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Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece / Maria Kampouri in Geocarto international, vol 34 n° 12 ([15/09/2019])  

Public [article]  inGeocarto international > vol 34 n° 12 [15/09/2019] . - pp 1273 - 1285 Titre : Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece Type de document : Article/Communication Auteurs : Maria Kampouri, Auteur ; Polychronis Kolokoussis, Auteur ; Demetre Argialas, Auteur ; Vassilia Karathanassi, Auteur Année de publication : 2019 Article en page(s) : pp 1273 - 1285 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] biomasse forestière [Termes IGN] conservation des ressources forestières [Termes IGN] écosystème forestier [Termes IGN] espèce végétale [Termes IGN] Grèce [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image Sentinel-MSI [Termes IGN] indicateur de biodiversité [Termes IGN] indice de diversité [Termes IGN] modèle numérique de surface [Termes IGN] réalité de terrain [Termes IGN] segmentation d'image Résumé : (Auteur) The aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson’s diversity index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson’s diversity index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritized for conservation. Numéro de notice : A2019-465 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1489424 Date de publication en ligne : 12/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1489424 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93616 [article]

An unsupervised classification approach for polarimetric SAR data based on the Chernoff distance for complex Wishart distribution / Mohammed Dabboor in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 2 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 2 (July 2013) . - pp 4200 - 4213 Titre : An unsupervised classification approach for polarimetric SAR data based on the Chernoff distance for complex Wishart distribution Type de document : Article/Communication Auteurs : Mohammed Dabboor, Auteur ; Michael Collins, Auteur ; Vassilia Karathanassi, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4200 - 4213 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image radar moirée [Termes IGN] loi de Wishart [Termes IGN] polarimétrie radar Résumé : (Auteur) A new unsupervised classification approach for polarimetric synthetic aperture radar (POLSAR) data is proposed in this paper. The Wishart-Chernoff distance is calculated and used in an agglomerative hierarchical clustering approach. Initial segmentation of POLSAR data into clusters is obtained based on the total backscattering power (SPAN) combined with the entropy, alpha angle, and anisotropy. The complex Wishart clustering is performed to optimize the initialization. Optimized clusters with minimum Wishart-Chernoff distance are merged hierarchically into an appropriate number of classes. The appropriate number of classes is estimated based on the data log-likelihood algorithm. Classification results show that the use of Wishart-Chernoff distance is superior to that of the Wishart test statistic distance. The effectiveness of the proposed Wishart-Chernoff distance is demonstrated using Advanced Land Observing Satellite POLSAR data. Numéro de notice : A2013-377 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227755 En ligne : https://doi.org/10.1109/TGRS.2012.2227755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32515 [article]

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