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Assessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa / Priyadarshi Upadhyay in Geocarto international, vol 35 n° 9 ([01/07/2020])  

Public [article]  inGeocarto international > vol 35 n° 9 [01/07/2020] . - pp 1018 - 1032 Titre : Assessment of USGS DEMs for modelling pothole inundation in the prairie pothole region of Iowa Type de document : Article/Communication Auteurs : Priyadarshi Upadhyay, Auteur ; Amy L. Kaleita, Auteur ; M. L. Soupir, Auteur Année de publication : 2020 Article en page(s) : pp 1018 - 1032 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] Global Multi-resolution Terrain Elevation Data 2010 [Termes IGN] inondation [Termes IGN] Iowa (Etats-Unis) [Termes IGN] mare [Termes IGN] modèle numérique de surface [Termes IGN] profondeur [Termes IGN] semis de points Résumé : (auteur) This study aims to compare inundation in two potholes using Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) with three Digital Elevation Models (DEMs): a 1 m DEM prepared from the LiDAR data which is readily available for the state of Iowa, USGS 1/9 arc-second DEM (∼3 m) which covers about 25% of the conterminous U.S. and USGS 1/3 arc-second DEM (∼10 m) which covers the entire USA. In this study, we found that the variations in water depth and presence/absence of ponding in the potholes of size greater than 1 ha can be predicted using USGS DEMs. The estimates of average water depths using USGS 3 m DEM was found to be 6% and 2% lower than the 1 m LiDAR DEM and the estimates of average water depths using USGS 10 m DEM was found to be 7% and 12% higher than the 1 m LiDAR DEM for the Walnut and Bunny potholes, respectively. Numéro de notice : A2020-429 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1573852 Date de publication en ligne : 06/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1573852 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95497 [article]

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059-2020091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach / Priyadarshi Upadhyay in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

Public [article]  inGeocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 278 - 295 Titre : Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach Type de document : Article/Communication Auteurs : Priyadarshi Upadhyay, Auteur ; Sanjay Kumar Ghosh, Auteur ; Anil Kumar, Auteur Année de publication : 2016 Article en page(s) : pp 278 - 295 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] blé (céréale) [Termes IGN] bruit rose [Termes IGN] classification automatique [Termes IGN] croissance végétale [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] surveillance agricole Résumé : (Auteur) In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 104 for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely. Numéro de notice : A2016-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047415 Date de publication en ligne : 26/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047415 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80381 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
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