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Intra-annual phenology for detecting understory plant invasion in urban forests / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 151 - 161 Titre : Intra-annual phenology for detecting understory plant invasion in urban forests Type de document : Article/Communication Auteurs : Kunwar K. Singh, Auteur ; Yin-Hsuen Chen, Auteur ; Lindsey Smart, Auteur ; Josh Gray, Auteur ; Ross K. Meentemeyer, Auteur Année de publication : 2018 Article en page(s) : pp 151 - 161 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Caroline du Nord (Etats-Unis) [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de la végétation [Termes IGN] détection d'anomalie [Termes IGN] espèce exotique envahissante [Termes IGN] flore urbaine [Termes IGN] forêt tempérée [Termes IGN] image Landsat-TM [Termes IGN] indice de végétation [Termes IGN] Ligustrum sinense [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] protection de la biodiversité [Termes IGN] surveillance forestière Résumé : (Auteur) Accurate and repeatable mapping of biological plant invasions is essential to develop successful management strategies for conserving native biodiversity. While overstory invasive plants have been successfully detected and mapped using multiple methods, understory invasive detection remains a challenge, particularly in dense forested environments. Very few studies have utilized an approach that identifies and aligns the acquisition timing of remote sensing imagery with peak phenological differences between understory and overstory vegetation types. We investigated this opportunity by analyzing a monthly time-series of 2011 Landsat TM data to identify acquisition periods with the highest phenological differences between understory and overstory vegetation for detecting the spatial distribution of the exotic understory plant Ligustrum sinense Lour., a rapidly spreading invader in urbanizing regions of the southeastern United States. We used vegetation indices (VI) to establish intra-annual phenological trends for L. sinense, evergreen forest, and deciduous forest located in Mecklenburg County, North Carolina, USA. We developed Random Forest (RF) models to detect L. sinense from those time steps exhibiting the highest phenological differences. We assessed the relative contribution of VI and topographic indices (TI) to the detection of L. sinense. We compared the top performing models and used the best overall model to produce a map of L. sinense for the study area. RF models that included VI, TI, and Landsat TM bands for March 13 and 29, 2011 (the periods with highest detected phenological differences), produced the highest overall accuracy and Kappa estimates, outperforming the combination of VI and TI by 8.5% in accuracy and 20.5% in Kappa. The top performing model from the RF produced a Kappa of 0.75. Our findings suggest that selecting remote sensing data for a period when phenological differences between L. sinense and forest types are at their peak can improve the detection and mapping of L. sinense. Numéro de notice : A2018-293 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.023 Date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90411 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018081	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018083	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018082	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A comprehensive cartographic approach to evacuation map creation for Hurricane Ike in Galveston County, Texas / Yin-Hsuen Chen in Cartography and Geographic Information Science, Vol 43 n° 1 (January 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 1 (January 2016) . - pp 68 - 85 Titre : A comprehensive cartographic approach to evacuation map creation for Hurricane Ike in Galveston County, Texas Type de document : Article/Communication Auteurs : Yin-Hsuen Chen, Auteur ; Stephanie E. Zick, Auteur ; Adam R. Benjamin, Auteur Année de publication : 2016 Article en page(s) : pp 68 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données lidar [Termes IGN] lecture de carte [Termes IGN] méthodologie [Termes IGN] modèle numérique de surface [Termes IGN] outil d'aide à la décision [Termes IGN] risque naturel [Termes IGN] style cartographique [Termes IGN] système d'information géographique [Termes IGN] tempête [Termes IGN] vitesse [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Hurricane evacuation maps raise awareness of the risks associated with potential inundation from storm surge and provide evacuation route information to residents leaving their residences for safety. To create the most effective hurricane evacuation map for communicating risk to the public, cartographic best practices must be integrated into the evacuation map visualization. This study addresses a current gap in the scientific literature by integrating cartographic best practices for color choice, symbol choice, and positive messaging, together with a complete GIS-based workflow utilizing LiDAR-derived digital elevation models, Sea, Lake and Overland Surges from Hurricanes (SLOSH) storm surge products, and vector shapefiles for creating hurricane evacuation maps. To evaluate the methodology, the primary study site was Galveston County, Texas, during Hurricane Ike in September 2008. The typical workflow for county and municipal emergency managers is to create pre-hurricane season generalized evacuation maps. This study shows that the probabilistic storm surge SLOSH forecasts can be seamlessly implemented in the 72–48 hours prior to a storm surge event to provide specialized evacuation maps that incorporate hurricane-specific parameters and more accurately show risk to residents. To verify the Hurricane Ike workflow, a secondary study site in Harrison County, Mississippi, was used to evaluate Hurricane Isaac in August 2012. This study provides a comprehensive cartographic methodology for evacuation zone mapping when the US coastline is threatened by a landfalling hurricane. Numéro de notice : A2016-111 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1014426 En ligne : https://doi.org/10.1080/15230406.2015.1014426 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79930 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2016011	RAB	Revue	Centre de documentation	En réserve L003	Disponible