven inductive approaches to geographic analysis. This article uses a loosely coupled strategy to combine the technique of self-organizing maps (SOM) with a new genetic algorithm (GA) for automatic identification of clusters in multidimensional environmental datasets. In the first stage, we employ the well-known classic SOM because it is able to handle the dimensional interactions and capture the number of clusters via visualization; and thus provide extraordinary insights into original data. In the second stage, this new GA rigorously delineates the cluster boundaries using a flexibly oriented elliptical search window. To test this approach, one synthetic and two real-world datasets are employed. The results confirm a more robust and reliable approach that provides a better understanding and interpretation of massive multivariate environmental datasets, thus maximizing our insights. Other key benefits include the fact that it provides a computationally fast and efficient environment to accurately detect clusters, and is highly flexible. In a nutshell, the article presents a computational approach to facilitate knowledge discovery of massive multivariate environmental datasets; as we are too familiar with their accelerating growth rate. Copyright Taylor & Francis Numéro de notice : A2010-054 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802711687 Date de publication en ligne : 14/04/2009 En ligne : https://doi.org/10.1080/10106040802711687 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30250 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2010011	RAB	Revue	Centre de documentation	En réserve L003	Disponible