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A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems / Dong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 63 - 77 Titre : A systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems Type de document : Article/Communication Auteurs : Dong Chen, Auteur ; Tatiana V. Loboda, Auteur ; Joanne V. Hall, Auteur Année de publication : 2020 Article en page(s) : pp 63 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Alaska (Etats-Unis) [Termes descripteurs IGN] Canada [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] perturbation écologique [Termes descripteurs IGN] Short Waves InfraRed [Termes descripteurs IGN] toundra Résumé : (Auteur) Satellite imagery has been widely used for the assessment of wildfire burn severity within the scientific community and fire management agencies. Multiple indices have been proposed to assess burn severity, among which the differenced Normalized Burn Ratio (dNBR) is arguably the most commonly used index that is expected to provide an objective and consistent assessment. However, although evidence of variability in the dNBR-based assessment of burn severity driven by image pair selection has been shown in many studies, the comprehensive examination of the extent of the bias resulting from the image selection has been lacking. In this study, we focus on three factors of the image selection process which are encountered by most Landsat-derived dNBR applications, including the sensor combination and the difference in timing of image acquisition (for both the year and seasonality) of pre- and post-fire image pairs. Through separate analyses, each targeting a single factor, we show that Landsat sensor combination between the pre- and post-fire images has a limited impact on the dNBR values. The difference in the year of acquisition between the images in the image pairs is shown to influence dNBR assessment with a noticeable increase in mean dNBR (>0.1) with only a single year difference between images compared to multi-year differences. However, differences in the image acquisition seasons and the resulting phenological differences is shown to impact dNBR values most considerably. Based on our results, we warn against the calculation of dNBR when the images are acquired in different seasons. We believe that despite the existence of multiple derivatives of dNBR, there remains a need for an improved version; one that is less susceptible to the phenological impacts introduced by the selected images. Numéro de notice : A2020-012 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.011 date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94400 [article]

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Modeling and communicating the conceptual intent of geo-analytical tasks for human-GIS interaction / G. Cai in Transactions in GIS, vol 17 n° 3 (June 2013)  

Public [article]  inTransactions in GIS > vol 17 n° 3 (June 2013) . - pp 353 - 368 Titre : Modeling and communicating the conceptual intent of geo-analytical tasks for human-GIS interaction Type de document : Article/Communication Auteurs : G. Cai, Auteur ; Bo Yu, Auteur ; Dong Chen, Auteur Année de publication : 2013 Article en page(s) : pp 353 - 368 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] agent (intelligence artificielle) [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] ArcGIS Desktop [Termes descripteurs IGN] interactivité [Termes descripteurs IGN] interface utilisateur [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] raisonnement spatial [Termes descripteurs IGN] système d'information géographique Résumé : (Auteur) One of the fundamental issues of geographical information science is to design GIS interfaces and functionalities in a way that is easy to understand, teach, and use. Unfortunately, current geographical information systems (including ArcGIS) remains very difficult to use as spatial analysis tools, because they organize and expose functionalities according to GIS data structures and processing algorithms. As a result, GIS interfaces are conceptually confusing, cognitively complex, and semantically disconnected from the way human reason about spatial analytical activities. In this article, we propose an approach that structures GIS analytical functions based on the notion of “analytical intent”. We describe an experiment that replaces ArcGIS desktop interface with a conversational interface, to enable mixed-initiative user-system interactions at the level of analytical intentions. We initially focus on the subset of GIS functions that are relevant to “finding what's inside” as described by Mitchell, but the general principles apply to other types of spatial analysis. This work demonstrates the feasibility of delegating some spatial thinking tasks to computational agents, and also raises future research questions that are key to building a better theory of spatial thinking with GIS. Numéro de notice : A2013-288 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12040 date de publication en ligne : 28/05/2013 En ligne : https://doi.org/10.1111/tgis.12040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32426 [article]

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