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Impacts of species misidentification on species distribution modeling with presence-only data / Hugo Costa in ISPRS International journal of geo-information, vol 4 n°4 (December 2015)  

Public [article]  inISPRS International journal of geo-information > vol 4 n°4 (December 2015) . - pp 2496 - 2518 Titre : Impacts of species misidentification on species distribution modeling with presence-only data Type de document : Article/Communication Auteurs : Hugo Costa, Auteur ; Giles M. Foody, Auteur ; Silvia Jiménez, Auteur ; Luis Silva, Auteur Année de publication : 2015 Article en page(s) : pp 2496 - 2518 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] entropie maximale [Termes IGN] erreur d'approximation [Termes IGN] identification automatique [Termes IGN] modèle de simulation [Termes IGN] propagation d'erreur Résumé : (auteur) Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data “contaminated” with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species’ niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling. Numéro de notice : A2015--004 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article DOI : 10.3390/ijgi4042496 Date de publication en ligne : 16/11/2015 En ligne : https://doi.org/10.3390/ijgi4042496 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101798 [article]

Integrating user needs on misclassification error sensitivity into image segmentation quality assessment / Hugo Costa in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 451 - 459 Titre : Integrating user needs on misclassification error sensitivity into image segmentation quality assessment Type de document : Article/Communication Auteurs : Hugo Costa, Auteur ; Giles M. Foody, Auteur ; Doreen S. Boyd, Auteur Année de publication : 2015 Article en page(s) : pp 451 - 459 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des besoins [Termes IGN] classification dirigée [Termes IGN] connaissance thématique [Termes IGN] objet géographique [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] similitude [Termes IGN] utilisateur Résumé : (auteur) Commonly the assessment of the quality of image segmentations used in object-based land cover classification uses the geometric match between the derived segmentation and a reference dataset. This paper argues that a more appropriate assessment of a segmentation is to also consider the thematic content of the objects generated. This allows the assessment to be tailored to the needs of the specific user. A new method for image segmentation quality assessment is described, which combines a traditional geometric-only method with the thematic similarity index (TSI), a metric that expresses the degree of thematic quality of objects from a user’s perspective. The perspectives of two users (a wolf researcher and a general user of land cover information) were adopted in a case study to demonstrate the new method. The results show that the new method allowed the production of more accurate land cover classifications for the two users than the use of the geometric-only approach Numéro de notice : A2015-976 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.451 En ligne : https://doi.org/10.14358/PERS.81.6.451 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80059 [article]